Socio-economic risk factors and wildfire crime in Italy: a quantile panel approach

Wildland arson threatens lives and the stability of the environmental ecosystems since fires change the characteristics of the landscape and cause an increase in carbon dioxide emissions, accelerating climate change events (Di Fonzo et al. 2015). There are several causes of forestland fires; however, human-induced fires have been recognised as the single most important cause of forest fires (Ganteaume 2013). This is particularly the case in European Mediterranean countries where it is estimated that more than 90% of forestland fires are caused by human action (Leone et al. 2002; Velez 2009).

Against this background, the objective of this study is to investigate the impact of socio-economic factors on wildfires in Italy. The analysis was conducted using annual regional level data for the period between 2006 and 2015. The country constitutes an interesting case study for several reasons. First, in Italy, more than 10 million hectares (ha) (about 32% of the national area) were reported as natural forestland in 2022. Every year, an average of 11,000 fires occur, destroying more than 50,000 ha of wood each year according to the World Bank collection of development indicators. Italy is one of the European regions most affected by wildfires and like other Mediterranean nations (such as Portugal and Spain), and the number of wildfires has steadily increased in recent years. However, wildfires in most cases do not originate from natural causes such as lightning, spontaneous ignition, or volcanic eruptions. Instead, they are an anthropogenic phenomenon (Lovreglio et al. 2010).

In the related literature, the relationship between socio-economic factors and the incidence of crime has for a long time been an important subject of study. Theoretical models on the determinants of crime point to rational choice factors that influence the likelihood of environmental crime. For example, in the literature pioneered by Becker (1968), a criminal’s choice is modelled as the standard microeconomic problem of expected utility where an individual chooses whether or not to commit a crime by comparing its expected benefits with its costs which can also include an opportunity cost, usually represented by the income from a legal activity. Ehrlich (1973) expanded on the basic analytical setting of Becker’s model by introducing the interaction between potential offenders (crime supply), deterrence, and prevention (government intervention). In Ehrlich’s (1973) theoretical framework, any factor that modifies the agents’ opportunity cost of legal activities can be included in the analysis of the determinants of crime. The results of the empirical research support the theoretical models in that socio–economic factors play a major role in establishing the incentives for engaging in crime (Enamorado et al. 2016; Coccia 2017; Fajnzylber et al. 2002; Gould et al. 2002).

In a recent paper, Canepa and Drogo (2021) evaluated whether the socio-economic variables suggested by the general crime literature also affect wildfire crime in Italy. The authors found that social economic factors such as unemployment play an important role in driving wildfire crime (Mancini et al. 2018; Pazienza and Beraldo 2004; Michetti and Pinar 2019). The study offers several insights into the impact of socio-economic factors on the occurrence of human-caused wildfires. A possible limitation of the analysis is that the regional discrepancy of the economic background is only marginally taken into consideration. Unlike other countries in the European Union, from an economic point of view, Italy is profoundly divided. As emerges from Fig. 3 in Appendix, the GDP per capita in some regions of northern Italy is amongst the highest in the European Union, whereas most of the southern regions have the lowest per capita GDP.

In Italy, the North–South divide has been a distinctive feature of the economic development since the beginning of the twentieth century. Historically, the gap in terms of GDP growth was relatively small just after the country unification in 1861 but increased steadily over time so much so that by the 1950s, it was close to 50% of the GDP per capita in the centre–north and never significantly changed thereafter, ranging from 55 to 60% until recently. Labour productivity has also historically been significantly lower in southern Italy (Musolino 2018). Similarly, a high unemployment rate has contributed to maintaining a significant gap in income inequality and poverty rate with respect of the northern and central regions. Not only does the North–South divide constitute the main source of heterogeneity but also the differences in terms of economic development and economic performance are quite remarkable within the macro-regions. Notably, the central and northern regions have been at the core of Italian economic development since the end of Second World War, but the development model has been quite different. On one hand, the north-western regions had a development model based on the Fordist organisation, heavily relying on large firms and heavy industry, whilst on the other, the economic growth in the north-eastern regions was based on the industrial district model (A’Hearn and Venables 2013). Different models of economic development have important differences in terms of income distribution and inequality.

