Theoretical Economics Letters, 2024, 14, 1532-1552 https://www.scirp.org/journal/tel ISSN Online: 2162-2086 ISSN Print: 2162-2078 DOI: 10.4236/tel.2024.144077 Aug. 15, 2024 1532 Theoretical Economics Letters Impact of Markup on Profitability Ratios: Evidence from Finland Erkki K. Laitinen School of Accounting and Finance, University of Vaasa, Vaasa, Finland Abstract The objective of the study is to show how the markup affects key ratios of profitability. Markup is an important tool for pricing planning and control, but little research has been done on it from a business management perspec- tive. In this study, a simple mathematical model is developed to demonstrate the connection between markup and profitability ratios. In addition to the markup, key factors influencing the ratios are the average lag between ex- penditures and revenues, which is connected to the company’s expenditure structure and industry. In addition to markup and average lag, key ratios are also affected by the company’s growth. The results of the study are illustrated in artificial data (100 randomized observations) and empirical data (733 Finnish companies). The results show that markup has a strong influence on profitability ratios, but other factors cause considerable fluctuations in the values of the ratios. However, profit margin is affected only by markup. Keywords Markup, Pricing, Profitability Ratios, Finnish Firms 1. Introduction Pricing is one of the most important decisions made by the management. When Finnish companies were asked how strategically important they consider pricing to be on a scale of 1 - 5 (1 = no importance and 5 = extremely important), the average of the answers (n = 206) was 4.34 and the median was 5 (Laitinen, 2009; Laitinen, 2013). Despite the strategic importance of pricing for companies, there seems to be a lack of academic interest in the field of pricing (Avlonitis & In- dounas, 2006). Nagle and Holden (1995) suggested that pricing is the most ne- glected element of the marketing mix. Several researchers have suggested that pricing is the only element of the marketing mix that generates revenue for the How to cite this paper: Laitinen, E. K. (2024). Impact of Markup on Profitability Ratios: Evidence from Finland. Theoretical Economics Letters, 14, 1532-1552. https://doi.org/10.4236/tel.2024.144077 Received: May 10, 2024 Accepted: August 12, 2024 Published: August 15, 2024 Copyright © 2024 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access https://www.scirp.org/journal/tel https://doi.org/10.4236/tel.2024.144077 https://www.scirp.org/ https://doi.org/10.4236/tel.2024.144077 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ E. K. Laitinen DOI: 10.4236/tel.2024.144077 1533 Theoretical Economics Letters company, when other elements of the marketing mix are related to costs (Finch, Becherer, & Casavant, 1998; Potter, 2000; O’Connor, 2003). Pricing is also the most flexible element of the marketing mix, as pricing decisions can be imple- mented relatively quickly and inexpensively (Urbany, 2001). Thus, pricing is a very important element of marketing mix increasing the need for academic re- search on pricing. Factually, pricing is a cornerstone of economic analysis (Chavas & Pagani, 2020). However, pricing is also a controversial issue that has raised a strong debate between economists and accountants (Laitinen, 2013; Lucas, 2003; Lucas & Raf- ferty, 2008). The neoclassical theory of the firm postulates that optimal pricing means to equate marginal product cost MC and marginal revenue MR con- forming to the Amoroso-Robinson rule (Amoroso, 1930; Robinson, 1933). The optimal condition MC = MR implies that the markup is defined as e/(1 + e), where e is the price elasticity of demand. Thus, the pricing theory suggests that the profit-maximizing firms should use marginal cost as the basis of pricing and add a markup following the Amoroso-Robinson rule. Thus, in this case, the profit-maximizing price is determined as MC∙e/(1 + e). Therefore, the ratio of the (marginal gross) profit to the price can be calculated through (P-MC)/P = −1/e which is known as Lerner index. According to the index, the profitability ratio is inversely proportional to the price elasticity of demand. Lerner index is 0 for a perfectly competitive firm that has no market power (profit = 0). Since both output prices and marginal costs are unobservable, researchers have typically estimated markups using data from firm-level financial statements through the expression M = P/MC = b∙Revenue/Cost of goods sold (COGS), where b is the output elasticity of the variable inputs to production estimated using the log production function (Bilyk, Grieder, & Khan, 2023; Konzcal & Lusiani, 2022). The controversial issue, a gap between (neoclassical) theory and practice, has arisen, since pricing practices do not follow the theoretical recommendations for profit maximization (Lucas, 2003; Lucas & Rafferty, 2008). The majority of companies do not use the theoretically recommended marginal pricing based on marginal costs MC, but