Governmental Revenue Compensation during COVID-19: Did Firm Resources and Institutional Factors Explain Who Received It?

Abstract

During COVID-19, the Norwegian Government provided compensation to amend firms’ decreased revenues, yet it should be neutral and tailored to losses only. However, firm resources or institutional factors may have influenced the compensation, which we study here. Survey data showed a high probability of compensation among large firms, although they were not hit particularly hard by COVID-19. Also, compensation was prevalent for firms in the hospitality, tourism, and culture industry, and a likely explanation is that they were hit hard. However, their probability of compensation was prevalent compared to firms in other industries, regardless of revenue losses. We assume that large firms’ compensation was due to their resources to apply for funding successfully, while institutional factors explained the compensation for firms in the hospitality, tourism, and culture industry.

1. Introduction

Worldwide, numerous firms and industries were hit hard by the COVID-19 pandemic [1,2], and as a response, many governments provided compensation to amend revenue losses. In Norway, “the aim of the Compensation Scheme was to avoid unnecessary bankruptcies and safeguard Norwegian jobs during the coronavirus crisis” [3]. The Government further emphasized that the compensation should be neutral, i.e., determined only as a function of revenue losses. However, we cannot rule out that particular firm- or industry characteristics may have influenced the distribution of the compensation beyond the amendment of revenue losses, and this study aims to shed light on these issues. For example, it has been debated whether the Scandic Hotel Group, a large Norwegian hospitality actor, should have been eligible for compensation, of which they indeed were a large recipient [4]. Going beyond this single case, can we know if factors apart from the amendment of revenue losses have been at play when the Government distributed compensations to firms in the wake of COVID-19?

To study this research question, we analyze survey data of 599 Norwegian firms and initially assess firm- or industry characteristics that eventually explain revenue losses due to the COVID-19 pandemic. Secondly, we assess firm- or industry characteristics that eventually explain the probability of receiving revenue compensation from the Government. Thirdly, we assess whether variation in firms’ revenue losses mediates and controls out firm- or industry characteristics’ effect on the probability of receiving revenue compensation.

The motive for our empirical approach is that from a neutral or objective standpoint, one would assume that those firm- or industry characteristics, which eventually explain revenue losses in the wake of COVID-19, would also explain the probability of receiving revenue compensation. In addition, one would assume that variation in firms’ revenue losses was to mediate and control out eventual firm- or industry characteristics’ effects on revenue compensation. That is, revenue losses would explain compensation received, not firm- or industry characteristics per se. Finally, one would assume that those firm- or industry characteristics that do not explain revenue losses neither would explain the probability of revenue compensation. Thus, observing deviations from these assumptions would instead indicate that the revenue compensation is not fully neutral or objective, i.e., not determined as a function of revenue losses only and at least partly tailored to particular firm- or industry characteristics.

In Section 4, we further address the above issues, but for now, we emphasize firm size in the number of employees as a characteristic of particular interest. Hence, we discuss whether revenue losses and governmental revenue compensation differ for large vs. small firms.

Also, we discuss whether revenue losses and compensation differ for firms in the hospitality, tourism, and culture industry compared to firms in the manufacturing industry and the consulting, finance, and insurance industry. In other words, the hospitality, tourism, and culture industry is of particular interest, and we include the others as reference groups.

Conceptually, we ground our study in literature streams emphasizing power structures [5,6,7], legitimacy [8,9,10], organizational slack [11], and taken-for-grantedness [12,13], which we further elaborate below. The study’s originality is that it investigates whether particular firm- or industry characteristics have influenced the distribution of government-provided monetary compensation in the wake of COVID-19 beyond the amendment of revenue losses, which, to our knowledge, has not been addressed in previous research.

2. Firm- and Industry Characteristics

2.1. Firm Size in Employees

An extensive body of literature examines whether large firms are more influential or powerful than small firms, but overall, the findings are mixed [5,6,7]. However, other research has shown that large firms are more productive in revenues generated per employee than small firms [14], possibly due to economies of scale and market power [15,16]. Historically, large firms are also considered to be in a relatively advantageous position and have been less exposed to what the literature labels as a “liability of smallness” due to vulnerability and lack of legitimacy in the marketplace and among stakeholders [8,9,10]. Large firms’ productivity gains furthermore induce organizational slack [11], which “allows an organization [or a firm] to adapt successfully to internal pressures for adjustment to external pressures for change in policy, as well as to initiate changes in strategy with respect to the external environment” [17], p. 30. Therefore, due to their abundant resources and organizational slack, large firms have likely been in an advantageous position when screening opportunities and eventually successfully applying for revenue compensation in the wake of COVID-19, which aligns with Gustafsson et al. [18] finding that they had a relatively high probability of receiving governmental grants.

