Persistent commodity shocks and transitory crime effects

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Persistent commodity shocks and transitory crime effects Alejandro Corvalan a,b, Matteo Pazzona a,b,∗ a b

Department of Economics, Diego Portales University, Santiago, Chile Department of Economics and Finance, Brunel University London, United Kingdom

a r t i c l e

i n f o

Article history: Received 27 October 2017 Revised 9 November 2018 Accepted 12 November 2018 Available online xxx JEL classification: E24 K42 054 Q33

a b s t r a c t This paper studies the dynamic response of crime to a positive income shock. We estimate the short- and medium-run effects that an increase in copper price had on the local economy and on criminal activity in Chile, which is the world’s leading copper producer. After a decade of high prices, mining municipalities did not exhibit lower crime rates compared to non-mining municipalities. To explain this counterintuitive result, we investigated heterogeneous dynamic effects and observed that property crimes decreased only at the beginning of the boom. As the dynamics of unskilled employment are consistent with the crime cycle, we argue that crime evolves due to temporary labor market adjustments. © 2018 Published by Elsevier B.V.

Keywords: Economic shocks Crime Mining

1. Introduction Unexpected economic shocks are the ideal setting to test the causal effects of income on crime, given concerns about endogeneity and reverse causality. Consistent with the economic theory of crime (Becker, 1968; Ehrlich, 1973), recent literature reports that crime rates increase with negative income shocks induced either by weather conditions,1 trade liberalizations,2 or other sources.3 When these papers exa. mine the persistence of the effect, they find that the surge in crime activities typically wanes over time There is, however, less literature available on positive income shocks. The existing papers show that individual cash transfers reduce crime, serving to target the immediate or short run effects.4 In this paper, we will examine the effects of permanent and aggregate positive income shock and the dynamic response this has on crime rate. This is something that, to the best of our knowledge, is a subject that has not been treated before in the existing literature. In this study, we will consider the impact of a fourfold increase in copper price between 2003 and 2013 in Chile, which is a country that relies heavily on copper production for income.5 The boom allows for a clean-cut identification because it

∗ Corresponding author at: Department of Economics and Finance, College of Business Arts and Social Science, Brunel University London, Uxbridge, Middlesex, United Kingdom UB8 3PH. E-mail addresses: [email protected] (A. Corvalan), [email protected] (M. Pazzona). 1 Miguel (2005), Melhum et al. (2006) and Blakeslee and Fishman (2017). 2 Iyer and Topalova (2014) and Dix-Carneiro et al., 2018. 3 Bignon et al. (2017), Cortés et al. (2016) and Fafchamps and Minten (2006). 4 Hannon and DeFronzo (1998), Foley (2011), Camacho et al. (2013) and Chioda et al. (2016). 5 Chile is the world’s top copper producer and exporter. In 2015, Chile accounted for about 30% of the world’s production of copper.

https://doi.org/10.1016/j.jebo.2018.11.015 0167-2681/© 2018 Published by Elsevier B.V.

Please cite this article as: A. Corvalan and M. Pazzona, Persistent commodity shocks and transitory crime effects, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2018.11.015

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heterogeneously affects both the mining and non-mining municipalities. In order to understand the effects of the price surge on crime, we will first study the effect of the boom on the local economies. We will focus specifically on the labor market conditions because they have direct consequences on crime and are likely to have been influenced by the mining boom.6 We will find that the commodity boom has a short-run effect over employment of the unskilled workers and a medium-run effect on wages. Next we will study the effect of the commodity boom on crime. Contrary to our expectations the commodity shock did not reduce crime rates in Chilean copper producing municipalities after a decade of high prices, when compared to non-mining municipalities over the entire period 2003 to 2013. To explain this counterintuitive result, we will investigate possible heterogeneous dynamic effects during this period. The boom had two distinct phases. From 2003 to 2008, the copper price increased significantly, and the price subsequently stabilized at a high level. When we test the dynamic response of this phenomenon on crime, we find that in the crescent period, the crime rates were significantly reduced as a consequence of the income shock. However, this reduction was eradicated and in fact the crime rate increased in the stabilization period, even though the boom was not reversed itself. Our findings suggest that a persistent positive income shock has an impact on crime but that this effect is only temporary. These findings are consistent with a stable long-run relationship between crime and its determinants. We suggest this pattern can be explained by adjustments in the labor market. In particular, the crime reductions observed in the first period might be due to a temporary increase in unskilled employment. On the contrary, the positive medium-run effects on wages might not be a determinant factor as the cost of living offset by this increase. We acknowledge that this discussion is only descriptive and, therefore, its implications must be interpreted cautiously. The main contribution of our paper is to analyze the dynamic response of crime to a positive income shock, and studying its short and medium-run effects separately. The same study has been done for the case of negative income shocks, and the results shown are similar to ours. For instance, Dix-Carneiro et al. (2018) considered the economic consequences of trade liberalization in Brazil between 1990 and 1995, showing that in the short run, in the regions that were more exposed to tariff cuts, there was a strong and significant negative impact on homicides. However, when the authors considered the longest period, 1990 to 2010, they found that the effect completely dissipated over time.7 Cortés et al. (2016) showed that the crash of a Ponzi scheme in Colombia triggered financial losses on a large fraction of the population, and that this resulted in an increase of both shoplifting and robbery. The following crime surge only lasted one quarter. Our evidence complements these findings, suggesting that the transient nature of the effect is also present in the case of a positive permanent income shock. This article also contributes to other strands of literature. First, our work fits into the recent literature that studies the relationship between income shocks and crime. As mentioned, almost all of this literature is related to negative income shocks, and they usually reported a positive relation between economic crisis and crime. However, the mechanisms that explain this behavior may not apply to the case of positive income shocks. For instance, Bali and Mocan (2010) showed that unemployment has asymmetric effects on crime. The elasticity of crime with respect to unemployment is greater when unemployment is increasing rather than decreasing. This is because it is difficult to revert investment in criminal capital when the economy improves. Nevertheless, empirical papers also show that positive shocks have a negative affect on crime. For example, Hannon and DeFronzo (1998) and Foley (2011) provided evidence that welfare payments have a negative impact on crimes in the US. Similarly, Camacho et al. (2013) and Chioda et al. (2016) studied the implications of conditional cash transfers on crime for both Colombia and Brazil, with the same results. Although these articles found significant negative effects on crime, they only cover two or three years and the authors did not explore the medium-run dynamics of these impacts. A second branch of the existing literature that is directly related to our study looks into the impact of price commodity booms on conflict. Several papers explored the relationship between commodity price shocks and conflict (Miguel et al., 2004; Bückner and Ciccone, 2010; De Luca et al., 2014; Berman et al., 2017; Bazzi and Blattman, 2014). In particular, Dube and Vargas (2013) related the impact of coffee and oil price shocks on the level of violent conflict in Colombia. For the same country, Idrobo et al. (2014) studied the casual relationship between illegal mining and violence as a result of the increase in gold prices. Meanwhile, Buonanno et al. (2015) analyzed the impact of an increase in sulphur prices on the creation of the Sicilian Mafia. All of these papers document the significant impact that price booms have on conflict. In a sense, these results are related to our findings when given that crime may be considered as a milder form of conflict, particularly in a middle-income country such as Chile. The rest of this paper is organized as follows. Section 2 presents our chosen scale of measure of commodity shock and studies how it spreads to the local economies. Section 3 explores how crime evolved over the entire period in both mining and non-mining municipalities, while the fourth deals with the dynamic effects that commodity shock has on crime. Section 5 argues and presents our interpretation of the findings from the previous sections. Finally, the last section presents our conclusion.

6 There is extensive literature on the role of unemployment and reduction in wages in an increase in crime. See Raphael and Winter-Ebmer (2001), Fougere et al. (2009), Machin and Meghir (2004) and Bali and Mocan (2010), among others. 7 They define the medium- and long-run periods as ten and twenty years, respectively. In our paper, we use the term medium-run to indicate a decade.

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Table 1 Summary statistics.