calculate the price according to the full cost principle, considering all the costs of the product in the price (Fitzpatrick, 1964; Smyth, 1967; Govindarajan & Anthony, 1983; Cunningham & Hornby, 1993; Shim & Sudit, 1995; Avlonitis & Indounas, 2006; Laitinen, 2013). The results from Finn- ish firms also give support to the existence of the gap, since variable (marginal) costing is only used by 14.0% of the firms (Laitinen, 2013). If the full-cost users (periodic and normal capacity) are summed up, the rate of full-cost adopters is 55.3%. In addition, 16.9% of the respondents use activity-based costs that may also refer to full-cost concept (but also in special circumstances, to marginal costs). Therefore, up to 72.1% of the Finnish firms may be full-cost adopters, which is close to the percentage (69.5%) got by Shim and Sudit (1995). Thus, cost accounting systems in practice are mostly not constructed to estimate mar- https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1534 Theoretical Economics Letters ginal costs but full-costs for the products (Drury & Tayles, 2000; Ahmed & Scapens, 2000). The gap between theory and practice can be solved considering the produc- tion of goods as a longer-term process. In the longer perspective, the amount of fixed costs will decrease and, finally, vanish: in the long-term, there are only variable costs. However, the approaches used by researchers and managers to utilize markup information differ significantly from each other. Firstly, re- searchers apply advanced econometric methods to estimate markups which are used to assess the structure of the markets. If product markets are characterised by a lack of competition, firms can charge a markup over their marginal costs and achieve monopoly rents which in the longer term can lead to higher prices and lower output. Secondly, managers use their own cost accounting systems to get an estimate of the full-cost of the product and use markups in pricing to plan and control profitability. However, more research is needed to show the relationship between markup and different profitability ratios. Markup is conceptually closely related to the profit margin that relates the difference between the price and the cost to the price whereas markup relates this difference to the cost. However, it is not clear how markup is related to such profitability measures as cash flow and return on assets. In this study, a simple longer-term model of production is developed and used to investigate the relationship between markup and different profitability ratios. The present model will be a steady approach having similarities with the approach used earlier by Laitinen (2024). Laitinen used the approach to show the impact of expenditure structure on profitability ratios. In this study, the steady framework is applied to investigate the impact of markup on these kinds of profitability ratios. Thus, in summary, the purpose of this study is to present a longer-term pro- cess model of production and use this process model to analyse the characteris- tics of markup. The analysis is concentrated on analysing the relationships be- tween markup and profitability ratios. The theoretical analysis is descriptive and normative analysis (for example, in the form of cost minimization) is not used. The relationships between markup and profitability ratios are also investigated using artificial data of 100 randomized observations and empirical data from 733 Finnish firms to support theoretical analysis. Empirical analysis is not, however, based on advanced econometric methods but utilizes only correlations between markup and profitability ratios. The contents of the paper are as follows. First, the background and the objective of the study were analyzed in this introductory section. In the second section, the process model of production is presented and the relationships between markup and profitability ratios are analyzed. The third section briefly discusses the artificial and empirical data and methods while the fourth section reviews empirical results. Finally, the last section summarizes the findings and outlines topics for further studies. https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1535 Theoretical Economics Letters 2. Framework for the Analysis Let us describe the simplified production and sales process of the firm assuming that the firm manufactures Q products at the unit cost of C in the period 0. These products are all to be sold for the unit price P in this and following peri- ods so that the sales volume follows an infinite geometric distribution by the pa- rameter q. Thus, the proportional markup M for the product is equal to P/C. In this case, the following identity can be used to determine the internal rate of re- turn r for the product to describe its profitability: ( ) ( ) ( ) 0 11 1 1 1 1 tt t rC Q C Q M q q r M q r q ∞ − = + ⋅ = ⋅ ⋅ ⋅ − + → = ⋅ − ⋅ + −∑ (1) where r > 0 and q/(1 + r) < 1 to make the geometric function converge. Thus, in this framework the markup M can be expressed simply as: ( )( ) ( )( ) 