Another issue favoring large firms is that their visibility for numerous stakeholders provides legitimacy that affects how people understand them. According to Suchman [12], p. 575, “audiences perceive the legitimate organization [or firm] not only as more worthy, but also as more meaningful, more predictable, and more trustworthy”. Assuming that size induces legitimacy, following Suchman’s argument, it is not farfetched to assume that large firms are perceived positively in the eye of the beholder, which further increases the probability of receiving revenue compensation from the Government. Therefore, we believe that large firms had a higher probability of successfully receiving revenue compensation from the Government than small firms.

Having argued that large firms have relatively abundant resources, organizational slack, and legitimacy, one may also assume they were more immune to revenue losses than small firms in the wake of COVID-19. However, COVID-19 largely altered firms’ revenues due to changes in market conditions and logistical bottlenecks [19], which we assume, at least in the short run, are challenging to handle independently of size. Research has indicated that firms, as a function of size, have been increasingly more prone to failure during a crisis than in its absence [20], yet other research found that small firms experienced larger revenue losses in a financial crisis than large firms [21]. This latter reported finding is echoed in recent research showing that large firms reported to be more positively and less negatively affected by COVID-19 than small firms, but firm size did not affect revenue changes [22]. Hence, overall, empirical findings are inconclusive, which may be explained by small firms using different responses during a crisis [23]. Therefore, as the first row in

Table 1. Concluding theoretical arguments suggesting directions of empirical results.

The absence of convincing theoretical arguments and mixed empirical findings induces us to conclude that the effect of firm size on revenue losses in the wake of COVID-19 can either be positive, negative, or unchanged.

We assume that the probability of compensation among firms in the hospitality, tourism, and culture industry is relatively high, even when accounting and controlling for the effect of revenue losses.

concludes, the absence of convincing theoretical arguments and mixed empirical findings induces us to conclude that the effect of firm size on revenue losses in the wake of COVID-19 can either be positive, negative, or unchanged.

2.2. The Hospitality, Tourism, and Culture Industry

The hospitality, tourism, and culture industry was hit particularly hard during COVID-19 [24,25,26,27,28], and the reasons are intuitive as travel- and physical proximity restrictions were strongly imposed worldwide. Also, the industry is generally vulnerable to economic downturns because they induce consumers and businesses to reduce discretionary spending on travel, culture, restaurants, and the like [29]. Consequently, we assume that firms in the hospitality, tourism, and culture industry have had a relatively high probability of experiencing revenue losses due to COVID-19. Firms in the two others—the manufacturing industry and the consulting, finance, and insurance industry—have had a relatively low probability of revenue losses as they have been less affected by travel- and physical proximity restrictions. Also, they are less vulnerable to discretionary spending cuts. Altogether, we conclude that firms in the hospitality, tourism, and culture industry have had a higher probability of revenue losses due to COVID-19 than firms in the two others.

Following the above reasoning, from a neutral or objective standpoint, to amend revenue losses, we further assume that firms in the hospitality, tourism, and culture industry have had a higher probability of receiving revenue compensation than firms in the two others. However, we do not rule out that the probability of compensation among firms in the hospitality, tourism, and culture industry is relatively high, even when accounting and controlling for the effect of revenue losses. At the core of this argument is what the literature describes as taken-for-granted cognitive legitimacy. The concept is largely attributed to assuming the absence of certain firms or institutions as unthinkable [12,13], and, therefore, by their very nature, they gain cognitive legitimacy because they are taken for granted. In our context, we do not find it farfetched to assume the need for compensation in the hospitality, tourism, and culture industry to be taken for granted due to an overall assumption that they were generally hit hard by COVID-19, which may have triggered revenue compensation going beyond de facto revenue losses. This taken-for-grantedness trigger can be attributed to both a pull and push effect: a pull effect for firms and industry stakeholders to encourage applications for revenue compensation and a push effect for the governmental body that provides compensation. Altogether, as the second row in Table 1 concludes, we assume that the probability of compensation among firms in the hospitality, tourism, and culture industry is relatively high, even when accounting and controlling for the effect of revenue losses.

