Thus far, I have established that a spike in drug-related violence, driven partly by the Mexican Drug War, had an effect on the economy in those states where the federal government implemented JOs. Using placebo studies, I have also proven that, once treated, said effect is statistically significant for the majority of the treated states that display an accurate synthetic control. Moreover, in Section 2, I have acknowledged that the spike in drug-related violence was simultaneously provoked by two foreign confounding factors: the 2004 expiration of the US Federal AWB, and a significant increase of cocaine seizure rates in Colombia after 2006. Therefore, the exogenous effect of drug-related violence on GDP per capita is not all a consequence of the Mexican Drug War.

To determine the direct causal effect of the Mexican Drug War on economic development, I run OLS on the variation of the normalized GDP per capita gap for the treated sample. Namely, the central model to evaluate the average treatment effect on the treated (ATT) is the following:

$$\widehat{N}{G}_{s\mathrm{,}t}=\alpha +\theta \widehat{N}{G}_{s\mathrm{,}t-1}+\beta J{O}_{s\mathrm{,}t-1}+\gamma {Z}_{s\mathrm{,}t-1}+{\epsilon}_{s\mathrm{,}t}\mathrm{,}\text{\hspace{1em}(6)}$$(6)

where $$\widehat{N}{G}_{s,t}$$ is the normalized GDP per capita gap, in percentage terms; $$\widehat{N}{G}_{s,t-1}$$ is the lagged dependent variable, which controls for tendency; *JO*_{s,t–1} is the lag value of my continuous proxy for the Mexican Drug War, the interaction term between the rollout of the JOs and the rate of interception operations; *Z*_{s},_{t–1} is a vector with the lag values of the two aforementioned confounding factors – the interaction between the 2004 expiration of the US Federal AWB and a dummy for AWB-bordering states (Chicoine 2011; Dube, Dube, and García-Ponce 2013), and cocaine seizure rates in Colombia (Castillo, Mejía, and Restrepo 2014); and *ε*_{s,t} are all other unobservables that influence the outcome. The coefficient of interest in equation (6) is *β*.

Alternatively, I include state fixed effects (*λ*_{s}) in equation (6) to control for possible systemic biases in the (normalized) GDP per capita gap, generated by the SCMs:

$$\widehat{N}{G}_{s\mathrm{,}t}={\lambda}_{s}+\theta \widehat{N}{G}_{s\mathrm{,}t-1}+\beta J{O}_{s\mathrm{,}t-1}+\gamma {Z}_{s\mathrm{,}t-1}+{\epsilon}_{s\mathrm{,}t}\mathrm{.}\text{\hspace{1em}(7)}$$(7)

However, the conditions for consistently estimating equation (7) are more complicated than OLS because, once state dummies are introduced, the error term (*ε*_{s,t}) becomes necessarily correlated with the lagged dependent variable $$\mathrm{(}\widehat{N}{G}_{s\mathrm{,}t-1}\mathrm{)}.$$ Following Angrist and Pischke (2009, ch. 5), I apply Arellano and Bond’s generalized method of moments procedure (ABGMM) to solve for serial correlation.

Finally, I run an additional two-stage least squares (2SLS) model in which the indicator for the Mexican Drug War (*JO*_{t–1}), along with the two identified confounding variables (*Z*_{t–1}), enter equation (6) indirectly through exogenous drug-related violence:

$$\begin{array}{c}\widehat{N}{G}_{s\mathrm{,}t}=\alpha +\theta \widehat{N}{G}_{s\mathrm{,}t-1}+\delta phomicide{s}_{ga{p}_{s\mathrm{,}t-1}}+{\epsilon}_{s\mathrm{,}t}\\ phomicide{s}_{ga{p}_{s\mathrm{,}t-1}}={\alpha}^{F}+{\theta}^{F}\widehat{N}{G}_{s\mathrm{,}t-1}+\pi J{O}_{s\mathrm{,}t-1}+\gamma {Z}_{s\mathrm{,}t-1}+{\nu}_{s\mathrm{,}t-1}\mathrm{,}\end{array}\text{\hspace{1em}(8)}$$(8)

where *phomicides*_{gap} is the gap in drug-related homicide rates between treated and synthetic control units. In this specification, the parameter of interest is *π*×*δ*.

Having established the mechanics of the minimization procedure in equations (2) and (3), I limit my sample observations from the end of the matching period onwards (2003–2012). I run equations (6), (7), and (8) for all treated states with a reliable synthetic control (Chihuahua, Durango, Guerrero, Michoacán, and Sinaloa), as well as for only those treated states that report a statistically significant GDP per capita gap (Chihuahua, Durango, and Guerrero). All together, my sample contains, at the most, 50 observations. Hence, equation (6) is more likely to provide the true ATT because OLS is consistent and unbiased for small samples, whereas the Arellano and Bond’s generalized method of moments procedure (ABGMM) and 2SLS estimators are only consistent in small-sample asymptotics (Angrist and Pischke 2009, ch. 4).

presents the main results for the effect of the Mexican Drug War on GDP per capita gap, in percentage units. Columns 1–3 show the estimations for all treated units that have an accurate synthetic control, whereas columns 4–6 reduce the sample to treated states with a statistically significant GDP per capita gap. The last row in presents the ATT of the Mexican Drug War on GDP per capita gap, in percentage terms. For most specifications, the coefficients for the Mexican Drug War and the confounding factors are statistically significant and move in the correct direction. What is more, there is little variation across estimators, implying no need for state dummies.

Table 5: Average effect of the Mexican Drug War on GDP per capita gap (%) between treated states and synthetic controls (2003–2012).

Given the properties of OLS and the number of states represented in the sample, my preferred specification is column 1. This specification explains around 69.1% of the outcome variation, and indicates a statistically significant ATT equal to –0.7% for Chihuahua, Durango, Guerrero, Michoacán, and Sinaloa, over the period 2003–2012. The 95% confidence interval of the ATT, under robust standard errors, is in the range of –1.4% and 0.4%. If there are zero spillovers, and a perfect linear relationship between the Mexican Drug War and GDP per capita, then an extrapolation of the ATT on all treated states amounts to a loss in GDP per capita equal to 0.5%, over the period 2003–2012. Given the share of treated states in Mexico’s economy (over one-third), this is a considerable effect for a single policy, which partially explains the poor performance of Mexico’s economy during Calderón’s administration.

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