Property crime Theft Robbery Violent crime Assault Homcide Rape Deterrence Production 2000 Price Production2000∗ Price Production2000∗ Price capita Log Production2000∗ Price capita Production2000All∗ Price Employment index Instrument Skilled employment Mining unskilled employment Not mining unskilled employment Unemployment Skilled wages Unskilled wages Median wages Income Income 20% Poverty Gini Schooling Housing prices Muncipal income Log population Density Young Rate

N

mean

sd

min

max

3776 3776 3776 3776 3776 3776 3776 3776 3776 3776 3776 3776 3776 3776 3135 3776 1904 1904 1904 1904 1900 1904 1904 1904 1904 1904 1584 1904 1897 3766 3776 3776 3776

1458.51 1323.96 134.27 516.25 500.19 1.57 14.48 26.52 3.53 1.27 4.48 1.42 −8.44 4.84 0.08 278.21 54.78 6.68 309.15 109.40 515,260.60 223,288.10 191,055.50 594,001.40 226,095.90 199.73 436.81 8971.52 11,063.92 6819.31 990.65 819.79 286.01

1,089.30 967.91 190.59 213.60 209.54 7.23 14.91 25.51 0.84 9.40 34.10 10.86 2.80 36.07 0.20 4,135.11 52.73 16.95 57.33 48.18 307,972.60 81,779.75 93,786.41 364,068.60 115,370.70 106.09 75.74 1489.41 521.61 13,391.55 137.78 2505.38 45.99

0 0 0 0 0 0 0 0 1.81 0 0 0 −9.21 0 0 0.65 0 0 33.58 0 71,260 82,944 71,863 149,394 48,484 0.00 190.12 4113.40 8987.20 466.55 549.31 0.02 4.33

10,819.54 9840.83 1636.03 1804.37 1804.37 323.10 297.62 90 0.0 0 4.43 117.90 521.87 200.82 6.73 573.90 1.86 120,188.10 502.96 167.72 658.88 404.04 7879,501 1027,716 1318,800 4466,245 1889,119 691.94 813.02 16,015.22 13,143.98 180,578.60 1374.42 15,407.64 613.38

2. The commodity shock and its effects on the local economy In this section we describe the mining boom generated by an upsurge in the price of copper during the mid 20 0 0s, and we analyze its effects on local economy in Chile. Our analysis is at a municipal level for the period between the years 20 0 0 and 2013.8 The choice to focus on this specific period was motivated by the fact that we wished to study the dynamics before and after the copper boom.

2.1. The copper mining boom From 2003 to 2008, the international price of copper increased from 0.8105 [USD/lb] to 3.1335 [USD/lb], which represents about a 400% increase. In order to measure the impact of this upsurge at the local level, we compute the current value of the copper production in year 20 0 0 in each municipality, into billions of Chilean pesos. That is to say, we construct our variable Production2000i ∗ Pricet using the copper production at the year 20 0 0, in order to have a pre-boom measure of production and avoid endogeneity problems. We then multiply this value with the current price of copper in billions of Chilean pesos. The value of copper production is expressed in metric tons and was obtained from the National Geological and Mining Service (Servicio Nacional de Geología y Minería, Sernageomin). The dollar price of the refined copper is reported by the Chilean Copper Commission (COCHILCO) (2014). We converted this value into Chilean pesos using the final year’s exchange rate, which we obtained from the Chilean Central Bank.9 Finally, we divided this measure so that the figures were given in billions of Chilean pesos. One billion Chilean pesos represents around 1500 millions US dollars. In Table 1 we report the summary statistics for Production2000i ∗ Pricet and all the other variables used in this study. The copper price boom is shown in Fig. 1, which displays the average of Production2000i ∗ Pricet for the period 20 0 0 to 2013.

8 A municipality is the smallest administrative unit in Chile. It resembles European Nuts3 and US counties. In Chile, there are currently 346 municipalities but we removed Easter Island from the study because of its excessive geographical distance from the mainland. 9 The exchange rate shows an opposite trend compared to the trend for price, which implies that in 2011, when the price was at its maximum, Production2000i ∗ Pricet was lower than in 2007.

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Fig. 1. Copper Price 20 0 0–2014.

In Fig. 1 we can distinguish two different sub-periods. In the first sub-period, from 2003 to 2008, the value of copper production shows a strong growth. We called this the rise period.10 In the second sub-period, after 2008, the production level remained stable at a high level compared with the beginning of the boom. We called this second instance the stabilization. Consequently, the entire boom encompasses both the rise and stabilization periods. A key assumption of our identification strategy is that the income shock induced by the international upsurge of copper price is not homogenous but, on the contrary, has spatial variation across Chilean municipalities. Fig. 2 below shows the value of copper production for a subset of municipalities, with darker colors indicating higher values of Production2000i ∗ Pricet . About 95% of the national production is concentrated in the Regions I–IV (the four most northern regions of the country) and the mining sector represents over 40% of the regional GDP. In regions V and VI (in the center of the country) as well as the XI and XII Regions, the mining sector represents between 10% and 25% of the regional GDP, respectively. Mining production is negligible in all of the other regions. From the map, we observe that there are clear geographic distinctions between mining and non-mining municipalities. From the total set of 345 municipalities, 25 have positive copper production in 20 0 0. Mining municipalities in Chile are not as isolated or scarcely populated as they are in other countries. In 2003, the average population in mining municipalities was 59,958 and 78,8% of them reside in urban areas, whereas for the non-mining population, the urbanization rates were 45,846 and 63,3%, respectively. Although there are alternative measures of income shocks based on employment (David et al., 2013; Dix-Carneiro et al., 2018), we prefer the use of production for a number of reasons. Firstly, the data on production is not extracted by surveys, while surveys are used for employment. Secondly, the presence of production in a municipality is local by definition. It exclusively captures the municipalities where there is a copper mine. That implies that we are not considering the municipalities that have mining employment but that are not producers. Consequently, the number of mining municipalities when using employment data is much higher. Finally, as Marchand and Weber (2018) explained, employment measures11 are hard to interpret as increases might be due to more extractions activity but can also be caused by contractions of other sectors, regardless of the price surge. While we are confident that production data are best suited for the purposes of this paper, we also evaluate the robustness of our results using an index based on employment. 2.2. Commodity booms and local economies Several papers documented the impact of natural resource extraction on local economic activity. Does mining activity lead to better local performance or not? From a theoretical point of view, the increase in extraction profitability led by

10 We observed that Production2000i ∗ Pricet decreased in 2008, the year of the financial crisis. However, we have decided to still consider the sub-period 2003 to 2008 for reasons that we will explain in this document. 11 Defined as dependence by the authors. The same paper proposed an interesting point of view on how to measure the exposure of the energy price shocks.

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Fig. 2. Production level in 20 0 0(pre-boom). The map shows the intensity of the variable Production2000i at the municipal level. Darker colors are associated with highest values of Production2000i .

higher prices would cause a rightward shift in the demand for labor in the extractive sector. As a result, employment, wages and earnings would go up, depending on the degrees of elasticity of the two curves. The extra workers needed to fulfill needs of the boom might come from other sectors, other geographical areas or be formerly unemployed people. At the same time, the boom may have different impacts on sectors other than the extractive one. One possibility is that the boom would affect sectors that are directly linked to the extraction one, such as input providers of the booming industry. Eventually, this would increase the demand for locally provided goods and services as more people are employed and that have more resources to spend. If more people are employed in the area, we also observe an increase in demand Please cite this article as: A. Corvalan and M. Pazzona, Persistent commodity shocks and transitory crime effects, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2018.11.015