1 1 11 1 1 1 1 r q q r rM Aq r q r r + − ⋅ = = + = + ⋅ − + − + + (2) where A = q/(1 − q) is the average lag of the sales distribution. Thus, the markup is a simple function of the average lag A and the internal rate of return r. The longer it takes to sell the products, the higher the markup M of the product has to be raised in order to make the product profitable at r. The result (2) also allows us to show the solution for the internal rate on re- turn r as a function of M and A as follows: ( ) ( ) 1 1 P C Mr C A P C A M − − = = ⋅ − − − − (3) and for standard solutions A > (M − 1) and M > 1 to ensure that r > 0. Thus, the faster the products are sold and the higher the markup (ceteris paribus), the better the profitability of the product. Then, let us include dynamics into the model assuming that the process is growing at a steady rate g over time. Let us denote total product cost in the pe- riod t by CQ(t) and assume that this cost is growing by g periodically. Every pe- riodic total cost CQ(t) is used to manufacture Q(t) units of products which are all sold following the identical geometric sales distribution. For these assump- tions the total periodic revenue PQ(t) can be solved in the following way: ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 0 11 1 1 1 1 1 1 1 1 1 1 1 tt t gPQ t CQ t M q q g CQ t M q g q PQ tr q g r q gCQ t r g q CQ t r g q ∞ − = + = ⋅ ⋅ − + = ⋅ ⋅ − ⋅ + − + − + + − + = ⋅ ⋅ → = ⋅ + + − + + − ∑ (4) This equation shows that the ratio of total revenue to total cost is symmetri- cally related to the internal rate of return r and the steady growth rate g. This ra- tio equals unity if r = g and exceeds unity if r > g. The expression (4) for the periodic total revenue makes it possible to calculate the cash flow margin CFM in the following way: https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1536 Theoretical Economics Letters ( ) ( ) ( ) ( ) ( ) 1 11 1 1 1 PQ t CQ t CQ t r g qCFM PQ t PQ t r q g − + + − = = − = − ⋅ + − + (5) This equation can be simplified further by replacing r by (3) which leads to the following result: ( )( ) 1 1 11 1 11 1 1 g q gCFM AM q g M g  + − = − ⋅ = − ⋅ + ⋅ − + +  (6) This result shows that CFM is simply an increasing function of the markup M but a decreasing function of the average lag A and the steady rate of growth g. If g = 0 or A = 0, CFM = (M − 1)/M. The definition of CFM is based on the expenditure concept that is independ- ent of periodization. However, the calculation of traditional profitability ratios requires that expenses and thus profits are defined in the process model. Because revenues are accumulated following the geometric sales function, it is logical to assume that expenses follow the same distribution. Thus, it is assumed that the value of the assets SQ(t) is obtained by summing up the unexpired portions of the periodic expenditures as follows: ( ) ( )( ) ( ) ( ) ( ) 0 1 11 1 1 v s v s v q gSQ t CQ t g q q CQ t g q ∞ ∞ − = = + + = + − = + −∑ ∑ (7) This result for the assets of the firm can be used to calculate expenses accord- ing to the selected valuation method. The accounting identity between expenses and expenditures says that periodic expenses can be calculated by deducting the change of assets from the periodic expenditure. Thus, the expenses DQ(t) can be calculated through the accounting identity in the following way: ( ) ( ) ( ) ( ) ( ) ( )1 1 gDQ t SQ t SQ t CQ t SQ t CQ tg= − − + = ⋅ + + (8) which leads to the result: ( ) ( ) ( )( )1 1 1 g qDQ t CQ t g q + − = ⋅ + − (9) Thus, the expense concept DQ(t) is a simple function of g and q but inde- pendent of r. The result (9) on the expense concept makes it possible to calculate the profit margin PRM as follows: ( ) ( ) ( ) 1 111 PQ t DQ t r q MPRMPQ t r q M M − ⋅ − = = = − = + − (10) which is independent of g. Thus, using the expensing method where expenses are accumulated at the same rate as revenues, profit margin PRM is determined solely by the markup M. Note that PRM = CFM when the steady growth rate g is equal to zero (or A = 0). When the growth rate is low, expenses and expendi- tures are generally close to each other, which is directly showed by the account- https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1537 Theoretical Economics Letters ing identity (8). The return on investment (assets) ROA is the most widely adopted profitabil- ity ratio. For the expensing method selected above it can be showed that the ratio where assets are defined at the beginning of the year basis is as follows: ( ) ( ) ( ) ( )( ) ( )1 1 11 1 11 1 PQ t DQ t g M MROA r g q gAQ t r q A − + − − = = ⋅ = ⋅ + − = ⋅ + − + (11) This equation shows that ROA is a decreasing function of the average lag A and an increasing function of the steady growth rate g and the markup M. In summary, the present process model of production makes it possible to describe profitability ratios CFM, PRM, and ROA in simple terms as functions of markup M as showed in Equations (6), (10), and (11), respectively. PRM is only dependent of M being closely associated with markup through a