Against this background, in this paper, we build on Canepa and Drogo (2021) and delve further on the impact of socio-economic factors on wildfire crime focusing on regional data. In principle, if the theoretical crime models hold true (Fajnzylber et al. 2002; Ehrlich 1973), we should see a stronger relationship between the socio-economic determinants and wildfire crime in the southern Italian regions with respect of the relatively wealthier northern regions. Therefore, the question we ask in this work is the following: ‘Do socio-economic factors play a greater role in driving environmental crime such as wildfire in more deprived regions?’ In other words, does the North–South economic divide pave the way for wildfire crime? Also, the consensus in the crime literature agrees that the crime rate has a countercyclical behaviour, trending upward during recessions and downward during economic expansions (Bushway et al. 2019; Mehlum et al. 2006). The supporting arguments for this inverted relationship point to several, sometime contrasting, reasons. First, the quality and quantity of legitimate employment opportunities are procyclical. The higher unemployment rate associated with economic recession may promote crime by lowering the opportunity cost of the time spent in criminal activity (Grogger 1998; Gould et al. 2002; Machin and Meghir 2004). Second, the literature empirically supports that the use of criminogenic commodities such as alcohol or drug abuse are both related to the business cycle (Johansson et al. 2006; Cook and Moore 1993; Cook and Durrance 2013). Accordingly, our third research question is whether wildfire crime is also related to the economic cycle. In other words, does the countercyclical relationship found in the crime literature also hold for the type of environmental crime considered in this paper?

To answer the above questions, we conducted a two-step investigation. In the first step of the empirical analysis, and we shed light on the socio-economic drivers of wildfire crime in Italy. We also investigated these risk factors to see whether they have a higher impact on the probability of wildfire crime occurrence in poorer regions. With this target in mind, we consider a quantile regression model with a nonadditive fixed effect as suggested by Powell (2022) (see also Powell 2020). The main advantage of the procedure introduced in Powell (2022) is that the quantile estimator maintains the non-separable disturbance term traditionally associated with quantile estimation, but the instruments can be arbitrarily correlated with the nonadditive fixed effects. In general, quantile regression models are procedures used to estimate a functional relationship between the response variable and the explanatory variables for all portions of the probability distribution (Koenker 2004). Therefore, quantile regression analysis allows us to investigate whether the socio–economic risk factors considered in this work have a greater impact on lower or upper tails of the crime distribution function. Traditionally, in the empirical literature, modellers have tried to identify which environmental and socio-economic factors influence fire occurrence by using linear or nonlinear models in the context of cross-sectional data (see, for example, Martinez et al. 2009; Levi and Bestelmeyer 2016; Marchal et al. 2017; Leone, et al. 2002).¹ However, the use of pooled linear or nonlinear specifications may leave the estimated model exposed to the unobserved heterogeneity problem, leading to biased and inconsistent estimators (Cameron and Trivedi 2013). Unlike the related literature, the model adopted in this paper allows for control of the fact that the marginal effects of the socio-economic risk factors are heterogeneous throughout the wildfire crime distribution. Moreover, the adopted model is robust in terms of heteroskedasticity. In particular, using quantile panel data estimation techniques, we can account for the time-invariant region-specific factors often omitted in related empirical works. Time-invariant heterogeneity is represented by all unobservable but relevant components characterising a region which are expected to be correlated with observed factors. Examples of region-specific factors are wildfire deterrence mechanisms, land use and type, and the presence of organised crime. Whilst these instances represent a persistent problem in cross-sectional analysis, using panel data estimation techniques allow us to control for regional heterogeneity.

The remainder of this paper is organised as follows. Section 2 presents the model specification and discusses the adopted estimation methodology. Section 3 presents the results of the quantile estimation regression. Section 4 presents the effects due to economic downturns. Finally, Sect. 5 gives some conclusive remarks.

In this section, we describe the model and estimation method undertaken for the empirical investigation. The proposed model specification complements and combines several approaches already developed in the related crime literature, but the estimation procedure proceeds in a different manner. Most of the empirical works investigate the relationship between wildfire crime and socio–economic factors considering how the conditional mean of wildfire crime distribution changes as a function of a given number of socio–economic risk factors (Mancini et al. 2018; Pazienza and Beraldo 2004; Michetti and Pinar 2019). To the best of our knowledge, this is the first paper that considers the causal relations and marginal effects in the tails of the wildfire crime distributions. We are particularly interested in the heterogeneity of these relationships, in addition to the relationship of the mean and median. Below, we describe the model specification and the estimation procedure in turn.