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in both hospitality, that includes businesses such as hotels, restaurants and bars, and real estate sector. This would lead to a positive effect on employment and wages throughout the economy. However, another alternative is that local economies experienced the so-called Dutch Disease. This theory suggests that when a sector is booming (copper in our case), the resulting shift of workers from one area of the economy to another will cause a decline in the employment level of the non-booming sectors. Moreover, as wages in the booming sector increase, there would be excessive pressure on non-booming sectors for wage to rise as well, which would reinforce their decline. Indeed, the characteristics of the local economy and the distribution of skills among workers in a local economy are also important. Whether an increase in commodity prices leads to improved labor market conditions in the entire local economy is, ultimately, an empirical question.12 The majority of works found positive employment effects. In a study closely related to ours, Black et al. (2005a) analyzed the impact of the coal mining activity on the local economy separating the three periods of price increase – or boom –, price stabilization, and price decrease during the 1970s and 1980s for four states in the United States. The author found that during the boom, mining municipalities experienced an increase of 6.8% per year on average in mining employment whereas only a 0.7% increase was experienced in non-mining employment. However, in the following period, when price stabilized, the authors found no effect. In a paper studying shale gas for the USA, Weber (2012) found that the boom in natural gas drilling increased total employment of 12% over an 8-year period in booming counties. Similarly for employment, the majority of empirical papers found that the increase in prices led to higher earnings. Black et al. (2005a) found increases in earnings in mining and non-mining sectors in mining countries. Positive results are also found in Weber (2012) and Lee (2015). However, a few studies do not find evidence of an increase in wages and income. For example, in a cross-section study of Brazilian municipalities, Caselli and Michaels (2013) could not find any positive impact of oil revenues on non-oil activities GDP. Paredes et al. (2015) studied the impact of the Pennsylvanian Marcellus shale fracking activities on income and employment. The authors did not find evidence of income effects. There are a number of studies that evaluate the impact of a boom in natural resources and their effects on socioeconomic indicators other than the labor market. First, there is consensus that booming local economy generates a lower level of poverty. Loayza and Rigolini (2016) evaluated the impact of mining activity on Peruvian districts and found that producing districts exhibited lower poverty rates. A similar result has been found in Chile by Álvarez et al. (2018). However, if the cost of living is increasing, price-adjusted-poverty might not necessarily decrease, as noted by Weber (2012). On the other hand, the consequence on inequality is ambigous. Some works found positive relationships (Loayza and Rigolini, 2016; Deaton and Niman, 2012), whereas others, like Howie and Atakhanova (2014) found that the boom decreases inequality. The economic shock might also affect schooling investment decisions, especially that of unskilled people. The possibility of getting a lowskill job easily might dissuade some young people from further studying in the short run (Black et al., 2005b). Finally, an increase in commodity prices might be associated with greater local government revenues (Aragon and Rud, 2013). This is particularly true for countries that allow the local government to retain part of the royalties and mining taxes. In turn this would alter the public spending at a local level (Asher and Novosad, 2014). Loayza and Rigolini (2016) documented such a case in Peru and Caselli and Michaels (2013) for Brazil. 2.3. The effect of the copper boom on chilean local economies In this section we analyze how the rise in copper prices affected the local economy in Chile. A comprehensive analysis of the impact of the boom is beyond the scope of this study. However, we will focus nevertheless on those variables that literature has identified as most likely to have an impact on crime. Labor market variables are supposed to have the strongest consequences on crime and because several papers documented that unskilled people are most likely to commit crimes(Gould et al., 2002; Yang, 2017; Raphael and Winter-Ebmer, 20 01; Machin and Meghir, 20 04), we chose to describe the evolution of labor market variables for both skilled and unskilled workers. For skilled employment, we consider workers with some post-secondary education. For unskilled, we compute the ratio of workers with high-school education or lower. In both cases, we also report the level of employment in the metalmining sector. In addition, we consider the unemployment rate (defined as individuals with no job but searching for one, divided by the total labor force).13 Regarding wages, we also separated our study between skilled and unskilled workers. In particular, wages of the unskilled might be interpreted as the opportunity costs of committing crime. We also include median wages that could be considered as the benefits from crime (Ehrlich, 1973). Moreover, we include two measures of income (which include both labor and non-labor sources), namely the average income and the one from the bottom 20% income percentile. We explore the impact on other crime determinants as well. First, we include the poverty rate (Bourguignon, 1999). We decide to include income inequality although the empirical literature did not reach a consensus on its own (Kelly, 20 0 0). We use the GINI coefficient based on household income before government transfer. As for education (Machin et al., 2011), we have chosen to consider the average years of schooling. A measure of housing prices is also included in order to document changes in the cost of living in mining municipalities. Finally, we report municipal income because we need to make sure that the presence of the mining sector does not lead indirectly to greater local government funds. 12 13

We focused our study on recent literature about variations within country. For cross-country literature, see Sachs and Warner (2001). All our measures of employment are in per 10 0 0 people.

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All data was obtained from the Chilean National Socioeconomic Survey (CASEN). The CASEN was conducted by the Chilean government at different frequencies during our study period; that is, every two or three years. In order to explore the impact of the boom on the economy, we consider a panel in which we interact Production2000i ∗ Pricet with year dummies. The panel considers all years with available CASEN survey data, that is 20 0 0, 20 03, 20 06, 20 09, 2011 and 2013. Our coefficients of interest are the θ s in the following equation:

Cr ime Deter minant it = αi + βt +

n 

θs Production20 0 0i ∗ Pricet ∗ Yearts + γ Xit + εit

(1)

s=1

where CrimeDeterminantit is a socio-economic variable in municipality i at time t and Production2000i ∗ Pricet is copper production in year 20 0 0 in municipality i times the copper price at time t. Yearts is a year dummy that equals 1 if t = s and 0 otherwise, n is the total number of CASEN years, Xit is a set of controls which includes log of the population and municipal density. α i and β t are municipal fixed effects and year dummies, respectively. We assume that the error term ε it is clustered at the municipal level.14 As we run multiple regressions using the same treatment, namely the rise in the copper price, we are aware that we have the well-known problem of testing multiple hypothesis with a single independent variable. Estimating the regressions separately for each variable and checking the significance of each coefficient by itself would lead to incorrectly over-rejecting the null hypothesis after the first test. In order to solve this problem, the literature uses the so-called family-wise error rate (FEWR) which considers the probability of making at least one type I error in the family of hypotheses. In the implementation, we followed the step-wise algorithm proposed by Romano and Wolf (2005a) and Romano and Wolf (2005b).15 The results of the estimation of Eq. (1) can be found in Table 2. Each row represents a single crime determinants’ estimation where the reported coefficients’ are the θ s for the year reported in the column title. Below each coefficient we report the traditional (uncorrected) and the Romano-Wolf p-values. We use the latter for significance analysis. As the omitted year in our estimations is 20 0 0, all coefficients are interpreted relative to this year. The first column which displays the results for 2003, allow us to verify the existence of pre-trend in crime determinants, before the increase in prices. As expected, Production2000i ∗ Pricet is not affecting crime determinants, except one. Looking at the employment variables, we notice that the boom is not affecting the overall or metal-mining skilled employment in any period, except for one case. The boom is having strong effects on total and metal mining unskilled employment, until 2011. Total unemployment decreased in the same period, but the coefficients, relative to the baseline year, are the highest in 2009 compared to the other periods. The effect on wages is almost the opposite. While skilled wages did not change due to the boom, unskilled and median wages increased after the copper price stabilized at a higher level. In both cases, the increase is gradual, reaching its maximum in 2013. Overall, the boom exhibits heterogeneous effects in the labor market over time. In the short run, employment is more affected; in the long run, the effects are concentrated on wages. These results are not limited to the metal-mining sector, suggesting the presence of spillover effects. Turning to non-labor market variables, we did not find a statistically significant change in poverty and inequality in the short and medium terms. Years of education have a negative and significant impact in the short term. This result is likely related to the increase in unskilled employment. The cost of living, captured by the in-house rent prices, increased in mining zones, only during the stabilization. This result is consistent with information from other sources that documented a housing boom in the north of Chile caused by the copper boom (López and Aroca, 2012). Finally, we also find that the local government budget was not statistically different between mining and non-mining municipalities. This is explained by the fact that in Chile, as in some other countries, the mining extraction royalties go directly to the central government (Asher and Novosad, 2014). In general, our results share important similarities with the literature we have presented earlier. In order to rationalize the consequences of the boom on the economy, we first provide some insight on why the economic shock affects unskilled employment only in the short term. The rise in copper prices created an incentive to increase extraction, which led to more workers in the metal-mining sector being employed. However, since the supply of labor was relatively elastic, this did not immediately translate into higher wages, at least in the short period (Borjas, 2013). The employment increase in the metal-mining sector generated positive spillovers into other sectors. This is the reason why unskilled employment in the non-metal-mining industry increased in 2006 and 2009, compared to the baseline year.16 Gradually, the marginal productivity of metal workers increased, which led to higher wages in the mining-metal sector. This had an effect on non-mining sectors, namely wage pressure, leading to higher earnings and income throughout the period. Such wage adjustments however were not cost-free, but led to a decrease in employment, as the marginal productivity in the non-mining sectors did not increase. Consistently, non-metal-mining unskilled employment did not increase in 2011 and 2013, although it did in the metal-mining sector.