3. Materials and Methods

3.1. Research Context and Data

To study our above arguments empirically, we analyzed data from an electronic survey carried out through telephone interviews in the first months of 2021 by Ipsos, a professional consulting and market research firm. Also, Ipsos was responsible for the data coding. Candidates for the survey were identified by the Brønnøysund Register Centre, “a government body under the [Norwegian] Ministry of Trade, Industry and Fisheries” [30]. The survey included complete data from 599 enterprises as legal entities, labeled as firms, 200 in each industry, except for the hospitality, culture, and tourism industry, which included 199 observations. The response rate was 25%. The Brønnøysund Register Centre data identified firm size in the number of employees and the industry sector where each unit operated. Below, we explain how we identified the three industries using Standard Industrial Classification (SIC) codes. The other variables were identified through the primary data gathered by the electronic survey, which we also explain in detail below. The target persons or informants of the survey were each firm’s CEO or deputy CEO.

3.2. Dependent Variables

The questionnaire included the following question to measure revenue losses (all translations from Norwegian into English are ours): “How would you say that COVID-19 overall has affected the firm’s revenues?”, where the respondents could indicate that they are reduced (coded 1), they are unchanged (coded 2), or they have increased (coded 3). We included the third alternative, as COVID-19 may have increased revenue for some firms. Therefore, we included the concept as an ordinal dependent variable and labeled it as revenue changes. An ordinal variable implies, in our case, that the response of unchanged revenues (coded 2) takes a higher value than the response of reduced revenues (coded 1) but a lower value than the response of increased revenues (coded 3). However, contrary to a continuous variable, where the difference between 1 and 2 is algebraically similar to that between 2 and 3, an ordinal variable instead emphasizes 2 is higher than 1 and lower than 3 without any constraint concerning the algebraic difference (for a formal explanation, please see, e.g., [31]). Below, we explain how we examined the variable revenue changes using maximum likelihood ordinal logistic regression. Also, in some models, we included the concept as a nominal independent variable. A nominal variable implies that a response coded 2 is different from a response coded 1 or 3, but there is no ranking in value between the responses. For instance, the coding of 2 does not take a higher value than the coding of 1 but a different value [31].

The questionnaire included the following question to measure whether a firm has received revenue compensation as a second dependent variable: “Has your firm, in relationship to COVID-19, used any of the following public support measures?”, of which one response alternative was labeled “Compensation for revenue losses” (yes coded 1, and no coded 0). In other words, the dependent variable is binary as it only takes two values. It is sometimes labeled as a dummy variable [32]. Below, we explain how we examined the dependent variable revenue compensation using maximum likelihood binary logistic regression or simply logistic regression.

3.3. Independent Variables

Firm size in the number of employees is an independent variable, and we have noted that it was gathered by using register data from the Brønnøysund Register Centre [30]. We log-transformed the variable by using the natural logarithm. For instance, the log-transformed value of a firm with one employee takes the value of 0, a firm with ten employees takes the value of 2.30, a firm with one hundred employees takes the value of 4.61, and a firm with one thousand employees takes the value of 6.91. Our motive for the log transformation is that a change of, for instance, ten employees would have a very different impact on a small than a large firm.

Another independent variable was the comparison of firms in the (1) hospitality, tourism, and culture industry, (2) manufacturing industry, and (3) consulting, finance, and insurance industry. In other words, we modeled the three industries as nominal variables. The first industry was identified by the Standard Industrial Classification (SIC) codes 55–56 and 90–93, the second by the SIC codes 10–32, and the third by the SIC codes 69–75 and 77–82 [33]. As noted, the survey included about 200 firms in each of the three industries.

3.4. Control Variables

We controlled for knowledge intensity since it may affect how firms face external challenges [34]. The concept was measured by summarizing responses in the questionnaire concerning whether or not the firm the last three years (1) introduced a new or substantially improved product or service to the market, (2) the product or service was also new for the market, (3) the firm the last three years introduced a new or substantially improved process innovation, (4) collaborated with other firms or institutions to develop new processes or products, and (5) at least half of the employees had higher education (college or university level education). I.e., the concept of knowledge intensity took a value varying between 0 and 5. For instance, an enterprise responding yes to two of the five questions, independent of which ones, was given a score of 2. Other research has similarly measured knowledge intensity [34].

Also, the survey included a question about whether the major owners are (1) located in the same region where the firm is located, (2) nationally beyond the region, or (3) internationally. We included ownership location as a dummy control variable, taking three different nominal values or classifications (please see our previous explanation of a nominal or dummy variable). The questionnaire further asked if the firm has exports, production abroad, or ownership in firms abroad. Responding yes to one or more of these questions indicated international engagements, and we included it as a final dummy control variable. It took the value of 1 if the firm had international engagements and 0 otherwise. Our motive for the control variables was to account for potential heterogeneity in the data beyond the effect of the independent variables described above.

