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Surviving the Recession with Efficiency Improvements: Hospitality Firms in Portugal

Surviving the recession with efficiency improvements: The case of hospitality firms in Portugal




The current study investigates what types of efficiency improvements contributed the most to hospitality establishments’ performance during the 2008 recession. During a recession, lower profitability limits investments in new initiatives, significant cost-cutting could deteriorate service levels, and sticky tangible and intangible resources limit a hospitality establishment’s ability to reduce its asset base. Limited ability to cut costs or reduce asset base shifts the focus on improving efficiency. Drawing on panel data of 1,647 Portuguese hospitality establishments from 2007 to 2014, we find that an increase in the return on fixed assets (ROFA) made the highest contribution to operating profit, followed by an increase in the return on intangible assets (ROIA). Increasing labour productivity during the recession had the lowest effect on operating profit.



















The Great Recession of 2008 had a devastating impact on the global hospitality industry. Sectors ranging from hotels and lodging to restaurants, and from tourist rentals to tourist activities faced a significant decline. According to’s 2015 Global Hospitality Report[1], during the Great Recession of 2008, the value per room declined by 15% in Europe and by 30% in the United States. Falling occupancy, a lack of new financing, erosion of property value, and lower revenue per available room led to a significant gap in supply and demand. Even in 2015, the occupancy rates were at 62.7% compared to the lowest, 54.8%, in 2009. Reduced discretionary income decreased restaurant visits and tourism (Smeral, 2010; Withiam, 1992a; Woodworth, 2009). Due to limited economic growth after 2008, the hospitality industry has barely reached pre-recession levels (Kahler and Lake, 2013; Smith, 2010).

A significant body of work has conceptually (Jones et al., 2009; Lesure, 1984; Smith, 2009, 2011; Woodworth, 2009) or qualitatively (Withiam, 1992b) focused on a hospitality establishment’s response to a recession. Others, based on empirical work, have focused on the role of marketing (del Mar Alonso-Almeida and Bremser, 2013; Singh and Dev, 2015) and branding (O’Neill and Carlbäck, 2011) or advertising (Spencer, 2013) expenditures on performance, whereas others have assessed the role of debt structure (Gu, 1992; Lee, 2007; Youn and Gu, 2010) in improving performance during a recession. Broader literature on firm response to a recession (Brown et al., 2013; Gulati, 2013; Gulati et al., 2010; Latham and Braun, 2011; MacCarthy et al., 2013) finds that firms focus on cost cutting and efficiency to lower risk or focus on increasing returns through new strategic investments (Cohen and Kunreuther, 2007; Melnyk et al., 2004; Seshadri and Subrahmanyam, 2005; Van Mieghem, 2003). Response to a recession also includes improved competitiveness, innovation and human resource management (Pappas, 2015).

However, these broader prescriptions are less applicable to hospitality establishments. Reduced discretionary spending lowers the availability of capital to pursue growth opportunities. During the Great Recession, due to a reduced supply of capital and lower demand, not only fewer hotels were built as of 2015, but there also continued to be a significant oversupply of hotel rooms. Similarly, fast food restaurants, due to lower prices, have experienced higher growth but traditional restaurants continue to have a higher failure rate (Youn and Gu, 2009). The tourism industry has also suffered similar declines (Zheng et al., 2016). Thus, in the fragmented and mature hospitality industry, ‘recession as an opportunity’ is less applicable to most hospitality establishments (West and Olsen, 1989).

Therefore, cost cutting and efficiency are the next candidates for consideration (cf. Woodworth and Mandelbaum, 2012). If hospitality establishments construe ‘recession as a threat’, they may engage in cost cutting. Except for labour costs, tangible and intangible assets can be sticky (Dalbor et al., 2004; Guilding et al., 2014). Customers spending their discretionary income during a recession might demand higher service levels, and while employee layoffs would immediately result in lower costs, it could negatively affect service quality and lower performance. The two other forms of assets – tangible and intangible – are sticky (Dyer and Singh, 1998; Mahoney and Pandian, 1992). As tangible assets are a part of tacit and explicit knowledge embedded in complex service chains, such assets may not be divested easily (Geieregger and Bader, 2007). Intangible assets are integral to the service experience and cannot be traded easily, and reductions could severely affect service quality. Overall, cost cutting and reducing the asset base may be detrimental to hospitality establishments. As growth opportunities are less possible with declining demand in the fragmented and mature industry, cost cutting may not be favourable and asset divestment not easy. Improving efficiency could be critical to improving performance during a recession.