14 Alternatively, we could have used a specification without including the price, which would produce equally significant coefficients. However, we preferred to include the price for two reasons. First, the inclusion of price allows us to evaluate the intensity of the shock on the dependent variables (crime determinants and crime). Secondly, since our baseline Eq. (2) includes the price, we prefer to be consistent throughout the paper. 15 See Clarke (2016) for the details. 16 A related study, Aragon and Rud (2013) showed that the unskilled workers were also the ones that benefited the most from the activities of the Yanacocha gold mine in Peru.

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A. Corvalan and M. Pazzona / Journal of Economic Behavior and Organization xxx (xxxx) xxx Table 2 The effect of the commodity shock and the economy. 2003 (1)

2006 (2)

2009 (3)

2011 (4)

2013 (5)

0.00 (0.99) [0.99] −0.02 (0.22) [0.48] 1.17 (0.02) [0.13] 0.01 (0.58) [0.86] −0.15 (0.04) [0.14]

−0.06∗ (0.00) [0.09] −0.03 (0.11) [0.29] 0.14∗ ∗ (0.00) [0.05] 0.03 (0.03) [0.13] −0.06 (0.03) [0.13]

−0.05 (0.06) [0.29] −0.02 (0.26) [0.54] 0.17∗ ∗ ∗ (0.00) [0.00] 0.06∗ (0.00) [0.08] −0.22∗ ∗ ∗ (0.00) [0.00]

−0.03 (0.14) [0.36] −0.01 (0.36) [0.63] 0.11∗ (0.00) [0.06] 0.07∗ ∗ ∗ (0.00) [0.00] −0.11∗ (0.00) [0.08]

−0.02 (0.75) [0.88] 0.00 (0.99) [0.99] 0.10 (0.07) [0.22] 0.04 (0.06) [0.21] −0.11 (0.04) [0.21]

−471.40 (0.34) [0.66] −93.38 (0.37) [0.66] 2.73 (0.96) [0.99] −911.40∗ (0.00) [0.08] −167.40 (0.12) [0.27]

−51.45 (0.85) [0.94] 22.08 (0.65) [0.85] 36.19 (0.07) [0.20] 47.26 (0.76) [0.94] 44.07 (0.60) [0.84]

−177.60 (0.48) [0.63] 101.21 (0.32) [0.59] 57.60 (0.36) [0.63] 146.21 (0.56) [0.63] 173.40 (0.10) [0.35]

−73.21 (0.66) [0.79] 173.24∗ ∗ (0.00) [0.02] 204.34∗ ∗ (0.00) [0.01] 443.44 (0.04) [0.21] 146.04 (0.01) [0.11]

155.48 (0.39) [0.67] 212.25∗ (0.00) [0.06] 212.68∗ ∗ (0.00) [0.02] 678.62 (0.05) [0.20] 310.88 (0.07) [0.22]

0.12 (0.41) [0.69] -0.15 (0.12) [0.86] -1.56 (0.16) [0.37] 5.88 (0.95) [0.99] 17.99 (0.03) [0.14]

0.08 (0.31) [0.59] 0.01 (0.60) [0.94] -1.01 (0.01) [0.12] 42.06 (0.36) [0.69] 16.38 (0.15) [0.34]

0.09 (0.35) [0.63] -0.11 (0.10) [0.63] -2.22∗ ∗ ∗ (0.00) [0.00] 73.90 (0.23) [0.51] 34.50 (0.10) [0.36]

0.05 (0.42) [0.63] 0.00 (0.01) [0.99] -1.29 (0.01) [0.14] 155.63∗ ∗ (0.00) [0.02] 32.10 (0.08) [0.33]

-0.18 (0.07) [0.22] 0.09 (0.07) [0.73] -2.30 (0.04) [0.18] 243.84∗ ∗ ∗ (0.00) [0.00] 65.17 (0.08) [0.22]

Employment Skilled employment

Metal mining skilled employment

Unskilled employment

Metal mining unskilled employment

Unemployment

Salary and Incomes Skilled wages

Unskilled wages

Median wages

Income

Income 20%

Other SES variables Poverty

Gini

Schooling

Housing Rental Prices

Municipal Budget

Notes. This table shows how the commodity boom shock impacted on a series of labor market and socio-economic variables. Each row reports the estimated coefficients of the dependent variable (in the row’s title) against the interactions between Production2000∗ Price and Year t dummies (year t in the column’s title). All estimations include controls for log population and density, and a full set of year dummies. To correct for Family Wise Error Rates from multiple hypothesis testing, we calculate the Romano and Wolf (2005a) and Romano and Wolf (2005b) p-values, using their stepdown methods. Traditional (uncorrected) and Romano-Wolf p-values are presented in round and square brackets, respectively. Significance are based on Romano-Wolf p-values. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively. Details on the regression equation and variables definitions are in Section 2.

3. The effect of the boom on crime: 2003–2013 In the previous section we showed how, between 20 0 0 and 2013, the commodity shock improved the living conditions in municipalities with greater mining production. We now explore how crime evolved over this period in mining and nonmining municipalities. Please cite this article as: A. Corvalan and M. Pazzona, Persistent commodity shocks and transitory crime effects, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2018.11.015

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Fig. 3. Property crime rates and copper price in mining and non-mining municipalities. This figure shows Property Crime and the copper price for mining and non-mining municipalities from 2001 until 2014. As we explain in the text, Property Crime includes fewer crime categories than the series that starts in 2003 and that we use in the regressions. We define a mining municipality as any municipality with positive production of copper in 20 0 0.

3.1. Crime variables We have yearly data at municipal level that shows the number of crime reports to the authorities per 10 0,0 0 0 inhabitants. The source is the Chilean Interior Ministry. Our main variable considers economically motivated crimes, which we labeled Property Crime. This variable includes two categories of reports: Theft and Robbery. The latter includes all the thefts where violence and intimidation against a victim were used. In the former, we include all other types of thefts, including burglary.17 As Table 1 shows, theft is the most frequent category.18 It is about 10 times more frequent than robbery. We have complete and disjoint data since 2003, but we also include data since 2001 and since 1999 for a reduced subset of municipalities. Accordingly, our preferred period of analysis starts in 2003, when the commodity boom started, but we use aggregated and reduced-sample data to explore the behavior of crime trends in the pre-treatment period. According to Table 1, the average property crime rate for the period is 1,458. In order to get an idea of the magnitude of crime in Chile, we may use comparable victimization surveys (United Nations Office on Drugs and Crime (UNODC), 2018). We conclude that the victimization rate in Chile is much higher when compared to, for instance, the USA. For example, car theft is five times more likely, burglary twice more likely to occur and robbery ten times more frequent. We first seek to provide a visual inspection of the different trends in property crime rates for mining and non-mining municipalities. We define mining municipalities as those that in the year 20 0 0 have a positive production of copper.19 Fig. 3 shows these trends from 2001 to 2014. As we can see, property crime rates between 2001 and 2003 increased in both mining and non-mining municipalities, although slightly more in mining ones. The graph illustrates some of the main findings of our paper: up until 2003 mining and non-mining municipalities have positive trends and then they start diverging. The trend of the latter group continues upwards whereas the trend of the former group shows a decline and then rises back up again. The distance between these two groups reaches its peak at the end of the rise period. However, after the entire period, the crime rates are very similar between the two groups. We now move on to a more structured exercise in order to quantify the impact of the mining cycle on crime rates. 3.2. Baseline econometric specification In the baseline analysis, we consider property crime rate as the dependent variable and Production2000i ∗ Pricet which is the current value of the pre-boom municipal copper production as the main explanatory variable. The choice of the pre-

17 18 19

The category theft includes theft with surprise, auto thefts, thefts from a car and other types of thefts without violence or intimidation against a victim. In Section 4 we will also consider violent crimes, which is the sum of assaults, homicides and rapes. There are 25 municipalities that satisfy this criteria.

