Abstract
We study how criminal organizations affect economic development. We exploit a natural experiment in El Salvador, where these criminal organizations emerged due to an exogenous shift in American immigration policy that led to the deportation of gang leaders from the United States to El Salvador. Using a spatial regression discontinuity design that focuses on the gang-created system of borders, we find that individuals in gang-controlled neighborhoods have less material well-being, income, and education than individuals living only 50 meters away but outside of gang territory. None of these discontinuities existed before the arrival of the gangs. A key mechanism behind the results is that gangs restrict individuals' mobility, affecting their labor-market options by preventing them from commuting to other parts of the city. The results are not determined by high rates of selective migration, differential exposure to extortion and violence, or differences in public goods provision.
| Original language | English |
|---|---|
| Pages (from-to) | 2083-2121 |
| Number of pages | 39 |
| Journal | Econometrica |
| Volume | 93 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Nov 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 8 Decent Work and Economic Growth -
SDG 10 Reduced Inequalities -
SDG 16 Peace, Justice and Strong Institutions
Keywords
- crime
- development
- Gangs
- mobility
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In: Econometrica, Vol. 93, No. 6, 11.2025, p. 2083-2121.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Gangs, labor mobility, and development
AU - Melnikov, Nikita
AU - Schmidt-Padilla, Carlos
AU - Sviatschi, María Micaela
N1 - Funding Information: Figure 4 presents the results of estimating Specification (3) for the three outcome variables.45 It shows that, before the 1996 change in United States immigration policy, areas with and without future gang presence experienced similar growth in economic activity. However, after the arrival of the gangs in 1996\u20131997, municipalities with gang presence experienced significantly lower economic growth. Gang presence and economic activity. Note: The figure presents an event study graph for the differences in economic growth between municipalities with and without gang presence. For all three outcome variables, the data are in percentage points, normalized to be equal to 100 percent in 1995\u20131996, before the change in the United States immigration policy. The magnitude of the effects is substantial. For example, by 2005, municipalities without gang presence had experienced a 105-percentage-point higher rate of new business openings. Additionally, after 1997, on average, these areas had an 82-percentage-point higher growth in nighttime light density and a 28.5-percentage-point higher growth in household income.46 Overall, these results confirm the notion that, after the arrival of the gangs, most economic growth has taken place in areas far away from gang territory, plausibly due to business owners' desire to avoid extortion and other forms of gang activity. We also complement the difference-in-differences results by using household income data from our 2019 survey in San Salvador and performing a back-of-the-envelope calculation that compares locations without gang presence separately to fully gang-controlled neighborhoods and places with only some gang activities.47 We find that, after 1997, areas with no gang presence experienced approximately 50 percentage points higher growth in household income than the former and approximately 9 percentage points higher growth than the latter. Thus, while proximity to places with the highest growth of employment opportunities positively affected individuals' earnings, it was residents of gang-controlled neighborhoods who were particularly negatively affected due to their inability to commute across the boundaries of gang territory. We also analyze whether the effects of gang presence are different in the largest urban centers (e.g., San Salvador) and in the rest of the country. To address this question, we follow AAHI+ (2021) and implement two types of synthetic difference-in-differences analyses. The first one defines the treatment variable in the same way as in the baseline difference-in-differences estimation. The second one narrows the treatment group to the four largest cities in El Salvador, all of which have had a substantial gang presence since the late 1990s: San Salvador, Soyapango, Santa Ana, and San Miguel. Supplemental Appendix Figure A.9 presents the two sets of results. In general, we find the two specifications to be quite similar, suggesting that the largest cities were not differentially affected compared to other places with gang presence. To clarify the distinction between gang presence and gang territorial control, we begin with presenting a simple conceptual framework for the interpretation of our results. Supplemental Appendix Section A.6 provides a more detailed discussion of the model behind this conceptual framework, as well as an analysis of two counterfactual scenarios: the removal of restrictions on individuals' mobility and the full removal of gang presence. We consider a one-dimensional city on a unit interval, where locations are characterized by their proximity to the gangs. Overall, the city is divided into three qualitatively different areas. Places in [0,b] are fully controlled by the gangs, and individuals living there cannot work in other parts of the city. These locations are the equivalent of gang territory in the regression discontinuity design. Places in [b,b+\u03B4] (\u03B4>0) are not controlled by the gangs. Individuals living there are free to work in any nongang part of the city, but firms in [b,b+\u03B4] are still exposed to extortion and other gang-related activities. Together, places in [0,b+\u03B4] comprise what we refer to as areas with gang presence. Finally, places in [b+\u03B4,1] do not have any gang presence. The differences in labor-market conditions between these three areas are determined by the production technology used by firms in that location. All firms can choose between two options: a simple technology that does not require any investment from the firms and a more productive technology that requires an initial investment at a fixed cost. In the absence of gangs, firms benefit from the adoption of the productive technology. However, productive firms in areas with gang presence face a high risk of their output being extorted, which makes them choose the simple technology instead. Thus, only firms in areas without gang presence choose to increase their productivity. In turn, as we show in Supplemental Appendix Section A.6, under a realistic set of parameters, this results in increased employment and higher wages in those firms. Despite labor-market conditions only being better in areas without gang presence, individuals living in [b,b+\u03B4] are still able to take advantage of them because of their ability to commute to [b+\u03B4,1], whereas people living in [0,b] cannot do so. This part of the mechanism highlights the importance of restrictions on mobility for people living in gang territory (i.e., [0,b]). At the same time, restrictions on mobility only matter due to higher economic growth in areas without gang presence: if firms in all parts of the city were the same, there would have been no need to commute to [b+\u03B4,1]. Based on this conclusion, we now analyze whether, after the arrival of the gangs, locations without gang presence indeed experienced more economic growth than places exposed to gang activity. As we previewed at the end of Section 5.3, the regression discontinuity results largely represent the socioeconomic costs of full territorial control by the gangs and not necessarily other forms of gang activity. In this section, we use data for all of El Salvador to analyze the broader consequences of gang presence on economic activity in the country. To clarify the distinction between gang presence and gang territorial control, we begin with presenting a simple conceptual framework for the interpretation of our results. Supplemental Appendix Section A.6 provides a more detailed discussion of the model behind this conceptual framework, as well as an analysis of two counterfactual scenarios: the removal of restrictions on individuals' mobility and the full removal of gang presence. We consider a one-dimensional city on a unit interval, where locations are characterized by their proximity to the gangs. Overall, the city is divided into three qualitatively different areas. Places in [0,b] are fully controlled by the gangs, and individuals living there cannot work in other parts of the city. These locations are the equivalent of gang territory in the regression discontinuity design. Places in [b,b+\u03B4] (\u03B4>0) are not controlled by the gangs. Individuals living there are free to work in any nongang part of the city, but firms in [b,b+\u03B4] are still exposed to extortion and other gang-related activities. Together, places in [0,b+\u03B4] comprise what we refer to as areas with gang presence. Finally, places in [b+\u03B4,1] do not have any gang presence. The differences in labor-market conditions between these three areas are determined by the production technology used by firms in that location. All firms can choose between two options: a simple technology that does not require any investment from the firms and a more productive technology that requires an initial investment at a fixed cost. In the absence of gangs, firms benefit from the adoption of the productive technology. However, productive firms in areas with gang presence face a high risk of their output being extorted, which makes them choose the simple technology instead. Thus, only firms in areas without gang presence choose to increase their productivity. In turn, as we show in Supplemental Appendix Section A.6, under a realistic set of parameters, this results in increased employment and higher wages in those firms. Despite labor-market conditions only being better in areas without gang presence, individuals living in [b,b+\u03B4] are still able to take advantage of them because of their ability to commute to [b+\u03B4,1], whereas people living in [0,b] cannot do so. This part of the mechanism highlights the importance of restrictions on mobility for people living in gang territory (i.e., [0,b]). At the same time, restrictions on mobility only matter due to higher economic growth in areas without gang presence: if firms in all parts of the city were the same, there would have been no need to commute to [b+\u03B4,1]. Based on this conclusion, we now analyze whether, after the arrival of the gangs, locations without gang presence indeed experienced more economic growth than places exposed to gang activity. To analyze the aggregate impact of gang activity, we use data from all of El Salvador to perform a difference-in-differences analysis, comparing the evolution of economic activity in areas with varying levels of gang activity after 1996. Our analysis exploits two sources of variation: the timing of gang members' deportation from the United States, which led to the emergence of gangs in El Salvador, and the geographic differences in exposure to organized crime. Our hypothesis is that prior to 1996, the year of the first wave of deportations from the United States, locations that would later experience different levels of gang activity had similar rates of economic development. However, after 1996, we expect to see higher rates of economic growth in areas with low levels of gang presence. We exploit the fact that, after being deported, many gang members who were born in El Salvador returned to their municipality of birth (Sviatschi (2022b)). Thus, we use the municipalities of birth of known gang leaders as a treatment variable for whether the municipality became exposed to gang activity.43 We then estimate the following event study model (Specification (3)) to measure the effect of gang presence on economic growth: 3Econ. growthi,t=gi+\u03B3t+\u2211j\u22601995\u03B2jgang presencei\u00D71{Year=j}t+\u03B5i,t. Econ. growth represents various measures of economic growth in municipality i at time t; gang presence is a dummy for whether a gang leader was born in municipality i; gi and \u03B3t represent municipality and year fixed effects, respectively. Standard errors are estimated using Conley standard errors with spatial correlation within a 5-km radius. The coefficients of interest are \u03B2j, which represent the differences in economic growth between locations with and without gang presence relative to 1995\u2014the year before the change in the United States immigration policy. We use three outcome variables to measure municipality-level growth in economic activity. The first one is the opening of new business establishments. Specifically, we use data from the 2005 economic census, which includes information on when the firms were opened.44 The second outcome variable is nighttime light density (or luminosity) which recent studies have found to be a good proxy for local-level economic activity (Chen and Nordhaus (2011), Henderson, Storeygard, and Weil (2012)). Finally, we use data on household income, which is based on annual household surveys conducted in 1992\u20132007 by DIGESTYC. In all three cases, the outcomes are measured in percentage points, normalized to be 100 percent in 1995\u20131996, both in areas with and without gang presence. In addition, given that the gangs were primarily attracted to large cities, to avoid the comparison between urban and rural locations, we limit our analysis to urban municipalities. Figure 4 presents the results of estimating Specification (3) for the three outcome variables.45 It shows that, before the 1996 change in United States immigration policy, areas with and without future gang presence experienced similar growth in economic activity. However, after the arrival of the gangs in 1996\u20131997, municipalities with gang presence experienced significantly lower economic growth. Gang presence and economic activity. Note: The figure presents an event study graph for the differences in economic growth between municipalities with and without gang presence. For all three outcome variables, the data are in percentage points, normalized to be equal to 100 percent in 1995\u20131996, before the change in the United States immigration policy. The magnitude of the effects is substantial. For example, by 2005, municipalities without gang presence had experienced a 105-percentage-point higher rate of new business openings. Additionally, after 1997, on average, these areas had an 82-percentage-point higher growth in nighttime light density and a 28.5-percentage-point higher growth in household income.46 Overall, these results confirm the notion that, after the arrival of the gangs, most economic growth has taken place in areas far away from gang territory, plausibly due to business owners' desire to avoid extortion and other forms of gang activity. We also complement the difference-in-differences results by using household income data from our 2019 survey in San Salvador and performing a back-of-the-envelope calculation that compares locations without gang presence separately to fully gang-controlled neighborhoods and places with only some gang activities.47 We find that, after 1997, areas with no gang presence experienced approximately 50 percentage points higher growth in household income than the former and approximately 9 percentage points higher growth than the latter. Thus, while proximity to places with the highest growth of employment opportunities positively affected individuals' earnings, it was residents of gang-controlled neighborhoods who were particularly negatively affected due to their inability to commute across the boundaries of gang territory. We also analyze whether the effects of gang presence are different in the largest urban centers (e.g., San Salvador) and in the rest of the country. To address this question, we follow AAHI+ (2021) and implement two types of synthetic difference-in-differences analyses. The first one defines the treatment variable in the same way as in the baseline difference-in-differences estimation. The second one narrows the treatment group to the four largest cities in El Salvador, all of which have had a substantial gang presence since the late 1990s: San Salvador, Soyapango, Santa Ana, and San Miguel. Supplemental Appendix Figure A.9 presents the two sets of results. In general, we find the two specifications to be quite similar, suggesting that the largest cities were not differentially affected compared to other places with gang presence. Next, we consider whether lower socioeconomic development in gang areas can be explained by higher levels of extortion or other violent crimes in gang territory. To address this question, first, we use geocoded data from the 2015 survey of firms conducted by the Salvadoran Foundation for Economic and Social Development to analyze whether firms in different parts of San Salvador were differentially exposed to extortion and other types of gang activity. Specifically, we estimate Specification (1) for the probabilities that a firm has been extorted and that the firm has generally experienced gang activity in the area where it is situated. Table V presents the results, showing that firms' exposure to extortion (column 1) and gang activity (column 2) does not change at the boundaries of gang territory. Firm was Firm experienced Amount firm Person was Amount person Gang homicides (per km2): Robbery extorted gang activity paid in extortion extorted paid in extortion All years Year \u22642007 (per km2) (1) (2) (3) (4) (5) (6) (7) (8) Gang territory \u22120.066 \u22120.036 0.261 0.017 \u22121.501 3.238 \u22120.101 1.867 (0.065) (0.061) (2.022) (0.036) (7.028) (2.537) (1.114) (8.415) [0.074] [0.068] [2.588] [0.035] [6.449] Observations 512 493 4120 1957 252 86 86 86 Mean dep. var 0.246 0.738 6.226 0.200 8.447 9.241 3.348 26.18 Note: The table presents the results of estimating Specification (1) for extortion and other gang-related violent crimes. In columns 1\u20132, the unit of observation is a firm in the 2015 survey of firms conducted by FUSADES. In column 3, the unit of observation is an instance when a firm had to make an extortion payment to the gang. These data come from confidential internal records of one of the larger firms in El Salvador. In columns 4\u20135, the unit of observation is an individual in our own 2020 survey. In columns 6\u20138, the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory, weighted by the size of the area of the distance bins. These data come from official police records. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). In columns 6\u20138, the Conley standard errors are not reported because there the location of the observations is not defined (the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory). Second, we address the possibility that, although firms on both sides of the gang-territory boundaries have the same probability of being extorted, the extortion amounts might be different. To analyze this question, we obtained confidential internal records on all the extortion payments that a large Salvadoran distribution firm, which operates in all parts of San Salvador, made to the gangs from 2012 through 2019. Column 3 of Table V presents the results of estimating Specification (1) for the size of the extortion payments, showing that they also do not change at the boundaries of gang territory.40 Third, we consider the possibility that, while firms on both sides of the boundaries of gang territory are equally extorted, individuals may be extorted more in gang-controlled neighborhoods. We use the data from our 2020 telephone survey in which we asked the respondents if they had ever had to pay extortion to the gangs and how much they had to pay. Columns 4 and 5 of Table V present the results of estimating Specification (1) for the probability that an individual has been extorted and for the amount of money paid in extortion, respectively. In both cases, we find no change at the boundaries of gang areas. Finally, we analyze whether neighborhoods on both sides of the gang-territory boundaries have similar levels of gang-related homicides and robberies. Columns 6\u20138 of Table V present the results of estimating Specification (1) for the number of gang-related homicides and robberies per square kilometer as the outcome variables; they show no differences in the rates of these crimes.41 The results in Table V are not surprising. They confirm the notion that both MS-13 and 18th Street operate not only in the areas they control but also in neighboring locations. Their territory is their \u201Cstronghold,\u201D a place where they do not need to hide and that, for this reason, needs to be protected from police informants and rival gang members. However, gang-controlled areas also serve as a bridgehead from which gang members and their collaborators\u2014who are not subject to the same mobility restrictions as other people living in their territory, especially when it comes to extortion and other gang-related activities\u2014can conduct regular raids into neighboring areas.42 The results in Table V have two important implications. The first one is that, since there are no changes in extortion and other criminal activities at the boundaries of gang territory, these factors cannot be driving the results in Table I. The second one is that, since the gangs are active in both the treatment and the control groups, the regression discontinuity results in Table I should not be interpreted to represent the overall effects of gang presence. Instead, they should be interpreted to denote the effects of gangs' territorial control and accompanying restrictions on mobility. In Section 6, we discuss this latter implication in much more detail and quantify the effects of exposure to overall gang presence, not just gang territorial control. Restrictions on individuals' mobility can account for a large part of the gap in labor-market outcomes between gang and nongang neighborhoods, but they are less likely to be driving the differences in educational attainment. Instead, these differences are likely to be explained by higher dropout rates and lower participation in educational programs in gang-controlled neighborhoods due to (i) recruitment by the gangs (e.g., see Sviatschi (2022a,b)), (ii) lower returns to education for people unable to work outside gang territory, and (iii) the poverty-induced need to work from a young age to help provide for one's family. To determine whether the gap in schooling can, indeed, be driven by higher dropout rates in gang territory, we perform the following analysis. We use administrative data from the 2005\u20132017 annual censuses of schools, in which the schools report the number of students enrolled at the beginning of the year and the number of students who dropped out without completing their grade. Using these data, we estimate Specification (1) with the outcome variable being the school's dropout rate, and the unit of observation\u2014a school in a year. Supplemental Appendix Table A.X