We assess gains in performance from changes in three types of efficiencies – an increase in the return on fixed assets, the return on intangible assets, and labour productivity. We draw on panel data of hospitality establishments in Portugal. The Portuguese sample offers a unique study context because Portugal, a service based economy, faced severe economic challenges during the Great Recession (Lane and Milesi-Ferretti, 2011). One of the most adverse effects of Portugal’s economic crisis was job destruction, resulting in an unemployment rate of more than 10%, with firm shutdowns accounting for approximately 36.4% of the total of job destruction (Carneiro et al., 2014). All sectors, including the hospitality sector, experienced severe decline (Pedroso, 2014), but as Esteves (2014) shows, the crisis caused severe damage to the food and beverage sector in Portugal.

Related to studying recessionary strategies of hospitality establishments, the Portuguese sample is also salient due to the resilience of its tourism industry. Between 2008 and 2014, the number of visitors increased, on average, 2% per year, a growth rate higher than that in the OECD countries and the EU countries (OECD, 2014). Even despite the recession and uncertainty, the tourism industry in Portugal reached record numbers in 2014. According to OECD (2016), the total number of hotel guests was 16.1 million in 2014, an increase of 12% from 2013. Total overnight stays increased 10% in 2014, totalling 45.9 million. In terms of the international overnight guests in hotels and similar establishments, the number reached 9.3 million (an increase of 11.8%), and the number of overnights by international visitors reached 32.1 million (an increase of 9%) in 2014. International tourism was responsible for 70% of the total demand in Portugal, but in terms of domestic occupancy, a similar trend was registered. Domestic guests were responsible for 13.8 million overnight stays in 2014 (an increase of 12.8%).

Overall, despite significant economic decline, the resilience of the Portuguese hospitality sector makes the sampling context particularly relevant to understanding how hospitality establishments in Portugal weathered the recession. In addition, to allow for reliable inferences on private hospitality establishments, in Portugal, financial information certified by a chartered account is publicly available.

The proposed framework is distinct from Singh and Dev (2015), who focus on a sample of 416 hotels over a three-year period, whereas we draw on the hospitality industry in Portugal from 2006 to 2014. Singh and Dev assess the correlation between marketing expenses and profitability, whereas we attempt to assess the relative effects of different efficiency improvements on performance using panel data regression. Overall, the study differs from Singh and Dev in terms of both sample scope (both industry and years covered) and the research question. Compared to del Mar Alonso-Almeida and Bremser (2013), who identify the value of branding and marketing during a recession and advise against cost cutting, we focus on the relative effects of change in different efficiencies on performance to inform firms in channelling their limited resources during a recession.

The proposed framework is important for three reasons. First, theoretically, a recession destabilizes the technical core of the firm, and efficiency, by maintaining the flow of service activities with necessary input levels, helps improve stability (cf. Schmenner, 2004). With the available resources – fixed, intangible, and labour – focusing on efficiency improvements that help provide most improvement and maintain alignment of internal resources, are the most important to understand. Second, there remains limited guidance in the hospitality literature on how establishments must respond to a recession – improving the alignment of resources (i.e., efficiency improvements) or lowering costs. As resources are scarce during a recession and efficiency helps better allocate limited resources (Latham and Braun, 2011), identifying the type of efficiency improvements that enhance performance the most helps manage limited managerial attention and resource scarcity during crisis (cf. Bansal et al., 2015). Third, we assess the joint effects of an increase in the return on fixed assets (ROFA) and an increase in labour productivity and joint effects of an increase in the return on intangible assets (ROIA) and an increase in labour productivity to understand gains in hospitality establishments from jointly improving different efficiency types. Orchestrating different forms of efficiencies could not only help align internal resources (Skinner, 1974) but also improve the alignment of service and strategic objectives (Boyer and McDermott, 1999) in hospitality establishments.



A recession is particularly challenging for the hospitality industry. Reduced income and economic contraction during a recession result in lower discretionary spending, which in turn, significantly reduce demand. In response to lower demand, hospitality establishments cannot immediately reduce their asset base for two reasons. First, as hospitality establishments leverage employees and fixed and intangible assets (Tang and Jang, 2007), divesting from these causally ambiguous assets may be difficult in the short run and have long-term implications on service quality (Hu et al., 2009; Ogbonna and Harris, 2002). If certain assets could be divested, due to economic decline, they may be sold in a fire sale, further weakening the financial position of the firm. Second, reducing the asset base during a recession and rebuilding it after the recession may be challenging due to the path dependent nature of service establishments where reputation, brand, and service quality are built over longer periods (Nieves et al., 2014).