Please cite this article as: A. Corvalan and M. Pazzona, Persistent commodity shocks and transitory crime effects, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2018.11.015

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A. Corvalan and M. Pazzona / Journal of Economic Behavior and Organization xxx (xxxx) xxx Table 3 Commodit y shock on crime: 2003-2013, baseline. Property crime rate

Production20 0 0∗ Price Deterrence Logpopulation Density Young rate Prop. crime rate (t–1) Macro trends Observations R-squared Sargan test Estimation

(1)

(2)

(3)

(4)

(5)

(6)

−1.16 [1.03] – – – – – – 3776 0.23

−1.17 [1.06]     – – 3776 0.26

−1.94 [1.23]     –  3776 0.29

−0.04 [0.77]       3772 0.49

−0.03 [0.96]      – 3768

−0.98 [0.95]     –  3776

135.60 (p=0.00) GMM

2SLS-FE

FE

FE

Instrument The Kleibergen Paap F-statistic R-squared

FE

FE First stage

0.15∗ ∗ ∗ [0.01] 234.06 0.29

Notes. This table shows the baseline results for the period 2003–2013. Property Crime is the dependent variable in all specifications. All estimations include year dummies. Estimation in column (1) includes only Production2000∗ Price; in columns (2) we add the control variables. For each control we interact the time-invariant municipal-specific pre-shock values with year dummies. In (3) we include linear and quadratic macro-region specific linear time trend. In (4) we add a lagged dependent variable. In (5) we consider an Arellano–Bond GMM estimator. In columns (6) we run a two stage least square estimation. In the same column, we report the first stage statistics. Details on how we constructed the instrument can be found in Eq. (3). Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively.

treatment production reduces concerns about the endogeneity of production.20 Our data specification is a standard fixed effect model with year dummies for the entire period. The equation we estimate is as follows:

Crimeit = αi + βt + θ Production2000i ∗ Pricet + γ Xit + εit

(2)

where Crimeit is the property crime rate for municipality i at year t, Production2000i ∗ Pricet is the value of copper production in 20 0 0 at municipality i times the price of copper in billion of Chilean pesos at the year t, Xit is a set of controls, and α i and β t are municipal fixed effects and year dummies, respectively. We assume that the error term ε it is clustered at the municipal level. As a measure of control, we include a deterrence variable. The ideal measure for this variable is the number of policemen, but the Chilean Interior Ministry does not provide these statistics for security reasons. Similar to Gould et al. (2002), we use the percentage of people apprehended for crime over the total number of crimes in that particular category. The idea is that, ceteris paribus, the more people are apprehended per crime, the more efficient the law enforcement system is (Di Tella and Schargrodsky, 2004). We are aware that this variable might suffer from endogeneity. Moreover, it has crime in the denominator (although total, not property), so any measurement error in crime rates automatically generates a negative correlation within said crime rates. As a consequence we abstain from any causal interpretation of the coefficients. All our results are robust to the exclusion of this deterrence measure. Other controls include log of population, density of population (Glaeser and Sacerdote, 1999) and percentage of young people (Levitt, 1999).21 Our source for demographic data is the Chilean National Statistical Institute (INE). For each control we interact the time-invariant municipal-specific pre-shock values with year dummies. In such a way we aim to reduce post-treament bias. The baseline regressions estimate an Eq. (2) for the entire boom period, from 2003 to 2013. Table 3 shows our results for property crime rate and we provide several robustness checks of the specification. We first include a parsimonious fixed effect specification which includes Production2000i ∗ Pricet and year dummies. In column (2) we include deterrence and all the controls, while in column (3) we also add linear and quadratic macro-region specific time trends.22 The objective of the region-specific trend is to relax the parallel trend assumption. In the same vein, we have changed the specification (2) to include a lagged dependent variable at the right-hand side. In order to correct Nickels’ bias, we report in column 20 Nevertheless, we notice that the production level is not that elastic with respect to the prices. For example, the crisis of the years 2007 to 2009 did not impact on the production level and, in fact, the production in 2008 was at similar levels to 2006. This behavior is unsurprising considering that production depends on long term investment decisions. 21 See Buonanno (2003) for a general discussion on the determinants of crime. 22 There are five macro-regions in Chile, specifically know as: The Large and Small North, The Middle regions, The South and the extreme South.

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(5) the Arellano–Bond GMM estimation.23 In all these cases, we find that the coefficient for Production2000i ∗ Pricet is nonsignificant, meaning that the boom had no effect on crime during the entire boom period; that is to say, from 2003 to 2013. Despite the positive effect that the boom had on the local economies, as documented in the previous section, we do not find a crime decrease in the entire period. The use of pre-treatment production in our explanatory variable served to avoid the issue with contemporary reverse causality. However, as both production and crime are persistent, even past production could have been affected by past crime trends in the medium to long run. In addition, even though the price of copper is mainly determined by the demand, the supply might also play a role. If this is the case, Chile could have an impact on price determination, threatening the exogeneity condition. In order to properly address this issue, we implemented an instrumental variable strategy and estimated a two-stage least squares specification. Following Dube and Vargas (2013), we constructed the instrument multiplying a municipal time-invariant part and a national time series one. The time-series variation is given by the export volume of the four other leading copper exporting countries, i.e. Australia, Canada, Peru and USA.24 The cross section dimension is captured by the inverse of the municipality’s centroid distance with the closest point being one of the two geological faults associated with the two most important copper’s metallogenic epochs.25 These two metallogenic epochs are the Late Eocene-early Oligocene, when the Domeyko fault was created, and the Late Miocene-early Pilocene. The belief is that copper production in a municipality will be higher the closer it is to one of the faults. This instrument is given by:

Instrumenti,t =

Total Exports Volumet Distance to Faulti

(3)

The results using these instrumental variable strategies can be found in the last column (6) of Table 3. Our instrument is strongly and positively associated with the Production2000i ∗ Pricet , with F-stats well above the rule of thumb suggested by Stock and Yogo (2005). Again, we find that the boom did not affect crime rates. The coefficients are slightly less negative than the ones in the previous columns but less precisely estimated. 3.3. Robustness As we explained in the previous section, our preferred measure for the boom considers the total value of mining production. However, in this section, we have tested whether our results are robust to a variety of alternative measures of the boom. We report our results in Table 4. In order to measure the relevance of the income shock to the local economy, we construct two measures in per capita terms. The first, Production2000i ∗ Pricet Capita, considers production weighed by municipal population, whereas LogProduction2000i ∗ Pricet Capita, as the name suggests, considers the logarithm of the production per capita. That fact aside, the variable is constructed in a similar fashion to the former. Columns (1) through (4) report the results first with OLS and then with 2SLS. Given the mentioned possible endogeneity problems, we are more interested in the instrumental variable results, i.e. columns (2) and (4). We did not find evidence of any effect on the per capita measurement related to property crime rates. A second identification assumption deals with the concerns over the possible effects the boom had beyond the coppermining municipalities. Even though copper is by far the most important mineral extracted in Chile, it is not the only mineral to be extracted. Chile is also a producer of iron and gold (Chilean Copper Commission (COCHILCO), 2014). If a municipality produced one of these metals but not copper, then it would also experience an economic shock given that metal prices are usually correlated. To deal with this potential threat, we first noticed that mining municipalities that produce gold and silver generally overlap with those that produce copper.26 In order to address this concern, in column (6) and (7) of Table 4, we consider a comprehensive measure of the value of the production of copper, iron and gold, and we call it Prod all. The results are quite similar to those in our baseline regression, given that copper production has the biggest share of the total production value. Another reason to suspect that the mining boom might not only be localized to the producing municipalities is that many mining workers often reside in municipalities where no production occurs. Usually, mining shifts consist of one or two weeks of continuous work followed by another week of rest. Accordingly, many workers travel from other municipalities, mainly neighboring ones. We address this issue by including a measure based on employment instead of production. Data on employment are obtained from the National Socioeconomic Survey (CASEN). There are 71 municipalities which, in 20 0 0, have more than 1% of employees in the metal production sector.27 To test the robustness of our results in an employment based measure, we use the index proposed by Álvarez et al. (2018) for Chile. The authors considered the five metals which

23

See Nickell (1981) and Arellano and Bond (1991) for a further discussion. Data is from The International Trade Centre (ITC) (2018). 25 Metallogenic epochs are units of geologic time during which conditions were particularly favorable for the formation of specific classes of mineral deposit. 26 There are only four municipalities that produce gold and three municipalities that produce silver but that do not produce copper. 27 That number is not directly comparable with the 25 using production because that refers to only copper but it does serve to give an idea that employment is more widespread than production. 24

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A. Corvalan and M. Pazzona / Journal of Economic Behavior and Organization xxx (xxxx) xxx Table 4 Commodity shock on property crime: 2003-2013, robustness.