presents the results of the estimation. Column 1 shows that, on average, the annual dropout rate in schools from gang territory was 2 percentage points higher than outside gang areas. The magnitude of the effect is almost the same both before and after 2007 (columns 2 and 3) and for male and female students (columns 4 and 5).38,39 Using the result from column 2 of Supplemental Appendix Table A.X as the baseline (i.e., the difference in dropout rates before 2007), one can estimate that, from 1997 to 2007, gang control resulted in a 2.1 \u00D7 10 = 21-percentage-point gap in school completion between students from gang and nongang areas. This estimate is fully consistent with the 14.6-percentage-point difference in school completion for the entire population reported in Table I. Although school education is usually associated with children, during the period under consideration, gang control also affected the educational attainment of many adult Salvadorans. From 1980 to 1992, El Salvador was in a state of civil war. During that period, much of the population was unable to get proper education: in 1992, only 31.4% of individuals in San Salvador had a high school degree (see Table II). For this reason, it is not surprising that after the end of the civil war, education of adults became an important priority for the government and was even explicitly mentioned in the Constitution, as well as in the General Law of Education (chapter VII, articles 28 to 33). From 1994 to 1997, the government rolled out the Program for Adult Literacy and Education (Programa de Alfabetizaci\u00F3n y Educaci\u00F3n B\u00E1sica de Adultos, PAEBA), a program designed to provide school-level education for the adult population. It was very popular: from 2000 to 2007, 726,000 people (approximately 12% of El Salvador's population) enrolled in PAEBA (Libreros, Antonio, and Carbajal (2010)). Comparing the levels of educational attainment in 1992 and 2007 in gang and nongang areas (Supplemental Appendix Figure A.2 and Figure S.4 in the Supplementary Materials), one can see that the share of population with a high school degree increased throughout San Salvador, but much more in areas outside of gang territory. In addition to being driven by higher dropout rates among school-age children, this difference likely reflects differential enrollment in PAEBA among adults in gang and nongang neighborhoods. We are unable to test this hypothesis directly because the implementation of PAEBA was largely community-based and was not centrally administered by any government agency. For instance, approximately 64% of classes were held in private homes, the locations of which are unknown, making it impossible to compare enrollment in gang and nongang areas (Libreros, Antonio, and Carbajal (2010)). However, PAEBA was also partly implemented by the schools, which reported program completion rates to the central government. We leverage administrative data from the 2005\u20132017 annual school censuses to compare the dropout rates among adults in gang and nongang areas. Column 6 of Supplemental Appendix Table A.X presents the results, showing that adults from gang territories were significantly more likely to drop out of the program. Moreover, on average, the difference in the dropout rate between gang and nongang neighborhoods was twice as large for adults as for school-age children, although the difference is not statistically significant. Overall, the results presented in this subsection suggest that the differences in educational attainment between gang territory and nongang areas are likely to be driven by differential rates of school completion in those locations. These results do not undermine the importance of the restrictions on individuals' mobility for labor-market outcomes (as shown in columns 3, 6, and 9 of Table A.VII, residents of gang neighborhoods have better labor-market outcomes if they are able to work outside of gang territory, even after controlling for the level of education), but they do indicate that even if those restrictions were to be eliminated, the gap in labor-market outcomes would not fully disappear because of the differences in the levels of education. Importantly, the differences in labor-market outcomes are not caused by a change in labor-market conditions at the boundaries of gang territory. To analyze this question, we use data from the 2005 economic census, which reported the location, number of employees, revenue, costs, and profits of all formal and informal firms in El Salvador. Using these data, we estimate Specification (1) and find that firm-level characteristics do not change at the boundaries of gang territory (columns 1\u20135 of Supplemental Appendix Table A.IX). In column 6 of Supplemental Appendix Table A.IX, we also demonstrate that, similarly, the number of business establishments per square kilometer is the same on both sides of the boundaries. This result is further verified in columns 7\u201310, using data from Google Maps instead of the 2005 economic census. How can the absence of a change in labor-market conditions at the boundaries of gang territory be consistent with residents of gang neighborhoods being unable to work in the largest and best-paying firms? The answer is that, as we demonstrate in Section 6, after the arrival of the gangs, most of the growth in economic activity has taken place in areas further away from gang-controlled neighborhoods. Thus, while there is no change in labor-market conditions directly at the boundaries of gang territory, these conditions gradually improve with distance from gang neighborhoods. Supplemental Appendix Figure A.8 illustrates these findings. These results highlight the salience of gang-imposed restrictions on individuals' mobility. Since firm characteristics do not change at the boundaries of gang territory, individuals living in nongang neighborhoods close to those boundaries have higher incomes not because of the differences in local labor-market conditions but because of their ability to commute to other parts of the city where the largest firms are located. To document the presence of restrictions on individuals' mobility, we estimate Specification (1) for mobility questions from three different sources: the 2019 survey (columns 1\u20135), a follow-up survey that we conducted in 2023 (column 6), and cell phone ping data from early 2022 (columns 7\u20138). Table IV presents the results. The estimates in column 1 suggest that the share of population working in gang-controlled neighborhoods dramatically increases by almost 50 percentage points (from 5.7% to 55.2%) at the boundaries of gang territory. Residents of gang territory are also more likely to work in the same neighborhood where they live and are less likely to have traveled outside of San Salvador: the share of individuals who have ever been to the beach or visited Santa Ana department, which are both 30 to 60 kilometers away, discontinuously decreases at the boundaries of gang territory. Works in Works in neighborhood Has been to Has been to gang territory where they live Santa Ana the beach (1) (2) (3) (4) Gang territory 0.495 0.111 \u22120.277 \u22120.064 (0.039) (0.031) (0.043) (0.031) [0.042] [0.050] [0.052] [0.032] Mean of dep. var. 0.334 0.302 0.495 0.872 Observations 1738 2071 2314 2314 Freedom of Gang borders prevented you Share of time spent in Mean distance away movement from getting jobs in large firms gang areas, excluding from home during where they live in other parts of the city time spent at home the day (in meters) (5) (6) (7) (8) Gang territory \u22120.097 0.100 0.213 \u221252.82 (0.039) (0.041) (0.039) (154.83) [0.039] [0.049] [0.034] [147.55] Mean of dep. var. 0.811 0.407 0.222 1955.62 Observations 2314 2313 9605 9605 Note: The table presents the results of estimating Specification (1) for the questions related to mobility. The outcome variables in columns 1\u20135 come from the 2019 survey, the outcome variable in column 6\u2014from the 2023 survey, and the outcome variables in columns 7\u20138 are based on cell phone ping data. Santa Ana is a neighboring municipality, which is approximately 60 kilometers away from San Salvador. The beach is approximately 30 kilometers away from San Salvador. The unit of observation is an individual. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted for spatial correlation within a 100-meter radius (Conley correction). To further demonstrate the salience of restrictions on individuals' mobility, we show that residents of gang areas acknowledge the presence of these restrictions. First, in 2019, they were significantly less likely to say that there is freedom of movement in the neighborhood where they live (column 5 of Table IV). Second, in 2023, we conducted a new survey in San Salvador, in which the respondents were asked whether, in the past, the gangs' \u201Cinvisible borders\u201D prevented them from finding jobs in large firms in other parts of the city. While individuals outside of gang territory were also affected (e.g., due to gang areas blocking the routes between some nongang parts of the city), the impact was significantly stronger for the residents of gang neighborhoods (column 6 of Table IV). Finally, in columns 7 and 8 of Table IV, we use cell phone ping data from early 2022 to illustrate that residents of gang territory are not generally less mobile than individuals living in other parts of the city, but that their movements are confined to gang-controlled areas. We begin with dividing the map of San Salvador into 100 \u00D7 100-meter grid cells and using the prevalence of pings during the night hours (from 9 p.m. to 7 a.m.) to identify the grid cell where an individual lives. Then, for each individual, we calculate the share of pings inside gang territory during the daytime (from 9 a.m. to 7 p.m.), excluding pings in their home grid cells.33 Similarly, we calculate the average distance that an individual travels away from their home during the daytime. Columns 7 and 8 of Table IV present the results of estimating Specification (1) for these outcome variables. They confirm that residents of gang-controlled neighborhoods spend a substantially larger share of their time in gang territory than individuals on the other side of the regression discontinuity cutoff. However, within the areas to which they are confined, both groups of individuals travel the same distance throughout the day, suggesting that residents of gang neighborhoods do not have a lower capacity to travel away from home. Figure 3 presents the regression discontinuity plots for the four most important variables in Table IV: the share of people working in gang territory, the share of time individuals spend in gang territory, the share of people who think there is freedom of movement in the area where they live, and the share of people who say that gang-imposed restrictions on mobility prevented them from finding jobs in large firms in other parts of the city. Gang control and mobility constraints. Note: The figure illustrates that residents of gang territory are more likely to work in a gang-controlled location, think that there are restrictions on the freedom of movement, and that these restrictions prevent them from finding jobs in large firms in other parts of the city. The vertical axis represents the average value of the outcomes variable; the horizontal axis\u2014distance (in meters) to the boundaries of gang territory. Neighborhoods to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. The dots represent the average value of the outcome variable in that 30-meter bin. We additionally investigate whether the results in Table IV can be determined by residents of gang areas having less access to personal transportation, such as cars and motorcycles. To address this question, we consider the heterogeneity of the effects of gang control on people who get to work by car or motorcycle and people who get to work in another way, controlling for the correlation between using personal transportation and the outcome variable.34 Supplemental Appendix Table A.V presents the regression estimates. The results suggest that, compared to their peers on the other side of the boundaries, residents of gang-controlled neighborhoods who get to work by car or motorcycle are still substantially more likely to work inside gang territory and, more generally, to have lower levels of mobility. They also have worse employment outcomes and are less likely to say that there is freedom of movement where they live. Finally, the results in columns 9\u201310 indicate that, regardless of car ownership, all residents of gang territory are significantly more likely to say that the gang-imposed borders prevented them from finding jobs in large firms in other parts of the city. Thus, differences in individuals' mobility cannot be explained by differential access to personal transportation in gang and nongang neighborhoods.35 Similarly, in Supplemental Appendix Table A.VI, we show that these results also cannot be explained by differences in educational attainment between the two groups. To document the presence of restrictions on individuals' mobility, we estimate Specification (1) for mobility questions from three different sources: the 2019 survey (columns 1\u20135), a follow-up survey that we conducted in 2023 (column 6), and cell phone ping data from early 2022 (columns 7\u20138). Table IV presents the results. The estimates in column 1 suggest that the share of population working in gang-controlled neighborhoods dramatically increases by almost 50 percentage points (from 5.7% to 55.2%) at the boundaries of gang territory. Residents of gang territory are also more likely to work in the same neighborhood where they live and are less likely to have traveled outside of San Salvador: the share of individuals who have ever been to the beach or visited Santa Ana department, which are both 30 to 60 kilometers away, discontinuously decreases at the boundaries of gang territory. Works in Works in neighborhood Has been to Has been to gang territory where they live Santa Ana the beach (1) (2) (3) (4) Gang territory 0.495 0.111 \u22120.277 \u22120.064 (0.039) (0.031) (0.043) (0.031) [0.042] [0.050] [0.052] [0.032] Mean of dep. var. 0.334 0.302 0.495 0.872 Observations 1738 2071 2314 2314 Freedom of Gang borders prevented you Share of time spent in Mean distance away movement from getting jobs in large firms gang areas, excluding from home during where they live in other parts of the city time spent at home the day (in meters) (5) (6) (7) (8) Gang territory \u22120.097 0.100 0.213 \u221252.82 (0.039) (0.041) (0.039) (154.83) [0.039] [0.049] [0.034] [147.55] Mean of dep. var. 0.811 0.407 0.222 1955.62 Observations 2314 2313 9605 9605 Note: The table presents the results of estimating Specification (1) for the questions related to mobility. The outcome variables in columns 1\u20135 come from the 2019 survey, the outcome variable in column 6\u2014from the 2023 survey, and the outcome variables in columns 7\u20138 are based on cell phone ping data. Santa Ana is a neighboring municipality, which is approximately 60 kilometers away from San Salvador. The beach is approximately 30 kilometers away from San Salvador. The unit of observation is an individual. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted for spatial correlation within a 100-meter radius (Conley correction). To further demonstrate the salience of restrictions on individuals' mobility, we show that residents of gang areas acknowledge the presence of these restrictions. First, in 2019, they were significantly less likely to say that there is freedom of movement in the neighborhood where they live (column 5 of Table IV). Second, in 2023, we conducted a new survey in San Salvador, in which the respondents were asked whether, in the past, the gangs' \u201Cinvisible borders\u201D prevented them from finding jobs in large firms in other parts of the city. While individuals outside of gang territory were also affected (e.g., due to gang areas blocking the routes between some nongang parts of the city), the impact was significantly stronger for the residents of gang neighborhoods (column 6 of Table IV). Finally, in columns 7 and 8 of Table IV, we use cell phone ping data from early 2022 to illustrate that residents of gang territory are not generally less mobile than individuals living in other parts of the city, but that their movements are confined to gang-controlled areas. We begin with dividing the map of San Salvador into 100 \u00D7 100-meter grid cells and using the prevalence of pings during the night hours (from 9 p.m. to 7 a.m.) to identify the grid cell where an individual lives. Then, for each individual, we calculate the share of pings inside gang territory during the daytime (from 9 a.m. to 7 p.m.), excluding pings in their home grid cells.33 Similarly, we calculate the average distance that an individual travels away from their home during the daytime. Columns 7 and 8 of Table IV present the results of estimating Specification (1) for these outcome variables. They confirm that residents of gang-controlled neighborhoods spend a substantially larger share of their time in gang territory than individuals on the other side of the regression discontinuity cutoff. However, within the areas to which they are confined, both groups of individuals travel the same distance throughout the day, suggesting that residents of gang neighborhoods do not have a lower capacity to travel away from home. Figure 3 presents the regression discontinuity plots for the four most important variables in Table IV: the share of people working in gang territory, the share of time individuals spend in gang territory, the share of people who think there is freedom of movement in the area where they live, and the share of people who say that gang-imposed restrictions on mobility prevented them from finding jobs in large firms in other parts of the city. Gang control and mobility constraints. Note: The figure illustrates that residents of gang territory are more likely to work in a gang-controlled location, think that there are restrictions on the freedom of movement, and that these restrictions prevent them from finding jobs in large firms in other parts of the city. The vertical axis represents the average value of the outcomes variable; the horizontal axis\u2014distance (in meters) to the boundaries of gang territory. Neighborhoods to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. The dots represent the average value of the outcome variable in that 30-meter bin. We additionally investigate whether the results in Table IV can be determined by residents of gang areas having less access to personal transportation, such as cars and motorcycles. To address this question, we consider the heterogeneity of the effects of gang control on people who get to work by car or motorcycle and people who get to work in another way, controlling for the correlation between using personal transportation and the outcome variable.34 Supplemental Appendix Table A.V presents the regression estimates. The results suggest that, compared to their peers on the other side of the boundaries, residents of gang-controlled neighborhoods who get to work by car or motorcycle are still substantially more likely to work inside gang territory and, more generally, to have lower levels of mobility. They also have worse employment outcomes and are less likely to say that there is freedom of movement where they live. Finally, the results in columns 9\u201310 indicate that, regardless of car ownership, all residents of gang territory are significantly more likely to say that the gang-imposed borders prevented them from finding jobs in large firms in other parts of the city. Thus, differences in individuals' mobility cannot be explained by differential access to personal transportation in gang and nongang neighborhoods.35 Similarly, in Supplemental Appendix Table A.VI, we show that these results also cannot be explained by differences in educational attainment between the two groups. The consequence of the mobility restrictions is that residents of gang neighborhoods often cannot work outside of gang territory, being forced to accept low-paying jobs in small firms because of their inability to commute to other parts of the city, where the largest firms are located. To demonstrate these negative effects of restrictions on individuals' mobility, we compare the labor-market outcomes for residents of gang areas who are able to work outside of gang territory and those who are not. Supplemental Appendix Table A.VII presents the results, showing that, while, on average, residents of gang-controlled neighborhoods earn less income and work in smaller firms than individuals from nongang locations, these gaps are significantly smaller for residents of gang territory who are able to work outside gang areas. In particular, we find that the latter are as likely to work in firms with 100 or more employees as individuals living outside of gang locations. They also have a 40% smaller gap in household income compared to other residents of gang territory.36 While these results should be treated as descriptive and interpreted with caution, they are fully consistent with gang-imposed restrictions on mobility being a major factor determining individuals' labor-market outcomes.37 Importantly, the differences in labor-market outcomes are not caused by a change in labor-market conditions at the boundaries of gang territory. To analyze this question, we use data from the 2005 economic census, which reported the location, number of employees, revenue, costs, and profits of all formal and informal firms in El Salvador. Using these data, we estimate Specification (1) and find that firm-level characteristics do not change at the boundaries of gang territory (columns 1\u20135 of Supplemental Appendix Table A.IX). In column 6 of Supplemental Appendix Table A.IX, we also demonstrate that, similarly, the number of business establishments per square kilometer is the same on both sides of the boundaries. This result is further verified in columns 7\u201310, using data from Google Maps instead of the 2005 economic census. How can the absence of a change in labor-market conditions at the boundaries of gang territory be consistent with residents of gang neighborhoods being unable to work in the largest and best-paying firms? The answer is that, as we demonstrate in Section 6, after the arrival of the gangs, most of the growth in economic activity has taken place in areas further away from gang-controlled neighborhoods. Thus, while there is no change in labor-market conditions directly at the boundaries of gang territory, these conditions gradually improve with distance from gang neighborhoods. Supplemental Appendix Figure A.8 illustrates these findings. These results highlight the salience of gang-imposed restrictions on individuals' mobility. Since firm characteristics do not change at the boundaries of gang territory, individuals living in nongang neighborhoods close to those boundaries have higher incomes not because of the differences in local labor-market conditions but because of their ability to commute to other parts of the city where the largest firms are