Killing Growth: Homicides and Corporate Investment in Brazil

Figures & data

Figure 1. A shock to homicides in Rio vs São Paulo.

Table 1. The effects of homicides on corporate investments

y = Investment	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Δ (LN) Homicides	−0.005**	−0.010**	−0.005**	−0.008***	−0.008**	−0.005***
	(0.002)	(0.005)	(0.002)	(0.002)	(0.003)	(0.002)
Homicides Increase							−0.003**	−0.004**
							(0.001)	(0.002)
Size	0.002***	0.003	0.001***	0.002***	0.003***	−0.008***	0.002***	−0.003
	(0.001)	(0.003)	(0.000)	(0.000)	(0.000)	(0.003)	(0.001)	(0.003)
Growth	0.004	0.005	0.004	0.006***	0.006***	0.003	0.004	0.005**
	(0.004)	(0.004)	(0.003)	(0.002)	(0.002)	(0.005)	(0.004)	(0.002)
Cashflows	−0.004	0.016*	0.008*	−0.001	−0.002	0.013	−0.004	−0.002
	(0.011)	(0.009)	(0.004)	(0.004)	(0.003)	(0.014)	(0.011)	(0.010)
Leverage	0.003	0.044***	0.005***	0.005*	0.006*	0.024	0.003	0.025***
	(0.008)	(0.016)	(0.001)	(0.003)	(0.003)	(0.016)	(0.007)	(0.009)
Investment (t-1)	0.443***	0.455***	0.494***	0.494***	0.493***	0.351***	0.442***
	(0.068)	(0.045)	(0.046)	(0.035)	(0.034)	(0.057)	(0.067)
GDP growth	0.087	0.061	0.087	0.102*	0.113*	0.096	0.084	0.087
	(0.065)	(0.071)	(0.071)	(0.057)	(0.059)	(0.077)	(0.065)	(0.070)
Employment growth	−0.602	−0.740*	−0.574	−0.741*	−0.874*	−0.660	−0.648	−0.702
	(0.408)	(0.420)	(0.455)	(0.428)	(0.451)	(0.468)	(0.433)	(0.412)
(LN) Population	−0.001	−0.016	−0.001	−0.002*	−0.002*	−0.070	−0.001	−0.025
	(0.001)	(0.011)	(0.001)	(0.001)	(0.001)	(0.200)	(0.001)	(0.096)
Constant	0.013	0.234	0.012	0.017	0.015	–	0.016	0.502
	(0.017)	(0.168)	(0.015)	(0.023)	(0.024)	–	(0.017)	(1.655)
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	2270	2270	2270	2270	2270	1741	2270	2618
Groups	447	447	447	447	447	384	447	429
Wald test (chi2)	4139037***	2983102***	1.00e + 07***	2170055***	2.41e + 07***	–	7185083***	–
F test								33.6574***
Instruments	448	157	38	499	499	399	448	–
Hansen’s J (chi2)	0.0004191	1.37e-08	4.15e-09	1.01e-07	4.18e-10	6.43e-12	5.24e-07	–
AR2 (z)	1.074857	1.17498	1.167257	1.107367	1.109075	1.016126	1.032795	–
Estimation	Sys. GMM	Sys. GMM	Sys. GMM	Sys. GMM	Sys. GMM	Diff. GMM	Sys. GMM	Static OLS