For the empirical investigation, we used the annual data for the period 2006–2016 for twenty Italian regions. Table 1 provides a description of the acronyms and some details of the authors’ calculations.

Before reporting the estimation results, we undertook a preliminary descriptive statistical analysis of the quantile distribution of the variables under consideration in order to investigate the properties of the cumulate distribution functions of wildfire crime and the related socio-economic determinants. Table 2 reports the estimates of the quantile shares of the socio-economic risk factors along with wildfire crime.⁵In particular, the first column reports the risk factors, whereas in columns 2–11, the estimated proportion of the total outcome that falls into each quantile is described. For ease of interpretation, the proportions are reported as a percentage and the quantiles are expressed in terms of percentile shares.

From Table 2, it appears that wildfire crime density function is highly skewed since approximately 60% of wildfire crimes are concentrated in the regions located in the higher quantiles of the cumulate distribution function. Namely, the regions in the top 30th quantile account for approximately 60% of the proportion of wildfire crime in the sample under consideration. On the other hand, the regions in the bottom 30th quantile experience less than 5% of wildfire crime. It is interesting to note that the distributions of organised crime and homicide rate, along with most of the other socio-economic indicators, seem to follow a similar pattern. In particular, Table 2 shows that the regions in the top 30th quantiles contribute to 52.98% of organised crime convictions and 53.31% of homicides out of the total crimes, respectively. This contrasts with the regions in the bottom 30th quantile where the percentile shares decrease to 13.02% for organised crime convictions and to 12.26% for homicide prosecutions, respectively. Similarly, the distribution of poverty and unemployment is highly skewed with the top 30% worst-performing regions in the sample receiving 55.24% and 48.93% of the total share in terms of poverty and unemployment, respectively. Looking at inequality, the ratio between the average income of the top quintile and the average income of the bottom quintile is also a left-tailed distribution with 37.74% of the share being in the higher top 30^th quantiles. Finally, the education distribution seems to be quite uniform across the Italian regions with only marginal differences between the top- and worst-performing regions.

In Table 3, the estimated coefficients of the four best fitting models M1, M2, M3, and M4 are reported. For brevity, only the estimated parameters for the quantiles p ∈ {0.25, 0.50, 0.75}, along with the associated standard errors, are reported. Note that most of the covariates are considered in natural logs.

In relation to our first question in Sect. 2.2, looking at the estimating results for the median quantile in Table 3 reveals interesting insights on the causal relation between socio-economic risk factors and wildfire crime. In particular, from the middle panel of Table 3, it appears that most of the socio-economic risk factors are significantly different from zero and have the expected sign. In particular, it appears that there is a positive relation between income inequality and wildfire crime as the estimated signs for INER_it in models M1–M4 are uniformly positive, and the estimated coefficients are statistically significant. Following a theoretical argument in Becker (1968), the positive effect of income inequality on wildfire crime can be interpreted according to the cost–benefit framework where the magnitude of the income inequality coefficient measures the impact of the difference between the return to crime and its opportunity cost (measured by the legal income of poor individuals). However, this argument is purely based on the rational behaviour of agents which, in the case of environmental crime, may underestimate the importance of the socio–economic environment in which the social actors interact. Following Fajnzylber (2002), an alternative interpretation of the positive link between inequality and crime is that in regions with higher income inequality, individuals have low expectations of the lifetime improvement of their socio-economic status through legal economic activities which decreases the opportunity cost of participating in illegal endeavours more generally.

Regarding the effect of violence, the estimated coefficients for ORGC_it in the median quantile are positive and statistically significant throughout the estimated models, whereas the estimated parameters for HOMR_it are of the expected sign but significant only in models M1, M2, and M3. These results are important since, according to a report by the Italian Antimafia Directorate, environmental crime is currently one of the most profitable forms of criminal activity, and eco-mafia has become a big business in the waste sector in Italy (Savona and Riccardi 2018). The previous literature has found that organised criminal networks are involved in the illegal disposal of commercial, industrial, and radioactive waste (Germani et al. 2018). However, the relation between organised and wildfire crime has not been previously investigated. Bearing in mind that organised crime is deeply rooted in Italy, and this result may have important implications when it comes to the crime deterrence policy.