located. In this section, we explore the mechanisms behind the negative effects of gangs' territorial control on development outcomes. In particular, we provide novel evidence on how gang-imposed mobility restrictions affect individuals' labor-market choices by preventing them from commuting to areas outside of gang territory, where the largest and best-paying firms are located. We also show that the differences in educational attainment between gang and nongang areas can be explained by higher dropout rates in gang-controlled neighborhoods. Finally, we investigate alternative mechanisms and find that the regression discontinuity results cannot be explained by differences in crime (i.e., homicides, extortion) or the composition of firms at the boundaries of gang territory. In Supplemental Appendix Section A.2, we show that our results are not driven by selective migration of individuals out of gang territory. Specifically, we estimate the rates of selective out-of-sample migration by considering the relationship between household wealth and the probability of a family member migrating abroad from 1997 through 2007, finding that selective migration accounts for no more than 14% of the gaps in socioeconomic development between gang and nongang areas.31 In Supplemental Appendix Subsections A.4 and A.5, we also demonstrate that the regression discontinuity results cannot be explained by differences in public goods provision or occupational structure (i.e., unemployment, informal employment, or hours worked), respectively.32 To document the presence of restrictions on individuals' mobility, we estimate Specification (1) for mobility questions from three different sources: the 2019 survey (columns 1\u20135), a follow-up survey that we conducted in 2023 (column 6), and cell phone ping data from early 2022 (columns 7\u20138). Table IV presents the results. The estimates in column 1 suggest that the share of population working in gang-controlled neighborhoods dramatically increases by almost 50 percentage points (from 5.7% to 55.2%) at the boundaries of gang territory. Residents of gang territory are also more likely to work in the same neighborhood where they live and are less likely to have traveled outside of San Salvador: the share of individuals who have ever been to the beach or visited Santa Ana department, which are both 30 to 60 kilometers away, discontinuously decreases at the boundaries of gang territory. Works in Works in neighborhood Has been to Has been to gang territory where they live Santa Ana the beach (1) (2) (3) (4) Gang territory 0.495 0.111 \u22120.277 \u22120.064 (0.039) (0.031) (0.043) (0.031) [0.042] [0.050] [0.052] [0.032] Mean of dep. var. 0.334 0.302 0.495 0.872 Observations 1738 2071 2314 2314 Freedom of Gang borders prevented you Share of time spent in Mean distance away movement from getting jobs in large firms gang areas, excluding from home during where they live in other parts of the city time spent at home the day (in meters) (5) (6) (7) (8) Gang territory \u22120.097 0.100 0.213 \u221252.82 (0.039) (0.041) (0.039) (154.83) [0.039] [0.049] [0.034] [147.55] Mean of dep. var. 0.811 0.407 0.222 1955.62 Observations 2314 2313 9605 9605 Note: The table presents the results of estimating Specification (1) for the questions related to mobility. The outcome variables in columns 1\u20135 come from the 2019 survey, the outcome variable in column 6\u2014from the 2023 survey, and the outcome variables in columns 7\u20138 are based on cell phone ping data. Santa Ana is a neighboring municipality, which is approximately 60 kilometers away from San Salvador. The beach is approximately 30 kilometers away from San Salvador. The unit of observation is an individual. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted for spatial correlation within a 100-meter radius (Conley correction). To further demonstrate the salience of restrictions on individuals' mobility, we show that residents of gang areas acknowledge the presence of these restrictions. First, in 2019, they were significantly less likely to say that there is freedom of movement in the neighborhood where they live (column 5 of Table IV). Second, in 2023, we conducted a new survey in San Salvador, in which the respondents were asked whether, in the past, the gangs' \u201Cinvisible borders\u201D prevented them from finding jobs in large firms in other parts of the city. While individuals outside of gang territory were also affected (e.g., due to gang areas blocking the routes between some nongang parts of the city), the impact was significantly stronger for the residents of gang neighborhoods (column 6 of Table IV). Finally, in columns 7 and 8 of Table IV, we use cell phone ping data from early 2022 to illustrate that residents of gang territory are not generally less mobile than individuals living in other parts of the city, but that their movements are confined to gang-controlled areas. We begin with dividing the map of San Salvador into 100 \u00D7 100-meter grid cells and using the prevalence of pings during the night hours (from 9 p.m. to 7 a.m.) to identify the grid cell where an individual lives. Then, for each individual, we calculate the share of pings inside gang territory during the daytime (from 9 a.m. to 7 p.m.), excluding pings in their home grid cells.33 Similarly, we calculate the average distance that an individual travels away from their home during the daytime. Columns 7 and 8 of Table IV present the results of estimating Specification (1) for these outcome variables. They confirm that residents of gang-controlled neighborhoods spend a substantially larger share of their time in gang territory than individuals on the other side of the regression discontinuity cutoff. However, within the areas to which they are confined, both groups of individuals travel the same distance throughout the day, suggesting that residents of gang neighborhoods do not have a lower capacity to travel away from home. Figure 3 presents the regression discontinuity plots for the four most important variables in Table IV: the share of people working in gang territory, the share of time individuals spend in gang territory, the share of people who think there is freedom of movement in the area where they live, and the share of people who say that gang-imposed restrictions on mobility prevented them from finding jobs in large firms in other parts of the city. Gang control and mobility constraints. Note: The figure illustrates that residents of gang territory are more likely to work in a gang-controlled location, think that there are restrictions on the freedom of movement, and that these restrictions prevent them from finding jobs in large firms in other parts of the city. The vertical axis represents the average value of the outcomes variable; the horizontal axis\u2014distance (in meters) to the boundaries of gang territory. Neighborhoods to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. The dots represent the average value of the outcome variable in that 30-meter bin. We additionally investigate whether the results in Table IV can be determined by residents of gang areas having less access to personal transportation, such as cars and motorcycles. To address this question, we consider the heterogeneity of the effects of gang control on people who get to work by car or motorcycle and people who get to work in another way, controlling for the correlation between using personal transportation and the outcome variable.34 Supplemental Appendix Table A.V presents the regression estimates. The results suggest that, compared to their peers on the other side of the boundaries, residents of gang-controlled neighborhoods who get to work by car or motorcycle are still substantially more likely to work inside gang territory and, more generally, to have lower levels of mobility. They also have worse employment outcomes and are less likely to say that there is freedom of movement where they live. Finally, the results in columns 9\u201310 indicate that, regardless of car ownership, all residents of gang territory are significantly more likely to say that the gang-imposed borders prevented them from finding jobs in large firms in other parts of the city. Thus, differences in individuals' mobility cannot be explained by differential access to personal transportation in gang and nongang neighborhoods.35 Similarly, in Supplemental Appendix Table A.VI, we show that these results also cannot be explained by differences in educational attainment between the two groups. The consequence of the mobility restrictions is that residents of gang neighborhoods often cannot work outside of gang territory, being forced to accept low-paying jobs in small firms because of their inability to commute to other parts of the city, where the largest firms are located. To demonstrate these negative effects of restrictions on individuals' mobility, we compare the labor-market outcomes for residents of gang areas who are able to work outside of gang territory and those who are not. Supplemental Appendix Table A.VII presents the results, showing that, while, on average, residents of gang-controlled neighborhoods earn less income and work in smaller firms than individuals from nongang locations, these gaps are significantly smaller for residents of gang territory who are able to work outside gang areas. In particular, we find that the latter are as likely to work in firms with 100 or more employees as individuals living outside of gang locations. They also have a 40% smaller gap in household income compared to other residents of gang territory.36 While these results should be treated as descriptive and interpreted with caution, they are fully consistent with gang-imposed restrictions on mobility being a major factor determining individuals' labor-market outcomes.37 Importantly, the differences in labor-market outcomes are not caused by a change in labor-market conditions at the boundaries of gang territory. To analyze this question, we use data from the 2005 economic census, which reported the location, number of employees, revenue, costs, and profits of all formal and informal firms in El Salvador. Using these data, we estimate Specification (1) and find that firm-level characteristics do not change at the boundaries of gang territory (columns 1\u20135 of Supplemental Appendix Table A.IX). In column 6 of Supplemental Appendix Table A.IX, we also demonstrate that, similarly, the number of business establishments per square kilometer is the same on both sides of the boundaries. This result is further verified in columns 7\u201310, using data from Google Maps instead of the 2005 economic census. How can the absence of a change in labor-market conditions at the boundaries of gang territory be consistent with residents of gang neighborhoods being unable to work in the largest and best-paying firms? The answer is that, as we demonstrate in Section 6, after the arrival of the gangs, most of the growth in economic activity has taken place in areas further away from gang-controlled neighborhoods. Thus, while there is no change in labor-market conditions directly at the boundaries of gang territory, these conditions gradually improve with distance from gang neighborhoods. Supplemental Appendix Figure A.8 illustrates these findings. These results highlight the salience of gang-imposed restrictions on individuals' mobility. Since firm characteristics do not change at the boundaries of gang territory, individuals living in nongang neighborhoods close to those boundaries have higher incomes not because of the differences in local labor-market conditions but because of their ability to commute to other parts of the city where the largest firms are located. Restrictions on individuals' mobility can account for a large part of the gap in labor-market outcomes between gang and nongang neighborhoods, but they are less likely to be driving the differences in educational attainment. Instead, these differences are likely to be explained by higher dropout rates and lower participation in educational programs in gang-controlled neighborhoods due to (i) recruitment by the gangs (e.g., see Sviatschi (2022a,b)), (ii) lower returns to education for people unable to work outside gang territory, and (iii) the poverty-induced need to work from a young age to help provide for one's family. To determine whether the gap in schooling can, indeed, be driven by higher dropout rates in gang territory, we perform the following analysis. We use administrative data from the 2005\u20132017 annual censuses of schools, in which the schools report the number of students enrolled at the beginning of the year and the number of students who dropped out without completing their grade. Using these data, we estimate Specification (1) with the outcome variable being the school's dropout rate, and the unit of observation\u2014a school in a year. Supplemental Appendix Table A.X presents the results of the estimation. Column 1 shows that, on average, the annual dropout rate in schools from gang territory was 2 percentage points higher than outside gang areas. The magnitude of the effect is almost the same both before and after 2007 (columns 2 and 3) and for male and female students (columns 4 and 5).38,39 Using the result from column 2 of Supplemental Appendix Table A.X as the baseline (i.e., the difference in dropout rates before 2007), one can estimate that, from 1997 to 2007, gang control resulted in a 2.1 \u00D7 10 = 21-percentage-point gap in school completion between students from gang and nongang areas. This estimate is fully consistent with the 14.6-percentage-point difference in school completion for the entire population reported in Table I. Although school education is usually associated with children, during the period under consideration, gang control also affected the educational attainment of many adult Salvadorans. From 1980 to 1992, El Salvador was in a state of civil war. During that period, much of the population was unable to get proper education: in 1992, only 31.4% of individuals in San Salvador had a high school degree (see Table II). For this reason, it is not surprising that after the end of the civil war, education of adults became an important priority for the government and was even explicitly mentioned in the Constitution, as well as in the General Law of Education (chapter VII, articles 28 to 33). From 1994 to 1997, the government rolled out the Program for Adult Literacy and Education (Programa de Alfabetizaci\u00F3n y Educaci\u00F3n B\u00E1sica de Adultos, PAEBA), a program designed to provide school-level education for the adult population. It was very popular: from 2000 to 2007, 726,000 people (approximately 12% of El Salvador's population) enrolled in PAEBA (Libreros, Antonio, and Carbajal (2010)). Comparing the levels of educational attainment in 1992 and 2007 in gang and nongang areas (Supplemental Appendix Figure A.2 and Figure S.4 in the Supplementary Materials), one can see that the share of population with a high school degree increased throughout San Salvador, but much more in areas outside of gang territory. In addition to being driven by higher dropout rates among school-age children, this difference likely reflects differential enrollment in PAEBA among adults in gang and nongang neighborhoods. We are unable to test this hypothesis directly because the implementation of PAEBA was largely community-based and was not centrally administered by any government agency. For instance, approximately 64% of classes were held in private homes, the locations of which are unknown, making it impossible to compare enrollment in gang and nongang areas (Libreros, Antonio, and Carbajal (2010)). However, PAEBA was also partly implemented by the schools, which reported program completion rates to the central government. We leverage administrative data from the 2005\u20132017 annual school censuses to compare the dropout rates among adults in gang and nongang areas. Column 6 of Supplemental Appendix Table A.X presents the results, showing that adults from gang territories were significantly more likely to drop out of the program. Moreover, on average, the difference in the dropout rate between gang and nongang neighborhoods was twice as large for adults as for school-age children, although the difference is not statistically significant. Overall, the results presented in this subsection suggest that the differences in educational attainment between gang territory and nongang areas are likely to be driven by differential rates of school completion in those locations. These results do not undermine the importance of the restrictions on individuals' mobility for labor-market outcomes (as shown in columns 3, 6, and 9 of Table A.VII, residents of gang neighborhoods have better labor-market outcomes if they are able to work outside of gang territory, even after controlling for the level of education), but they do indicate that even if those restrictions were to be eliminated, the gap in labor-market outcomes would not fully disappear because of the differences in the levels of education. Next, we consider whether lower socioeconomic development in gang areas can be explained by higher levels of extortion or other violent crimes in gang territory. To address this question, first, we use geocoded data from the 2015 survey of firms conducted by the Salvadoran Foundation for Economic and Social Development to analyze whether firms in different parts of San Salvador were differentially exposed to extortion and other types of gang activity. Specifically, we estimate Specification (1) for the probabilities that a firm has been extorted and that the firm has generally experienced gang activity in the area where it is situated. Table V presents the results, showing that firms' exposure to extortion (column 1) and gang activity (column 2) does not change at the boundaries of gang territory. Firm was Firm experienced Amount firm Person was Amount person Gang homicides (per km2): Robbery extorted gang activity paid in extortion extorted paid in extortion All years Year \u22642007 (per km2) (1) (2) (3) (4) (5) (6) (7) (8) Gang territory \u22120.066 \u22120.036 0.261 0.017 \u22121.501 3.238 \u22120.101 1.867 (0.065) (0.061) (2.022) (0.036) (7.028) (2.537) (1.114) (8.415) [0.074] [0.068] [2.588] [0.035] [6.449] Observations 512 493 4120 1957 252 86 86 86 Mean dep. var 0.246 0.738 6.226 0.200 8.447 9.241 3.348 26.18 Note: The table presents the results of estimating Specification (1) for extortion and other gang-related violent crimes. In columns 1\u20132, the unit of observation is a firm in the 2015 survey of firms conducted by FUSADES. In column 3, the unit of observation is an instance when a firm had to make an extortion payment to the gang. These data come from confidential internal records of one of the larger firms in El Salvador. In columns 4\u20135, the unit of observation is an individual in our own 2020 survey. In columns 6\u20138, the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory, weighted by the size of the area of the distance bins. These data come from official police records. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). In columns 6\u20138, the Conley standard errors are not reported because there the location of the observations is not defined (the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory). Second, we address the possibility that, although firms on both sides of the gang-territory boundaries have the same probability of being extorted, the extortion amounts might be different. To analyze this question, we obtained confidential internal records on all the extortion payments that a large Salvadoran distribution firm, which operates in all parts of San Salvador, made to the gangs from 2012 through 2019. Column 3 of Table V presents the results of estimating Specification (1) for the size of the extortion payments, showing that they also do not change at the boundaries of gang territory.40 Third, we consider the possibility that, while firms on both sides of the boundaries of gang territory are equally extorted, individuals may be extorted more in gang-controlled neighborhoods. We use the data from our 2020 telephone survey in which we asked the respondents if they had ever had to pay extortion to the gangs and how much they had to pay. Columns 4 and 5 of Table V present the results of estimating Specification (1) for the probability that an individual has been extorted and for the amount of money paid in extortion, respectively. In both cases, we find no change at the boundaries of gang areas. Finally, we analyze whether neighborhoods on both sides of the gang-territory boundaries have similar levels of gang-related homicides and robberies. Columns 6\u20138 of Table V present the results of estimating Specification (1) for the number of gang-related homicides and robberies per square kilometer as the outcome variables; they show no differences in the rates of these crimes.41 The results in Table V are not surprising. They confirm the notion that both MS-13 and 18th Street operate not only in the areas they control but also in neighboring locations. Their territory is their \u201Cstronghold,\u201D a place where they do not need to hide and that, for this reason, needs to be protected from police informants and rival gang members. However, gang-controlled areas also serve as a bridgehead from which gang members and their collaborators\u2014who are not subject to the same mobility restrictions as other people living in their territory, especially when it comes to extortion and other gang-related activities\u2014can conduct regular raids into neighboring areas.42 The results in Table V have two important implications. The first one is that, since there are no changes in extortion and other criminal activities at the boundaries of gang territory, these factors cannot be driving the results in Table I. The second one is that, since the gangs are active in both the treatment and the control groups, the regression discontinuity results in Table I should not be interpreted to represent the overall effects of gang presence. Instead, they should be interpreted to denote the effects of gangs' territorial control and accompanying restrictions on mobility. In Section 6, we discuss this latter implication in much more detail and quantify the effects of exposure to overall gang presence, not just gang territorial control. Another assumption that needs to be satisfied for our estimates to be interpreted as causal is that there has been no selective migration of individuals across the regression discontinuity threshold. Selective migration can affect our results in two ways. The first is what we call in-sample migration: individuals moving from a neighborhood on one side of the boundaries to an area on the other side of the boundaries while remaining in San Salvador and, thus, in our sample. This type of migration would be a direct threat to identification because it would imply that individuals can manipulate their treatment status. The second is what we call out-of-sample migration: individuals moving from San Salvador to a different municipality in El Salvador or abroad. This type of migration does not invalidate the identification strategy, but it changes the interpretation of the mechanism through which the gangs affect local socioeconomic conditions (i.e., that gang control makes wealthy, educated individuals leave San Salvador). In this subsection, we consider the direct threat to identification that comes from in-sample migration. To show that in-sample migration is not driving our findings, we leverage our 2019 survey, where, among other questions, we asked individuals whether they had lived in the exact same place their entire life: 77% of respondents said they had. This information allows us to compare the results for the full sample and for the subsample of respondents for whom we know the ex ante treatment status (i.e., that they lived in the location before the arrival of the gangs). In the absence of in-sample migration, the two sets of results would be quite similar, whereas, if the results are determined by in-sample migration, the discontinuities would appear only in the full sample. Notably, this exercise also allows us to determine that the results are not driven by wealthy and educated newcomers choosing to settle in nongang parts of San Salvador. By restricting the sample to individuals who have lived in the same neighborhood their entire life, by definition, we exclude all newcomers. When we limit the sample this way, the results of the regression discontinuity analysis are practically unchanged. Supplemental Appendix Figure A.4 illustrates this fact by showing the two regression discontinuity plots for household income. The left-hand side of the figure presents the results for the full sample; the right-hand side presents the subsample of never-movers. The two plots are quite similar, suggesting that the results are not driven by selective in-sample migration. Supplemental Appendix Table A.I reports the regression estimates for the socioeconomic characteristics from our 2019 survey, both for the full sample and for the sample of never-movers; Figure S.5 in the Supplementary Materials illustrates these results.30 For a detailed discussion of out-of-sample migration (i.e., individuals moving from San Salvador to a different municipality or abroad), see Supplemental Appendix Section A.2. To address any remaining concerns regarding the potential endogeneity of the boundaries, we perform the following analysis. We identify three major multilane roads\u2014Bulevar Venezuela, 49 Avenida Sur, and Autopista Comalapa\u2014which together form more than 45 kilometers of natural barriers that largely determined the southern and western boundaries of gang territory.26 Table III reports the results of estimating Specification (1) using these three roads, rather than the actual boundaries of gang territory, to predict the location of the borders. The results remain highly significant, demonstrating that they are not driven by the potential endogeneity of some gang-territory boundaries. Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Has internet (1) (2) (3) (4) (5) (6) Gang territory \u22120.096 0.047 \u22120.064 \u22120.226 0.004 \u22120.287 (0.014) (0.009) (0.014) (0.056) (0.002) (0.029) [0.021] [0.012] [0.022] [0.102] [0.002] [0.101] Mean of dep. var. 0.947 0.021 0.966 0.050 0.002 0.097 Observations 7424 6312 6348 6348 6348 6056 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a computer Number of rooms (7) (8) (9) (10) (11) (12) Gang territory \u22120.021 \u22120.606 \u22120.385 \u22120.045 \u22120.556 \u22122.061 (0.009) (0.052) (0.032) (0.014) (0.062) (0.321) [0.013] [0.155] [0.053] [0.014] [0.118] [0.398] Mean of dep. var. 0.033 0.305 0.671 0.957 0.256 2.814 Observations 6021 6080 6098 6119 6086 6348 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (13) (14) (15) (16) (17) (18) Gang territory \u22120.167 \u22120.406 \u22120.233 \u22120.071 \u22120.246 \u22120.266 (0.039) (0.032) (0.054) (0.009) (0.025) (0.028) [0.038] [0.049] [0.107] [0.014] [0.059] [0.051] Mean of dep. var. 0.926 0.406 0.146 0.964 0.335 0.486 Observations 21,488 20,722 20,722 6312 5933 20,722 Note: The table presents the results of estimating Specification (1), using the locations of major roads and boulevards (geographical barriers) as the predicted boundaries of gang territory. To ensure comparability of the census tracts on both sides of the regression discontinuity threshold, we exclude 25% of the largest census tracts, which are disproportionately present outside gang territory. We also include dummies for the three remaining quartiles of the census tract size distribution. Table S.II in the Supplementary Materials reports the results of estimating the same regression specification without excluding the largest census tracts and, instead, including dummies for all four quartiles of the census tract size distribution. All the variables come from the 2007 census. The unit of observation is a dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). We also perform a placebo analysis in which we use major multilane roads that did not define the boundaries of gang territory to ensure that these geographical barriers did not affect socioeconomic development through factors unrelated to the gang boundaries. The analysis focuses on a series of consecutive roads, ranging from Redondel Masferrer in the west to Avenida Independencia in the east, that split San Salvador into two similar-size parts (see Figure 1). We then estimate whether the level of socioeconomic development changes at the placebo boundaries.27 Supplemental Appendix Table A.II presents the results, confirming the notion that major roads do not affect development outcomes through factors unrelated to the gang boundaries. To ensure that nongang areas close to the boundaries of gang territory are the appropriate counterfactual for gang-controlled neighborhoods, we check whether, before the arrival of the gangs, those locations had any preexisting differences in geography, socioeconomic development, or crime. First, we estimate Specification (1) for potentially important neighborhood characteristics (e.g., elevation, access to waterways, road density) and the socioeconomic characteristics from the 1992 census (e.g., dwelling conditions, having a TV).22 Columns 1\u201324 of Table II present the results. There are no discontinuities in any of the variables, confirming the notion that, initially, the locations on opposite sides of the boundaries were not different from one another. Supplemental Appendix Figure A.5 illustrates these results for the first principal components of dwelling, household, and individual characteristics.23 Neighborhood characteristics Has access to Territory used for Urban territory Road density the waterways Elevation Tree coverage coffee production (1) (2) (3) (4) (5) (6) Gang territory \u22120.018 \u22120.571 0.027 \u22120.193 0.008 \u22120.005 (0.060) (0.953) (0.069) (16.599) (0.019) (0.026) [0.052] [1.849] [0.095] [17.439] [0.023] [0.027] Mean of dep. var. 0.814 17.84 0.326 720.4 0.049 0.029 Observations 476 476 476 476 476 476 Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Shared bathroom (7) (8) (9) (10) (11) (12) Gang territory \u22120.015 \u22120.003 \u22120.032 \u22120.036 \u22120.007 0.021 (0.036) (0.028) (0.047) (0.039) (0.017) (0.032) [0.035] [0.030] [0.046] [0.030] [0.013] [0.029] Mean of dep. var. 0.813 0.100 0.816 0.182 0.030 0.142 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a blender Number of rooms (13) (14) (15) (16) (17) (18) Gang territory \u22120.004 \u22120.049 \u22120.030 0.009 0.014 \u22120.069 (0.009) (0.051) (0.054) (0.019) (0.032) (0.170) [0.007] [0.043] [0.049] [0.019] [0.034] [0.172] Mean of dep. var. 0.034 0.285 0.320 0.860 0.625 2.670 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (19) (20) (21) (22) (23) (24) Gang territory \u22120.000 \u22120.014 \u22120.019 \u22120.005 \u22120.016 \u22120.013 (0.011) (0.028) (0.017) (0.031) (0.030) (0.018) [0.009] [0.028] [0.017] [0.031] [0.026] [0.018] Mean of dep. var. 0.904 0.314 0.112 0.863 0.525 0.380 Observations 234,749 227,281 227,281 64,899 64,899 227,281 Number of incarcerations per km2 prior to 1997: All crimes Homicide Robbery Sex crimes Assault Other violent crimes (25) (26) (27) (28) (29) (30) Gang territory \u22122.096 1.548 \u22120.640 \u22121.404 \u22120.823 \u22121.777 (18.200) (1.291) (3.979) (1.321) (3.400) (1.873) Mean of dep. var. 114.6 4.476 21.61 6.147 19.78 9.275 Observations 86 86 86 86 86 86 Note: Before the arrival of the gangs, locations on either side of the boundaries of gang territory had similar geographic and socioeconomic characteristics. The table presents the results of estimating Specification (1) for the neighborhood characteristics and the variables from the 1992 census. The unit of observation is a census tract, dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). In columns 25\u201330, the Conley standard errors are not reported because there the location of the observations is not defined (the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory). Next, we estimate Specification (1) for the level of crime prior to the arrival of the gangs, measured by the number of people incarcerated in different parts of the city. Using the incarceration records from San Salvador's prisons, we geocode the residential addresses of the 4726 individuals who had been incarcerated prior to 1997. Then, we calculate the number of incarcerations per square kilometer for each 10-meter bin, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries).24 Columns 25\u201330 of Table II present the results of estimating Specification (1) for different types of crimes, showing that locations on both sides of the boundaries had similar levels of crime prior to the arrival of the gangs.25 Overall, before the mid-1990s, gang and nongang locations had similar levels of socioeconomic development and crime, allowing us to conclude that nongang areas close to the boundaries are the appropriate counterfactual for gang neighborhoods in the absence of gang control. In this subsection, we analyze the assumptions that need to be satisfied for the estimates in Table I to represent the causal effect of gang control on socioeconomic development. To ensure that nongang areas close to the boundaries of gang territory are the appropriate counterfactual for gang-controlled neighborhoods, we check whether, before the arrival of the gangs, those locations had any preexisting differences in geography, socioeconomic development, or crime. First, we estimate Specification (1) for potentially important neighborhood characteristics (e.g., elevation, access to waterways, road density) and the socioeconomic characteristics from the 1992 census (e.g., dwelling conditions, having a TV).22 Columns 1\u201324 of Table II present the results. There are no discontinuities in any of the variables, confirming the notion that, initially, the locations on opposite sides of the boundaries were not different from one another. Supplemental Appendix Figure A.5 illustrates these results for the first principal components of dwelling, household, and individual characteristics.23 Neighborhood characteristics Has access to Territory used for Urban territory Road density the waterways Elevation Tree coverage coffee production (1) (2) (3) (4) (5) (6) Gang territory \u22120.018 \u22120.571 0.027 \u22120.193 0.008 \u22120.005 (0.060) (0.953) (0.069) (16.599) (0.019) (0.026) [0.052] [1.849] [0.095] [17.439] [0.023] [0.027] Mean of dep. var. 0.814 17.84 0.326 720.4 0.049 0.029 Observations 476 476 476 476 476 476 Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Shared bathroom (7) (8) (9) (10) (11) (12) Gang territory \u22120.015 \u22120.003 \u22120.032 \u22120.036 \u22120.007 0.021 (0.036) (0.028) (0.047) (0.039) (0.017) (0.032) [0.035] [0.030] [0.046] [0.030] [0.013] [0.029] Mean of dep. var. 0.813 0.100 0.816 0.182 0.030 0.142 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a blender Number of rooms (13) (14) (15) (16) (17) (18) Gang territory \u22120.004 \u22120.049 \u22120.030 0.009 0.014 \u22120.069 (0.009) (0.051) (0.054) (0.019) (0.032) (0.170) [0.007] [0.043] [0.049] [0.019] [0.034] [0.172] Mean of dep. var. 0.034 0.285 0.320 0.860 0.625 2.670 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (19) (20) (21) (22) (23) (24) Gang territory \u22120.000 \u22120.014 \u22120.019 \u22120.005 \u22120.016 \u22120.013 (0.011) (0.028) (0.017) (0.031) (0.030) (0.018) [0.009] [0.028] [0.017] [0.031] [0.026] [0.018] Mean of dep. var. 0.904 0.314 0.112 0.863 0.525 0.380 Observations 234,749 227,281 227,281 64,899 64,899 227,281 Number of incarcerations per km2 prior to 1997: All crimes Homicide Robbery Sex crimes Assault Other violent crimes (25) (26) (27) (28) (29) (30) Gang territory \u22122.096 1.548 \u22120.640 \u22121.404 \u22120.823 \u22121.777 (18.200) (1.291) (3.979) (1.321) (3.400) (1.873) Mean of dep. var. 114.6 4.476 21.61 6.147 19.78 9.275 Observations 86 86 86 86 86 86 Note: Before the arrival of the gangs, locations on either side of the boundaries of gang territory had similar geographic and socioeconomic characteristics. The table presents the results of estimating Specification (1) for the neighborhood characteristics and the variables from the 1992 census. The unit of observation is a census tract, dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). In columns 25\u201330, the Conley standard errors are not reported because there the location of the observations is not defined (the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory). Next, we estimate Specification (1) for the level of crime prior to the arrival of the gangs, measured by the number of people incarcerated in different parts of the city. Using the incarceration records from San Salvador's prisons, we geocode the residential addresses of the 4726 individuals who had been incarcerated prior to 1997. Then, we calculate the number of incarcerations per square kilometer for each 10-meter bin, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries).24 Columns 25\u201330 of Table II present the results of estimating Specification (1) for different types of crimes, showing that locations on both sides of the boundaries had similar levels of crime prior to the arrival of the gangs.25 Overall, before the mid-1990s, gang and nongang locations had similar levels of socioeconomic development and crime, allowing us to conclude that nongang areas close to the boundaries are the appropriate counterfactual for gang neighborhoods in the absence of gang control. To address any remaining concerns regarding the potential endogeneity of the boundaries, we perform the following analysis. We identify three major multilane roads\u2014Bulevar Venezuela, 49 Avenida Sur, and Autopista Comalapa\u2014which together form more than 45 kilometers of natural barriers that largely determined the southern and western boundaries of gang territory.26 Table III reports the results of estimating Specification (1) using these three roads, rather than the actual boundaries of gang territory, to predict the location of the borders. The results remain highly significant, demonstrating that they are not driven by the potential endogeneity of some gang-territory boundaries. Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Has internet (1) (2) (3) (4) (5) (6) Gang territory \u22120.096 0.047 \u22120.064 \u22120.226 0.004 \u22120.287 (0.014) (0.009) (0.014) (0.056) (0.002) (0.029) [0.021] [0.012] [0.022] [0.102] [0.002] [0.101] Mean of dep. var. 0.947 0.021 0.966 0.050 0.002 0.097 Observations 7424 6312 6348 6348 6348 6056 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a computer Number of rooms (7) (8) (9) (10) (11) (12) Gang territory \u22120.021 \u22120.606 \u22120.385 \u22120.045 \u22120.556 \u22122.061 (0.009) (0.052) (0.032) (0.014) (0.062) (0.321) [0.013] [0.155] [0.053] [0.014] [0.118] [0.398] Mean of dep. var. 0.033 0.305 0.671 0.957 0.256 2.814 Observations 6021 6080 6098 6119 6086 6348 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (13) (14) (15) (16) (17) (18) Gang territory \u22120.167 \u22120.406 \u22120.233 \u22120.071 \u22120.246 \u22120.266 (0.039) (0.032) (0.054) (0.009) (0.025) (0.028) [0.038] [0.049] [0.107] [0.014] [0.059] [0.051] Mean of dep. var. 0.926 0.406 0.146 0.964 0.335 0.486 Observations 21,488 20,722 20,722 6312 5933 20,722 Note: The table presents the results of estimating Specification (1), using the locations of major roads and boulevards (geographical barriers) as the predicted boundaries of gang territory. To ensure comparability of the census tracts on both sides of the regression discontinuity threshold, we exclude 25% of the largest census tracts, which are disproportionately present outside gang territory. We also include dummies for the three remaining quartiles of the census tract size distribution. Table S.II in the Supplementary Materials reports the results of estimating the same regression specification without excluding the largest census tracts and, instead, including dummies for all four quartiles of the census tract size distribution. All the variables come from the 2007 census. The unit of observation is a dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). We also perform a placebo analysis in which we use major multilane roads that did not define the boundaries of gang territory to ensure that these geographical barriers did not affect socioeconomic development through factors unrelated to the gang boundaries. The analysis focuses on a series of consecutive roads, ranging from Redondel Masferrer in the west to Avenida Independencia in the east, that split San Salvador into two similar-size parts (see Figure 1). We then estimate whether the level of socioeconomic development changes at the placebo boundaries.27 Supplemental Appendix Table A.II presents the results, confirming the notion that major roads do not affect development outcomes through factors unrelated to the gang boundaries. A potential concern is that the boundaries of gang territory may not have remained stable between the time they were formed (soon after the gangs emerged) and 2015, when EDH published the map of gang territory. If the EDH map does not accurately reflect which areas were controlled by the gangs in 2007, the estimates in Table I would be biased toward zero (i.e., against finding an effect).28 Thus, the results in Table I should be interpreted as the lower bound of the effects of gang control. Nevertheless, in Supplemental Appendix Section A.1, we demonstrate that the gang-territory boundaries have remained stable since they were first formed. Specifically, we exploit the fact that most gang-related homicides take place precisely at the boundaries of gang territory because of people attempting to enter or leave gang-controlled neighborhoods without permission.29 As a result, by showing that, throughout the years, gang-related homicides consistently take place right at the boundaries from the EDH map, we are able to confirm the validity of that map and demonstrate the stability of those boundaries. In addition, in 2023, we conducted a new survey of individuals from gang and nongang neighborhoods, in which, among other questions, the respondents were asked whether their neighborhood had been controlled by gangs 20 years ago, during the presidency of Francisco Flores P\u00E9rez (President of El Salvador in 1999\u20132004). As shown in Supplemental Appendix Figure A.6, the share of respondents answering in the affirmative significantly increases at the boundaries of gang territory, suggesting that the borders have remained stable over time. Another assumption that needs to be satisfied for our estimates to be interpreted as causal is that there has been no selective migration of individuals across the regression discontinuity threshold. Selective migration can affect our results in two ways. The first is what we call in-sample migration: individuals moving from a neighborhood on one side of the boundaries to an area on the other side of the boundaries while remaining in San Salvador and, thus, in our sample. This type of migration would be a direct threat to identification because it would imply that individuals can manipulate their treatment status. The second is what we call out-of-sample migration: individuals moving from San Salvador to a different municipality in El Salvador or abroad. This type of migration does not invalidate the identification strategy, but it changes the interpretation of the mechanism through which the gangs affect local socioeconomic conditions (i.e., that gang control makes wealthy, educated individuals leave San Salvador). In this subsection, we consider the direct threat to identification that comes from in-sample migration. To show that in-sample migration is not driving our findings, we leverage our 2019 survey, where, among other questions, we asked individuals whether they had lived in the exact same place their entire life: 77% of respondents said they had. This information allows us to compare the results for the full sample and for the subsample of respondents for whom we know the ex ante treatment status (i.e., that they lived in the location before the arrival of the gangs). In the absence of in-sample migration, the two sets of results would be quite similar, whereas, if the results are determined by in-sample migration, the discontinuities would appear only in the full sample. Notably, this exercise also allows us to determine that the results are not driven by wealthy and educated newcomers choosing to settle in nongang parts of San Salvador. By restricting the sample to individuals who have lived in the same neighborhood their entire life, by definition, we exclude all newcomers. When we limit the sample this way, the results of the regression discontinuity analysis are practically unchanged. Supplemental Appendix Figure A.4 illustrates this fact by showing the two regression discontinuity plots for household income. The left-hand side of the figure presents the results for the full sample; the right-hand side presents the subsample of never-movers. The two plots are quite similar, suggesting that the results are not driven by selective in-sample migration. Supplemental Appendix Table A.I reports the regression estimates for the socioeconomic characteristics from our 2019 survey, both for the full sample and for the sample of never-movers; Figure S.5 in the Supplementary Materials illustrates these results.30 For a detailed discussion of out-of-sample migration (i.e., individuals moving from San Salvador to a different municipality or abroad), see Supplemental Appendix Section A.2. In Section 6, we also demonstrate the absence of pretrends in socioeconomic development between areas with and without gang presence. Specifically, we perform a difference-in-differences analysis using nighttime light density, household income, and firm openings to show that these variables only started to change after the deportation of the gang leaders from the United States to El Salvador. In Supplemental Appendix Section A.3, we also present a wide range of additional robustness checks to ensure that the estimates in Table I represent the causal effect of gang control on socioeconomic development. Table I presents the results of estimating Specification (1) using the 2007 census data. It shows that, after experiencing gang rule, individuals living in gang-controlled neighborhoods have significantly worse dwelling conditions, lower levels of education, and are less wealthy than their peers on the other side of the boundaries. For instance, residents of gang territory are estimated to have a 21-percentage-point lower probability of owning a car, a 15-percentage-point lower probability of having a high school degree, and a 5-percentage-point lower probability of living in a house with concrete walls than individuals living less than 50 meters away but not under the control of gangs.20 The results for the other measures of socioeconomic development present the same pattern. Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Has internet (1) (2) (3) (4) (5) (6) Gang territory \u22120.047 0.026 \u22120.050 \u22120.079 0.006 \u22120.131 (0.015) (0.010) (0.021) (0.021) (0.002) (0.029) [0.017] [0.010] [0.027] [0.027] [0.003] [0.039] Mean of dep. var. 0.932 0.028 0.941 0.108 0.005 0.181 Observations 72,087 60,675 62,169 62,169 62,169 59,776 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a computer Number of rooms (7) (8) (9) (10) (11) (12) Gang territory \u22120.013 \u22120.207 \u22120.135 \u22120.021 \u22120.173 \u22120.693 (0.006) (0.046) (0.033) (0.006) (0.035) (0.195) [0.005] [0.057] [0.040] [0.008] [0.045] [0.203] Mean of dep. var. 0.033 0.429 0.697 0.952 0.346 3.093 Observations 59,096 60,045 60,168 60,384 60,020 62,169 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (13) (14) (15) (16) (17) (18) Gang territory \u22120.032 \u22120.153 \u22120.121 \u22120.036 \u22120.089 \u22120.101 (0.007) (0.029) (0.026) (0.012) (0.019) (0.020) [0.008] [0.033] [0.030] [0.013] [0.024] [0.023] Mean of dep. var. 0.928 0.449 0.208 0.952 0.378 0.522 Observations 208,416 202,935 202,935 60,675 58,293 202,935 Note: After experiencing gang control, gang-controlled areas have worse socioeconomic conditions than neighboring areas that were not under the control of gangs. The table presents the results of estimating Specification (1) for the variables from the 2007 census. The unit of observation is a dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). Figure 2 illustrates the findings from Table I for the first principal components of the dwelling, household, and individual characteristics. The vertical axis represents the average value of the outcome variables; the horizontal axis represents distance (in meters) to the boundaries of gang territory. Areas to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. For all the outcome variables, there is a clear discontinuity at the boundaries of gang-controlled neighborhoods.21 Socioeconomic conditions after 10 years of gang control. Note: By 2007, socioeconomic conditions had become significantly worse in gang-controlled areas. The figure illustrates the results for the first principal components of the dwelling, household, and individual characteristics from Table I. All the variables come from the 2007 census. The unit of observation is a dwelling, a household, and an individual, depending on the specification. All the variables are normalized to vary between zero and 1 with higher values representing better outcomes. The vertical axis represents the average value of the outcomes variable; the horizontal axis\u2014distance (in meters) to the boundaries of gang territory. Neighborhoods to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. The dots represent the average value of the outcome variable in that 30-meter bin. Overall, the results suggest that gangs have had a significant negative effect on socioeconomic development in the neighborhoods they control. To estimate the total monetary cost of this effect, we consider a variable that potentially aggregates all the effects of living under gang control into one\u2014household income\u2014the data for which come from the 2019 survey. The left part of Supplemental Appendix Figure A.4 presents the regression discontinuity plot for this variable. The results suggest that residents of gang neighborhoods earn approximately $350 less each month compared to residents of nongang areas. Given that the average monthly income in our sample is $625, this discontinuity implies a substantial reduction in earnings. Supplemental Appendix Table A.I presents the regression estimates for household income and the other socioeconomic characteristics from the 2019 survey. We begin by estimating the effect of gangs' territorial control on socioeconomic development using data from the 2007 census. For each census tract, we calculate the distance from its centroid to the boundaries of gang territory (in tens of meters) and implement a spatial regression discontinuity design, using this distance as the forcing variable (Specification (1)): 1yic=\u03B10+\u03B11distancec+\u03B12gang territorycdistancec+\u03B13gang territoryc+\u03B5ic. Depending on the specification, i denotes individuals, dwellings, or households, and c denotes census tracts. In turn, gang territory is a dummy variable for whether the location is controlled by the gangs, distance represents the distance to the boundaries of gang territory, and y is the outcome variable of interest. As a baseline, standard errors in parentheses are clustered by 30-meter bins denoting the distance to the boundaries of gang territory, separately for locations inside and outside of gang territory.17 The assumption behind this way of clustering the standard errors is that the correlation between the error terms depends primarily on the distance to the boundaries of gang territory (e.g., because of differential spillovers of gang activity). The alternative possibility is that the error terms are correlated only within neighboring areas. Therefore, in the main regression tables, when it is possible, we also report Conley standard errors (in brackets), which allow for spatial correlation within a 100-meter radius.18 Throughout the paper, the significance of the results remains the same regardless of which standard errors we use.19 The coefficient of interest is \u03B13, which represents the effect of living in a gang-controlled neighborhood. The two assumptions for interpreting this effect as causal are as follows. First, nongang areas close to the boundaries of gang territory should provide the appropriate counterfactual for socioeconomic development in the absence of gang control. In Section 4.3, we validate this assumption by showing that, before the arrival of the gangs, locations on both sides of the current boundaries of gang territory had similar geographic and socioeconomic characteristics as well as the same number of incarcerated individuals. We also identify places where the locations of the boundaries were determined by the presence of natural barriers that prevented the gangs from expanding further. We then use these natural boundaries of gang territory to verify that our results are not driven by the potential endogeneity of some of the other boundaries. The second assumption is that residents of gang territory did not selectively migrate from those areas to neighboring locations in the control group. Section 4.3 and Supplemental Appendix Section A.2 provide a detailed discussion of this assumption, showing that selective migration can explain no more than 14% of the socioeconomic gaps between gang and nongang areas. To estimate the effects of gangs' territorial control on socioeconomic development, we implement a spatial regression discontinuity design, focusing on San Salvador municipality. We begin by estimating the effect of gangs' territorial control on socioeconomic development using data from the 2007 census. For each census tract, we calculate the distance from its centroid to the boundaries of gang territory (in tens of meters) and implement a spatial regression discontinuity design, using this distance as the forcing variable (Specification (1)): 1yic=\u03B10+\u03B11distancec+\u03B12gang territorycdistancec+\u03B13gang territoryc+\u03B5ic. Depending on the specification, i denotes individuals, dwellings, or households, and c denotes census tracts. In turn, gang territory is a dummy variable for whether the location is controlled by the gangs, distance represents the distance to the boundaries of gang territory, and y is the outcome variable of interest. As a baseline, standard errors in parentheses are clustered by 30-meter bins denoting the distance to the boundaries of gang territory, separately for locations inside and outside of gang territory.17 The assumption behind this way of clustering the standard errors is that the correlation between the error terms depends primarily on the distance to the boundaries of gang territory (e.g., because of differential spillovers of gang activity). The alternative possibility is that the error terms are correlated only within neighboring areas. Therefore, in the main regression tables, when it is possible, we also report Conley standard errors (in brackets), which allow for spatial correlation within a 100-meter radius.18 Throughout the paper, the significance of the results remains the same regardless of which standard errors we use.19 The coefficient of interest is \u03B13, which represents the effect of living in a gang-controlled neighborhood. The two assumptions for interpreting this effect as causal are as follows. First, nongang areas close to the boundaries of gang territory should provide the appropriate counterfactual for socioeconomic development in the absence of gang control. In Section 4.3, we validate this assumption by showing that, before the arrival of the gangs, locations on both sides of the current boundaries of gang territory had similar geographic and socioeconomic characteristics as well as the same number of incarcerated individuals. We also identify places where the locations of the boundaries were determined by the presence of natural barriers that prevented the gangs from expanding further. We then use these natural boundaries of gang territory to verify that our results are not driven by the potential endogeneity of some of the other boundaries. The second assumption is that residents of gang territory did not selectively migrate from those areas to neighboring locations in the control group. Section 4.3 and Supplemental Appendix Section A.2 provide a detailed discussion of this assumption, showing that selective migration can explain no more than 14% of the socioeconomic gaps between gang and nongang areas. Table I presents the results of estimating Specification (1) using the 2007 census data. It shows that, after experiencing gang rule, individuals living in gang-controlled neighborhoods have significantly worse dwelling conditions, lower levels of education, and are less wealthy than their peers on the other side of the boundaries. For instance, residents of gang territory are estimated to have a 21-percentage-point lower probability of owning a car, a 15-percentage-point lower probability of having a high school degree, and a 5-percentage-point lower probability of living in a house with concrete walls than individuals living less than 50 meters away but not under the control of gangs.20 The results for the other measures of socioeconomic development present the same pattern. Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Has internet (1) (2) (3) (4) (5) (6) Gang territory \u22120.047 0.026 \u22120.050 \u22120.079 0.006 \u22120.131 (0.015) (0.010) (0.021) (0.021) (0.002) (0.029) [0.017] [0.010] [0.027] [0.027] [0.003] [0.039] Mean of dep. var. 0.932 0.028 0.941 0.108 0.005 0.181 Observations 72,087 60,675 62,169 62,169 62,169 59,776 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a computer Number of rooms (7) (8) (9) (10) (11) (12) Gang territory \u22120.013 \u22120.207 \u22120.135 \u22120.021 \u22120.173 \u22120.693 (0.006) (0.046) (0.033) (0.006) (0.035) (0.195) [0.005] [0.057] [0.040] [0.008] [0.045] [0.203] Mean of dep. var. 0.033 0.429 0.697 0.952 0.346 3.093 Observations 59,096 60,045 60,168 60,384 60,020 62,169 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (13) (14) (15) (16) (17) (18) Gang territory \u22120.032 \u22120.153 \u22120.121 \u22120.036 \u22120.089 \u22120.101 (0.007) (0.029) (0.026) (0.012) (0.019) (0.020) [0.008] [0.033] [0.030] [0.013] [0.024] [0.023] Mean of dep. var. 0.928 0.449 0.208 0.952 0.378 0.522 Observations 208,416 202,935 202,935 60,675 58,293 202,935 Note: After experiencing gang control, gang-controlled areas have worse socioeconomic conditions than neighboring areas that were not under the control of gangs. The table presents the results of estimating Specification (1) for the variables from the 2007 census. The unit of observation is a dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). Figure 2 illustrates the findings from Table I for the first principal components of the dwelling, household, and individual characteristics. The vertical axis represents the average value of the outcome variables; the horizontal axis represents distance (in meters) to the boundaries of gang territory. Areas to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. For all the outcome variables, there is a clear discontinuity at the boundaries of gang-controlled neighborhoods.21 Socioeconomic conditions after 10 years of gang control. Note: By 2007, socioeconomic conditions had become significantly worse in gang-controlled areas. The figure illustrates the results for the first principal components of the dwelling, household, and individual characteristics from Table I. All the variables come from the 2007 census. The unit of observation is a dwelling, a household, and an individual, depending on the specification. All the variables are normalized to vary between zero and 1 with higher values representing better outcomes. The vertical axis represents the average value of the outcomes variable; the horizontal axis\u2014distance (in meters) to the boundaries of gang territory. Neighborhoods to the left of the dashed line are located outside of gang territory; areas to the right are controlled by the gangs. The dots represent the average value of the outcome variable in that 30-meter bin. Overall, the results suggest that gangs have had a significant negative effect on socioeconomic development in the neighborhoods they control. To estimate the total monetary cost of this effect, we consider a variable that potentially aggregates all the effects of living under gang control into one\u2014household income\u2014the data for which come from the 2019 survey. The left part of Supplemental Appendix Figure A.4 presents the regression discontinuity plot for this variable. The results suggest that residents of gang neighborhoods earn approximately $350 less each month compared to residents of nongang areas. Given that the average monthly income in our sample is $625, this discontinuity implies a substantial reduction in earnings. Supplemental Appendix Table A.I presents the regression estimates for household income and the other socioeconomic characteristics from the 2019 survey. In this subsection, we analyze the assumptions that need to be satisfied for the estimates in Table I to represent the causal effect of gang control on socioeconomic development. To ensure that nongang areas close to the boundaries of gang territory are the appropriate counterfactual for gang-controlled neighborhoods, we check whether, before the arrival of the gangs, those locations had any preexisting differences in geography, socioeconomic development, or crime. First, we estimate Specification (1) for potentially important neighborhood characteristics (e.g., elevation, access to waterways, road density) and the socioeconomic characteristics from the 1992 census (e.g., dwelling conditions, having a TV).22 Columns 1\u201324 of Table II present the results. There are no discontinuities in any of the variables, confirming the notion that, initially, the locations on opposite sides of the boundaries were not different from one another. Supplemental Appendix Figure A.5 illustrates these results for the first principal components of dwelling, household, and individual characteristics.23 Neighborhood characteristics Has access to Territory used for Urban territory Road density the waterways Elevation Tree coverage coffee production (1) (2) (3) (4) (5) (6) Gang territory \u22120.018 \u22120.571 0.027 \u22120.193 0.008 \u22120.005 (0.060) (0.953) (0.069) (16.599) (0.019) (0.026) [0.052] [1.849] [0.095] [17.439] [0.023] [0.027] Mean of dep. var. 0.814 17.84 0.326 720.4 0.049 0.029 Observations 476 476 476 476 476 476 Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Shared bathroom (7) (8) (9) (10) (11) (12) Gang territory \u22120.015 \u22120.003 \u22120.032 \u22120.036 \u22120.007 0.021 (0.036) (0.028) (0.047) (0.039) (0.017) (0.032) [0.035] [0.030] [0.046] [0.030] [0.013] [0.029] Mean of dep. var. 0.813 0.100 0.816 0.182 0.030 0.142 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a blender Number of rooms (13) (14) (15) (16) (17) (18) Gang territory \u22120.004 \u22120.049 \u22120.030 0.009 0.014 \u22120.069 (0.009) (0.051) (0.054) (0.019) (0.032) (0.170) [0.007] [0.043] [0.049] [0.019] [0.034] [0.172] Mean of dep. var. 0.034 0.285 0.320 0.860 0.625 2.670 Observations 64,899 64,899 64,899 64,899 64,899 64,899 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (19) (20) (21) (22) (23) (24) Gang territory \u22120.000 \u22120.014 \u22120.019 \u22120.005 \u22120.016 \u22120.013 (0.011) (0.028) (0.017) (0.031) (0.030) (0.018) [0.009] [0.028] [0.017] [0.031] [0.026] [0.018] Mean of dep. var. 0.904 0.314 0.112 0.863 0.525 0.380 Observations 234,749 227,281 227,281 64,899 64,899 227,281 Number of incarcerations per km2 prior to 1997: All crimes Homicide Robbery Sex crimes Assault Other violent crimes (25) (26) (27) (28) (29) (30) Gang territory \u22122.096 1.548 \u22120.640 \u22121.404 \u22120.823 \u22121.777 (18.200) (1.291) (3.979) (1.321) (3.400) (1.873) Mean of dep. var. 114.6 4.476 21.61 6.147 19.78 9.275 Observations 86 86 86 86 86 86 Note: Before the arrival of the gangs, locations on either side of the boundaries of gang territory had similar geographic and socioeconomic characteristics. The table presents the results of estimating Specification (1) for the neighborhood characteristics and the variables from the 1992 census. The unit of observation is a census tract, dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). In columns 25\u201330, the Conley standard errors are not reported because there the location of the observations is not defined (the unit of observation is a 10-meter bin, denoting the distance to the boundaries of gang territory). Next, we estimate Specification (1) for the level of crime prior to the arrival of the gangs, measured by the number of people incarcerated in different parts of the city. Using the incarceration records from San Salvador's prisons, we geocode the residential addresses of the 4726 individuals who had been incarcerated prior to 1997. Then, we calculate the number of incarcerations per square kilometer for each 10-meter bin, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries).24 Columns 25\u201330 of Table II present the results of estimating Specification (1) for different types of crimes, showing that locations on both sides of the boundaries had similar levels of crime prior to the arrival of the gangs.25 Overall, before the mid-1990s, gang and nongang locations had similar levels of socioeconomic development and crime, allowing us to conclude that nongang areas close to the boundaries are the appropriate counterfactual for gang neighborhoods in the absence of gang control. To address any remaining concerns regarding the potential endogeneity of the boundaries, we perform the following analysis. We identify three major multilane roads\u2014Bulevar Venezuela, 49 Avenida Sur, and Autopista Comalapa\u2014which together form more than 45 kilometers of natural barriers that largely determined the southern and western boundaries of gang territory.26 Table III reports the results of estimating Specification (1) using these three roads, rather than the actual boundaries of gang territory, to predict the location of the borders. The results remain highly significant, demonstrating that they are not driven by the potential endogeneity of some gang-territory boundaries. Dwelling characteristics Household characteristics Walls made Has sewerage Use electricity for of concrete Bare floor infrastructure lighting and cooking No bathroom Has internet (1) (2) (3) (4) (5) (6) Gang territory \u22120.096 0.047 \u22120.064 \u22120.226 0.004 \u22120.287 (0.014) (0.009) (0.014) (0.056) (0.002) (0.029) [0.021] [0.012] [0.022] [0.102] [0.002] [0.101] Mean of dep. var. 0.947 0.021 0.966 0.050 0.002 0.097 Observations 7424 6312 6348 6348 6348 6056 Household characteristics Has a motorcycle Has a car Has a phone Has a TV Has a computer Number of rooms (7) (8) (9) (10) (11) (12) Gang territory \u22120.021 \u22120.606 \u22120.385 \u22120.045 \u22120.556 \u22122.061 (0.009) (0.052) (0.032) (0.014) (0.062) (0.321) [0.013] [0.155] [0.053] [0.014] [0.118] [0.398] Mean of dep. var. 0.033 0.305 0.671 0.957 0.256 2.814 Observations 6021 6080 6098 6119 6086 6348 Individual characteristics First principal component of the: Can read Has a high Has a university Dwelling Household Individual and write school degree degree characteristics characteristics characteristics (13) (14) (15) (16) (17) (18) Gang territory \u22120.167 \u22120.406 \u22120.233 \u22120.071 \u22120.246 \u22120.266 (0.039) (0.032) (0.054) (0.009) (0.025) (0.028) [0.038] [0.049] [0.107] [0.014] [0.059] [0.051] Mean of dep. var. 0.926 0.406 0.146 0.964 0.335 0.486 Observations 21,488 20,722 20,722 6312 5933 20,722 Note: The table presents the results of estimating Specification (1), using the locations of major roads and boulevards (geographical barriers) as the predicted boundaries of gang territory. To ensure comparability of the census tracts on both sides of the regression discontinuity threshold, we exclude 25% of the largest census tracts, which are disproportionately present outside gang territory. We also include dummies for the three remaining quartiles of the census tract size distribution. Table S.II in the Supplementary Materials reports the results of estimating the same regression specification without excluding the largest census tracts and, instead, including dummies for all four quartiles of the census tract size distribution. All the variables come from the 2007 census. The unit of observation is a dwelling, household, or individual, depending on which characteristics are being considered. In the individual-level regressions, the sample consists of the entire population. Omitted controls include a linear trend in distance to the boundaries of gang territory, separately for locations on each side of the boundaries. Standard errors in parentheses are clustered by 30-meter bins, denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). Standard errors in brackets are adjusted to allow for spatial correlation within a 100-meter radius (Conley correction). We also perform a placebo analysis in which we use major multilane roads that did not define the boundaries of gang territory to ensure that these geographical