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From Collective Punishment to Constraints on Authority

Rethinking the Impact of US Sanctions on Venezuela

Published online by Cambridge University Press:  16 May 2026

Miguel Angel Santos
Affiliation:
Instituto Tecnológico y de Estudios Superiores de Monterrey
José Morales-Arilla
Affiliation:
Instituto Tecnológico y de Estudios Superiores de Monterrey
Zinedine Partipilo Cornielles
Affiliation:
Harvard University

Summary

This Element critically examines the claim that United States economic sanctions on Venezuela constituted 'collective punishment' of the Venezuelan population, contributing significantly to the country's economic collapse and humanitarian crisis. Through comprehensive analysis of economic, developmental, and welfare indicators from 2013 to 2023, it demonstrates that the bulk of Venezuela's economic devastation - including 52 percent of GDP losses and 98 percent of import declines - largely occurred before financial sanctions were imposed in August 2017. Key welfare indicators such as infant mortality, undernourishment, and life expectancy had deteriorated substantially by 2017 and subsequently stabilized or improved following sanctions implementation, contradicting narratives that attribute Venezuela's collapse primarily to external economic pressure. The Element provides a timeline of Venezuelan economic and political events around sanctions and a critical review of the literature on their economic effects. This title is also available as Open Access on Cambridge Core.

Information

Figure 0

Figure 1(a) Real GDP Index

Figure 1

Figure 1(b) Imports

Sources: IMF World Economic Outlook Database for real GDP; Harvard Growth Lab’s Atlas of Economic Complexity for import data. Authors’ calculations.
Figure 2

Figure 2(a) Infant mortality rates

Figure 3

Figure 2(b) Undernourishment rate

Sources: World Bank’s World Development Indicators (WDI) and Economist Intelligence Unit (EIU) for infant mortality rates; WDI for undernourishment rates. Authors’ calculations.
Figure 4

Figure 3(a) Life expectancy

Figure 5

Figure 3(b) Crude death rates

Sources: World Bank’s World Development Indicators (WDI). Authors’ calculations.
Figure 6

Table 1 Studies supporting the “sanctions as collective punishment” viewpointTable 1 long description.

Figure 7

Figure 4 Intensity of nightlights by municipality typeNote: Monthly per capita nighttime light intensity (nW/(cm 2⋅ sr)) for oil-producing and non-oil-producing municipalities from January 2015 to December 2018. The vertical dashed line indicates July 2017, the month preceding the imposition of US financial sanctions.

Source: NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS). Authors’ calculations.
Figure 8

Figure 5 Evolution of cornflour sales by municipality typeNote: Monthly per capita cornflour sales in kilograms for oil-producing and non-oil-producing municipalities from January 2015 to December 2018. The vertical dashed line indicates July 2017, the month preceding the imposition of US financial sanctions.

Source: Proprietary data from a large food producer in Venezuela. Authors’ calculations.
Figure 9

Figure 6 Voter turnout rates by municipality typeNote: Average voter turnout rates (percentage of registered voters who cast ballots) by election year for oil-producing and non-oil-producing municipalities. Data cover presidential elections (2012, 2013, 2018, 2024) and National Assembly elections (2015).

Sources: Venezuela’s Consejo Nacional Electoral (CNE) for 2012-2018 elections; ComandoConVenezuela for 2024 election. Authors’ calculations.
Figure 10

Table 3 Impact of sanctions on outcomesTable 3 long description.

Sources: VIIRS for nighttime lights; proprietary data for cornflour sales; CNE and ComandoConVenezuela for electoral data. Authors’ calculations.
Figure 11

Figure 7 Event study: Per capita nightlightsNote: Event study coefficients based on Equation 2 showing the difference in per capita nighttime light intensity between oil-producing and non-oil-producing municipalities relative to July 2017 (month 0). Each point represents the interaction coefficient between month indicators and the oil municipality indicator. The regression includes municipality and month fixed effects, as well as controls for baseline population density, poverty rate, and regime support. Dashed lines indicate 95% confidence intervals. Standard errors are clustered at the municipality level.

Source: NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS). Authors’ calculations.
Figure 12

Figure 8 Event study: Cornflour salesNote: Event study coefficients based on Equation 2 showing the difference in per capita cornflour sales (kilograms) between oil-producing and non-oil-producing municipalities relative to July 2017 (month 0). Each point represents the interaction coefficient between month indicators and the oil municipality indicator. The regression includes municipality and month fixed effects, as well as controls for baseline population density, poverty rate, and regime support. Dashed lines indicate 95% confidence intervals. Standard errors are clustered at the municipality level.

Source: Proprietary data from a large food producer in Venezuela. Authors’ calculations.
Figure 13

Figure 9 Event study: Voter turnout ratesNote: Event study coefficients based on Equation 2 showing the difference in voter turnout rates (percentage of registered voters) between oil-producing and non-oil-producing municipalities relative to the 2015 election (election 0). Each point represents the interaction coefficient between election year indicators and the oil municipality indicator. The regression includes municipality and election fixed effects, as well as controls for baseline population density, poverty rate, and regime support. Dashed lines indicate 95% confidence intervals. Standard errors are clustered at the municipality level.

