Panel Data Analysis
Jaime de Diego 1, Mercedes Fernández 2, Antonio Rúa 3
1 University Institute of Studies on Migration, Comillas Pontifical University, 28015 Madrid, Spain
2 University Institute of Studies on Migration, Comillas Pontifical University, 28015 Madrid, Spain
3 Faculty of Economics and Business Administration, Comillas Pontifical University, 28105 Madrid, Spain
Abstract. We analyzed the influence of variables associated to socioeconomic vulnerability over some factors related to wildfires. We employed panel data techniques in order to capture the dynamic of wildfires and socioeconomic vulnerability over fifteen years (2001-2015) in the municipalities of Galicia, which is the most historically affected region by wildfires in Spain. We used meteorological variables together with socioeconomic variables in order to show the relation with vulnerability aspects. The results show an existing relationship between these variables and the number of wildfires, the burned hectares and the virulence of forest fires. So-cioeconomic factors like aged population and livestock farms or rustic hectares, linked with temperature, humidity and wind velocity, affect the number of occurrences of wildfires and their destructive capacities. The study raises awareness about all those aspects that cause this kind of disasters in order to reduce risks.
Keywords: Forest fires, wildfires, socioeconomic variables, panel data, Galicia, Spain.
1
Introduction
Human action can be the cause of different disaster events that usually occurs around the world. This is, sometimes, due to an environmental specific change or a climate alteration, consequence of a long-term contamination process. However, some of these events are isolated and normally related to the anthropogenic risk that pro-duces this natural event, like forest fires.
Forests are spaces with enormous importance in the functioning of the environment, not only because of the vital benefits but also because of the social and economic ser-vices. This services and benefits are water and climate regulation, carbon sequestra-tion, and commercial and recreational uses. The problem is when some of them are no longer functioning, so there is a disturbance of the capacities, often due to an impact like forest fires, concerning different territories. [1]
The territory and the population within the exposed areas determine whether a natural event is a disaster or not. [2]. This situation shows the importance of the population
and the characteristics related to the social and economic structure, like class, ethnic-ity, sex or poverty. Some of these social vulnerability variables increase the risk of suffering from the effects of a disaster like a wildfire [3]. So, in disaster management it is important to minimize the risk of a natural disaster event [4]. This situation shows that preventive actions should be targeted to people’s characteristics. Therefore, this reinforces the idea of studying the population’s peculiarities in the analysis of forest fire risk [5]
2
Materials and Methods
Spain in one of the most affected European countries by forest fires. Every year, many hectares are burned, considering this as a natural disaster. However, the origin of forest fires is usually a human cause, around 96% in Spain [6].
The Autonomous Community of Galicia is the region with the highest number of for-est fires in the 21st century. According to different studies [7,8,9], wildfires might be directly related to social and economic factors mixed with environmental factors. Year in year out, these disasters continue happening in this region and there is a large difference with other communities of Spain [10]. Subsequently, it is necessary to in-vestigate the relationship between socioeconomic variables and forest fires in Galicia.
Specifically, how these variables affect the impact of wildfires in extension and in severity. Therefore, along with climatological characteristics, these actions need to be studied in order to determine and predict the occurrence of the fires. [11]. In this sense we have chosen a panel data model which is not often used in wildfire research; how-ever, some author used this kind of model, demonstrating is a useful tool in risk anal-ysis [1, 4, 12].
Using panel data, we explained the relation of wildfires and socioeconomic variables during fifteen years in 314 municipalities in Galicia. The data shows the number of forest fires, the hectares burned and the virulence (ratio hectares burned/ number of wildfires), that have occurred between 2001–2015. This Panel data allow seeing sta-tistics for every municipality each year, the information is very clear because we can see the variation among time, while analyzing the dynamic during the years of obser-vation [13].
The variables used are explained in the results, based on different studies about this region [7, 8, 10]. These reflect the reality of Galician municipalities, showing the ables related to vulnerability. Also, we used the most important meteorological vari-ables during summer [9]. The panel data model is represented as follows [13]:
yit
xit
uit i
N
,
t
1, ...
T
Where i is the number of independent variables used and t represents years.
y, in this case, represents the dependent variables: Number of wildfires, Hectares Burned and Virulence (Hectares burned/ Number of wildfires), therefore, three mod-els are run.