Notes: This table reports estimation results. Investment is the dependent variable. The main explanatory variable is Δ (LN) Homicides (city homicides growth). The vector of firm controls includes Size, Growth, Cashflows, Leverage, and Investment (t-1). The vector of state controls includes GDP growth, Employment Growth, and (LN) Population. Model (1) is the baseline System GMM estimation, in which Investment (t-1) is set as endogenous, and Growth, Cashflows, and Leverage are set as predetermined (thus instrumented via GMM-style instruments), whereas Δ (LN) Homicides, Size, GDP growth, Employment Growth, (LN) Population, and the Year Dummies are instrumented by IV-style instruments. Model (2) is a sensitivity check in which homicides and all controls are set as predetermined and instrumented by GMM-style instruments (except for lagged investment which is set as endogenous and the Year Dummies which are instrumented with IV-style instruments), and the instruments are Collapsed as to reduce the number of instruments and thus mitigate over-instrumentation. In Model (3) the number of instruments is significantly reduced by instrumenting only Investment (t-1) as endogenous, with Collapsed GMM-style instruments. Model (4) is a sensitivity check estimated with the Orthogonal deviations option as to account for the gaps in the panel. Model (5) repeats model (4), but setting Homicides as Endogenous (instrumented via GMM-style instruments) whilst all the controls remain predetermined. Model (6) is a sensitivity analysis employing the Difference GMM estimator. In Model (7) we focus on strictly positive growth (increases) in homicides (=1 if Δ (LN) Homicides¿0, =0 otherwise). In Model (8) we repeat the estimation in (7), this time via static OLS (without lagged investment). All models include firm and year fixed effects. Test statistics for the Hansen’s J over-identifying restrictions test (the chi2 statistic) and for the Arellano-Bond AR2 test (the z statistic) are reported for all the GMM models. State clustered standard errors are shown in parenthesis below the coefficients. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 2. The effects of homicides on corporate policies

	(1)	(2)	(3)
Dependent variable	Cash Holdings	R&D	Dividends
Δ (LN) Homicides (City)	0.005*	−0.006**	−0.007***
	(0.003)	(0.003)	(0.002)
y (t-1)	0.543***	0.766***	0.639***
	(0.026)	(0.040)	(0.131)
Constant	−0.054**	−0.065**	−0.015
	(0.023)	(0.028)	(0.026)
Firm controls	Yes	Yes	Yes
State controls	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Observations	4561	4579	2779
Groups	687	687	485
Wald test (chi2)	2.26e + 07***	2.83e + 07***	6978374***
Instruments	541	532	541
Hansen’s J (chi2)	1.85e-06	0.0102589	2.95e-07
AR2 (z)	0.2018589	1.280789	0.9042549

Notes: This table reports system GMM results where we examine the effects of Δ (LN) Homicides (the growth in city homicides) on key corporate policies as dependent variables: (1) Cash Holdings (Cash/Assets), (2) R&D, (=1 if firms report R&D expenditures¿0, =0 otherwise), and (3) Dividends (Dividends/Sales). The models follow our baseline specification, with y (t-1), Growth, Cashflows, and Leverage instrumented by GMM-style instruments, and Δ (LN) Homicides, Size, GDP growth, Employment Growth, (LN) Population, and the Year Dummies instrumented by IV-style instruments. All models include firm and year fixed effects and the same controls as in Table 1. State clustered standard errors are shown in parenthesis below the coefficients. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 3. Heterogeneous effects

	(Main Regression)	25th pc.	50th pc.	75th pc.
y = Investment	(A)	(B)	(C)	(D)
Δ (LN) Homicides × Size	0.004***	0.001	0.006	0.010
	(0.001)
Δ (LN) Homicides × Leverage	−0.010*	−0.048	−0.050	−0.051
	(0.006)
Δ (LN) Homicides × Cashflow	−0.016***	−0.048	−0.049	−0.050
	(0.006)
Δ (LN) Homicides × Growth	0.003	−0.048	−0.048	−0.048
	(0.005)
Δ (LN) Homicides	−0.048***
	(0.017)
Size	0.002***
	(0.000)
Leverage	0.001*
	(0.001)
Cashflow	−0.002
	(0.005)
Growth	0.006**
	(0.003)
Investment (t-1)	0.447***
	(0.047)
Constant	0.004
	(0.013)
Firm FE	Yes
Year FE	Yes
State Controls	Yes
Observations	2270
Groups	447
Wald test (chi2)	1741674***
Instruments	755
Hansen’s J (chi2)	7.10e-07
AR2 (z)	0.988093