simple monotonic (but not linear) transformation. Thus, it is obvious that statistical association to M is highest for PRM. If g = 0, CFM equals PRM. The deviations of CFM from PRM are thus largely due to growth g but also to average lag A. Therefore, it can be hypothesized that the statistical association of markup M is lower to CFM than to PRM. ROA is also closely related to markup M but, in ad- dition, this relationship is affected by growth g and average lag A, which weak- ens statistical association. Therefore, it is expected that the association of markup M to ROA is about as strong as to CFM. In summary, it is expected that statistical association of markup M is strongest to PRM but also strong to CFM and ROA, which are comparable with respect to the strength of association. If growth g is very low, the fluctuations in CFM are small, because then CFM is close to PRM. Therefore, this low growth does not have as strong an effect on the fluctuations of ROA. However, ROA is sensitive to the variations in A, be- cause A acts as a divisor in the ROA formula. 3. Data, Methods and Variables The theoretical results are illustrated in two different data sets, artificial experi- mental data and empirical data from Finnish firms. The purpose of the artificial experiment is to illustrate the connections between markup M and the (artificial) profitability indicators calculated on the basis of derived formulas in random- ized data. The values of the central variables of the model are drawn with the random number generator (randbetween) in the Excel software in such a way that they are uniformly distributed in the given relevant range. The profitability ratios are calculated based on these randomized values. The dependence between the ratios and markup M is investigated using Pearson correlation and Spear- man rank correlation coefficients. The goal is to evaluate the direction (correla- tions) and intensity (graphical analysis) of the variation in the ratios with respect to M. The variables of the theoretical model are ranged so that markup M var- ies randomly between 1.00 and 1.20, average lag A between 0.25 and 2.5, and growth g between 0.00 and 0.10. These ranges are consistent with the results https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1538 Theoretical Economics Letters from the empirical data. Internal rate of interest r is calculated based on the val- ues of variables M and A according to Equation (3). The experiment includes a total of 100 observations which are randomly drawn for analysis. The experi- mental data are presented in Appendix 1. The experimental data allows us only to use artificial observations of markup and profitability ratios based on uniformly distributed artificial variables which are assumed independent. Therefore, the relationships between markup M and profitability ratios are also analyzed using financial statement data from Finnish firms. The mathematical analysis showed that the relationships between M and the three ratios is expected to be strong but is it an empirical question of how strong they are in reality. The present mathematical model is based on assump- tions related to a steady state and steady long-term growth which usually ap- proximately hold only for larger firms. Therefore, empirical evidence for this study is gathered from a sample of middle-sized and large companies having longer time-series of successive financial statements publicly available (see Laitinen, 2024). The sample is extracted from the ORBIS database of van Dijk (BvD) (Bureau van Dijk, 2023). ORBIS includes financial and other information on more than 489 million companies across the globe. ORBIS captures and blends data from more than 170 different sources and makes the data standardized and compara- ble. In extracting the sample, a restriction that the selected company must be Finnish and employ more than 50 employees was applied. In addition, the se- lected company must have a longer time series of total expenditure and net sales available, to estimate the long-term growth rate. The sample was extracted from a period before the COVID-19 pandemic to avoid its potential effects on the data on M (see Bilyk, Grieder, & Khan, 2023). The sample selected in this way con- sisted of 957 limited companies. However, the companies which did not fulfil the convergence conditions A > M-1 and M > 1 in (3) were deleted, so that the final sample includes 733 companies. The following series shows the stages in the sampling: Orbis data base Finnish firms More than 50 employees Longer time series Preliminary sample: 957 firms Convergence conditions Final sample: 733 firms The final sample mainly consist of limited private firms (88.1%) and there are only few limited public firms (11.9%). The size distribution of the sample com- panies is skewed, since the average number of employees is 938 whereas the me- dian is only 219. The average net sales of the companies are 286.388 Teur the median being only 55.010 Teur. The average total assets in the sample are 278.357 Teur while the median is 