Focusing on the role of education, the results for the median quantile in Table 3 suggest that a negative relationship exists between education attainment and wildfire crime throughout the estimated models. A negative relation between education level and fire risk was also found to be significant by Torres et al. (2012). Regarding unemployment, the estimated parameters for the median quantile in models M3 and M4 support the view that the worsening of the labour market conditions increases wildfire crime. In general, the findings in Table 3 confirm the previous empirical results in the related literature that unemployment is an important risk factor causing wildfire crime (Mercer and Prestemon 2005; Prestemon and Butry 2005; Martınez et al. 2009).

Coming now to TRIAL_it, and EQI_it, we see that higher enforcement levels and institution quality reduce the probability of committing a crime since the estimated coefficients of these covariates are negative throughout the estimated the models, as postulated by the seminal paper by Becker (1968). In particular, we see that the wildfire crime rate is negatively related to the share of suspected wildfire criminals who have had to stand trial. This implies that the successful identification of suspected offenders increases the expected costs of wildfire crime for the average potential arsonist. Overall, the estimation results strongly suggest that a higher level of apprehension and good governance helps to reduce wildfire crime.

Focusing on the demographic covariates, the estimated coefficients of DEN_it for the median quantile have a negative sign and are statistically significant. However, the estimated parameters in the models are very small. Such a small magnitude of estimated coefficients casts some doubts on the actual impact of this covariate on wildfire crime for the data at hand.

Coming to weather-related factors, the results mainly confirm the findings in the related literature as the estimated parameters in Table 3 are significant and of the expected sign. It is well known that the Mediterranean summer weather conditions (high temperature, prolonged drought periods, and strong winds) facilitate wildfire occurrence (Vasilakos et al. 2009).

Turning now to the second question in Sect. 2.2, comparing the estimated coefficients of the lower and top quantiles reveal some interesting results. Starting with income inequality, it appears that the magnitude of the estimated coefficients for INER_it is relatively low in the bottom quantile of wildfire crime but increase sharply for the regions located in the top quantile. Therefore, the marginal effect on wildfire crime of one unit increase in inequality in the regions in the top quantile is much greater than the marginal effect in the regions that enjoy the more desirable position in the bottom quantile. Similarly, the marginal effect of an increase in unemployment and material deprivation is much higher in the top quantile, meaning that a unit increase in unemployment (or material deprivation) in regions with high unemployment (or material deprivation) has much greater impact on wildfire crime than a unit increase in the regions in the bottom quantile.

Looking at the effect of organised crime and homicide rates, once again the estimated coefficients for the top and bottom quantiles have positive signs and are significant. The estimated parameters for HOMR_it are particularly informative since the estimated coefficients for the top quantile are much greater in magnitude than those in the bottom quantile, thus indicating that the marginal effect of homicide rate on wildfire crime are much higher in regions with a high criminality level. This result highlights the fact that wildfire crime is strongly related to local criminal organisations, confirming the literature findings for other types of environmental crime (Germani et al. 2018).

Considering the effect of education on wildfire crime, it appears that EDUC_it has the expected negative signs throughout the estimated quantiles in all estimated models but model M1 (for the bottom quantile only), where the estimated parameters, is not significant. A comparison of the magnitude of the estimated coefficients in the bottom and top quantiles suggests that the marginal effects of the level of education increase in the top quantile, suggesting that an increase in one year in education in regions with higher wildfire crime has a greater impact than an extra year in education in regions with a lower level of wildfire crime. Looking at the estimated coefficients for UNIV_it, these are also significantly different from zero throughout the estimated models but the marginal effects are less clear.

