Killing Growth: Homicides and Corporate Investment in Brazil

Abstract

We examine the effects of violent crime on corporate investment and financing decisions of Brazilian firms. Exploring city variation in homicides, we find that an increase in the growth rate of homicides is associated with significantly lower corporate investments, with lower labour investments, and with a higher likelihood of layoffs. Spikes in violent crime are also associated with more conservative financing policies, reflected in higher cash holdings, in lower R&D (research and development) expenditures, and in lower dividend payments. Homicides further affect investment efficiency and financing choices, decoupling investment from debt finance and profitability. Moreover, the negative association between homicides and investment is significantly stronger in smaller firms, which highlights the uneven costs of violent crime in reducing firm growth.

Keywords:

• corporate investment
• homicides
• crime
• financing policies
• Brazil

Acknowledgments

We are grateful to the editor and the two anonymous referees for the insightful and helpful comments. Any errors are our own. The firm-level data used in the study is from Osiris (Bureau van Dijk). The data is proprietary, and it can be purchased from BVD by subscription. The city-level crime and economic data is maintained by IPEA and can be download at www.ipea.gov.br. Code available upon request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 We only observe the city location of the firms once at the headquarters level, and the sample includes both active and inactive firms, hence allowing for entry and exit.

2 Logically, other types of pettier crimes (like robberies) could impact investment decisions too, but our interpretation from the extant literature is that such lighter crimes are more relevant in contexts where investment is conducted by individuals or by smaller businesses, whereas for larger corporations (our focus) the literature suggests that violent crimes are more relevant.

3 We use Sales growth primarily since our data includes public and private firms.

4 We also test models using Bank Debt/Assets as an alternative proxy for credit (not reported for brevity), observing similar results.

5 Unemployment rates are periodically estimated for certain metropolitan areas in selected states only. Thus we use Employment creation as a broader (substitute) measure available to all states.

6 Such state variables capture economic conditions that could correlate with Homicides and affect Investment (for example in cities or states with higher unemployment firms may invest more as labour costs are cheaper, but higher unemployment may increase crime), and that are available for our full sample period. Moreover, city fixed effects (encapsulated by the firm effects) control for unobservable city characteristics that are time invariant, like secular attitude towards violence.

7 As we do not observe shifts in the locations of firms in our data, city fixed effects are encapsulated by the firm fixed effects.

8 We test for over-identifying restrictions with the Hansen’s J test (which is robust to heteroskedastic errors) and for second-order serial correlation in the error term with the Arellano-Bond test. We observe small chi2 test statistics for the Hansen’s J test, and small z test statistics for the Arellano-Bond test, which in both cases suggest accepting the null hypotheses of both tests in all the models. Furthermore, the chi2 statistic remains small and statistically insignificant even when strongly reducing the instrument count (for example model (3)), which suggests that instrument proliferation is unlikely to be affecting the reliability of the tests’ diagnostics.

9 To ensure we fully capture the uncertainties surrounding investment which are related to crime, we test additional models (not reported for brevity) including homicides in levels both contemporaneously and lagged by one and two periods. In all cases, we find coefficients equal to 0.000, which corroborate the view that managers care more about changes in crime than about levels.

10 One important issue to be considered is that crime can also impact the location of firms. To control for location preferences, we estimate an additional model (not reported for brevity) where we control for the number of peer firms (from the same industry) that also locate in the same city. We find a significantly negative correlation (of about -0.11) between the number of peer firms present in cities and the cities’ homicides (which suggests that firms locate less often in more violent cities). We then include the number of peer firms locating in the same state as a control variable in our investment models. While we observe an insignificant impact on firm investment, this control variable cleanses the correlation between location preferences and homicides from the investment equation.

11 Furthermore, we also tested interactions with variables capturing differences in the ownership structure of firms: we interact homicides with government ownership, and with foreign ownership. However, we did not find any significant interactions, which suggests that ownership structure does not affect the relation between investment and city homicides growth.

12 The RAIS dataset has been widely employed in studies relying on micro-level data (for instance, see Colonnelli et al, 2022).

13 Briefly commenting on key summary statistics: on average sample firms operate 2 plants per city, the average (standard deviation) number of firm-city employees is 552 (2514), whereas the average (standard deviation) growth rate in the number of employees is 0.035 (0.284).

14 The full story is entitled Rio violence exposes Brazil’s missed chance.

15 The individual coefficients of Treat_i and Post_t are subsumed by the firm and year fixed effects.