In response to the recession, a hospitality establishment must first stabilize its operational core (the processes and activities of converting service inputs into outputs) from outside recessionary disturbances (Thompson, 1967). Variation in external demand could be managed through improved internal efficiency. Efficiency refers to increasing output for a given level of input or maintaining the same level of output for reduced inputs. As service space, fixtures, and inventories cannot be liquidated at cost in the short term; efficiency from fixed assets must be increased by increasing output from a given level of fixed assets. Intangible assets such as brand, trademarks, software to manage customer relations, and long-term client contracts, among others are also difficult to value and liquidate it the short term (Erickson and McCall, 2008). Similar to increasing efficiency from fixed assets, improving efficiency from intangible assets would be desirable to maintain performance levels.

Given the stickiness of fixed and intangible assets, due to the variability of labour costs, hospitality establishments may aim to maintain similar output levels with fewer employees. However, the logic is not straightforward. Relying on fewer employees to maintain the same level of output could increase task demands, stress, and errors. In a service setting, ‘making-do-with-less’ in a recessionary period could further reduce demand and lower reputation (Collison and Sheldon, 1991; Karatepe, 2013). Fewer employees to maintain the same service levels may not suffice (Wu et al., 2008).

Based on the above discussion, we propose that an increase in the return on fixed assets (ROFA), the return on intangible assets (ROIA), and labour productivity would be positively associated with firm performance.

Hypothesis 1a: An increase in the return on fixed assets (ROFA) is positively associated with performance.

Hypothesis 1b: An increase in the return on intangible assets (ROIA) is positively associated with performance.

Hypothesis 1c: An increase in labour productivity is positively associated with performance.


Effects of joint pursuit of efficiencies

While improvements in efficiency in the three areas may be desirable, whether hospitality establishments could use the three efficiencies as levers could be further explored. Pursuing two efficiency types jointly could help further re-align resources and improve performance (Boyer and McDermott, 1999).

We propose that an increase in labour productivity considered in tandem with an increase in ROFA or ROIA could be positively associated with performance as follows. The stickiness of ROFA and ROIA could be complemented by the lower stickiness of labour productivity. In improving returns from ROFA, improving labour productivity could help develop recombinations of fixed assets and service interaction loci of employees to improve performance. For example, in a restaurant’s efforts to improve customer turnover (increasing ROFA) in tandem with increasing labour productivity would improve service levels (cf. Namasivayam and Denizci, 2006). Faster turnover and improved productivity would lower costs and increase performance. If an establishment pursues an increase in ROFA in tandem with increased labour productivity, it signals to employees that the owners are committed to improving performance not only by making employees improve productivity but also by being committed to improving performance of fixed assets. Such joint improvement efforts create a feeling of ‘we are in it together’ and elicit pro-social and organizational citizenship behaviours (Lavelle et al., 2007). Furthermore, improving ROFA and labour productivity jointly may create a ‘buffer’ effect where resulting shortfalls from one type of efficiency could be picked by the other type (cf. Ravichandran et al., 2007). Continuing from the previous example, if a restaurant increases turnover to improve ROFA, service quality may decline; however, employee productivity could make up for the perception of reduced service quality resulting from faster customer turnover.

Similarly, increasing ROIA while improving labour productivity would create richer loci of service provision by further leveraging intangible assets (Kwansa et al., 2008). During a recession, intangible assets are particularly salient in maintaining sales (cf. King, 2010; Mary Tzortzaki and Mihiotis, 2012), and increasing labour productivity implies employees are willing to go above and beyond, further strengthening customer relationships embedded in intangible assets. Improvements in ROIA suggest that the firm is leveraging its brand and reputation more intensively, improving collaboration with clients (e.g., longer-term or favourable contracts), and leveraging relationship management software, among other practices, to improve returns from intangible assets. Higher labour productivity increases interdependence, and cooperation (Evans and Davis, 2005) and collaboration also lead to improved customer relationships with increasing ROIA.

Based on the above discussion we propose the following:

Hypothesis 2a: With an increase in ROFA, an increase in labour productivity is positively associated with performance.

Hypothesis 2b: With an increase in ROIA, an increase in labour productivity is positively associated with performance.