Finally, focusing on the results for AGRI_it, the estimated coefficients are statistically significant for all estimated models. However, the estimated signs change from mostly negative in the bottom quantile to mostly positive in the top quantile. These results suggest that in the regions with a high level of employment in the agricultural sector, a further increase in employment also increases the rate of wildfire crime. On the contrary, in the regions in the bottom quantile, a rise in employment in agriculture is likely to provide a protective effect concerning wild forests. In the literature, the impact of this covariate is not clear and empirical works reports contradicting results. For example, Leone et al. (2002) found a positive relation between wildfire crime and agricultural expansion in the Mediterranean regions. The authors suggest that forests have been voluntarily set on fire to gain land for agriculture and pasture which were retained due to being more valuable than the land used for logging. Similarly, Moreira et al. (2009) describe burning shrubland as a widespread practice in rural areas across the Mediterranean region used to renovate livestock pastures. On the other hand, Martinez et al. (2009) suggest that land abandonment and rural exodus were found to be positively related to wildfire occurrence. To the best of our knowledge, the results of the quantile panel estimation bring a new perspective to the related literature as it suggests that the regions in bottom quantile may benefit from agricultural expansion. On the other hand, the regions in the top quantiles further expanding the possibility of employment in agriculture may induce greater wildfire occurrences. However, such a strong conclusion calls for further investigation. For example, the high degree of abandonment and depopulation of the high hill and mountain areas, and the abandonment of agronomic and silvicultural practices may have an influence on fire occurrence. The investigation of accidental wild forest fires is outside the scope of this work. However, the results in Table 3 warrant further investigation in future research.

The empirical investigation presented in this work complements the literature on environmental crime by providing new evidence on the determinants of human-induced wildfire. In particular, by using panel data at the regional level for the period between 2006 and 2015, this study provides new insights into the role of socio-economic factors when explaining wildfire crime in Italy.

The contribution of this paper to the extant literature can be summarised as follows. First, it conducts an extensive analysis of the relationship between socio-economic determinants and wildfire crime. In the literature, empirical works on the socio-economic factors influencing wildland arson are still quite limited. However, understanding the socio-economic drivers behind wildfires are crucial for the planning and management of fire prevention policies. In this respect, the empirical investigation carried out in this work reveals that socio-economic factors are important determinants of wildfire crime in Italy. In particular, the estimation results presented in this paper clearly suggest that higher levels of material deprivation, unemployment and income inequality increase wildfire crime. Also, risk factors such as organised crime and homicide were seen to affect the probability of crime in the same direction. On the other hand, a negative relationship between the level of education and wildfire crime was found. These findings are important since, according to the Italian environmental group Legambiente (2010), more than half of Italy’s fires are started deliberately.

The proposed methodological approach is the second contribution of this paper. Most of the related literature uses linear or nonlinear regression models to investigate the relationship between human-induced wildfire crime and the risk factors by modelling the conditional mean function of the wildfire distribution (see for example, Mancini et al. 2018; Pazienza and Beraldo 2004; Michetti and Pinar 2019). However, the conditional mean modelling framework used in these works has some important limitations since it cannot be readily extended to the noncentral locations of the wildfire distribution. In this paper, we are interested in understanding the heterogeneity of the relationship across the wildfire distribution, in addition to the relationship at the mean or median. Accordingly, the adopted quantile panel model allows the investigator to move away from the central moment and consider the tails of the distribution where Italian regions with the highest and least levels of crime are located. This different perspective is certainly interesting from a policy point of view, since policymakers are more interested in knowing what happens at the extremes of a distribution than at the centre. The estimation results suggest that the impact of a one unit increase in inequality in the regions in the top quantile (where the inequality level is the highest) is much greater than the marginal effect in the regions that enjoy the desirable position at the bottom quantile, (where the level of inequality is the lowest). Similarly, the marginal effects of increases in unemployment and material deprivation are much higher for the regions in the top quantile, meaning that a unit increase in unemployment (or material deprivation) in these regions has a much greater impact on wildfire crime than a unit increase in the regions in the bottom quantile.

The findings from the quantile panel and Poisson models also highlight the fact that wildfire crime data cannot be easily estimated using models that do not consider fat tails, such as ordinary least-squared regressions. This is because the least-squared methods do not have a bounded influence and cannot accommodate for heterogeneity, which is an important characteristic of the Italian data. Heavy tails and outliers can make the estimators inconsistent and, even when the estimator itself is consistent, the standard errors require higher moments to exist (Cameron and Trivedi 2013). In this context, the focus on the conditional mean may become a misleading measure of the central location because it is heavily influenced by outliers. In this respect, the quantile regression method adopted in this paper is robust in terms of departures from the normality of innovations assumption (Chernozhukov and Hansen 2008).