barriers did not affect socioeconomic development through factors unrelated to the gang boundaries. The analysis focuses on a series of consecutive roads, ranging from Redondel Masferrer in the west to Avenida Independencia in the east, that split San Salvador into two similar-size parts (see Figure 1). We then estimate whether the level of socioeconomic development changes at the placebo boundaries.27 Supplemental Appendix Table A.II presents the results, confirming the notion that major roads do not affect development outcomes through factors unrelated to the gang boundaries. A potential concern is that the boundaries of gang territory may not have remained stable between the time they were formed (soon after the gangs emerged) and 2015, when EDH published the map of gang territory. If the EDH map does not accurately reflect which areas were controlled by the gangs in 2007, the estimates in Table I would be biased toward zero (i.e., against finding an effect).28 Thus, the results in Table I should be interpreted as the lower bound of the effects of gang control. Nevertheless, in Supplemental Appendix Section A.1, we demonstrate that the gang-territory boundaries have remained stable since they were first formed. Specifically, we exploit the fact that most gang-related homicides take place precisely at the boundaries of gang territory because of people attempting to enter or leave gang-controlled neighborhoods without permission.29 As a result, by showing that, throughout the years, gang-related homicides consistently take place right at the boundaries from the EDH map, we are able to confirm the validity of that map and demonstrate the stability of those boundaries. In addition, in 2023, we conducted a new survey of individuals from gang and nongang neighborhoods, in which, among other questions, the respondents were asked whether their neighborhood had been controlled by gangs 20 years ago, during the presidency of Francisco Flores P\u00E9rez (President of El Salvador in 1999\u20132004). As shown in Supplemental Appendix Figure A.6, the share of respondents answering in the affirmative significantly increases at the boundaries of gang territory, suggesting that the borders have remained stable over time. Another assumption that needs to be satisfied for our estimates to be interpreted as causal is that there has been no selective migration of individuals across the regression discontinuity threshold. Selective migration can affect our results in two ways. The first is what we call in-sample migration: individuals moving from a neighborhood on one side of the boundaries to an area on the other side of the boundaries while remaining in San Salvador and, thus, in our sample. This type of migration would be a direct threat to identification because it would imply that individuals can manipulate their treatment status. The second is what we call out-of-sample migration: individuals moving from San Salvador to a different municipality in El Salvador or abroad. This type of migration does not invalidate the identification strategy, but it changes the interpretation of the mechanism through which the gangs affect local socioeconomic conditions (i.e., that gang control makes wealthy, educated individuals leave San Salvador). In this subsection, we consider the direct threat to identification that comes from in-sample migration. To show that in-sample migration is not driving our findings, we leverage our 2019 survey, where, among other questions, we asked individuals whether they had lived in the exact same place their entire life: 77% of respondents said they had. This information allows us to compare the results for the full sample and for the subsample of respondents for whom we know the ex ante treatment status (i.e., that they lived in the location before the arrival of the gangs). In the absence of in-sample migration, the two sets of results would be quite similar, whereas, if the results are determined by in-sample migration, the discontinuities would appear only in the full sample. Notably, this exercise also allows us to determine that the results are not driven by wealthy and educated newcomers choosing to settle in nongang parts of San Salvador. By restricting the sample to individuals who have lived in the same neighborhood their entire life, by definition, we exclude all newcomers. When we limit the sample this way, the results of the regression discontinuity analysis are practically unchanged. Supplemental Appendix Figure A.4 illustrates this fact by showing the two regression discontinuity plots for household income. The left-hand side of the figure presents the results for the full sample; the right-hand side presents the subsample of never-movers. The two plots are quite similar, suggesting that the results are not driven by selective in-sample migration. Supplemental Appendix Table A.I reports the regression estimates for the socioeconomic characteristics from our 2019 survey, both for the full sample and for the sample of never-movers; Figure S.5 in the Supplementary Materials illustrates these results.30 For a detailed discussion of out-of-sample migration (i.e., individuals moving from San Salvador to a different municipality or abroad), see Supplemental Appendix Section A.2. In Section 6, we also demonstrate the absence of pretrends in socioeconomic development between areas with and without gang presence. Specifically, we perform a difference-in-differences analysis using nighttime light density, household income, and firm openings to show that these variables only started to change after the deportation of the gang leaders from the United States to El Salvador. In Supplemental Appendix Section A.3, we also present a wide range of additional robustness checks to ensure that the estimates in Table I represent the causal effect of gang control on socioeconomic development. We obtained annual school census data from the Ministry of Education covering 2005 to 2017 (MINED (2017)). These censuses include annual information on the number of students enrolled in each grade at the beginning of the year and the number of students who graduated from each grade, allowing us to calculate the dropout rate for each school-year in our sample. Some of the schools also participated in the Program for Adult Literacy and Education, which provides school-level education for adults without a degree. For these schools, we also calculate the dropout rate among adults. DIGESTYC also provided us with maps of the census tracts (segmentos censales) for the 1992 and 2007 censuses (DIGESTYC (2007a)). Each census tract represents a tiny area with a fixed geographic perimeter. In 2007, the average census tract in our sample included 131 households and 473 individuals. This small size allows us to accurately estimate the location of the respondents using the geographic coordinates of the census tracts' centroids. In addition, because of the difficulty with attributing treatment status, we exclude 27 census tracts (4% of the census tracts in San Salvador) whose centroids are outside gang neighborhoods but have at least 25% of their territory controlled by the gangs. Finally, we limit our analysis to census tracts located within 420 meters of the boundaries of gang territory because, after that, there are gaps in the distribution of observations both inside and outside of gang-controlled areas.16 The General Directorate of Statistics and Censuses (Direcci\u00F3n General de Estad\u00EDsticas y Censos, DIGESTYC) provided us with de-identified microdata for the 1992 (DIGESTYC (1992)) and 2007 (DIGESTYC (2007b)) censuses. The data cover the socioeconomic characteristics of all the country's households and individuals, including educational attainment and material ownership (e.g., having a car and a TV). Both censuses also recorded the characteristics of all the dwellings in El Salvador. Notably, the data for these variables were recorded by the enumerators based on their observations, not self-reported by the respondents. For most outcome variables, both censuses worded the questions exactly the same. Hence, the data are directly comparable across census exercises.15 In this section, we document our primary sources of data. For further details on these data, as well as a description of our ancillary data sources, see the Supplementary Materials. Table S.I in the Supplementary Materials presents the summary statistics of the outcome variables used in our analysis. In 2015, a local newspaper\u2014El Diario de Hoy (EDH)\u2014published the map that we use in this study (see Figure 1). It delimited the locations controlled by MS-13 and 18th Street in San Salvador (EDH (2015)). EDH based its report on information and cartography from the Ministry of Justice and Public Security and the PNC. The newspaper further validated the map of gang boundaries by confirming that the gang-controlled neighborhoods on the map are also the places where its distribution network had periodic encounters with gang members. We, too, have independently verified the accuracy of the map published by EDH.14 Moreover, in Supplemental Appendix Section A.1, we present evidence on how the boundaries of gang territory had remained stable between the time they were formed in the late 1990s and 2015, when EDH published its map. The General Directorate of Statistics and Censuses (Direcci\u00F3n General de Estad\u00EDsticas y Censos, DIGESTYC) provided us with de-identified microdata for the 1992 (DIGESTYC (1992)) and 2007 (DIGESTYC (2007b)) censuses. The data cover the socioeconomic characteristics of all the country's households and individuals, including educational attainment and material ownership (e.g., having a car and a TV). Both censuses also recorded the characteristics of all the dwellings in El Salvador. Notably, the data for these variables were recorded by the enumerators based on their observations, not self-reported by the respondents. For most outcome variables, both censuses worded the questions exactly the same. Hence, the data are directly comparable across census exercises.15 DIGESTYC also provided us with maps of the census tracts (segmentos censales) for the 1992 and 2007 censuses (DIGESTYC (2007a)). Each census tract represents a tiny area with a fixed geographic perimeter. In 2007, the average census tract in our sample included 131 households and 473 individuals. This small size allows us to accurately estimate the location of the respondents using the geographic coordinates of the census tracts' centroids. In addition, because of the difficulty with attributing treatment status, we exclude 27 census tracts (4% of the census tracts in San Salvador) whose centroids are outside gang neighborhoods but have at least 25% of their territory controlled by the gangs. Finally, we limit our analysis to census tracts located within 420 meters of the boundaries of gang territory because, after that, there are gaps in the distribution of observations both inside and outside of gang-controlled areas.16 To document the mechanisms through which gangs affect socioeconomic development, we conducted our own geocoded survey in San Salvador in 2019. To be consistent with the census data, we conducted the survey in areas within 420 meters of the boundaries of gang territory. The survey was designed to be representative by 30-meter bins denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). It consisted of in-person interviews and contained questions related to individuals' mobility, employment, income, satisfaction with public goods provision, and the role of formal (i.e., government) and informal institutions in resolving neighborhood problems. However, for security reasons, we were unable to ask individuals direct questions related to gang activity. The data on the extortion payments to the gangs made by firms and individuals in San Salvador come from the following three sources: (i) a geocoded survey of small and medium-sized enterprises conducted by a local think tank in 2015 (FUSADES (2015)); (ii) geocoded confidential internal records of a large Salvadoran distribution firm on all the extortion payments it made to the gangs from 2012 to 2019 (see Brown, Montero, Schmidt-Padilla, and Sviatschi (2020)); and (iii) our own geocoded telephone survey, which we conducted in San Salvador in 2020. For more information on these data sources, see the Supplemental Appendix. We obtained annual school census data from the Ministry of Education covering 2005 to 2017 (MINED (2017)). These censuses include annual information on the number of students enrolled in each grade at the beginning of the year and the number of students who graduated from each grade, allowing us to calculate the dropout rate for each school-year in our sample. Some of the schools also participated in the Program for Adult Literacy and Education, which provides school-level education for adults without a degree. For these schools, we also calculate the dropout rate among adults. Once the gangs assert control over a particular neighborhood, they zealously protect it from outside influence. The main threat to the gangs' security comes from rival gang members and police informants entering their territory and arresting or assassinating them. A related fear is that residents of their territory will defect and provide information about the gangs' whereabouts and activities to the police or the rival gang. Therefore, to improve their security, both MS-13 and 18th Street instituted a system of checkpoints, requiring individuals attempting to enter or exit the area to show their identification cards, which have the residential address printed on them (ICG (2018)). To make this system work, the gangs dispatch junior gang members and collaborators (banderas) to patrol the boundaries of their territory (ICG (2018), Boerman and Golob (2020)).10 This system of territorial control, which existed since at least 1999 (Palma (1999)) until 2022, was supported by the gangs' ability to entice and coerce new banderas to join their criminal structures. Both MS-13 and 18th Street also used sophisticated techniques to track down defectors; many end up killed.11 Overall, gang-imposed restrictions on mobility were such a prominent issue in El Salvador that, in 2016, the criminal code was reformed to introduce the crime of \u201Cillegal restriction of freedom of movement,\u201D which penalizes \u201Cany person who, by violence, intimidation or threat to persons or property, prevents another from freely moving, entering, remaining or leaving any place in the territory of the Republic.\u201D In addition to improving security, checkpoints also allowed the gangs to extort individuals and businesses that have been allowed to enter or exit their territory (e.g., distribution and transportation companies). Mart\u00EDnez (2016) describes the situation as follows: \u201COne of the great advantages of having borders between rival gangs is imposing taxes. Everyone pays: companies that install cable television, the women that sell in the central markets, taxi drivers.\u201D12 Both MS-13 and 18th Street relied on extortion as their main source of revenue; they collected regular payments from individuals and businesses throughout San Salvador, including nongang parts of the city (InSight Crime and CLALS (2018)).13 As a result of restrictions on their mobility, many residents of gang-controlled neighborhoods have poor labor-market outcomes, being unable to work in locations outside of gang territory. However, as we show in Section 5.1, this does not happen due to a change in labor-market conditions directly at the boundaries of gang territory. Instead, people living in nongang areas close to the boundaries have better jobs due to their ability to commute to other parts of the city, where the largest and best-paying firms are located. The reason for the absence of a change in local labor-market conditions is that, when it comes to collecting extortion payments (and other gang-related activities), gang members and their collaborators do not face restrictions on their mobility. As we show in Section 5.3, individuals and businesses in nongang areas close to the boundaries of gang territory have the same exposure to extortion and other gang-related crimes as residents of gang areas. Thus, territorial control also functions as a \u201Cbridgehead\u201D from which the gangs can extort nearby locations that are not under their control. As the de facto authorities in their territories, gangs claim to be \u201Cproviding a \u2018community service\u2019 by protecting locals from other criminals and corrupt police\u201D (ICG (2018)). In reality, while such claims are not totally misleading, we find that, for two reasons, the gangs provide limited public services. First, unlike many other criminal organizations such as drug cartels or the Italian Mafia, Salvadoran gangs are quite poor; a rank-and-file gang member earns, at most, $15 a week, half the minimum wage of an agricultural day laborer (Mart\u00EDnez, Lemus, Mart\u00EDnez, and Sontag (2016)). Thus, the gangs lack sufficient resources to invest in improving the economic conditions in the areas they control. The second reason relates to one of the peculiarities of the urban context in which the gangs and the state coexist. Given the state's proximity to gang territory, in the absence of mobility restrictions, government workers can provide public goods throughout the city, not just in areas controlled by the state. Moreover, the government has had at least two reasons to continue investing in infrastructure and social and educational programs in gang-controlled neighborhoods, even before the 2022 crackdowns. First, if the government were to stop providing public goods in gang territory, its legitimacy in the eyes of the local population would likely be undermined, increasing support for the gangs (Zoethout (2015)). Second, such a move could be costly for incumbent politicians: \u201CGangs serve as intermediaries between political parties and residents in controlled neighborhoods [\u2026] offer[ing] political candidates what no other broker or intermediary can provide\u2014the use of coercive violence to sway elections in their favor\u201D (C\u00F3rdova (2019)). Thus, politicians who do not provide social programs in gang areas would likely see their reelection prospects dwindle, and their lives endangered. In turn, the gangs have been willing to allow non-police-related government workers to enter their territory to provide public services, both because gang members directly benefit from their availability and because government investment indirectly contributes to higher revenues from extortion. For example, the construction and repair of roads in gang-controlled neighborhoods has allowed the gangs to collect more extortion payments from trucks and transportation companies passing through their territory (ICG (2017)). In this section, we present an overview of how MS-13 and 18th Street developed in Salvadoran migrant communities in the United States and how criminal capital was exported from these communities to El Salvador following a shift in American immigration policy in 1996. We then describe how, once in El Salvador, the gangs quickly reestablished their criminal structures, began recruiting, and gained territorial control over many urban neighborhoods throughout the country, most notably in the capital, San Salvador. We also provide qualitative evidence on how the boundaries of gang territory were formed soon after the arrival of the criminal deportees, based on the system of territorial control that the gangs had developed in the United States. Southern California, especially Los Angeles, became home for thousands of Salvadorans fleeing the country's descent into civil war in the 1980s (Stanley (1987)). Lacking an established support network, Salvadoran migrants lived in poor, overcrowded neighborhoods and often faced discrimination from other migrant groups (Brettell (2011)). In a typical family, both parents worked, often leaving the children unsupervised (Savenije (2009)). Left on their own and facing prejudice from other migrant groups and their gangs, some Salvadoran youth formed the precursors to MS-13\u2014self-defense groups that were initially better known for petty crimes and for their affinity to cannabis and heavy metal, rather than for brutal violence\u2014while others joined 18th Street, an existing Mexican gang (Dunn (2007), Cruz (2010), Mart\u00EDnez and Mart\u00EDnez (2018)). As membership in MS-13 and 18th Street grew across Salvadoran immigrant communities, the gangs became known to the local authorities. Some of their members were sent to prison, where they gained criminal capital and social connections that helped them solidify their structures (Womer and Bunker (2010), Mart\u00EDnez and Mart\u00EDnez (2018)). By the mid-1980s, both MS-13 and 18th Street had developed independent identities, organizational structures revolving around territory-based cliques (clicas), and a fierce rivalry that continues to this day (Ward (2013)). Many gangs in 1980s Los Angeles shared a noteworthy trait: they precisely demarcated their territory, which greatly contributed to their identity and development (Coughlin and Venkatesh (2003)). For example, they used graffiti to define the territories under their control and to project authority over their rivals and the local population (Tita, Cohen, and Engberg (2005), Artsy (2018)). This demarcation had a profound impact on the mobility and decisions of individuals living in gang territories: \u201COne of the really important things to think about is how the invisible borders [\u2026] add costs we often don't think about. If I'm a young person growing up in a particular neighborhood [in Los Angeles] and the closest movie theater or the closest shopping mall is claimed by a rival gang, [\u2026] I'm going to have to spend more time on a bus, put more gas in my car, to travel to other areas\u201D (Artsy (2018)). In an observational study of incarcerated MS-13 gang members in Los Angeles County, Nu\u00F1o and Maguire (2021) highlight how \u201Cmost MS-13 members are involved in cliques that claim certain turf or territory (96.3%) and would be willing to use violence to defend it against others (92.6%),\u201D relying on graffiti and outposts to mark and control their territories.5 This facet of gang culture became a fundamental trait of gang structures in El Salvador. In 1996, to reduce crime in urban areas and address the surge in irregular migration, the United States passed the Illegal Immigration Reform and Immigration Responsibility Act (IIRIRA) (Chac\u00F3n (2009), ACMM+ (2017)). IIRIRA drastically increased immigration enforcement, creating procedures for expedited removal, adding new grounds for deportation, and increasing the number of border patrol agents. This shift in American policy had a profound impact on El Salvador. During the first wave of deportations in 1996, over 500 Salvadoran gang members were deported from the United States, leading to devastating changes in Salvadoran communities (Sviatschi (2022b)). Given that they did not have criminal records in El Salvador, the repatriated gang members\u2014many of whom were serving or had previously served