Sample and Procedures

The data were retrieved from the IES (Informação Empresarial Simplificada) Form available from the Informa D&B database. The IES form is a document that Portuguese firms have to file annually with financial and performance information. The availability of reliable financial information is critical to test the proposed hypotheses and reduce effects of common method bias. We focus on the entire recessionary period from 2007 to 2014. The data ranges from 2006 to 2014, as we take the change in efficiency from the previous period year 2006, which is not dropped in the analysis.

The sample includes the entire population of firms in the tourism industry (CAE – Código das Actividades Empresariais, codes 55111 to 55900, 56101 to 56305, 77110 to 79120, 91041 to 91042, 93110 to 93294 and 96040). We do not use any filters, and based on case-wise deletion, the final sample from 2007 to 2014 included 1,647 firms and 3,076 firm-year observations.


The outcome variable is the natural log of operating income (EBITDA). The predictors are (i) change in the return on fixed assets [ln(net income)/ln(fixed assets)] from the previous year; (ii) change in the return on intangible assets [ln(net income)/ln(intangible assets)] from the previous year; and (iii) change in labour productivity [ln(sales)/ln(employees)] from the previous year. The measure of labour productivity, sales per employee, has been widely used in the previous literature (Baker and Riley, 1994; Cho et al., 2006; D’Annunzio-Green et al., 2008; Datta et al., 2005).

To control for industry effects, we include 5-digit industry dummies. As size is an important determinant of success in a recession (Perez‐Quiros and Timmermann, 2000), we include two proxies for size – the natural log of the number of employees and the natural log of total assets. As advertising expenditures proxies for marketing expenditures (to proxy for marketing expenditures in Singh and Dev, 2015 study), we control for natural log of advertising expenditures. Finally, the natural log of interest paid and the natural log of retained earnings, serve as proxies for resources available to make changes.

Table 1 presents the mean, standard deviations and pairwise correlations based on case-wise deletions from Model 7 in Table 2. In the Appendix, we provide a detailed summary of the key variables at the industry level.


Insert Tables 1-4 about here



We use random-effects regression (xtreg Stata 14.1) to test the proposed hypotheses. Hypothesis 1a proposed that an increase in the return on fixed assets is positively related to performance (Table 2, Model 2: β = 1.228, p < 0.01). Hypothesis 1b and 1c proposed that an increase in the return on intangible assets (Table 2, Model 3: β = 0.240, p < 0.01) and an increase in labour productivity (Table 2, Model 4: β = 0.0143, p < 0.01) are positively associated with firm performance, respectively. As the outcome and predictor are both on a logarithmic scale, the interpretation is a 1% change in x is associated with a y% change in the outcome variable. Among the three direct effects, an increase in ROFA has the highest effect on operating income (1.228%), whereas the change in labour productivity (0.014%) has a minimal impact on operating income. The change in intangible assets has a moderate (0.240%) effect on firm performance.

Hypothesis 2a proposed that with increasing ROFA, a greater increase in labour productivity would be more positively associated with performance (Table 2, Model 5: β = 0.0667, p < 0.01). To interpret the effects, in Exhibit 1, using the margins command in Stata 14.1, for the positive change zone (right side of the vertical line passing through zero of x-axis), the dashed line has a positive slope; however, the slope of the solid line is more positive – indicating a substitution effect where firms that must focus on increasing ROFA may have lower returns from increasing labour productivity; the gains related to the dashed line are nevertheless in a positive direction. Similarly, to the left of the vertical line, for firms realizing a lower increase in ROFA, greater improvements in labour productivity improve performance. However, in Model 7, the effect of H2a reverses in direction. Hypothesis 2b proposed that with increasing ROIA, a greater change in labour productivity would be positively associated with performance (Table 2, Model 6: β = 0.0340, p > 0.10).

Overall, all the hypotheses, except H2b, are supported. The highest effect sizes are for an increase in ROFA (H1a), followed by an increase in ROIA (H1b), and the lowest effect sizes are for an increase in labour productivity (H1c).


To control for autoregressive effect at ar(1), we use xtregar function in Stata 14.1. As presented in Model 1 of Table 3, the inferences are similar to the main model. Specifically, H1a (Table 3, Model 1: β = 1.2027, p < 0.01 vs. Table 2, Model 7: β = 1.2618, p < 0.01), H1b (Table 3, Model 1: β = 0.0532, p > 0.10 vs. Table 2, Model 7: β = 0.0398, p > 0.10), H1c (Table 3, Model 1: β = 0.0222, p < 0.01 vs. Table 2, Model 7: β = 0.0205, p < 0.01), H2a (Table 3, Model 1: β = -0.1979, p < 0.01 vs. Table 2, Model 7: β = -0.1687, p < 0.01), and H2b (Table 3, Model 1: β = 0.0416, p > 0.10 vs. Table 2, Model 7: β = 0.0306, p > 0.10) have similar inferences in line with the main results.