4. Results

Table 2. Operationalization of the variables.

Variable	Operationalization
Revenues increased	Survey question asking, “How would you say that COVID-19 overall has affected the firm’s revenues?”, where respondents could indicate that they have increased.
Revenues unchanged	Survey question asking, “How would you say that COVID-19 overall has affected the firm’s revenues?”, where respondents could indicate that they are unchanged.
Revenues decreased	Survey question asking, “How would you say that COVID-19 overall has affected the firm’s revenues?”, where respondents could indicate that they have decreased.
Firm size in employees (ln)	Register data from the Brønnøysund Register Centre. Log-transformed by using the natural logarithm.
Hospitality, tourism, and culture	Standard Industrial Classification (SIC) codes 55–56 and 90–93.
Manufacturing industry	SIC codes 10–32.
Consulting, finance, and insurance	SIC codes 69–75 and 77–82.
Knowledge intensity	Summarizing responses in the questionnaire concerning whether or not the firm the last three years (1) introduced a new or substantially improved product or service to the market, (2) the product or service was also new for the market, (3) the firm the last three years introduced a new or substantially improved process innovation, (4) collaborated with other firms or institutions to develop new processes or products, and (5) at least half of the employees had higher education (college or university level education).
Major ownership locally or regionally	Survey questions concerning whether the major owners are located in the same region where the firm is located.
Major ownership nationally beyond the region	Survey questions concerning whether the major owners are located nationally beyond the region where the firm is located.
Major ownership internationally	Survey questions concerning whether the major owners are located internationally, i.e., beyond Norway.
International engagements	Survey questions concerning whether the firm has exports, production abroad, or ownership in firms abroad. Responding yes to one or more of these questions indicates international engagements.
Received revenue compensation	Survey question concerning whether the firm has received revenue compensation: “Has your firm, in relationship to COVID-19, used any of the following public support measures?”, of which one response alternative was labeled “Compensation for revenue losses” (yes coded 1, and no coded 0).

reports the operationalization of the variables,

Table 3. Descriptive statistics.

	Whole Sample, N = 599				Hospitality, N = 199				Manufacturing, N = 200				Consulting, N = 200
	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD
Revenues increased	0	1	0.179		0	1	0.085		0	1	0.275		0	1	0.175
Revenues unchanged	0	1	0.295		0	1	0.126		0	1	0.335		0	1	0.425
Revenues decreased	0	1	0.526		0	1	0.789		0	1	0.390		0	1	0.400
Firm size in employees (ln)	0	8.38	2.45	1.16	0	5.00	2.57	1.08	0	5.96	2.49	1.14	0	8.38	2.28	8.38
Hospitality, tourism, and culture	0	1	0.332
Manufacturing industry	0	1	0.334
Consulting, finance, and insurance	0	1	0.334
Knowledge intensity	0	5	2.37	1.42	0	5	2.09	1.38	0	5	2.55	1.44	0	5	2.48	1.41
Major ownership locally or regionally	0	1	0.890		0	1	0.955		0	1	0.830		0	1	0.885
Major ownership nationally beyond the region	0	1	0.060		0	1	0.040		0	1	0.09		0	1	0.050
Major ownership internationally	0	1	0.050		0	1	0.005		0	1	0.08		0	1	0.065
International engagements	0	1	0.232		0	1	0.070		0	1	0.365		0	1	0.260
Received revenue compensation	0	1	0.402		0	1	0.724		0	1	0.380		0	1	0.395

reports descriptive statistics, and

Table 4. Correlation matrix.

	1	2	3	4	5	6	7	8	9	10	11	12
Revenues increased (1)
Revenues unchanged (2)	–0.302 ***
Revenues decreased (3)	−0.491 ***	−0.682 ***
Firm size in employees (ln) (4)	0.020	−0.026	0.009
Hospitality, tourism, and culture (5)	−0.172 ***	−0.263 ***	0.372 ***	0.077 †
Manufacturing industry (6)	0.178 ***	0.061	−0.193 ***	0.025	−0.499 ***
Consulting, finance, and insurance (7)	−0.007	0.201 ***	−0.179 ***	−0.102 *	−0.499 ***	−0.501 ***
Knowledge intensity (8)	0.084 *	0.019	−0.082 *	0.138 ***	−0.139 ***	0.087 *	0.052
Major ownership locally or regionally (9)	−0.031	0.006	0.018	−0.158 ***	0.146 ***	−0.135 ***	−0.011	−0.137 ***
Major ownership nationally beyond the region (10)	0.029	0.006	−0.027	0.081 *	−0.059	0.089 *	−0.030	0.087 *	−0.719 ***
Major ownership internationally (11)	0.013	−0.015	0.003	0.139 ***	−0.146 ***	0.097 *	0.048	0.102 *	−0.653 ***	−0.058
International engagements (12)	0.095 *	−0.035	−0.040	0.159 ***	−0.270 ***	0.223 ***	0.047	0.241 ***	−0.211 ***	0.044	0.255 ***
Received revenue compensation	−0.196 ***	−0.308 ***	0.431 ***	0.174 ***	0.512 ***	−0.249 ***	−0.271 ***	−0.049	0.039	−0.021	−0.032	−0.104 *

