Defining Sample and Variables

Now that the hypotheses have been defined, we will focus on gathering the sample needed to test them, specifically, the subset of firms we will be looking at, the variables needed and how to calculate them, and the data collection process.

3.2.1 Defining the touristic sector

To isolate the firms that we will be looking at and gathering data from, we will use the NACE codes that the Eurostat attributes to the touristic sector. NACE codes belong to the industry classification system standardly used in the European Union that aggregates firms based on their activity under a label, i.e.,

17 the NACE code, for statistical purposes. In Table 1 you can find every code attributed to the touristic sector and its respective label. The codes are composed of a letter, which broadly identifies a specific economic activity, and 4 numbers to better specify different subsectors within the economic activity specified. The sectors listed below belong to section H, I and N, which, respectively, refer to:

Transportation and storage; Accommodation and food service activities; Administrative and support service activities.

As it was mentioned in the previous chapter, Eurostat makes a distinction between firms based on their degree of exposure and dependence on touristic activity, with firms that belong to a subsector with high exposure being labelled intensive tourism firms and the ones that belong to a subsector that is less dependent on touristic activity being labelled partially tourism firms. The subsectors that belong to mainly tourism firms are as follows: passenger air transport (H5110); hotels and similar accommodation (I5510); holiday and other short-stay accommodation (I5520); camping grounds, recreational vehicle parks and trailer parks (I5530); travel agency and tour operator activities (N7910).

NACE CODE

DESCRIPTION TYPE OF

SUBSECTOR H4910 Passenger rail transport, interurban Partially Tourism

H4932 Taxi operation Partially Tourism

H4939 Other passenger land transport n.e.c. Partially Tourism H5010 Sea and coastal passenger water transport Partially Tourism H5030 Inland passenger water transport Partially Tourism

H5110 Passenger air transport Intensive Tourism

I5510 Hotels and similar accommodation Intensive Tourism I5520 Holiday and other short-stay accommodation Intensive Tourism I5530 Camping grounds, recreational vehicle parks and trailer

parks

Intensive Tourism I5610 Restaurants and mobile food service activities Partially Tourism

I5630 Beverage serving activities Partially Tourism

N7710 Renting and leasing of cars and trucks Partially Tourism N7721 Renting and leasing of recreational and sports goods Partially Tourism N7910 Travel agency and tour operator activities Intensive Tourism N7990 Other reservation service and related activities Partially Tourism

Table 1 - Tourism Sector

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3.2.2 Variables

The main dependent variable this study focuses on is leverage, calculated by the ratio between debt and total assets. As for the explanatory variables traditionally associated with leverage in capital structure theory, we will use the ones already mentioned in the traditional research hypotheses and presented in more detail in Table 2 with the respective formula. Also, in the same table, you will find the dummy variables.

Type of variable Variable Formula

Dependent Variable

Leverage (Loans + Long Term Debt)/ Total Assets

Tangibility Tangible fixed assets/ Total Assets

Size Natural logarithm of SALES

(lnSALES) Profitability EBIT/ Total Assets Explanatory

Variables

Growth Opportunities [Total Assets(t)-Total Assets(t-1)]

/ Total Assets(t-1) Non-Debt Tax Shield Depreciation and Amortization /

EBITDA

Liquidity Cash and cash equivalents/ Total Assets

Age Age of the firm in years Covid Equal to 1 if the observation

belongs to the year 2020 Intensive Tourism Equal to 1 if the firm belongs to

the intensive tourism firms Explanatory

Dummy Variables

Geographical 7 dummy variables. Equal to 1 if the firm is in the corresponding

location (NUTS 2) Previously levered Equal to 1 if the firm was levered

in the previous year Size classification 4 dummy variables. Equal to 1 if

it fits the size classification criteria

Table 2 - Variables

19 As it is possible to see in Table 2, we will use the sum of long-term debt and loans as the proxy for debt. Traditionally in literature either total debt or only long-term debt is used, however we decided to include loans to try to capture the short-term impact of covid on debt, given that we only have one period that is relevant to the analysis of said impact.

The explanatory variables are calculated in line with what you would find in Kumar et al. (2017) and Ramalho and Silva (2009), although some alternative formulas could have been used that are somewhat interchangeable with the ones above - just as an example for tangibility using fixed assets instead of tangible fixed assets, for size using the natural logarithm of total assets instead of sales and for NDTS some divide by total assets instead of EBITDA.

As for the dummy variables, covid is equal to one in observation that were registered in 2020, given that the first official reported covid case happened in the thirty-first of December 2019, while intensive tourism is equal to one if the firm in question belongs to one of the five NACE codes identified as mainly tourism (H5110, I5510, I5520, I5530 and N7910). The geographical variable is meant to control for the firm’s location, given that a sector like tourism, and as we saw in the literature review, is incredibly location dependent. There will be seven dummy variables for each respective NUTS 2, which are: North of Portugal; Central Portugal; Lisbon Metropolitan Area; Alentejo; Algarve; Azores;

Madeira. The size classification variable is meant to control whether the firm observed is micro, small, medium, or large according to the standards set by the European Commission (2004), in which: firms that employ under 10 employees and present an annual turnover or total assets inferior to 2 million euros are considered micro firms; firms that employ less than 50 people and have an annual turnover or total assets inferior to 10 million euros are considered small firms, excluding the ones that fall under the micro firm criteria; medium firms employ less than 250 people and have an annual turnover inferior to 50 million or total assets inferior to 43 million, but also not counting the firms that are considered micro or small ; and firms who employ over 250 people or have an annual turnover superior to 50 million euros or total assets over 43 million are considered large firms. These dummy variables will be calculated independently for each individual year meaning that the same firm might have a different size classification in different years. The variable named “Previously levered” is equal to one if the firm had any debt in its capital structure in the previous year.

To test for the specific research hypotheses is also important to define interaction variables between the covid variable and the other dummy variables, since the specific hypotheses tests for possible changes in the effects said variables had on leverage during the pandemic. In Table 3, you will find said interaction variables and a brief description.

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Table 3 - Interaction Variables