We tested whether the inferences hold for the log of net income and ROS. As presented in Table 3, compared to Model 7 in Table 2, H1c is not supported for the log of net income and is now significant in the opposite direction for ROS. The negative effect of labour productivity on ROS could be because focusing on worker efficiency is less compatible with the external customer demand metric of ROS

H2b, which was not supported in the main results, is supported for both the log of net income and ROS. Support for H2b for Models 2 and 3 in Table 3 suggests that our outcome of operating income is less influenced by changes in capital structure and loss write-offs that are absorbed in net income (Table 3, Model 2) and more conservative than ROS (Table 3, Model 3) that includes the net profit component that could result from asset and capital structure changes. Based on estimates in Tables 2 and 3, we discuss our inferences.


Considering the estimates from Tables 2 and 3, the most conservative inferences are that changes in ROFA, followed by ROIA, provide the most important performance gains. The remaining effects are somewhat mixed across different outcome measures.

Our analyses are based on accounting data, and therefore, we cannot provide details on the microdynamics of allocating resources during a recession. Increases in ROFA and ROIA significantly influence performance, and the gains from a change in ROFA (1.228) are far greater than those from a change in ROIA (0.244), almost by a factor of five. This finding suggests that in allocating scarce resources during a recession, preference should be towards increasing ROFA. Overall, gains from increasing ROFA are the highest, and increasing ROIA, although beneficial, must be only a second priority.



The current study focuses not only on the hospitality industry in a country hit hardest by the Great Recession, Portugal, but also an industry that was more resilient than similar industries in Europe. We attempted to identify types of efficiency improvements that result in the highest performance gains during a recession. The results show that improving returns on fixed assets has the highest impact on operating profit (1.228%), followed by gains from increasing returns from intangible assets (0.240%). The inferences have implications for hospitality firms during a recession. We discuss the practical implications below.

Recession is a challenging period for the hospitality industry. As dining and vacationing are discretionary expenses, due to lower income, customers cut back significantly on such expenses. As divesting from fixed and intangible assets in the short term is less feasible and cost cutting could deteriorate service levels, a recession could lead to an increased focus on efficiency improvements. There are three areas – the efficiency of fixed assets, the efficiency of intangible assets, and labour productivity. While cutting back on labour is easier and more immediate than divesting from fixed and intangible assets, our findings show that improvements in labour productivity have the smallest effect and are inconclusive on performance when combined with a change in ROFA or a change in ROIA. Therefore, while variable labour costs could be cut easily and the remaining workers at an establishment could be made to work harder, gains in profitability may not be very high. In the choice between improving returns from fixed assets or intangible assets, managers could focus on improving returns from fixed assets. The inferences provide guidance to managers of hospitality establishments in allocating limited attention and resources in the face of a recession.

In closing, limited studies have focused on how hospitality establishments could improve performance in a recession. In the current study, using a panel dataset and inferences robust to several controls and alternate specifications, we aimed to show the relative gains in efficiency improvements for hospitality establishments. During a recession, limited resources and declining demand pose a double threat for these establishments, and allocating efforts towards areas of improvements that lead to the highest returns is an important area of research. The inferences must be interpreted in light of their limitations. First, while the panel data with certified financial information are valuable in drawing reliable inferences, we do not focus on the rich qualitative data that could further shed light on the recessionary response by hospitality establishments. Second, while we draw the sample from a country hit hardest by the Great Recession, Portugal, the generalizability of the inferences could be ascertained through future studies drawing on samples from different countries. Finally, although we control for 5-digit industry dummies, the intra-industry variations in the recessionary response could be further studied. We hope that the current study fosters future research on the efficacy of strategies adopted by hospitality establishments during a recession and the consequences of such strategies on performance.