N = 599. † p < 0.10; * p < 0.05; *** p < 0.001.

reports correlations. Empirically, we studied our research questions using ordinal and binary logistic regression and report the results in

Table 5. Ordinal (Model A) and binary (Models B1–B6) logistic regressions.

Dependent Variable	Revenue Changes	Received Revenue Compensation
	Model A	Model B1	Model B2	Model B3	Model B4	Model B5	Model B6
Firm size in employees (ln)	0.076	0.315 ***	0.372 ***	0.315 ***	0.372 ***	0.351 ***	0.407 ***
	(0.075)	(0.090)	(0.094)	(0.090)	(0.094)	(0.091)	(0.096)
Manufacturing industry (MI) 1	1.86 ***	−2.45 *** [11.6]	−2.02 *** [7.53]
	(0.232)	(0.255)	(0.267)
Consulting, finance, and insurance (CFI) 1	1.62 ***	−2.47 *** [11.9]	−2.12 *** [8.29]
	(0.226)	(0.250)	(0.266)
MI and CFI merged 1				−2.47 *** [11.8]	−2.07 *** [7.91]	−1.81 *** [6.09]	−2.05 *** [7.76]
				(0.221)	(0.232)	(0.204)	(0.227)
Knowledge intensity	0.079	0.004	0.036	0.004	0.036	−0.023	0.035
	(0.060)	(0.072)	(0.077)	(0.072)	(0.077)	(0.071)	(0.076)
Major ownership nationally beyond the region 2	−0.024	−0.025	−0.030	−0.023	−0.019	0.080	0.122
	(0.339)	(0.420)	(0.437)	(0.420)	(0.437)	(0.387)	(0.403)
Major ownership internationally 2	−0.493	0.222	0.085	0.222	0.086	0.273	0.146
	(0.384)	(0.441)	(0.460)	(0.441)	(0.461)	(0.440)	(0.463)
International engagements	−0.193	0.050	−0.078	0.052	−0.072	0.088	−0.077
	(0.210)	(0.256)	(0.265)	(0.255)	(0.265)	(0.249)	(0.261)
Revenues unchanged 3			−1.65 ***		−1.65 ***		−1.81 ***
			(0.262)		(0.262)		(0.246)
Revenues increased 3			−1.49 ***		−1.48 ***		−1.42 ***
			(0.300)		(0.299)		(0.269)
Likelihood ratio χ2	88.1 ***	181.0 ***	236.4 ***	181.0 ***	236.2 ***	108.9 ***	176.9 ***
Maximum/avg. variance inflation factor (VIF)	1.49/1.21	1.49/1.21	1.68/1.26	1.21/1.10	1.30/1.16	1.21/1.10	1.24/1.13
Coarsened exact matching (CEM)						Yes	Yes

N = 599 (one observation concerning ownership for a firm in the hospitality, tourism, and culture industry is missing). Two-tailed tests of significance for regression coefficients. *** p < 0.001. Standard errors in parenthesis. Intercepts omitted. ¹ Default is the hospitality, tourism, and culture industry, and we include odds ratios in brackets concerning this industry. ² Default is major ownership locally or regionally. ³ Default is revenues decreased.

. All analyses were carried out in Stata 17 [35].

4.1. Revenue Changes as a Dependent Variable

Model A in Table 5 includes revenue changes as an ordinal dependent variable, i.e., it takes the three different values: (1) reduced revenues, (2) unchanged revenues, or (3) increased revenues, cf. our previous explanation and discussion. We observe that the effects on the dependent variable are significant and positive for both the manufacturing industry and the consulting, finance, and insurance industry. As these are compared to the hospitality, tourism, and culture industry as a baseline or reference category, it indicates that the latter has experienced a negative revenue change compared to the two other industries. Thus, the probability of a negative change in revenues is higher for firms in the hospitality, tourism, and culture industry than in the others.

Table 6. Logit predictions of the percentage of firms experiencing decreased, unchanged, or increased revenues in the three industries.