Production20 0 0∗ Price capita

OLS (1)

2SLS (2)

1.84 [4.54]

13.58 [12.86]

∗

Log production20 0 0 Price capita

OLS (3)

2SLS (4)

−221.01 [176.84]

537.90 [460.06]

Production20 0 0All∗ Price

OLS (5)

2SLS (6)

−1.56 [1.28]

1.03 [0.98]

Employment index

OLS (7)

2SLS (8)

1,356.11 [1,275.96]

830.05 [1,481.94]]

∗

Production20 0 0 Price W∗ Production20 0 0∗ Price

ρ Observations R-squared

3776 0.29

3776

3776 0.29

3776

3776 0.29

3776

3135 0.36

3135

2SLS (9)

−1.73 [1.11] 0.29 [2.86] 0.13∗ ∗ ∗ [0.03] 3400 0.16

Notes. This table reports the robustness exercises spelled out in Section 4. For each alternative commodity shock measure we report two columns: one with fixed effect (OLS) and the other with instrumental variable(2SLS). We use the same instrument for each 2SLS regression. Details on how we constructed the instrument can be found in Eq. 3. Column (9) reports the results of a spatial durbin model (SDM), which includes a spatial lag for the dependent variable and the controls. The number of observations is lower than columns (1) through (6) as it requires balanced panel data. All estimations includes municipal fixed effects, year dummies, linear and quadratic trends and a full set of controls which include deterrence, population, density and young rate. For each control we interact the time-invariant municipal-specific pre-shock values with year dummies. We do not report the first stage statistics. These are available upon requests. Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively.

represented more than 1% of the overall production value in 20 0 0, and constructed a measurement of the price weighed in growth. They then created an index based on these growth measurements, setting the initial value of the index to 100 in 2003. Finally, the authors took the log of this index and multiplied it by the exposure of each municipality to the shock, as proxied by the share of the metal-mining sector on each municipality’s overall employment. We label this variable as the Employment Based Index. The results are displayed in Table 4, columns (7) and (8). Again, this measurement of employment presents similar results to the ones of production. The final concern we had relates to the fact that municipalities’ boundaries might not necessarily represent the local criminal labor markets. Criminals might operate in their own municipality but may also travel to close-by municipalities in their search for illegal opportunities. Consequently, crime rates would tend to be spatially clustered, with municipalities reporting a high level of crime situated geographically close to others with similar high crime rates. Moreover, mining municipalities might attract criminals from close-by non-mining ones.28 If present, all these spillover effects would bias our fixed effects estimates (Anselin, 2013). To take into consideration the likely spatial nature of crime, in column (9) of Table 4 we show the results of a spatial durbin model (SDM), which includes a spatial lag for the dependent variable and the controls. We use a quasi-fixed maximum likelihood estimator for fixed effects for balanced panel data, as detailed by Belotti et al. (2016), and report the results of the matrix queen rook, although employing other types of matrices leaves the results basically unchanged. Letting our model have a spatial structure does not affect the results we have found so far. Production2000i ∗ Pricet is still not relevant in the medium-run. The point estimate is −1.73 whereas in Table 3, column 3, it was −1.94. The positive and significant coefficient ρ confirms that there are positive and significant crime spillovers. Municipalities with high levels of crime are located close-by to others with similar high levels. Moreover, the level of mineral production of the neighboring municipalities does not affect crime rates. This further testifies the robustness of our findings. 4. Dynamic response of crime & short-run analysis Our result that the commodity price driven income shock had no effect on crime rates seems to be counterintuitive. How is it possible that a fourfold increase in the price of copper does not lead to a decrease in crime rates? To provide additional insight into this pattern, we explored the dynamic response of crime—that is to say, the short-run effects—to the income shock. In Section 3 we already checked that the boom has heterogeneous time effects on several local economic variables. The question here is whether crime is also exhibiting a particular dynamic response during the entire boom period. 4.1. Dynamic response of crime In order to explore heterogeneous effects over time, we run the same regression as in (2), but we substitute Production2000i ∗ Pricet by a series of interaction between Production2000i ∗ Pricet with year dummies, as in the specification 28

We think that criminals do not travel to distant municipalities in order to commit crimes.

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Fig. 4. Effects of production value on property crime, since 2003. This figure explores the heterogeneous effect of the commodity boom on property crime. In particular, it shows of the regression of crime on the interaction between Production2000i ∗ Pricet and year dummies for the period 2003–2013. Slashed line represents the 95% confidence interval. Further details can be found in Section 4. The exact coefficients, and standard errors, can be found in Table 5.

(1). The equation is the following:

Crimeit = αi + βt +

n 

θs Production2000i ∗ Pricet ∗ Yearts + γ Xit + εit

(4)

s=1

where Yearts is a year dummy that equals 1 if t = s and 0 otherwise, and n is the total number of years. In Fig. 4 we present a graphic description of our results. We plot the set of coefficients θ t , one for each interaction except the final year, including the 95% confidence interval. That is, the omitted year in our estimations is 2013 and all coefficients are interpreted relative to this year.29 From our findings, the boom evidently has a negative and significant effect in 2004 until 2008, which is roughly the period where the price of copper surged. The year 2005 is the year with highest crime differential between copper-miningproduction municipalities and the other ones. Interestingly, from 2008 onward, Production2000i ∗ Pricet ceases to have an impact on property crimes. A possible threat to identification is that crime rates in mining municipalities were decreasing before the surge in the price of copper. Although the visual inspection and the use of macro trends in (2) suggest the absence of pre-trends, we considered this factor with two additional tests. In Table 5 we exhibit the results reported in 4, but in addition we estimate the same specification for years before 2003 using our aggregated and reduced-sample data of crime. In column (1) the coefficients displayed in [Fig. 4] are shown. In column (2) we consider property crime from 2001 for all the municipalities. We confirm the results of column (1), except for the year 2004 which is now slightly above the 10% threshold (p-value=.116). The coefficients for the year 2002 and 2003 are negative and not significantly different from zero, compared to the baseline year. Nevertheless, crime rates in mining municipalities were already rising before 2003 which provides evidence that if anything the shock was so strong that it reverted positive trends in mining municipalities. In column (3) we consider the property crime rate for the reduced-sample data which encompasses about 70 municipalities starting in 1999. Again, we find similar results for the surge period, while prior to the price shock the confidence intervals overlap with zero. Overall, we find strong support for the non-existence of pre-trends in crime. 4.2. The short-run effects of the boom on crime So far, we have two results: first, the medium-run effect of the commodity boom over crime is null; and second, there exists a negative and significant short-run effect, which occurred on the rise period until the year 2008. In order to isolate the effect of Production2000i ∗ Pricet on this period, we ran a specification (2) only for the period between 2003 and 2008. We report the OLS and 2SLS fixed effects results. We included the full set of controls and the errors were clustered at a 29

We used 2013 as the omitted category because the initial year is different across the three models.