3
Results
We used the software GRETL to run the models, establishing the data as a panel or cross-sections of individuals observed over time. The models run for each dependent variable are different but show the effect of these variables over the factors related to forest fires. In the case of “number of wildfires,” the model is a fixed effect model, but for the two other variables, we used random effects models. The use of fixed ef-fects derives from contrast of Hausmann [13], which demonstrated that fixed efef-fects in this case are better than random effects. Also, robust standard deviations were used to solve the heteroscedasticity problems. Logarithms were taken when it was neces-sary. Results are shown in table 1:
Table 1. models. Fixed effects (Nº WF) and Random effects (Ha Bur and Virulence). Source: Own elaboration
Variables description Variables Nº WF HaBur Virulence
(β) (β) (β)
const 324.709 463.386 24.4388
-Population>64 years LnPopulation>64 9.18480 34.3890* -0.0685981
-Nº of people/Has LnDensity -13.9555 -70.1520*** -2.34745***
-Population between 40 and 64 / population between 15 and 39
LnI.active -43.0789*** -137.357*** -7.52846**
-Population between 60 and 64 / population between 15 and 19.
I.replacement -0.0134874 -0.122134** 0.00203237
-Foreign population / total LnP.Foreign -1.12728 -2.60633 -1.14064**
-Plots in thousands of euros / people registered in the Real Es-tate Cadastre
LnParcelVal -1.90664 -25.4787*** -0.0137364
-Buildings and dwellings of a singular entity/set of towns with less ten buildings, which are forming streets, squares or other urban roads.
LnDisCenter -0.997631 -17.4087*** -0.381264**
-Rustic Has per municipality LnRusticHa -5.28444*** 15.0364* 1.06768**
-Livestock farms per municipal-ity
LnRanch 8.84043*** 4.30928 -0.154480
-Cattle heads per municipality LnLivestock -2.80215** -10.8609* -1.12829**
-Gross Income per habitant IncCap -3.25679 -2.87839 0.109766
-The debt of the town councils / habitants of each municipality
LnDebtHab 0.0824813 -1.84496 -0.0659858
-Gross Domestic Product LnGDP -3.86586 8.08287 0.621286
-Average summer temperature TºSum -0.229005*** 4.92970*** 0.210467***
-Average summer Humidity HuSum -0.142831*** 0.935749*** 0.074704***
(***: significance at 1%, **: significant at 5%, *: significant at 10%.)
4
Discussion
As said earlier, the objective of this paper is to establish the relation between the vari-ables that reflect the vulnerability in Galician municipalities and the three dependent variables selected. Analyzing the coefficients (β) sign, we can see the relations, direct or inverse, with the dependent variables. As we can show in the results the models represent this relation but, there is a difference between them:
In the case of Number of wildfires (Nº WF), all meteorological variables are signifi-cant, but one of them stand out: if the temperature (‘TºSum’) is lower, there are more wildfires, so this reinforces the idea of the anthropogenic origin of the wildfires. In addition, the variables related to rural areas (‘LnRusticHa’, ‘LnRanch’ and ‘LnLive-stock’) have higher importance with the lack of young population, caused by the abandonment of these regions.
In the case of Hectares burned (HaBur), the most important variables are those re-lated to the management of the territory (‘LnParcelVal’, ‘LnDisCenter’ and ‘LnRus-ticHa’) and the population living in these areas (‘LnPopulation>64’, ‘LnDensity’ and ‘LnI.active’). There is an abandonment of the forest and there are lands more prone to big wildfires, due to the fuel available.
In the case of the Virulence, the results are like ‘HaBur’, although the variable ‘LnP.-Foreign’ is noteworthy. This is because they are normally living in areas with high vulnerability, like rural areas, due to fewer economy capacities. Also, these areas are populated with aged people (‘LnI.active’), who are not capable of managing their lands, so they abandon them (‘LnDensity’). This makes these lands more prone to wildfires with higher intensity.
5
Conclusions
The results established a relationship between socioeconomic variables and depen-dent variables related to wildfires. However, they must be considered in different points related to wildfires.
Sustainable development is necessary, considering the social aspects that stand out as a cause of forest wildfires. Therefore, improving rural life quality in the areas af-fected, predictably would alter the variables positively towards their relation to forest fires. It is also important to give value to the forest through measures, such as agro-forestry mosaics systems together with holdings investments. Then, it is a must the in-crease funding for prevention and the elaboration of strategies for it. Climate, envi-ronmental and natural issues are not the only main points, so focus should be put on all aspects that may influence this kind of disasters, to reduce risk.
In Spain, competences have been transferred to the autonomous communities, but the central government is responsible for basic legislation and public policy design. Therefore, it is necessary to involve local bodies, considering the socioeconomic vari-ables. We need to promote interactions between different population sections and age
cohorts since this could influence harmful traditional behavior associated with aging societies. However, all these changes should be planned for the mid–long term. Finally, we prove the influence of social characteristics in the factors related to forest fires. Therefore, intervening on these social problems can make a difference regarding the risk associated with forest fires.
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