sentences in the United States\u2014gained their freedom after returning to their home country (Ward (2013)). El Salvador was still recovering from its civil war, which ended in 1992, and the Salvadoran state did not have the resources to prevent the gangs from expanding. The 1992 Peace Accords mandated the creation of a new police force\u2014the National Civil Police (Polic\u00EDa Nacional Civil, PNC)\u2014and at the time of the repatriations, the structure of the PNC was still being defined (e.g., no rural police units existed until 2004). The repatriated gang leaders exploited this low level of state capacity and expanded their operations to many urban areas. Most of the repatriated MS-13 and 18th Street gang members had lived in the United States since a young age and knew little about their home country. For this reason, most of them returned to their birth municipalities, relying on their family networks to resettle in a new environment (DeCesare (1998), Sviatschi (2022b)). Seeking social acceptance and status, the gang deportees banded together and tapped into local youth groups to replicate the gang structures they had in California. Even though only a few hundred gang members were repatriated from the United States in 1996, they quickly expanded their ranks, recruiting new members from the local population. Many locals were attracted by the camaraderie and respect that the gangs offered; others sought more tangible material gains such as money and drugs (Cruz and Portillo Pe\u00F1a (1998), Mart\u00EDnez and Mart\u00EDnez (2018)). Sviatschi (2022b), in particular, shows how, after the MS-13 and 18th Street gang members arrived, and began recruiting adolescents to join their structures, El Salvador experienced an immediate increase in gang-related activities. According to the local authorities, by the end of 1996, at least 20 thousand individuals had joined the two gangs (Cruz and Portillo Pe\u00F1a (1998)). Taking advantage of the postwar environment and widespread destitution, both MS-13 and 18th Street quickly expanded their influence over many neighborhoods, particularly in the capital, San Salvador, and other urban areas, \u201Cgain[ing] complete control of [certain] localities\u201D (Zoethout (2015)). This rapid formation and enforcement of boundaries was possible due to four main factors: (i) the gangs' experience in implementing a system of territorial control in California, (ii) the importance of territorial control for the gangs' identity and long-term survival, (iii) the gangs' ability to recruit new members from the local population, and (iv) El Salvador's low state capacity in the 1990s. The system of territorial control built upon the strategy the gangs honed in California, where demarcation, largely based on natural barriers, split urban areas into small geographical confines known as cliques (Miguel Cruz (2010)). In El Salvador, the gangs also defined their territory based on natural barriers such as major roads and boulevards (Tenorio (2002), Vega (2015)). We identify and take advantage of three such major roads (see Figure 1)\u2014Bulevar Venezuela, 49 Avenida Sur, and Autopista Comalapa, all of which existed in 1996\u2014that largely determined the southern and western boundaries of gang territory. All of these multilane roads hinder the gangs from expanding beyond them to exert control over neighborhoods on both their sides. Gang territory in San Salvador. In Section 4.3, we take advantage of these natural boundaries of gang territory to verify that the results of the regression discontinuity analysis are not determined by the potential endogeneity of some of the other boundaries. We also show (i) that the borders of gang-controlled neighborhoods were not formed as a result of preexisting spatial differences in socioeconomic conditions or crime before the arrival of the criminal deportees and (ii) that the natural barriers that did not contribute to the formation of the gang boundaries do not affect socioeconomic outcomes. Our conversations with the police and individuals living in gang areas suggest that, in San Salvador, the boundaries of gang territory have remained stable since they were formed.6,7 The police repeatedly tried to regain control over those locations, but their attempts succeeded only in 2022, after the period studied in this paper.8,9 In part, the pre-2022 efforts failed because the gangs had formed ties with the local population, cultivating a network of informants that allowed them to elude capture (Cruz (2010), Ward (2013), Boerman and Golob (2020)). The importance of the boundaries of gang territory has been widely documented. International Crisis Group (ICG) describes the situation as follows: \u201CIn some areas, gangs have accumulated so much power that they have become de facto custodians of these localities, setting up road-blocks, supervising everyday life and imposing their own law\u201D (ICG (2017)). In another interview, a resident of San Salvador is even more direct: \u201CDo you see that place across the road? I could never get in there since it's the 18th Street gang's territory. If they see me in there, they might think I'm a spy [\u2026] and I could easily get killed\u201D (ICG (2018)). Once the gangs assert control over a particular neighborhood, they zealously protect it from outside influence. The main threat to the gangs' security comes from rival gang members and police informants entering their territory and arresting or assassinating them. A related fear is that residents of their territory will defect and provide information about the gangs' whereabouts and activities to the police or the rival gang. Therefore, to improve their security, both MS-13 and 18th Street instituted a system of checkpoints, requiring individuals attempting to enter or exit the area to show their identification cards, which have the residential address printed on them (ICG (2018)). To make this system work, the gangs dispatch junior gang members and collaborators (banderas) to patrol the boundaries of their territory (ICG (2018), Boerman and Golob (2020)).10 This system of territorial control, which existed since at least 1999 (Palma (1999)) until 2022, was supported by the gangs' ability to entice and coerce new banderas to join their criminal structures. Both MS-13 and 18th Street also used sophisticated techniques to track down defectors; many end up killed.11 Overall, gang-imposed restrictions on mobility were such a prominent issue in El Salvador that, in 2016, the criminal code was reformed to introduce the crime of \u201Cillegal restriction of freedom of movement,\u201D which penalizes \u201Cany person who, by violence, intimidation or threat to persons or property, prevents another from freely moving, entering, remaining or leaving any place in the territory of the Republic.\u201D In addition to improving security, checkpoints also allowed the gangs to extort individuals and businesses that have been allowed to enter or exit their territory (e.g., distribution and transportation companies). Mart\u00EDnez (2016) describes the situation as follows: \u201COne of the great advantages of having borders between rival gangs is imposing taxes. Everyone pays: companies that install cable television, the women that sell in the central markets, taxi drivers.\u201D12 Both MS-13 and 18th Street relied on extortion as their main source of revenue; they collected regular payments from individuals and businesses throughout San Salvador, including nongang parts of the city (InSight Crime and CLALS (2018)).13 As a result of restrictions on their mobility, many residents of gang-controlled neighborhoods have poor labor-market outcomes, being unable to work in locations outside of gang territory. However, as we show in Section 5.1, this does not happen due to a change in labor-market conditions directly at the boundaries of gang territory. Instead, people living in nongang areas close to the boundaries have better jobs due to their ability to commute to other parts of the city, where the largest and best-paying firms are located. The reason for the absence of a change in local labor-market conditions is that, when it comes to collecting extortion payments (and other gang-related activities), gang members and their collaborators do not face restrictions on their mobility. As we show in Section 5.3, individuals and businesses in nongang areas close to the boundaries of gang territory have the same exposure to extortion and other gang-related crimes as residents of gang areas. Thus, territorial control also functions as a \u201Cbridgehead\u201D from which the gangs can extort nearby locations that are not under their control. As the de facto authorities in their territories, gangs claim to be \u201Cproviding a \u2018community service\u2019 by protecting locals from other criminals and corrupt police\u201D (ICG (2018)). In reality, while such claims are not totally misleading, we find that, for two reasons, the gangs provide limited public services. First, unlike many other criminal organizations such as drug cartels or the Italian Mafia, Salvadoran gangs are quite poor; a rank-and-file gang member earns, at most, $15 a week, half the minimum wage of an agricultural day laborer (Mart\u00EDnez, Lemus, Mart\u00EDnez, and Sontag (2016)). Thus, the gangs lack sufficient resources to invest in improving the economic conditions in the areas they control. The second reason relates to one of the peculiarities of the urban context in which the gangs and the state coexist. Given the state's proximity to gang territory, in the absence of mobility restrictions, government workers can provide public goods throughout the city, not just in areas controlled by the state. Moreover, the government has had at least two reasons to continue investing in infrastructure and social and educational programs in gang-controlled neighborhoods, even before the 2022 crackdowns. First, if the government were to stop providing public goods in gang territory, its legitimacy in the eyes of the local population would likely be undermined, increasing support for the gangs (Zoethout (2015)). Second, such a move could be costly for incumbent politicians: \u201CGangs serve as intermediaries between political parties and residents in controlled neighborhoods [\u2026] offer[ing] political candidates what no other broker or intermediary can provide\u2014the use of coercive violence to sway elections in their favor\u201D (C\u00F3rdova (2019)). Thus, politicians who do not provide social programs in gang areas would likely see their reelection prospects dwindle, and their lives endangered. In turn, the gangs have been willing to allow non-police-related government workers to enter their territory to provide public services, both because gang members directly benefit from their availability and because government investment indirectly contributes to higher revenues from extortion. For example, the construction and repair of roads in gang-controlled neighborhoods has allowed the gangs to collect more extortion payments from trucks and transportation companies passing through their territory (ICG (2017)). How do nonstate armed actors affect economic development? On the one hand, they can impede the state from providing public goods, enforcing property rights and contracts, and preventing violence (Acemoglu, Johnson, and Robinson (2001), Michalopoulos and Papaioannou (2013)). On the other hand, if the state is weak and unable to control parts of its territory, nonstate armed actors may take on the role of the state in fulfilling essential institutional functions, potentially enabling economic growth (Tilly (1985), Olson (1993), Bates, Greif, and Singh (2002), IACCA+ (2019), De la Sierra (2020)) and competing for the \u201Chearts and minds\u201D of civilians (IACCA+ (2019), De la Sierra (2020), Blattman, Duncan, Lessing, and Tobon (2022)). Overall, how and why nonstate armed actors affect development remains an open question. In this paper, we study how a specific type of nonstate armed actor\u2014namely, criminal organizations\u2014affects socioeconomic development. In urban areas in the developing world, millions of people live under some form of criminal governance (Lessing (2021), Blattman, Duncan, Lessing, and Tobon (2022)). Criminal organizations function mainly in urban centers, often controlling parts of the city, while other parts are controlled by the state. In particular, this paper analyzes how two of the world's most prolific gangs\u2014MS-13 (Mara Salvatrucha) and 18th Street (Barrio 18)\u2014affected socioeconomic development in El Salvador.1 We exploit a natural experiment that took place in El Salvador. Before the mid-1990s, El Salvador had no significant criminal organizations. However, in 1996, after a shift in American immigration policy that made it easier to deport individuals\u2014especially those with criminal backgrounds\u2014back to their country of origin, many Salvadoran migrants who were members of California-based gangs (specifically, MS-13 and 18th Street) were deported back to El Salvador. These deported gang members reestablished their gangs in El Salvador and quickly gained control over certain parts of the country. To protect their territory from outsiders, the gangs also re-created a system of borders and checkpoints that they used to establish territorial dominance in California (Nu\u00F1o and Maguire (2021)), resulting in the division of urban areas between the gangs and the state. To estimate the effects of gangs' territorial control, we use the boundaries of gang-controlled neighborhoods in El Salvador's capital, San Salvador, to implement a spatial regression discontinuity design. These territorial demarcations were formed soon after the gang leaders arrived in 1996, and they roughly coincide with existing natural barriers, such as boulevards and highways. We measure the outcome variables using the 2007 census and our own geocoded survey, which we conducted in 2019 in both gang and nongang neighborhoods. Our results indicate that residents of gang-controlled neighborhoods in San Salvador have worse dwelling conditions, less income, and a lower probability of owning durable goods compared to individuals living just 50 meters away but outside of gang territory. They are also less likely to work in large firms. For instance, we find that residents of gang areas have $350 less monthly household income (the sample mean is $625) compared to individuals living in neighboring nongang locations and have a 12-percentage-point lower probability of working in a firm with at least 100 employees. The results are highly robust to the choice of empirical specifications. These differences in living standards did not exist before the gang leaders arrived. We replicate the regression discontinuity design with data from the 1992 census to show that, before the gangs emerged, areas on both sides of the gang borders had similar socioeconomic and geographic characteristics, as well as similar levels of crime. These results are consistent with the fact that the boundaries of gang territory were not formed based on preexisting socioeconomic differences, but rather on the availability of natural barriers (i.e., major roads). We also show that the natural barriers are not associated with differences in socioeconomic conditions when they do not determine gang territorial control. An important mechanism through which gangs affect socioeconomic development in the neighborhoods they control is related to restrictions on individuals' mobility. The gangs' long-term survival depends on their ability to secure the borders of their territory and prevent the police and rival gang members from arresting or killing them. Therefore, to maintain secure control over their territory, both MS-13 and 18th Street instituted a system of checkpoints, not allowing individuals to freely enter or leave gang-controlled neighborhoods (ICG (2018)). The security of their territory also allows the gangs to use it as a bridgehead from which they conduct extortion raids to neighboring areas. Using the data from our geocoded survey, we perform a spatial regression discontinuity design to document the presence of restrictions on individuals' mobility. We show that residents of gang areas are 50 percentage points more likely to work in gang territory compared to individuals living only 50 meters away but on the nongang side of the boundaries. They are also more likely to say that gang-imposed borders prevented them from getting jobs in large firms in other parts of the city, less likely to say that there is freedom of movement in the neighborhood where they live, and less likely to have been to places outside of San Salvador. However, those individuals do not have lower levels of mobility per se. Using cell phone ping data, we show that, while residents of gang-controlled neighborhoods are largely confined in their movements to gang territory, they travel the same distance as their peers on the other side of the gang boundaries. These mobility restrictions affect labor-market outcomes: residents of gang territory end up working in smaller firms and earning lower wages because they cannot commute to the areas where the largest and best-paying firms are located. Notably, labor-market conditions do not change directly at the boundaries of gang territory (i.e., there is no change in firm size, wages, profitability, or the number of business establishments). Instead, we show that, after the emergence of the gangs, new business establishments increasingly opened in areas far away from gang territory. Nonetheless, residents of nongang neighborhoods close to the boundaries were able to take advantage of these new labor-market opportunities by commuting to parts of the city where the largest firms are located, whereas individuals living in gang areas were prevented from doing so by the restrictions on their mobility. Another factor limiting socioeconomic development in gang-controlled neighborhoods is related to educational attainment. Using school census data, we show that the annual school dropout rate is 2 percentage points higher in gang territory than in nongang areas. The differences in educational attainment contribute to further widening the income gap between gang and nongang territories. We also examine other potential determinants of lower socioeconomic development in gang-controlled neighborhoods, but we find that, in this context, they cannot explain the results. In particular, we demonstrate that individuals and firms on both sides of the boundaries are equally exposed to extortion and other violent crimes. This result is explained by the fact that, since gang members are not subject to the same mobility restrictions as the other people living on their territory, they conduct regular raids into neighboring areas outside their immediate control. This result is fully consistent with the finding that labor-market conditions do not change directly at the boundaries of gang territory. Similarly, we find no differences in the availability and quality of public goods provision (e.g., schools and hospitals), consistent with the qualitative evidence suggesting that the government has been willing to provide public goods in gang areas to avoid ostracizing the residents of those locations.2 In turn, because the gangs benefit from public goods provision in their neighborhoods, they have been willing to allow the government to provide (nonpolice-related) services in the areas they control.3 Finally, we show that the results are not driven by higher levels of unemployment (or informal employment) in gang-controlled neighborhoods and that selective migration of individuals across the boundaries of gang territory can explain no more than 14% of the gap in socioeconomic development between the gang and nongang neighborhoods. Finally, we use data from all of El Salvador to perform a difference-in-differences design that analyzes how gang presence affected the spatial allocation of economic activity in the country. We find that, after the arrival of the gangs, municipalities least exposed to gang activity experience significantly more openings of new business establishments, as well as higher growth in nighttime light density and household income. These results highlight how the economic costs of mobility restrictions increased over time: as employment opportunities improved in places without gang activity, it became increasingly important to be able to commute to work in those areas. Our paper is related to several strands of the existing literature. First, it contributes to the literature studying the origins and consequences of organized crime and other nonstate armed actors (e.g., Gambetta (1996), Frye and Zhuravskaya (2000), Bandiera (2003), Daniele and Marani (2011), Acemoglu, Robinson, and Santos (2013), Daniele and Geys (2015), Buonanno, Durante, Prarolo, and Vanin (2015), Buonanno, Prarolo, and Vanin (2016), Dell (2015), Pinotti (2015), Daniele and Dipoppa (2017), De Feo and Davide De Luca (2017), Acemoglu, De Feo, and Davide De Luca (2019), Alesina, Piccolo, and Pinotti (2019), De la Sierra (2020), Murphy and Rossi (2020), Mirenda, Mocetti, and Rizzica (2022), Sviatschi (2022a,b)). Most of this literature has focused on violence, or the potential thereof, as the channel behind the effects of organized crime on politics, investment, migration, and other aspects of socioeconomic development. We complement this literature by presenting novel evidence on one specific aspect of criminal organizations that is increasingly prevalent in the developing world: territorial control in urban settings. By looking at urban areas where the territory is divided between the state and the gangs, we document a previously ignored mechanism through which criminal organizations affect socioeconomic development: restrictions on mobility. As Glaeser and Sims (2015) point out, little is known about the consequences of crime in the urbanized, developing world. In these contexts, because criminal organizations constantly face the potential for territorial challenges both from rival criminal groups and from the state, they need to implement stringent security measures to protect the borders of the neighborhoods they control (e.g., imposing restrictions on individuals' mobility). As a result, residents of these neighborhoods end up having significantly worse labor-market outcomes because of their inability to work in other parts of the city. Second, our paper is related to the literature on criminal governance and the organizational structure of criminal enterprises (Levitt and Venkatesh (2000), Skarbek (2011), Carvalho and Soares (2016), IACCA+ (2019), Lessing and Willis (2019), Magaloni, Vivanco, and Melo (2020), Lessing (2021), Blattman, Duncan, Lessing, and Tobon (2022)). Much of the existing literature has shown how nonstate armed actors emerge to fill the void left by the state and provide security and other public goods to the local population in exchange for political influence (e.g., Blattman and Miguel (2010)), taxation (e.g., Olson (1993), De la Sierra (2020)), and the opportunity to conduct their illegal activities. Our paper analyzes how these relationships are altered in an urban