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A. Corvalan and M. Pazzona / Journal of Economic Behavior and Organization xxx (xxxx) xxx Table 5 Commodity shock on property crime: dynamic effects. 2003 (1)

2001 (2)

1999 (3)

−1.98 [3.25] −1.25 [−2.19] 0.80 [1.36] −2.02 [1.29] −3.23∗ ∗ ∗ [0.86] −2.34∗ ∗ ∗ [0.46] −2.36∗ ∗ ∗ [0.65] −1.78∗ ∗ ∗ [0.65] −0.90 [0.75] 0.21 [0.77] −0.17 [0.69] 0.21 [0.31] 4456 0.40

−7.19 [5.44] −7.65 [4.81] −3.49 [2.65] −1.04 [2.07] 1.55 [1.55] −1.60 [1.03] −2.46∗ ∗ ∗ [0.48] −1.89∗ ∗ ∗ [0.30] −2.13∗ ∗ ∗ [0.56] −1.17∗ [0.69] −0.27 [0.79] 0.46 [0.89] 0.03 [0.81] 0.48 [0.32] 1170 0.03

Production20 0 0∗ Price∗ 1999 ∗

∗

Production20 0 0 Price 20 0 0 Production20 0 0∗ Price∗ 2001 Production20 0 0∗ Price∗ 2002 Production20 0 0∗ Price∗ 2003 Production20 0 0∗ Price∗ 2004 Production20 0 0∗ Price∗ 2005 Production20 0 0∗ Price∗ 2006 Production20 0 0∗ Price∗ 2007 Production20 0 0∗ Price∗ 2008 Production20 0 0∗ Price∗ 2009 Production20 0 0∗ Price∗ 2010 Production20 0 0∗ Price∗ 2011 Production20 0 0∗ Price∗ 2012 Observations R-squared

1.25 [1.66] −2.29∗ [1.30] −3.48∗ ∗ ∗ [0.90] −2.42∗ ∗ ∗ [0.52] −2.44∗ ∗ ∗ [0.69] −1.89∗ ∗ ∗ [0.66] −1.00 [0.78] 0.22 [0.79] −0.21 [0.70] 0.20 [0.32] 3776 0.28

Notes. This table explores the heterogeneous dynamic effects of the commodity shock on property crime. We multiplied Production2000∗ Price by year time effects, i.e., Production2000∗ Price∗ 2003 stands for Production2000∗ Price multiplied by the 2003 year dummy. Column (1) considers the period 2003–2013. Column (2) analyses the period 2001– 2013, whereas the last column, (3), from 1999 until 2013. This latest considers a subset of municipalities, 70, for which data are available. All regressions include controls for population, density, young population and year dummies. For each control we interact the timeinvariant municipal-specific pre-shock values with year dummies. Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively. Further details can be found in Section 4.

municipal level. Along with property crimes, we considered its subcategories Robbery and Theft. We also studied violent crimes, with its subcategories Assault, Homicide and Rape. First of all, we confirm that Production2000i ∗ Pricet has a negative effect on the property crime rate in the short run, as displayed in column (1). An increase by about 1 billion Chilean pesos (about 1500 millions US dollars) in the value of copper production leads to a decrease in the crime rate of 2.25. To have a more precise idea of this value, we computed the average of Production2000i ∗ Pricet for the mining municipalities between 2003 and 2008– which is 36.4 – and we multiplied this average by the estimated coefficient. The result is −81.9. Considering that in 2003 the property crime rate was about 1333 in mining municipalities, the reduction of crime due to the price shock was about 6.14%. The disjointed categories of theft and robbery revealed some differences between property crime categories. In particular, the instrumental variable analysis revealed that it is Theft that was affected by the boom rather than Robbery. An average increase in Production2000i ∗ Pricet in the period 2003 to 2008, led to a decrease of 7.8% for Theft in the 25 mining municipalities.30 In contrast, violent crimes are not affected by mining shocks. For all categories, there was only a significantly negative effect in Homicide. This result requires further investigation because generally homicides are not economically motivated in Chile.31 Indeed, Production2000i ∗ Pricet better predicts property crimes than it does violent crimes. In conclusion, we found that the negative effect of the boom on crime was present only during the rise of the copper price and it mainly affected economically motivated crimes.

30 31

In 2003 the theft rate was 1,043. This is not a general feature of crime in Latin America. See Dix-Carneiro et al. (2018) for a discussion of the Brazilian case.

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Table 6 Commodity shock on crime: 20 03–20 08, baseline with categories.

Production20 0 0∗ Price R-squared Production20 0 0∗ Price Observations

Property (1)

Theft (2)

Robbery (3)

−2.65∗ ∗ [0.95] 0.16

−2.33∗ ∗ [0.92] 0.14

−0.37∗ ∗ ∗ [0.09] 0.28

−2.25∗ ∗ ∗ [0.66] 2056

−2.25∗ ∗ ∗ [0.66] 2056

−0.10 [0.07] 2056

Violent (4) OLS −0.69∗ ∗ ∗ [0.23] 0.10 2SLS −0.30 [0.22] 2056

Assault (5)

Homicide (6)

Rape (7)

−0.72∗ ∗ ∗ [0.23] 0.10

−0.01∗ [0.00] 0.04

0.03∗ [0.02] 0.07

−0.29 [0.22] 2056

−0.01∗ [0.01] 2056

0.01 [0.02] 2056

Notes. This table presents the short run (20 03–20 08) effects of the commodity boom on several crime categories. The crime categories considered are total property crime and its subcategories, Theft and Robbery, along with total violent crime and its subcategories, Assault, Homicide and Rape. For each crime category, we present the fixed effect(OLS) and instrumental variable(2SLS) results. Details on how we constructed the instrument can be found in Eq. (3). All estimations include municipal fixed effects, year dummies, linear and quadratic trends, and a full set of controls which include deterrence, population, density and young rate. For each control we interact the time-invariant municipal-specific pre-shock values with year dummies. We do not report the first stage statistics. These are available upon requests. Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively. Table 7 Commodity shock on crime: 20 03-20 08, robustness.

Production20 0 0∗ Price capita

OLS (1)

2SLS (2)

−3.23 [3.12]

−37.53∗ ∗ ∗ [12.22]

Log Production20 0 0∗ Price capita

OLS (3)

2SLS (4)

−305.19∗ ∗ [141.47]

−1048.82∗ ∗ ∗ [358.23]

Production20 0 0All∗ Price

OLS (5)

2SLS (6)

−2.86∗ ∗ ∗ [0.90]

−2.26∗ ∗ ∗ [0.67]

Employment index

OLS (7)

2SLS (8)

−930.00 [709.41]

−3,810.50∗ ∗ ∗ [453.27]

Production20 0 0∗ Price W∗ Production20 0 0∗ Price

ρ Observations R-squared

2056 0.15

2056

2056 0.15

2056

2056 0.15

2056

1710 0.20

1710

2SLS (9)

−2.48∗ ∗ ∗ [1.18] −0.99 [3.00] −0.02 [0.04] 2040 0.00

Notes. This table reports the robustness exercises spelled out in Section 4. For each alternative commodity shock measure we report two columns: one with fixed effect (OLS) and the other with instrumental variable (2SLS). We use the same instrument for each 2SLS regression. Details on how we constructed the instrument can be found in Eq. 3. Column (9) reports the results of a spatial durbin model (SDM), which includes a spatial lag for the dependent variable and the controls. The number of observations is lower than columns (1) through (6) as it requires balanced panel data. All estimations includes municipal fixed effects, year dummies, linear and quadratic trends and a full set of controls which include deterrence, population, density and young rate. For each control we interact the time-invariant municipal-specific pre-shock values with year dummies. We do not report the first stage statistics. These are available upon requests. Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively.