	Manufacturing Industry	Consulting, Finance, and Insurance	Hospitality, Tourism, and Culture
Decreased revenues	37.7	43.1	78.2
Unchanged revenues	36.4	35.2	16.3
Increased revenues	25.8	21.8	5.50
Sum	100	100	100

Estimates are based on Model A in Table 5.

gives more detailed information concerning the industry effect as it displays logit prediction probabilities in percent from the estimates in Model A. The table shows that the percentage of firms experiencing decreased revenues is roughly twice as high in the hospitality, tourism, and culture industry (78.2%) compared to the others (37.7% and 43.1%). The percentage experiencing unchanged revenues is about half in the hospitality, tourism, and culture industry (16.3%) compared to the others (36.4% and 35.2%). Finally, the percentage experiencing increased revenues is about one-fifth in the hospitality, tourism, and culture industry (5.50%) compared to the manufacturing industry (25.8%) and about one-fourth compared to the consulting, finance, and insurance industry (21.8%).

Figure 1, also based on the estimates in Model A (Table 5), provides information similar to Table 6 but also gives a graphic display of 95% confidence intervals (CIs) in brackets. For the manufacturing industry, the figure shows that revenues have not changed significantly in either direction. The percentage or probability of firms reporting decreased and unchanged revenues is higher than for those reporting increased revenues, but the difference is non-significant (as illustrated by the overlap in the brackets displaying 95% CIs). Moreover, the percentage or probability of firms reporting decreased vs. unchanged revenues is practically identical in the manufacturing industry.

For the consulting, finance, and insurance industry, Figure 1 shows that the probability or percentage of firms reporting decreased revenues is higher than for those reporting unchanged revenues, but the difference is non-significant (as illustrated by the overlap in the brackets displaying 95% CIs). However, the probability or percentage of firms reporting increased revenues is lower than for those reporting both unchanged and increased revenues, and here, the difference is significant (as illustrated by the absence of overlap in the brackets displaying 95% CIs).

For the hospitality, tourism, and culture industry, Figure 1 shows that the percentage or probability of firms reporting decreased revenues is higher than for those reporting both unchanged and increased revenues, and the differences are strongly significant (as illustrated by the “widely” absent overlap in the brackets displaying 95% CIs). Also, the probability or percentage of firms reporting unchanged revenues is significantly higher than for those reporting increased revenues (as illustrated by the absence of overlap in the brackets displaying 95% CIs).

More interestingly, however, is perhaps the observation in Figure 1 that the percentage or proportion of firms reporting unchanged or increased revenues is significantly lower in the hospitality, tourism, and culture industry than in the two others, while the opposite is the case concerning the proportion or percentage of firms reporting decreased revenues. In other words, the percentage or proportion reporting decreased revenues is significantly higher in the hospitality, tourism, and culture industry than in the two others. Altogether, we conclude that revenue losses are significantly more prevalent in the hospitality, tourism, and culture industry than in the others.

Returning to Table 5, none of the other variables in Model A significantly affect the dependent variable. The likelihood ratio χ² is strongly significant, implying a robust model fit (the following models will also show robust and significant model fits). Maximum and average variance inflation factors (VIFs) taking relatively low values concerning the independent variables indicate that multicollinearity is not a problem (the following models will also show relatively low VIFs). Theoretically, the lowest value the VIF can take is 1, implying that an independent variable is not correlated with any of the other independent variables in the model. The more an independent variable is correlated with other independent variables in a model, the higher the VIF. The literature suggests that VIFs taking values higher than 4, sometimes 10, can be problematic, creating unstable standard errors and regression estimates [36]. Thus, the VIFs that we report are far below these critical values.

4.2. Received Revenue Compensation (or Not) as a Dependent Variable

In Model B1, Table 5, we include the binary dependent variable that indicates whether a firm has received revenue compensation and observe that firm size in employees has a significant positive effect. Based on the model, Figure 2 displays the proportion or percentage of firms receiving compensation as a function of firm size. Also, it includes 95% CIs. A tiny firm with one employee has a probability little higher than 0.2 (i.e., 20%) of receiving compensation, a small firm with ten employees a probability of about 0.35 (i.e., 35%), a medium-sized firm with 100 employees a probability of more than 0.5 (i.e., 50%), and a large with 1000 employees a probability of about 0.7 (i.e., 70%). I.e., firm size in employees strongly predicts the probability of revenue compensation, and the results are particularly interesting as large firms did not report a significantly higher probability of revenue losses than small firms (cf. Model A). Moreover, the firm size effect on the probability of revenue compensation in Model B1 is conservative as it is stronger in all the later models Table 5 reports on.