Sources: Venezuela’s Consejo Nacional Electoral (CNE) for 2012–2018 elections; ComandoConVenezuela for 2024 election. Authors’ calculations.
Figure 14

Figure 10(a) Daily oil production (bpd)

Figure 15

Figure 10(b) Indexed Differences

Source: Proprietary daily field-level production data from an oil consulting firm. Authors’ calculations.
Figure 16

Figure 11(a) Effect on the average field

Figure 17

Figure 11(b) Average effect by field type

Source: Proprietary daily field-level production data from an oil consulting firm. Authors’ calculations.
Figure 18

Figure 12 Distribution of field-specific effectsNote: Overlapping histograms showing the distribution of field-specific regression discontinuity estimates of the March 7, 2019, blackout effect on oil production. Each estimate represents the percentage point change in production index for an individual oil field. “Pump” fields require steam injection for extraction; “Gas” fields rely on associated natural gas. Estimates are from separate regression discontinuity specifications for each of the sixty-four oil fields in the dataset, using a two-week bandwidth.

Source: Proprietary daily field-level production data from an oil consulting firm. Authors’ calculations.
Figure 19

Figure 13(a) Daily

Figure 20

Figure 13(b) Cumulative

Source: Proprietary daily field-level production data from an oil consulting firm; WTI oil prices from commodity markets. Authors’ calculations.
Figure 21

Figure 14 Evolution of essential importsNote: Annual Venezuelan imports in billions of USD from 2012 to 2023. The black line shows food imports (HS codes < 25, excluding 23, 13, 12, 05, and 0101). The light gray line shows essential imports, combining food and medicine imports (HS code 30). Vertical dashed lines indicate the imposition of US financial sanctions (August 2017).

Source: Harvard Growth Lab’s Atlas of Economic Complexity. Authors’ calculations.
Figure 22

Figure 15(a) Actual vs. Synthetic

Figure 23

Figure 15(b) Difference

Source: Harvard Growth Lab’s Atlas of Economic Complexity. Authors’ calculations using synthetic control methodology (Abadie et al., 2010).
Figure 24

Figure 16(a) Venezuela vs. placebo differences

Figure 25

Figure 16(b) Significance of post-treatment estimates

Source: Harvard Growth Lab’s Atlas of Economic Complexity. Authors’ calculations using the synthetic control methodology (Abadie et al., 2010).
Figure 26

Figure 17 Yearly Venezuelan import overprice estimatesNote: Figure shows yearly estimates of overpricing of Colombian exports to Venezuela in comparison to other importer countries between 2001 and 2024. Each point represents the coefficient from a year-specific regression following Equation 4. Log FOB prices are estimated as a function of a Venezuelan-importer indicator variable, controlling for firm-product-week fixed effects. The coefficient captures the average percent price difference when the same Colombian exporter sells the same product during the same week to Venezuela versus other countries. Dark and light gray vertical lines capture 90% and 95% confidence intervals. Observations are weighted by transaction value.

Source: Colombia’s Dirección de Impuestos y Aduanas Nacionales (DIAN) transaction-level export data. Authors’ calculations.
Figure 27

Figure A.18 Coefficient plot - nighttime lightsNote: Difference-in-differences coefficient estimates and 95% confidence intervals for the effect of the August 2017 financial sanctions on nighttime light intensity in oil-producing versus non-oil-producing municipalities. Three specifications are shown: (1) Per capita nighttime lights excluding controls; (2) Per capita nighttime lights with additional controls for baseline population density, poverty rate, and regime support (2013 chavista vote share); (3) Log nighttime lights using total radiance; (4) Per capita nighttime lights including the Caracas metropolitan area municipalities. The difference-in-differences term is the interaction between a post-July 2017 indicator and an oil municipality indicator. All specifications include municipality and month fixed effects. Standard errors are clustered at the municipality level.

Source: NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS). Authors’ calculations.
Figure 28

Figure A.19 Coefficient plot - cornflour salesNote: Difference-in-differences coefficient estimates and 95% confidence intervals for the effect of the August 2017 financial sanctions on cornflour sales in oil-producing versus non-oil-producing municipalities. Three specifications are shown: (1) Per capita cornflour sold excluding Caracas metropolitan area municipalities; (2) Per capita cornflour sold with additional controls for baseline population density, poverty rate, and regime support (2013 chavista vote share) (3) Log kilograms of cornflour; (4) Per capita cornflour sold including the Caracas metropolitan area municipalities. The difference-in-differences term is the interaction between a post-July 2017 indicator and an oil municipality indicator. All specifications include municipality and month fixed effects. Standard errors are clustered at the municipality level.

Source: Proprietary data from a large food producer in Venezuela. Authors’ calculations.
Figure 29

Figure A.20 Coefficient plot - voter turnoutNote: Difference-in-differences coefficient estimates and 95% confidence intervals for the effect of the August 2017 financial sanctions on electoral turnout in oil-producing versus non-oil-producing municipalities. Three specifications are shown: (1) Turnout without control variables (2) Turnout with additional controls for baseline population density, poverty rate, and regime support (2013 chavista vote share); (3) Turnout including municipalities in Caracas; (4) Turnout including both controls and Caracas; (5) Turnout excluding the 2018 election; (6) Turnout excluding the 2024 election. The difference-in-differences term is the interaction between a post-July 2017 indicator and an oil municipality indicator. All specifications include municipality and month fixed effects. Standard errors are clustered at the municipality level.

Source: CNE and ComandoConVenezuela. Authors’ calculations.

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