Notes: In column (A), we show system GMM results in which we interact City Homicides growth with (i) Size; (ii) Leverage; (iii) Cashflows; and (iv) Growth. In columns (B), C), and (D) we show the marginal effects of the interactions at the 25th (B), 50th (C), and 75th (D) percentiles of the distributions of the firm covariates (respectively). The values reported in (B), (C) and (D) show how the marginal effects of City Homicides Growth on investment change as we move from the lower (25th), to the median (50th), and finally to the higher (75th) values of each of the covariates’ distribution. Investment (t-1), Growth, Cashflows, Leverage, and the interactions of these variables with Homicides are instrumented via GMM-style instruments, whereas Δ (LN) Homicides, Size, the interaction of Size with Homicides, GDP growth, Employment Growth, (LN) Population, and the Year Dummies are instrumented by IV-style instruments. State clustered standard errors are shown in parenthesis below the coefficients. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels respectively.

Table 4. Plant-level evidence

	(1)	(2)	(3)	(4)
Dependent variable	(LN) Employees	Δ (LN) Employees	Δ (LN) Employees	Layoffs
Δ (LN) Homicides	−0.012***	−0.015***	−0.023***
	(0.004)	(0.003)	(0.003)
Δ (LN) Homicides x (LN) Size			0.029***
			(0.008)
Homicides increases (0/1)				0.052***
				(0.017)
GDP growth (city)	0.090**	0.059*	0.062*	−0.115***
	(0.036)	(0.034)	(0.032)	(0.040)
y (t-1)	0.934***	0.081***	0.075***	0.116***
	(0.008)	(0.010)	(0.010)	(0.005)
(LN) Size			−0.051**
			(0.020)
Constant	0.381***	−0.022***	−0.007	0.446***
	(0.056)	(0.008)	(0.008)	(0.012)
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	35851	30444	30444	29762
Groups	5472	5456	5456	5378
Wald test (chi2)	1.60e + 08***	6217.602***	13339.25***	183484***
Instruments	255	238	334	217
Hansen’s J (chi2)	15.14662	20.10147	16.22894	13.81689
AR2 (z)	−1.388491	0.6949623	0.5396031	1.562659

Notes: This table shows system GMM results on the effects of homicides on plant size and labour investments. In model (1), the dependent variable is plant size ((LN) Employees)); in model (2), the growth rate of the number of employees is a proxy for labour investments (Δ (LN) Employees)); in model (3), we test for the role of firm size interacting Homicides and Firm Size (as proxied by the log of the total number of plants); in model (4), the dependent variable is Layoffs (=1 if the growth rate of employees¡0, and =0 otherwise), and the explanatory variable is Homicides increase (=1 if the growth of homicides¿0, and =0 otherwise). All models include firm and year fixed effects, and control for city GDP growth. The explanatory variables are instrumented by GMM-style instrments (except year dummies, instrumented by IV-style instruments). State clustered standard errors are shown in parenthesis below the coefficients. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels respectively.

Table 5. A shock to homicides in Rio vs São Paulo

y = Investment	(1)	(2)	(3)	(4)	(5)	(6)
Treated (Rio) × Post	−0.004***	−0.005**	−0.007*	−0.012**	−0.011*	−0.008**
	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.000)
Size		−0.004	0.001	−0.003	−0.001	0.001**
		(0.002)	(0.000)	(0.003)	(0.004)	(0.000)
Growth		0.003	0.004	0.002	0.008	0.005
		(0.002)	(0.003)	(0.002)	(0.002)	(0.002)
Cashflow		0.000	−0.009	0.000	0.018	−0.000
		(0.014)	(0.015)	(0.015)	(0.006)	(0.015)
Leverage		0.023	−0.033	0.023	0.003	0.004
		(0.015)	(0.056)	(0.015)	(0.025)	(0.006)
GDP Growth (t)				−0.014	−0.354**	−0.244
				(0.020)	(0.018)	(0.098)
GDP Growth (t-1)				0.018	−0.065	−0.221
				(0.013)	(0.014)	(0.081)
Employment (t)				−1.194	2.526*	0.100
				(0.196)	(0.264)	(0.714)
Employment (t-1)				−0.964***	−1.426**	0.180
				(0.001)	(0.107)	(0.230)
State Ownership (0/1)					0.025***
					(0.000)
Treated (Rio) × State Ownership (0/1)					−0.034**
					(0.001)
Post × State Ownership (0/1)					0.026**
					(0.001)
Treated (Rio) × Post × State Ownership (0/1)					−0.120**
					(0.008)
Firm FE	Yes	Yes	Yes	Yes	Yes	No
Year FE	Yes	Yes	No	Yes	Yes	Yes
State FE	No	No	Yes	No	No	Yes
Industry FE	No	No	Yes	No	No	Yes
PSM matched samples	No	No	Yes	No	No	No
Adjusted R-square	0.457	0.459	0.522	0.458	0.531	0.252
N	1953	1670	703	1670	846	1703

