ANTENAS PLAGAS

The aim of this study is to enhance the quantitative firm failure process literature

on SMEs, by incorporating elements from the qualitative failure process and the wider firm failure literature.

To achieve this aim , a number of different techniques will be used. First, factor and cluster analysis will be performed to identify the alternative firm failure processes in firms from the EU countries and within the UK regions. Secondly, panel data ordered logistic regression will be used to investigate the determinants of firms’ transition to failure in the alternative firm failure processes, both in the EU countries and within the UK regions. Finally, linear spatial panel data analysis will be used to investigate the potential existence of spatial effects between firm failure processes in the EU countries and within the UK regions. Such a technique is used for the first time in the quantitative firm failure process literature and the wider SME failure literature. For this reason the hypotheses associated with this are introduced in this chapter. A number of associated statistical tests are also performed prior to these techniques. The statistical tests are discussed in each individual empirical chapter.

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i) Factor and Cluster Analysis

Factor and cluster analysis will be used to identify the alternative firm failure processes that are present in EU and UK firms, using a number of firm-specific characteristics proposed in the literature. This is associated with the main aim of the study and the first/second objectives. It considers financial ratios and the impact of firms’ management characteristics to identify alternative firm failure processes in the EU and the UK. It also investigates the determinants of firms’ transition to failure in the EU and the UK. The combination of factor and cluster analysis has been selected as a way to identify firm failure processes (Laitinen, 1991; Lukason and Hoffman, 2014; Lukason and Laitinen, 2016; Laitinen et al., 2014).

Factor analysis is a technique whose purpose is “to define the underlying structure

among the variables in the analysis” (Hair et al., 2006, p.104). In this thesis, factor

analysis is used to summarize the firm specific-characteristics to reduce the number of variables that enter the cluster analysis process. Cluster analysis will then be used identify the alternative firm failure processes for the firms in the EU countries and the UK regions.

ii) Panel Data Ordered Regression

Panel data ordered regression models will be used to investigate the determinants of firms’ transition to failure in the alternative firm failure processes. This will enable the thesis to address part of its aim which is to identify the determinants of firms’ transition towards failure. Moreover, panel ordered regressions will assist in addressing the second and third objectives related to the impact of firms’ management characteristics (in addition to the financial ratios), and the influence of business environment and excessive growth in firms’ transition to failure. The advantages of panel data analysis in business finance and firm failure studies are well documented in the literature as discussed in section 3.4.4. However, there has been limited work undertaken to identify the determinants of firms’ transition to failure in the quantitative firm failure process studies. Panel data can add value by controlling the existence of individual heterogeneity between firms. In addition, the traditional logistic regression based on cross sectional data remains one of the most popular techniques in firm failure studies (Balcaen and Ooghe, 2006).

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Results from simple ordered regression without the panel element will be used as benchmark for robustness checking purposes. One should note that the majority of the firm failure studies use binary definitions for the dependent variable (usually within the context of logistic regression). As it was explained in Section 3.3, this study uses ordered logistic regression in panel and spatial panel contexts.

iii) Linear Spatial Panel Analysis

The aim of the third empirical chapter (Chapter 7) is associated with the fourth objective of this study. In particular, it employs spatial panel data analysis to investigate the impact of spatial location in aggregated firm failures from the sample.

The quantitative approach that is chosen is a linear spatial panel model. The theoretical advantage of this approach is associate with the advantages of spatial data in general. Spatial panel data account for potentially spatially correlated disturbances (across European countries and U.K. countries/regions) in addition to the normal time wise correlation (Arnold and Wied, 2014).

The usage of spatial econometric techniques has seen growing interest within economic studies because these models introduce a different angle to the analysis of relationships between agents. In fact, the focus is shifted from the individual agent (in this case the firm) where decisions are made in isolation to an approach where the interaction between agents matters (Anselin, 1999; Diggle, 2013). Wang et al. (2012) argued that spatial data are particularly relevant for economic- related studies, especially when considering different geographical locations. In an increasingly inter-connected economy the cross-sectional independence assumption between a sample’s observations (e.g. firms) is becoming less relevant (Wang et al., 2012). Similarly Cravo et al., (2014) showed the presence of spatial dependence in growth patterns on a sample of Brazilian SMEs. Likewise, spatial interactions could be due to competition between cross sectional units (in this case, businesses), business network issues, spill-overs of issues affecting firms’ failure, and regional issues (Kapoor et al., 2007).

For the purpose of this study, the focus on the spatial effects will be at firm failure process in EU countries’ and the UK regions’ level. Firm failures are therefore

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aggregated at the alternative firm failure processes of the EU countries and at the alternative firm failure processes of the UK regions. However, the focus of the spatial analysis (which is discussed in Chapter 7) is on the existence of spatial effects. As such this chapter introduces 2 hypotheses, with two variations each, which will be further investigated in Chapter 7:

Hypothesis 13a: There are statistically significant spatial effects associated with

EU firm failures.

Hypothesis 13b: Spatial effects are the same between alternative firm failure

processes in EU firms, in terms of statistical significance.

Hypothesis 14a: There are statistically significant spatial effects associated with

the UK firm failures.

Hypothesis 14b: Spatial effects are the same between alternative firm failure

processes in UK firms, in terms of statistical significance.

In order to investigate and address the above hypotheses, a maximum likelihood estimator will be used, in line with STATA 15 procedures for spatial panel data analysis. In addition, the Greene and Hensher (2010) likelihood ratio test as well as the Hausman test for the existence of fixed or random effects will be employed. The model specification will include a spatial weights matrix and will control for spatial dependencies in the dependent variable and in the error terms. This approach is chosen because the study is interested in identifying the existence of spatial interaction in firm failures in the alternative firm failure processes. As such, Anselin et al., (2008) suggest that the spatial lag model is appropriate in that case. On the other hand, controlling for spatial effects in the residuals is also helpful for ensuring there is no bias arising from spatial autocorrelation resulting from the use of spatial data (Elhorst, 2014). In addition, a simple, non-spatial model will be used for robustness check purposes.