Also, Model B1, Table 5, shows that the probability of revenue compensation is significant and negative for both the manufacturing industry and the consulting, finance, and insurance industry. As these two industries are compared to the hospitality, tourism, and culture industry as a baseline or reference group, it indicates that the latter had a higher probability of receiving compensation than the others. Similarly, the odds ratios in brackets reveal that the hospitality, tourism, and culture industry had a much higher probability of receiving compensation than the others. Specifically, the odds of receiving revenue compensation were 11.6 times higher for a firm in the hospitality, tourism, and culture industry than for a firm in the manufacturing industry. Similarly, the odds of receiving revenue compensation were 11.9 times higher for a firm in the hospitality, tourism, and culture industry than for a firm in the consulting, finance, and insurance industry. (Note that the odds ratios we report are related to the hospitality, tourism, and culture industry compared to the others.)

The high probability of revenue compensation in the hospitality, tourism, and culture industry (Model B1) coincides with the high probability of revenue losses, as shown in the previous model (Model A). However, when controlling for revenue changes as a nominal dummy variable in Model B2, taking the three different values of revenues increased, decreased, or reduced, we still observe that the industry effects are significantly robust. Nonetheless, the absolute value of the estimates and the odds ratios are lower in Model B2 than in B1 (which does not control for revenue changes). It indicates that revenue changes in the hospitality, tourism, and culture industry partly explain the probability of revenue compensation (due to the lower absolute value of regression estimates and odds ratios in Model B2 compared to Model B1). Yet, still observing significantly robust industry estimates nonetheless indicates a relatively high probability of receiving revenue compensation in the hospitality, tourism, and culture industry independent of whether firms had revenue losses.

Expectedly, the nominal dummy variable of revenue changes shows a lower probability of revenue compensation for firms experiencing unchanged or increased revenues than those observing decreased revenues as a baseline or reference category and, therefore, not visible (Model B2). Interestingly, firms with increased revenues have a slightly higher probability of revenue compensation than firms with unchanged revenues (as the regression estimate is lower for the latter group). It may indicate that firms with increased revenues are in a more favorable position to apply for and negotiate compensation than firms with unchanged revenues, but we emphasize that the difference is marginal. The control variables have non-significant effects on the probability of revenue compensation (which is also the case in the following models).

Models B3 and B4 replicate the two previous models, except that the manufacturing industry (MI) and the consulting, finance, and insurance industry (CI) are merged into one group that we compare with the hospitality, tourism, and culture industry. None of the statistical conclusions are altered compared to the two previous models.

Models B5 and B6 replicate the two previous models, except that we balance firm observations in the merged industries with firm observations in the hospitality, tourism, and culture industry using the coarsened exact matching (CEM) procedure [37]. CEM “prunes” two different groups—in our case, the merged industries and the hospitality, culture, and tourism industry—to become as similar as possible, and King and Nielsen demonstrate that it has better properties than propensity score matching [38]. In our study, we balanced the groups according to firm size and revenue changes, as they were the only significant predictors except for the industry variables. Thus, we divide the firms into five groups according to size, from the smallest to the largest, each including the same number of firms. Also, we divide the firms into three groups according to whether they reported decreased, unchanged, or increased revenues. Altogether, the divisions generate 15 bins or strata (5 * 3 = 15). If a bin or stratum were to include observations from one industry only, they would be excluded as the observations cannot be matched with the observations from the other industry due to their very absence in the stratum (but in our data, all 15 strata included observations from both industries). Next, observations in the 15 strata were weighed according to the number of firms in each industry. For instance, if observations in a stratum are seven for firms in the merged industries and three in the other as the baseline group, each observation in the merged industries is given a weight of 0.43 (3/7). If the opposite is the case in another stratum, each firm in the merged industry is given a 2.3 (7/3) weight. We used the algorithm by Blackwell et al. [39] to execute the CEM, and the weighted regression estimates in Models B5 and B6 based on the “pruned” strata do not alter the statistical conclusions from the previous models. The only exception is that the probability of revenue compensation in the hospitality, tourism, and culture industry is somewhat lower when the sample is matched. We conclude that the probability of receiving revenue compensation is higher in the hospitality, tourism, and culture industry than in the other industries, regardless of revenue losses. (Based on Greene’s [40] limited dependent (LIMDEP) variable model, we also carried out unreported seemingly unrelated probit regression analyses by simultaneously adding and estimating separate models where revenues unchanged and revenues increased are also dependent variables. The independent variables in these unreported analyses were the same as in Models A and B1 (Table 5), and no statistical conclusion was altered concerning the study’s research questions).