context, where the proximity of the state, on the one hand, poses a threat to the gangs' territorial control but, on the other hand, allows the gangs to rely on the provision of most public goods by the government.4 Third, our paper contributes to the literature studying the causes and consequences of the formation of extractive institutions, which can have a long-lasting impact on socioeconomic development (e.g., Acemoglu, Johnson, and Robinson (2001, 2002), Dell (2010), Michalopoulos and Papaioannou (2013), Dell, Lane, and Querubin (2018), Dell and Olken (2020), Lowes and Montero (2021)). Specifically, we show how the deportation of criminal leaders from the United States to El Salvador has resulted in their establishing extortionary gangs that significantly limited socioeconomic development in El Salvador. It also contributes to a long-standing debate on whether individual leaders\u2014in this case, gang leaders\u2014affect economic growth in developing countries (Jones and Olken (2005)). Finally, our work is related to the literature analyzing the economic effects of barriers to geographical mobility. The existing literature has focused on the effects of international borders (e.g., Clemons, Montenegro, and Pritchett (2008), McKenzie, Stillman, and Gibson (2010), Mergo (2016), Cal\u00EC and Miaari (2018), Alsawady, Hassan, and Turunen-Red (2022)) and the absence of transportation infrastructure (e.g., Donaldson (2018), Asher and Novosad (2020)). We complement this work by showing how gang-imposed restrictions on individuals' freedom of movement can significantly affect socioeconomic development, even within an integrated metropolitan area and in the absence of direct transportation costs and legal borders. Given the global prevalence of similar intracountry barriers to mobility, our results provide important policy implications for many developing countries. In particular, nonstate armed actors restrict individuals' freedom of movement in Brazil, Colombia, Guatemala, and Honduras (e.g., IACCA+ (2019), Magaloni, Vivanco, and Melo (2020)); many other countries, too, experience various forms of mobility restrictions (e.g., see Walther, Dambo, Kon\u00E9, and van Eupen (2020)). The rest of this paper is structured as follows. Section 2 describes the rise and organization of criminal groups in El Salvador. Section 3 describes the main data sources. Section 4 presents the identification strategy and the main results. Section 5 examines the mechanisms driving the results. Section 6 analyzes the aggregate effects of gang presence. Section 7 concludes. The Supplemental Material (Melnikov, Schmidt-Padilla, and Sviatschi (2025)) comprises the Appendix and a section of Supplementary Materials. Overall, the results presented in this paper indicate that, via a combination of restrictions on individuals' mobility and displacement of economic activity, nonstate armed actors can have a considerable negative impact on socioeconomic development. These findings have broad policy implications, shedding light on the long-term consequences of deporting individuals with criminal records to a country with low state capacity, suggesting that improvements in state capacity can significantly improve economic growth, and highlighting the importance of freedom of movement for socioeconomic development. To analyze the aggregate impact of gang activity, we use data from all of El Salvador to perform a difference-in-differences analysis, comparing the evolution of economic activity in areas with varying levels of gang activity after 1996. Our analysis exploits two sources of variation: the timing of gang members' deportation from the United States, which led to the emergence of gangs in El Salvador, and the geographic differences in exposure to organized crime. Our hypothesis is that prior to 1996, the year of the first wave of deportations from the United States, locations that would later experience different levels of gang activity had similar rates of economic development. However, after 1996, we expect to see higher rates of economic growth in areas with low levels of gang presence. We exploit the fact that, after being deported, many gang members who were born in El Salvador returned to their municipality of birth (Sviatschi (2022b)). Thus, we use the municipalities of birth of known gang leaders as a treatment variable for whether the municipality became exposed to gang activity.43 We then estimate the following event study model (Specification (3)) to measure the effect of gang presence on economic growth: 3Econ. growthi,t=gi+\u03B3t+\u2211j\u22601995\u03B2jgang presencei\u00D71{Year=j}t+\u03B5i,t. Econ. growth represents various measures of economic growth in municipality i at time t; gang presence is a dummy for whether a gang leader was born in municipality i; gi and \u03B3t represent municipality and year fixed effects, respectively. Standard errors are estimated using Conley standard errors with spatial correlation within a 5-km radius. The coefficients of interest are \u03B2j, which represent the differences in economic growth between locations with and without gang presence relative to 1995\u2014the year before the change in the United States immigration policy. We use three outcome variables to measure municipality-level growth in economic activity. The first one is the opening of new business establishments. Specifically, we use data from the 2005 economic census, which includes information on when the firms were opened.44 The second outcome variable is nighttime light density (or luminosity) which recent studies have found to be a good proxy for local-level economic activity (Chen and Nordhaus (2011), Henderson, Storeygard, and Weil (2012)). Finally, we use data on household income, which is based on annual household surveys conducted in 1992\u20132007 by DIGESTYC. In all three cases, the outcomes are measured in percentage points, normalized to be 100 percent in 1995\u20131996, both in areas with and without gang presence. In addition, given that the gangs were primarily attracted to large cities, to avoid the comparison between urban and rural locations, we limit our analysis to urban municipalities. The consequence of the mobility restrictions is that residents of gang neighborhoods often cannot work outside of gang territory, being forced to accept low-paying jobs in small firms because of their inability to commute to other parts of the city, where the largest firms are located. To demonstrate these negative effects of restrictions on individuals' mobility, we compare the labor-market outcomes for residents of gang areas who are able to work outside of gang territory and those who are not. Supplemental Appendix Table A.VII presents the results, showing that, while, on average, residents of gang-controlled neighborhoods earn less income and work in smaller firms than individuals from nongang locations, these gaps are significantly smaller for residents of gang territory who are able to work outside gang areas. In particular, we find that the latter are as likely to work in firms with 100 or more employees as individuals living outside of gang locations. They also have a 40% smaller gap in household income compared to other residents of gang territory.36 While these results should be treated as descriptive and interpreted with caution, they are fully consistent with gang-imposed restrictions on mobility being a major factor determining individuals' labor-market outcomes.37 In Supplemental Appendix Section A.3, we also present a wide range of additional robustness checks to ensure that the estimates in Table I represent the causal effect of gang control on socioeconomic development. In Section 6, we also demonstrate the absence of pretrends in socioeconomic development between areas with and without gang presence. Specifically, we perform a difference-in-differences analysis using nighttime light density, household income, and firm openings to show that these variables only started to change after the deportation of the gang leaders from the United States to El Salvador. A potential concern is that the boundaries of gang territory may not have remained stable between the time they were formed (soon after the gangs emerged) and 2015, when EDH published the map of gang territory. If the EDH map does not accurately reflect which areas were controlled by the gangs in 2007, the estimates in Table I would be biased toward zero (i.e., against finding an effect).28 Thus, the results in Table I should be interpreted as the lower bound of the effects of gang control. Nevertheless, in Supplemental Appendix Section A.1, we demonstrate that the gang-territory boundaries have remained stable since they were first formed. Specifically, we exploit the fact that most gang-related homicides take place precisely at the boundaries of gang territory because of people attempting to enter or leave gang-controlled neighborhoods without permission.29 As a result, by showing that, throughout the years, gang-related homicides consistently take place right at the boundaries from the EDH map, we are able to confirm the validity of that map and demonstrate the stability of those boundaries. In addition, in 2023, we conducted a new survey of individuals from gang and nongang neighborhoods, in which, among other questions, the respondents were asked whether their neighborhood had been controlled by gangs 20 years ago, during the presidency of Francisco Flores P\u00E9rez (President of El Salvador in 1999\u20132004). As shown in Supplemental Appendix Figure A.6, the share of respondents answering in the affirmative significantly increases at the boundaries of gang territory, suggesting that the borders have remained stable over time. The data on the extortion payments to the gangs made by firms and individuals in San Salvador come from the following three sources: (i) a geocoded survey of small and medium-sized enterprises conducted by a local think tank in 2015 (FUSADES (2015)); (ii) geocoded confidential internal records of a large Salvadoran distribution firm on all the extortion payments it made to the gangs from 2012 to 2019 (see Brown, Montero, Schmidt-Padilla, and Sviatschi (2020)); and (iii) our own geocoded telephone survey, which we conducted in San Salvador in 2020. For more information on these data sources, see the Supplemental Appendix. To document the mechanisms through which gangs affect socioeconomic development, we conducted our own geocoded survey in San Salvador in 2019. To be consistent with the census data, we conducted the survey in areas within 420 meters of the boundaries of gang territory. The survey was designed to be representative by 30-meter bins denoting the distance to the boundaries of gang territory (separately for each side of the boundaries). It consisted of in-person interviews and contained questions related to individuals' mobility, employment, income, satisfaction with public goods provision, and the role of formal (i.e., government) and informal institutions in resolving neighborhood problems. However, for security reasons, we were unable to ask individuals direct questions related to gang activity. In 2015, a local newspaper\u2014El Diario de Hoy (EDH)\u2014published the map that we use in this study (see Figure 1). It delimited the locations controlled by MS-13 and 18th Street in San Salvador (EDH (2015)). EDH based its report on information and cartography from the Ministry of Justice and Public Security and the PNC. The newspaper further validated the map of gang boundaries by confirming that the gang-controlled neighborhoods on the map are also the places where its distribution network had periodic encounters with gang members. We, too, have independently verified the accuracy of the map published by EDH.14 Moreover, in Supplemental Appendix Section A.1, we present evidence on how the boundaries of gang territory had remained stable between the time they were formed in the late 1990s and 2015, when EDH published its map. Taking advantage of the postwar environment and widespread destitution, both MS-13 and 18th Street quickly expanded their influence over many neighborhoods, particularly in the capital, San Salvador, and other urban areas, \u201Cgain[ing] complete control of [certain] localities\u201D (Zoethout (2015)). This rapid formation and enforcement of boundaries was possible due to four main factors: (i) the gangs' experience in implementing a system of territorial control in California, (ii) the importance of territorial control for the gangs' identity and long-term survival, (iii) the gangs' ability to recruit new members from the local population, and (iv) El Salvador's low state capacity in the 1990s. The system of territorial control built upon the strategy the gangs honed in California, where demarcation, largely based on natural barriers, split urban areas into small geographical confines known as cliques (Miguel Cruz (2010)). In El Salvador, the gangs also defined their territory based on natural barriers such as major roads and boulevards (Tenorio (2002), Vega (2015)). We identify and take advantage of three such major roads (see Figure 1)\u2014Bulevar Venezuela, 49 Avenida Sur, and Autopista Comalapa, all of which existed in 1996\u2014that largely determined the southern and western boundaries of gang territory. All of these multilane roads hinder the gangs from expanding beyond them to exert control over neighborhoods on both their sides. Gang territory in San Salvador. In Section 4.3, we take advantage of these natural boundaries of gang territory to verify that the results of the regression discontinuity analysis are not determined by the potential endogeneity of some of the other boundaries. We also show (i) that the borders of gang-controlled neighborhoods were not formed as a result of preexisting spatial differences in socioeconomic conditions or crime before the arrival of the criminal deportees and (ii) that the natural barriers that did not contribute to the formation of the gang boundaries do not affect socioeconomic outcomes. Our conversations with the police and individuals living in gang areas suggest that, in San Salvador, the boundaries of gang territory have remained stable since they were formed.6,7 The police repeatedly tried to regain control over those locations, but their attempts succeeded only in 2022, after the period studied in this paper.8,9 In part, the pre-2022 efforts failed because the gangs had formed ties with the local population, cultivating a network of informants that allowed them to elude capture (Cruz (2010), Ward (2013), Boerman and Golob (2020)). The importance of the boundaries of gang territory has been widely documented. International Crisis Group (ICG) describes the situation as follows: \u201CIn some areas, gangs have accumulated so much power that they have become de facto custodians of these localities, setting up road-blocks, supervising everyday life and imposing their own law\u201D (ICG (2017)). In another interview, a resident of San Salvador is even more direct: \u201CDo you see that place across the road? I could never get in there since it's the 18th Street gang's territory. If they see me in there, they might think I'm a spy [\u2026] and I could easily get killed\u201D (ICG (2018)). In 1996, to reduce crime in urban areas and address the surge in irregular migration, the United States passed the Illegal Immigration Reform and Immigration Responsibility Act (IIRIRA) (Chac\u00F3n (2009), ACMM+ (2017)). IIRIRA drastically increased immigration enforcement, creating procedures for expedited removal, adding new grounds for deportation, and increasing the number of border patrol agents. This shift in American policy had a profound impact on El Salvador. During the first wave of deportations in 1996, over 500 Salvadoran gang members were deported from the United States, leading to devastating changes in Salvadoran communities (Sviatschi (2022b)). Given that they did not have criminal records in El Salvador, the repatriated gang members\u2014many of whom were serving or had previously served sentences in the United States\u2014gained their freedom after returning to their home country (Ward (2013)). El Salvador was still recovering from its civil war, which ended in 1992, and the Salvadoran state did not have the resources to prevent the gangs from expanding. The 1992 Peace Accords mandated the creation of a new police force\u2014the National Civil Police (Polic\u00EDa Nacional Civil, PNC)\u2014and at the time of the repatriations, the structure of the PNC was still being defined (e.g., no rural police units existed until 2004). The repatriated gang leaders exploited this low level of state capacity and expanded their operations to many urban areas. Most of the repatriated MS-13 and 18th Street gang members had lived in the United States since a young age and knew little about their home country. For this reason, most of them returned to their birth municipalities, relying on their family networks to resettle in a new environment (DeCesare (1998), Sviatschi (2022b)). Seeking social acceptance and status, the gang deportees banded together and tapped into local youth groups to replicate the gang structures they had in California. Even though only a few hundred gang members were repatriated from the United States in 1996, they quickly expanded their ranks, recruiting new members from the local population. Many locals were attracted by the camaraderie and respect that the gangs offered; others sought more tangible material gains such as money and drugs (Cruz and Portillo Pe\u00F1a (1998), Mart\u00EDnez and Mart\u00EDnez (2018)). Sviatschi (2022b), in particular, shows how, after the MS-13 and 18th Street gang members arrived, and began recruiting adolescents to join their structures, El Salvador experienced an immediate increase in gang-related activities. According to the local authorities, by the end of 1996, at least 20 thousand individuals had joined the two gangs (Cruz and Portillo Pe\u00F1a (1998)). Southern California, especially Los Angeles, became home for thousands of Salvadorans fleeing the country's descent into civil war in the 1980s (Stanley (1987)). Lacking an established support network, Salvadoran migrants lived in poor, overcrowded neighborhoods and often faced discrimination from other migrant groups (Brettell (2011)). In a typical family, both parents worked, often leaving the children unsupervised (Savenije (2009)). Left on their own and facing prejudice from other migrant groups and their gangs, some Salvadoran youth formed the precursors to MS-13\u2014self-defense groups that were initially better known for petty crimes and for their affinity to cannabis and heavy metal, rather than for brutal violence\u2014while others joined 18th Street, an existing Mexican gang (Dunn (2007), Cruz (2010), Mart\u00EDnez and Mart\u00EDnez (2018)). As membership in MS-13 and 18th Street grew across Salvadoran immigrant communities, the gangs became known to the local authorities. Some of their members were sent to prison, where they gained criminal capital and social connections that helped them solidify their structures (Womer and Bunker (2010), Mart\u00EDnez and Mart\u00EDnez (2018)). By the mid-1980s, both MS-13 and 18th Street had developed independent identities, organizational structures revolving around territory-based cliques (clicas), and a fierce rivalry that continues to this day (Ward (2013)). Many gangs in 1980s Los Angeles shared a noteworthy trait: they precisely demarcated their territory, which greatly contributed to their identity and development (Coughlin and Venkatesh (2003)). For example, they used graffiti to define the territories under their control and to project authority over their rivals and the local population (Tita, Cohen, and Engberg (2005), Artsy (2018)). This demarcation had a profound impact on the mobility and decisions of individuals living in gang territories: \u201COne of the really important things to think about is how the invisible borders [\u2026] add costs we often don't think about. If I'm a young person growing up in a particular neighborhood [in Los Angeles] and the closest movie theater or the closest shopping mall is claimed by a rival gang, [\u2026] I'm going to have to spend more time on a bus, put more gas in my car, to travel to other areas\u201D (Artsy (2018)). In an observational study of incarcerated MS-13 gang members in Los Angeles County, Nu\u00F1o and Maguire (2021) highlight how \u201Cmost MS-13 members are involved in cliques that claim certain turf or territory (96.3%) and would be willing to use violence to defend it against others (92.6%),\u201D relying on graffiti and outposts to mark and control their territories.5 This facet of gang culture became a fundamental trait of gang structures in El Salvador. Publisher Copyright: © 2025 The Authors. Econometrica published by John Wiley & Sons Ltd on behalf of The Econometric Society.
PY - 2025/11
Y1 - 2025/11
N2 - We study how criminal organizations affect economic development. We exploit a natural experiment in El Salvador, where these criminal organizations emerged due to an exogenous shift in American immigration policy that led to the deportation of gang leaders from the United States to El Salvador. Using a spatial regression discontinuity design that focuses on the gang-created system of borders, we find that individuals in gang-controlled neighborhoods have less material well-being, income, and education than individuals living only 50 meters away but outside of gang territory. None of these discontinuities existed before the arrival of the gangs. A key mechanism behind the results is that gangs restrict individuals' mobility, affecting their labor-market options by preventing them from commuting to other parts of the city. The results are not determined by high rates of selective migration, differential exposure to extortion and violence, or differences in public goods provision.
AB - We study how criminal organizations affect economic development. We exploit a natural experiment in El Salvador, where these criminal organizations emerged due to an exogenous shift in American immigration policy that led to the deportation of gang leaders from the United States to El Salvador. Using a spatial regression discontinuity design that focuses on the gang-created system of borders, we find that individuals in gang-controlled neighborhoods have less material well-being, income, and education than individuals living only 50 meters away but outside of gang territory. None of these discontinuities existed before the arrival of the gangs. A key mechanism behind the results is that gangs restrict individuals' mobility, affecting their labor-market options by preventing them from commuting to other parts of the city. The results are not determined by high rates of selective migration, differential exposure to extortion and violence, or differences in public goods provision.
KW - crime
KW - development
KW - Gangs
KW - mobility
UR - https://www.scopus.com/pages/publications/105023330760
U2 - 10.3982/ECTA21305
DO - 10.3982/ECTA21305
M3 - Article
AN - SCOPUS:105023330760
SN - 0012-9682
VL - 93
SP - 2083
EP - 2121
JO - Econometrica
JF - Econometrica
IS - 6
ER -