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Table 1

Sample Descriptives

Mean SD Min Max 1 2 3 4 5 6 7 8 9
ln(EBITDA) 10.57 1.65 1.73 18.76 1.0000
ln(Employees) 2.28 1.13 0.69 7.45 0.7713* 1.0000
ln(Advertising) 6.69 2.31 0.91 15.21 0.6988* 0.6643* 1.0000
ln(Assets) 12.69 1.59 9.27 20.34 0.8829* 0.7798* 0.7056* 1.0000
ln(Retained earnings) 10.27 2.07 -4.61 18.59 0.5812* 0.5169* 0.4093* 0.6283* 1.0000
ln(interest paid) 7.79 2.48 -3.91 17.19 0.6734* 0.5531* 0.5309* 0.7078* 0.3536* 1.0000
Change ROFA -0.01 0.13 -1.18 1.62 0.0967* 0.0152 -0.0329 -0.0155 -0.0707* -0.0183 1.0000
Change ROIA -0.02 0.32 -5.93 3.55 0.0809* 0.0316 0.0077 0.0205 -0.0347 0.0090 0.5044* 1.0000
Change labour productivity -0.09 1.59 -12.81 12.74 0.0030 -0.0743* -0.0100 0.0018 0.0368* -0.0155 0.0617* 0.0546* 1.0000


N = 1,647 firms with 3,076 firm-year observations

* p < 0.05







Table 2

Random Effects Regression

(1) (2) (3) (4) (5) (6) (7)
VARIABLES ln_ebitda ln_ebitda ln_ebitda ln_ebitda ln_ebitda ln_ebitda ln_ebitda
ln(employees) 0.2458*** 0.2447*** 0.2521*** 0.2938*** 0.2867*** 0.3144*** 0.2930***
(22.0249) (21.9860) (15.2340) (14.2973) (15.6885) (12.0056) (11.4729)
ln(advertising) 0.0572*** 0.0489*** 0.0497*** 0.0541*** 0.0490*** 0.0447*** 0.0491***
(13.4523) (12.1063) (7.9872) (7.5732) (8.0164) (4.8374) (5.4939)
ln(assets) 0.5696*** 0.5593*** 0.5462*** 0.5782*** 0.5565*** 0.5493*** 0.5507***
(56.2624) (54.8884) (34.7835) (33.0349) (35.3502) (23.1408) (23.8749)
ln(retained earnings) 0.0041 0.0259*** 0.0315*** 0.0061 0.0214*** 0.0200** 0.0301***
(0.9555) (6.0665) (4.9635) (0.8511) (3.3876) (2.1814) (3.3669)
ln(interests paid) 0.0601*** 0.0681*** 0.0751*** 0.0577*** 0.0722*** 0.0683*** 0.0699***
(18.4636) (22.1939) (13.7365) (10.8986) (15.9770) (8.7719) (9.2883)
Δ ROFA [H1a] 1.2280*** 1.2690*** 1.2618***
(41.2819) (27.8365) (14.4888)
Δ ROIA [H1b] 0.2400*** 0.2972*** 0.0398
(13.1454) (9.0747) (1.0452)
Δ Labour productivity [H1c] 0.0143*** 0.0098** 0.0272*** 0.0205***
(2.6964) (2.2926) (4.0701) (3.1727)
Δ ROFA × Δ Labour productivity [H2a] 0.0667*** -0.1687***
(3.0254) (-2.6152)
Δ ROIA × Δ Labour productivity [H2b] 0.0340 0.0306
(1.3584) (0.9563)
Constant 1.7747*** 1.9589*** 2.0213*** 1.3629*** 1.7461*** 1.8969*** 1.7674***
(14.8095) (16.3132) (11.3743) (6.5720) (9.2444) (6.9281) (6.6175)
Industry dummies (5-digit CAE) Included Included Included Included Included Included Included
Observations 23,713 14,847 6,674 8,754 6,745 3,084 3,076
Number of firms (DUNS number) 8,130 6,078 3,346 3,537 2,971 1,654 1,647
R2-within 0.0522 0.1920 0.0912 0.0379 0.1890 0.1040 0.1890
R2_between 0.711 0.782 0.789 0.722 0.794 0.796 0.806
Chi2 22998 24645 13218 10149 12656 6741 7256
df 48 48 47 47 47 47 49
p-value 0 0 0 0 0 0 0
z-statistics in parentheses; DUNS is the unique firm identifier in Informa D&B. 