Third, this study takes a novel approach with respect to the extant literature by investigating the relationship between the business cycle and wildfire crime. The well-established literature on business cycles advocates the viewpoint that the type of socio-economic risk factors considered in the paper is all overwhelmingly cyclical. We therefore speculate that wildfire crime patterns over time may also stem from the properties of socio-economic determinants. It was found that per capita income and wildfire crime occurrence share similar patterns over time. In addition, the analysis of the time-varying correlation coefficients reveals that regions in the top quantile distribution of the wildfire distribution (regions with higher level of wildfire crime) have also mostly positive estimated time-varying correlation coefficients between socio-economic risk factors and wildfire.

The joint interpretation of the empirical results presented in this paper provides several insights that may be useful when designing more effective policy interventions designed to reduce the risks of forest fire crime and their environmental impact. First, the overall findings in this paper confirm the strong correlation between risk factors such as inequality, poverty and unemployment and wildfire crime as was found in the related literature. These results emphasise the need for a strong policy framework to reduce income inequality and promote economic growth which in turn reduces unemployment and poverty. Consensus in the general crime literature suggests that a high crime rate hampers standard of living and overall quality of life (Coccia 2017; Grogger and Michael 2000). This literature also suggests that there is a potential vicious cycle between crime unemployment and poverty. Prevalent criminal activities erode employment opportunities and are exacerbated by the high unemployment rates. In this respect, social policies should be formulated to strengthen the equal distribution of income which will ultimately reduce the wildfire crime rates and poverty incidence across the country, especially in the Italian southern regions.

Second, the quantile panel estimation results suggest that level of education can affect wildfire crime. Regions characterised by a lower education level show a higher wildfire crime risk. Although the relation between wildfire risk and education is still an open question in the wildfire literature (see, for example, Lochner 2004; Lochner and Moretti 2004; Hjalmarsson 2008; Oreopoulos 2006; Lochner 2004; Usher 1997), it is largely accepted that level of education plays an important role in determining the distribution of income: a higher level of educational attainment improves the income level and reduces income inequality and poverty (see, for example, Glaeser et al. 2004). In this respect, a higher education level may affect wildfires indirectly by reducing the income inequality and increasing the income, resulting in less incentive to engage in environmental crime.

Third, the estimated negative signs for the trial rate suggest that there is a negative relationship between wildfire crime occurrence and level of apprehension. An inspection of the magnitude of the estimated coefficients across the quantiles reveals that regions with a higher level of law enforcement also experience lower levels of wildfire crime occurrence. Italy is also characterised by the presence of organised crime which controls the economic activities connected to agriculture and pasture practices. The findings in this paper corroborate the view that the presence of illegal associations increases the occurrence of wildfire crime. Taken together, these results suggest that human-induced wildfire occurrences may benefit from a greater level of law enforcement. In this respect, the wildfire crime deterrence law in Italy establishes temporal binding constraints on the use of the area damaged by forest fire. However, evidence suggests that these regulations have only been partially enforced in some Italian regions (the EFFACE 2016 report). In this respect, a greater enforcement of the deterrence regulations may reduce the wildfire crime occurrence. The issue of increasing law enforcement and other forms of crime apprehension has become particularly urgent after the Covid-19 pandemic, since Italy has received a large post-pandemic economic recovery fund from the European Union as support towards the green transition which may attract criminal organisations looking to benefit from lucrative reforestation contracts to repurpose the land for solar panels.

Overall, the results presented in this study enhance our understanding of the relationship between socio-economic risk factors and wildfires. A possible limitation of this study is that the use of data at an annual frequency may conceal the impact of weather-related causes of wildfire, which is overwhelmingly seasonal. Weather elements are undeniably recognised as major determinants of wildfire risk in the relevant literature (see Ganteaume et al. 2013; and the references therein). Previous studies suggest that socio-economic factors are determinants of the fire frequency, whereas the surface burned per fire is more related to weather patterns (Michetti and Pinar 2019). In this respect, a robustness check may be undertaken in the future to check whether increasing the frequency of the data still confirms the finding of this paper. Another possible limitation of this study is that the analysis carried out only relates to wildfire crime in Italy. However, there are other countries in Europe that are plagued by a high rate of wildfire crime and have a great socio-economic divide such as Spain, for example (see Fig. 3 in the Appendix). Therefore, our future research will explore whether the risk factors that we have identified for Italy also hold for other countries in a similar situation.