5. Conclusions and Implications

In response to the COVID-19 pandemic, the Norwegian Government compensated many firms for revenue losses. The compensation was to be neutral, i.e., determined as a function of revenue losses only, but particular firm- or industry characteristics may have influenced its distribution. Grounding our study in literature streams emphasizing power structures [5,6,7], legitimacy [8,9,10], organizational slack [11], and taken-for-grantedness [12,13], we, therefore, investigated if firm size and industry sector influenced the compensation distribution beyond the amendment of revenue losses.

5.1. Discussion of the Findings in Light of Different Literature Streams

Survey data of 599 firms across three different industries showed that in the wake of COVID-19, large firms had a relatively high probability of receiving revenue compensation from the Norwegian Government, although they were not hit harder by the pandemic than smaller firms. We attribute this finding to research emphasizing that large firms are powerful [5,6,7], are not exposed to “liability of smallness” and lack of legitimacy [8,9,10], and likely have organizational slack [11]. We argue that large firms can have used those characteristics as vehicles to apply for monetary compensation successfully.

Secondly, we found that firms in the hospitality, tourism, and culture industry had a relatively high probability of monetary compensation. A likely explanation is that they, indeed, were hit hard by the pandemic, which is also shown in other research [24,25,26,27,28]. However, the probability of compensation was high in this industry, even when accounting and controlling for revenue changes. This implies that firms in the hospitality, tourism, and culture industry had a relatively high probability of compensation regardless of whether they were hit hard. Possible explanations of the finding are institutional factors, e.g., cognitive legitimacy [8,9,10] and taken-for-grantedness [12,13]. In other words, the industry may have been prone to compensation beyond a de facto probability of revenue losses due to an overall assumption that they were generally hit hard by COVID-19, which was chiefly the case but not always. Altogether, large firms’ high probability of revenue compensation was likely due to their resources to apply for funding successfully, while institutional factors, for instance, cognitive legitimacy and taken-for-grantedness, likely explained the high probability of compensation in the hospitality, tourism, and culture industry.

5.2. Practical Implications, Limitations, and Future Research

Our study indicates that the government revenue compensation after COVID-19 was not always tailored to the most intended candidates. Accordingly, a practical implication is that the Government needs to develop efficient support schemes and coherent systems for monitoring and policy learning if similar compensation systems are to be used in the future [41]. Moreover, as we find that taxpayer money is not always spent according to its intention, in the future, one needs to evaluate the use of such programs altogether if scenarios similar to those experienced during the COVID-19 pandemic were to occur. For example, it is now debated in Norway whether the Government should provide firms compensation due to high electricity prices, but some are skeptical, arguing that the compensation will not be fairly distributed, which is in accordance with our findings [4].

A further practical implication is that policymakers should be aware of the tendency for large firms with legitimacy [8,9,10] and organizational slack [11] to gain easier access to public benefits than small firms can or are willing to. The marketplace for private enterprises should be neutral, where certain firms, such as the large firms in our study, are not put in an advantageous position. In turn, this may skew the competitive advantage in favor of large firms and, hence, be harmful for small and medium-sized firms.

Despite the study being conducted in Norway, we argue that the findings are relevant beyond the current context. That is, we have no strong reasons to believe that firms of different sizes or operating in particular industries would behave substantially differently in another national context than the Norwegian. The reason for our argument is that the theoretical explanations of our findings are generic and not tailored to specific national or cultural contexts.

A limitation of our study is non-response bias, as we do not know whether those responding to the survey are substantially different from those not responding. It may particularly skew the descriptive statistics (Table 3), but also the regressions if the tendency to respond differs substantially across industries, and the association between firm size and the tendency of having received revenue compensation differs substantially among those participating vs. not participating in the survey. To amend the limitation, future research should aim to achieve a higher response rate or apply similar analyses using registerdata.

A further limitation is that we rely on perceptual data concerning revenue losses and compensation. A related limitation is that we use relatively crude measures to study the concepts. Hence, modeling revenue losses and compensation by using register data as continuous variables can amend these limitations, which we encourage scholars to pursue in future research. Another topic for future research is to investigate further potential reasons why the monetary compensation, in the wake of COVID-19, was not always distributed according to what was intended. Through the lenses of different theoretical perspectives, we have discussed potential reasons, but beyond this, we do not know which ones, if any, are the more likely explanations. Also, there may be explanations beyond what we have discussed in this paper, which we encourage future research to look into.