*** p<0.01, ** p<0.05, * p<0.1

Table 3

Robustness Tests

(1) (2) (3)
VARIABLES ln_ebitda ln_netincome ROS
Autoregressive ar(1)
Stata 14.1 model xtregar xtreg xtreg
ln(employees) 0.2918*** 0.3107*** -0.0272**
(11.6699) (7.3637) (-2.2384)
ln(advertising) 0.0456*** 0.0847*** 0.0065*
(5.1760) (5.6626) (1.8626)
ln(assets) 0.5660*** 0.3656*** 0.0160
(24.8328) (9.5805) (1.5133)
ln(retained earnings) 0.0312*** 0.1637*** 0.0126***
(3.5172) (10.9791) (3.2320)
ln(interests paid) 0.0660*** -0.0359*** -0.0060**
(8.7454) (-2.8511) (-2.0024)
Δ ROFA [H1a] 1.2027*** 4.4137*** 0.4117***
(13.9811) (29.8420) (14.0566)
Δ ROIA [H1b] 0.0532 0.2970*** 0.0296**
(1.4200) (4.5570) (2.2698)
Δ Labour productivity [H1c] 0.0222*** -0.0018 -0.0126***
(3.5254) (-0.1656) (-5.6670)
Δ ROFA × Δ Labour productivity [H2a] -0.1979*** -0.3934*** -0.0415*
(-3.0921) (-3.5716) (-1.8458)
Δ ROIA × Δ Labour productivity [H2b] 0.0416 0.1773*** 0.0198*
(1.3129) (3.2251) (1.8383)
Industry dummies (5-digit CAE) Included Included Included
Constant 1.5988*** 1.9325*** 0.8720***
(6.1166) (4.4032) (6.4036)
Observations 3,076 3,146 3,146
Number of firms (DUNS number) 1,647 1,677 1,677
R2-within 0.187 0.405 0.181
R2_between 0.806 0.583 0.112
Chi2 7847 3358 530.8
df 50 49 49
p-value 0 0 0
z-statistics in parentheses; DUNS is the unique firm identifier in Informa D&B. 