4.3. Robustness short-run In this subsection, we repeated the same exercises that we did in Section 3.3, but only for the period between 2003 and 2008. Results in Table 7 confirm the findings with Production2000i ∗ Pricet . The instrumental variable approach provided significant negative effects in all cases. The spatial analysis confirmed that Production2000i ∗ Pricet negatively affected property crime rates. As we found earlier, the level of mining production did not spillover onto other municipalities. Contrary to the previous results, however, we did not find crime spillover effects. 5. Discussion In the two previous sections, we documented the consequences of the rise of the copper price on crime and found that the effect is only temporary. Also, in Section 2, we discussed the consequences on crime determinants, summarized in Table 2. In this section, we have combined all these findings in order to discuss whether the crime cycles can be explained by the dynamics response of the crime determinants to the economic shock and how this can be done. To achieve this objective, we refer to Dix-Carneiro et al. (2018), where it is assumed that there is a stable long-term relationship between Please cite this article as: A. Corvalan and M. Pazzona, Persistent commodity shocks and transitory crime effects, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2018.11.015

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A. Corvalan and M. Pazzona / Journal of Economic Behavior and Organization xxx (xxxx) xxx Table 8 Mediation analysis: impact of employment on property crime. Baseline (1)

Skiled employment (2)

Unkiled employment (3)

Miningunskilled employment (4)

Unemployment (5)

Production20 0 0∗ Price

−3.15∗ ∗ ∗

Observations R-squared

301 0.14

−3.13∗ ∗ ∗ [0.84] 301 0.16

−3.11∗ ∗ ∗ [0.84] 301 0.16

−2.89∗ ∗ ∗ [0.75] 301 0.17

−3.14∗ ∗ ∗ [0.82] 301 0.16

Notes. This table presents the short run effects of the commodity boom on property crime between 20 0 0 and 2006. All estimations include year dummies and a full set of controls which include deterrence, population, density and young rate. Column (1) is the baseline regression. From column (2) until (5) we add one labour market variable at a time. Errors are clustered at the municipality level. ∗ ,∗ ∗ , ∗ ∗ ∗ , significance at the 10%, 5% and 1% levels, respectively.

crime and its determinants and there is a short-term response of these same determinants to the commodity shock.32 That is to say, the shock has transitory and permanent effects over different economic variables, and this in turn has an effect on crime that can be heterogeneous over time. First we notice that the copper boom had significant effects on the labor market and, in particular, on the unskilled labor force. The employment of unskilled workers grew during the rise of the copper price, and their salaries increased during the stabilization period. The importance of this behavior is that a large strand of literature points to employment and wages – especially of the unskilled workers – as main determinants of crime (Raphael and Winter-Ebmer, 2001, Gould et al., 2002; Machin and Meghir, 2004; Lin, 2008; Fougere et al., 2009; Bali and Mocan, 2010; Buonanno et al., 2014; Schnepel, 2016). In this sense, we observed that the boom had marked dynamic effects on some of the key determinants of crime. During the rise period, the effect of the Production2000i ∗ Pricet was through employment. Along with the increase in metal-mining unskilled employment, we observed spillover effects on all unskilled employment. Our study suggests that crime reduction over the period 20 03–20 08, could have been due to the increase in unskilled employment. In turn, this led to less incentive for people supposedly more at risk to commit crime to perform illicit acts. A rational criminal choosing to allocate his time between legal and illegal activities (Ehrlich, 1973; Block and Heineke, 1975), will react to the increase in employment opportunities by devoting more time to legal activities, as its opportunity cost rises. Moreover, the booming economy might have absorbed into the labor force those people who were at the margins between legal and illegal jobs. As the effect on employment is not persistent in the medium run, the effect is only temporary. As a further exercise, we report to Table 8, which shows how the coefficient of Production2000i ∗ Pricet changes as we add labour markets determinants in the short-run period. We clearly see that the crime determinants that reduce the baseline coefficient the most (−3.15) are those related to unskilled employment (−2.89 and −3.11). This is suggestive evidence that these variables are acting as a mechanisms. A more interesting question is why the effect of crime is reverted at the end of our first period of analysis. Following DixCarneiro et al. (2018), if the boom had an effect on crime determinants in the medium run, we should observe a change in crimes rates in the medium run as well. However, crime was not affected by the boom in the medium run even though the salaries of the unskilled workers increased after a decade of higher prices. An immediate explanation is that employment and not wages are playing a more impacting role on crime. However, this result is not documented in the literature, and as a matter of fact authors such as Gould et al. (2002) suggest the opposite is true. An alternative explanation considers the cost of living. We already mentioned that the literature found positive effects on living prices in mining areas. In Table 2 we found that housing rent prices, an indirect measure for price level, increased more in mining areas, but that this only occurred in the second part of the boom. However, the real wages and incomes that we considered in the same table, are computed using a national inflation index.33 A simple exercise can be illustrative. If we compute the ratio of wages over housing prices, we found no effect of the boom in 2013, which shows that salaries were increasing at the same rate as the cost of renting a house increased.34 Even though inflation did not revert the effect of the boom on the wages entirely, it certainly attenuated the real salaries’ growth. We acknowledge that this discussion is only descriptive and, therefore, its implications must be interpreted with caution. In a sense, our findings suggest a mean reversion process. In the first period, the impact of the economic boom is so strong that it is able to deviate the positive trends that all Chilean municipalities are experiencing. When the effect of the boom vanishes, the crime rate begins its rise back to a positive trend. A second point that we would like to discuss here is how we chose to compare our findings with the ones found by the literature on booms and conflict. Although many studies detected a positive effect, there is no consensus on the validity of this, as witnessed by the diversity of the estimates. Such heterogeneity, as explained by Dube and Vargas (2013), could be due to the action of two opposing theories: the opportunity cost and rapacity. The former states that when there is a

32 We deviate from them in the sense that they have a one-time trade shock while we have a commodity shock that is persistent over time. However, the baseline argument remains the same. 33 There are no regional or municipal deflectors in Chile. 34 Results are available upon request.

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positive economic shock driven by an increase in the price of a commodity, the opportunity cost of individuals/households that commit crimes goes up. In fact, higher prices lead to greater profits which generally improve economic conditions. Therefore, as shown by scholars as Becker (1968) and Ehrlich (1973), people at the margin will be pushed into the legal sector, decreasing the probability of committing a crime. On the other hand, the rapacity hypothesis links positive income shocks to increases in conflict and violence. This happens because when the price of commodities goes up, the rents to be extracted also increase. This attracts violent criminals and push their expected utility up (Grossman, 1991). For the case of Chile, we reckon that there is a relatively high level of institutions which make it very unlikely that the economic shocks will lead to appropriation by violent organized crime groups. 6. Conclusions This paper analyses how a permanent economic shock might only decrease crime rates transiently. We exploited the recent commodity boom as a natural experiment to document the effect that an increase in the copper price would have on the criminal activity in Chile, which is the world’s top copper exporter. Before analyzing its crime effects, we evaluated how the effects of the economic shock transmitted to the local economies. In particular, we focused on the variables that the literature had identified as the most relevant crime determinants. Our analysis reveals that Chilean mining municipalities gradually improved their living conditions over the years, compared to non-mining municipalities. We find a heterogeneous response of the crime determinants to the economic shock. In the first years of the sustained price growth, the shock had spillover effects on the employment of unskilled workers outside of the metal-mining sectors. Gradually, such effects vanished, because a high productivity sector such as the metal-mining industry, put pressure on other sectors to also increase wages. In the third section of this work, we sought to show that, despite a fourfold increase in copper prices over the period 2003 to 2013, the producing municipalities did not experience lower property crime rates when compared to non-mining ones. This finding is robust with the use of various econometric techniques and an instrumental variable approach. Moreover, results are confirmed by the use of alternative economic shock measures such as in per capita or employment ones. We also tested for the presence of production spillovers but did not find any. In order to understand whether the shock had a transitory effect, we studied its crime dynamic patterns. Tables 5 and 6 show that crime did decrease in the mining municipalities only during the first part of the boom, when the living price increased significantly. On average, from 2003 until 2008, the boom caused a reduction in property crime rates of 6.14% in the mining municipalities. These results are consistent with various specifications and identification threats. The fact that an income shock has no effect on the medium run does not contradict the economics of crime theory. A permanent shock might have a short-run effect on crime determinants, as well as a temporary one might have a mediumrun effect. In our case, the decade-long commodity shock increased unskilled employment but only in the first years of the boom and not in the entire period. Our work suggests that a permanent boom can have temporary effects on crime even though the fundamental relationship between crime determinants and crime is constant throughout time. Acknowledgments We would like to thank Roberto Alvarez, Paolo Buonanno, Damian Clarke and Alvaro García for their valuable feedback. 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