Notes: This table reports results of OLS difference-in-differences (DiD) regressions. Investment is the dependent variable. We set Rio firms as treated group (=1), and São Paulo firms as control group (=0). Post is a dummy =1 from 2016 onward (the shock to Rio’s State public finances), and =0 before that. State clustered standard errors are shown in parenthesis below the coefficients. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels respectively.

Table A1. Variable definitions and summary statistics

A: Variables	Definition	Mean	SD	25p	50p	75p	Count
Investment	Capex/Assets	0.041	0.051	0.006	0.025	0.055	3121
City Homicides growth	Δ (LN) Homicides	−0.065	0.265	−0.192	−0.084	0.055	3106
Size	Natural log of Assets	13.444	2.007	12.384	13.596	14.712	3121
Growth	Δ Sales	0.022	0.503	−0.144	0.022	0.196	2870
Cash flows	EBITDA/Assets	0.075	0.258	0.037	0.088	0.142	3121
Leverage	Debt/Assets	0.257	0.510	0.078	0.203	0.335	2901
Employment growth	Δ Net employment/pop	−0.000	0.005	−0.004	0.001	0.003	3120
GDP growth	Δ (LN) GDP	0.010	0.033	−0.013	0.014	0.027	3118
Population	(LN) Population	16.923	0.735	16.480	16.849	17.598	3121
B: By State	Investment	City Homicides	Δ City Homicides	Δ Employment	Δ GDP	(LN) Population	Firms
Alagoas	0.014	52.362	−0.589	0.003	0.011	15.016	1.000
Amazonas	0.026	50.385	0.040	0.001	0.014	15.157	2.000
Bahia	0.035	67.157	0.025	0.000	0.008	16.494	16.000
Ceara	0.047	64.136	0.056	−0.000	0.016	15.995	13.000
Distrito Federal	0.055	27.321	−0.075	0.000	0.019	14.845	6.000
Espirito Santo	0.055	35.319	−0.167	−0.001	0.022	15.145	3.000
Goias	0.008	43.596	−0.038	−0.000	−0.003	15.719	2.000
Marañhao	0.030	54.863	0.033	−0.001	0.028	15.735	3.000
Minas Gerais	0.043	24.010	−0.051	−0.000	0.007	16.835	39.000
Mato Grosso	0.079	34.292	−0.127	0.001	0.036	15.021	1.000
Para	0.011	61.303	0.087	−0.002	0.012	15.911	2.000
Paraiba	0.026	70.196	−0.076	0.000	0.003	15.189	2.000
Pernambuco	0.040	41.859	−0.038	−0.000	0.014	16.042	6.000
Parana	0.041	30.285	−0.052	−0.000	0.014	16.215	22.000
Rio de Janeiro	0.047	25.639	−0.011	−0.001	0.008	16.625	91.000
Rio Grande do Norte	0.043	52.169	0.063	−0.000	0.016	15.023	2.000
Rio Grande do Sul	0.041	33.883	−0.007	−0.001	0.015	16.223	37.000
Santa Catarina	0.036	13.101	−0.005	−0.000	0.020	15.710	31.000
Sergipe	0.022	51.068	0.047	0.001	−0.003	14.599	2.000
São Paulo	0.039	12.335	−0.118	−0.001	0.009	17.595	257.000
Tocantins	0.005	32.033	0.166	−0.001	0.010	14.217	1.000