*** p<0.01, ** p<0.05, * p<0.1

Figure 1

Moderation Effects



Industry-wise descriptives

Industry Firm-years Employees Assets EBIDTA Change in Return on Fixed Assets Change in Return on Intangible Assets Change in labour productivity
CAE 55111 Hotels with restaurants 145 Mean 92.234           25,300,000    1,616,880 -0.014 -0.037 0.047
SD 133.117 69,500,000    4,480,635 0.096 0.544 1.297
CAE 55112 Low-cost hotel with restaurant 22 11.227  763,333     82,794 0.016 -0.099 -0.076
6.414  749,960     61,292 0.079 0.738 0.796
CAE55113 Inns with restaurant 2 27.500    3,045,671  142,377 0.052 0.089 0.778
13.435    3,743,467  108,116 0.001 0.044 1.088
CAE 55115 Motels with restaurant 1 29.000    3,004,105  674,841 -0.004 -0.013 -0.040
 –  –
CAE 55116 Apartment-hotel with restaurant 17 384.471 69,300,000    7,733,659 -0.025 -0.053 0.339
477.837 90,100,000 10,700,000 0.092 0.180 1.747
CAE 55117 Holiday village with restaurant 3 59.667    7,882,481  749,751 0.160 0.322 -0.030
43.822    6,202,886  609,961 0.226 0.526 0.139
CAE 55118 Touristic apartments with restaurant 23 76.261    4,807,112  311,475 -0.028 -0.010 0.017
110.716    7,466,141  494,194 0.128 0.198 0.437
CAE 55119 Other establishments with restaurant 13 16.077    2,656,438  164,881 -0.038 -0.109 0.600
14.591    3,649,223  171,799 0.134 0.276 2.043
CAE 55121 Hotels without restaurants 28 15.536    1,814,328  182,991 0.002 0.006 -0.045
9.155    2,110,318  184,747 0.103 0.187 0.695
CAE 55122 Low-cost hotel without restaurant 2 8.000  196,596     17,112 -0.036 -0.038 0.053
0.000  7,605  9,956 0.184 0.210 0.748
CAE 55123 Touristic apartments without restaurant 15 38.533    9,838,078  367,997 -0.002 0.098 0.001
52.824 16,400,000  421,634 0.114 0.265 0.670
CAE 55124 Other establishments without restaurant 12 6.833    1,342,238     82,913 -0.035 -0.058 -0.105
3.486    1,685,665     84,653 0.067 0.120 1.403
CAE 55201 Serviced accommodation to tourists 6 3.333  352,034     28,889 -0.013 -0.021 -0.494
1.211  299,361     21,413 0.074 0.089 4.699
CAE 55202 Rural tourism 17 4.706  460,082     25,642 -0.115 -0.096 0.585
4.104  371,625     19,538 0.249 0.219 2.488
CAE 55204 Other short-term accommodation 6 38.500    1,777,663  256,021 0.004 0.031 -0.114
34.547    1,624,155  246,445 0.032 0.062 0.198
CAE 55300 Camping and caravans 41 27.317    1,880,993  199,450 -0.017 -0.002 -0.223
25.893    2,764,108  186,833 0.083 0.153 0.839
CAE 56101 Standard restaurants 703 21.036  772,431  121,089 -0.007 -0.010 -0.127
57.364    2,510,468  455,495 0.129 0.283 1.352
CAE 56102 Restaurants with bar service 189 12.725  287,017     45,062 -0.005 -0.014 -0.098
15.790  340,020     79,975 0.120 0.222 2.078
CAE 56103 Restaurants without table service 39 344.692 22,500,000    4,878,874 -0.002 -0.018 -0.353
579.449 44,700,000    9,691,660 0.072 0.199 1.105
CAE 56104 Traditional restaurants 40 13.200  792,864     83,202 -0.027 -0.053 0.034
10.115  976,999     95,424 0.109 0.184 1.555
CAE 56105 Restaurant with dance floor 17 22.588    2,524,674  150,975 0.001 0.076 -0.208
34.044    4,874,705  135,157 0.119 0.404 1.222
CAE 56106 Takeaways 55 20.600    1,003,484  100,754 -0.016 0.008 -0.151
39.996    2,218,274  177,525 0.191 0.255 0.569
CAE 56107 Other restaurants 312 22.724  686,856  120,787 -0.013 -0.019 -0.006
58.238    2,237,065  477,494 0.124 0.204 1.757
CAE 56301 Cafes 475 7.463  214,806     27,039 -0.010 -0.045 0.001
9.320  359,516     41,836 0.162 0.413 1.904
CAE 56302 Bars 113 7.920  466,862     50,389 -0.019 -0.039 -0.383
9.482  885,726     84,866 0.148 0.255 1.775
CAE 56303 Cake shops and tea houses 461 12.907  334,757     39,327 -0.014 -0.032 -0.057
12.562  412,895     47,528 0.129 0.335 1.300
CAE 56304 Other establishments without shows 51 8.745  289,572     43,810 -0.014 -0.011 0.064
7.891  404,366     53,047 0.120 0.242 1.337
CAE 56305 Drink establishments with dance floor 24 11.792  383,303     67,343 -0.027 -0.053 0.206
8.762  416,822     77,661 0.102 0.150 2.553
CAE 77110 Rent a car 56 41.250 38,100,000    7,662,632 0.003 0.049 -0.549
74.853    128,000,000 25,900,000 0.095 0.435 2.274
CAE 77120 Rent a car – trucks 3 9.000  324,956     83,283 0.025 0.018 -0.657
0.000     16,969     10,976 0.020 0.057 0.040
CAE 77210 Rent of sports and recreational equipment 7 8.571    1,516,624  254,608 0.012 0.023 0.014
5.287  979,087  180,364 0.225 0.293 0.740
CAE 79110 Travel agencies 40 10.275    2,073,010  135,553 -0.003 -0.005 -0.392
10.311    3,817,814  184,551 0.133 0.205 1.245
CAE 91041 Zoos, botanic and aquariums activities 8 126.250 15,400,000    1,716,068 -0.108 -0.109 -0.026
115.803 14,700,000  726,409 0.247 0.330 0.049
CAE 93110 Management of sport facilities 40 62.975 14,800,000    1,262,660 -0.029 -0.102 -0.116
66.717 36,100,000    2,083,334 0.094 0.414 0.473
CAE 93192 Other sport activities 38 8.895  855,149  104,316 -0.009 -0.017 -0.632
7.866  883,035  171,597 0.128 0.248 2.852
CAE 93210 Theme parks activities 9 58.778    4,021,320  998,060 0.006 0.016 0.236
44.969    2,216,177  747,387 0.053 0.066 0.642
CAE 93292 Marina activities 6 31.833 25,300,000    2,052,040 -0.056 -0.052
9.786    5,817,279    1,167,177 0.095 0.126
CAE 93293 Other tourism entertainment activities 4 14.750    1,601,038  123,031 0.016 0.012 1.014
22.867    2,565,224     98,037 0.068 0.098 1.343
CAE 93294 Other amusement and recreation activities 31 7.710    3,128,119  138,303 -0.031 -0.054 -0.392
4.627    7,141,368  237,260 0.125 0.303 1.783
CAE 96040 Well-being activities 2 2.000  155,269     43,717 0.007 0.544 -0.061

[1] Rushmore (2015):

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