32.454 Teur. The industrial classification of the https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1539 Theoretical Economics Letters sample companies is presented in Appendix 2. Most companies are from man- ufacturing (36.4%), wholesale and retail trade (17.2%), professional, scientific and technical activities (7.8%), or information and communication (7.1%) in- dustries. The sample is with respect to size and industrial distribution quite rep- resentative sample of Finnish middle-sized and large companies. The variables of the analyses were calculated following the theoretical con- cepts. Total expenditure (expense) is defined as the sum of current and fixed ex- penditures (expenses) following the accounting identity (9). The total revenue concept was measured by net sales. Then, the three profitability ratios were cal- culated as follows: CFM = (Total revenue − Total expenditure)/Total revenue PMR = (Total revenue − Total expense)/Total revenue = EBIT/Total revenue ROA = (Total revenue − Total expense)/Total assets =EBIT/Total assets In these definitions, total revenue (net sales) does not include other revenue and total expense does not include interest expenses or taxes. Total assets in ROA are defined on the beginning of the year basis. The variables markup M, (long-term) growth g, the average lag A, and the in- ternal rate of return r are approximated following simple procedures. First, markup M is calculated through (10) as M = 1/(1 − PMR). Secondly, estimates of long-term growth rate in total revenue and total expenditures are estimated ap- plying the regression analysis on nine-year time series. The final estimate of g is calculated as a weighted sum of these estimates using the sum of time-series as weights. Third, the average lag A is approximated using the multiple of total assets to total expenditure. In the theoretical framework, it is showed in (7) that the ratio of total assets SQ(t) to total expenditure CQ(t) can be used to approximate first q and further A in the following way: ( ) ( ) ( ) ( )1 1: 1 1 1 SQ t q g V g qV q ACQ t g q V g q + + = = → = → = + − + + − (12) where V is defined as the ratio of total assets to total expenditure. Fourth, internal rate of return r is approximated through (3) using the esti- mates of M and A. The estimates of q, A, and r are only rough approximations (see Laitinen & Laitinen, 2022). For the statistical analyses, all model variables are winzorized at the 2.5/97.5 percentiles level to minimize the impact of ex- treme outliers on the results. The effect of M on profitability ratios is analyzed in the same way as in the artificial data using correlations and graphical analysis. 4. Empirical Results Table 1 presents descriptive statistics for the artificial data of 100 randomized observations. Because the variables M, A, and g are drawn from the uniform dis- tributions, skewness and kurtosis for these variables are quite low. However, in- ternal rate of return r estimate that is calculated through (3) using these three es- https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1540 Theoretical Economics Letters timates has a high kurtosis. In general, the estimates of r are higher than the es- timates of g due to the range constraint set on g. The values of ROA are however comparable with those of r. The lower quartile of CFM is negative due to the observations where g exceeds r, which is theoretically shown by (5). The average M is in this experiment only 1.094 that is below the values got for markup in empirical studies. However, markup is here added to total cost, not on marginal cost which makes it smaller. Table 1. Descriptive statistics for the artificial experiment (uniform distributions) (n = 100). Mean Std. Deviation Skewness Kurtosis Quartiles 25 50 75 M 1.094 0.059 0.176 −1.055 1.043 1.090 1.140 A 1.259 0.662 0.250 −1.210 0.665 1.130 1.858 g 0.050 0.032 −0.106 −1.236 0.020 0.050 0.080 r 0.121 0.129 2.313 7.067 0.049 0.079 0.158 CFM 0.027 0.065 −0.604 1.053 −0.005 0.029 0.065 PRM 0.083 0.049 0.029 −1.074 0.041 0.083 0.123 ROA 0.103 0.091 1.574 2.759 0.049 0.077 0.137 Table 2 and Table 3 present the Pearson and Spearman rank correlation coef- ficients for the artificial data. The statistical results support expectations, since markup M is almost perfectly correlated with PRM (0.999) when the Pearson correlation is considered. The transformation (10) between M and PRM is not linear but it is monotonic making the Spearman rank correlation equal unity (1.000). The Pearson and rank correlations of M to CFM (0.643 & 0.656) and ROA (0.589 & 0.724) are consistent with the expectations. These correlations are high and comparable with each other. M is not strongly correlated with the av- erage lag A or the growth rate g, since Pearson and rank correlations to A are 0.178 & 0.185 and to g 0.130 & 0.124. However, is closely positively correlated with internal rate of return r (0.531 & 0.721). This rate r is strongly positively correlated with all profitability ratios, especially with ROA (0.988 & 0.998). These results are also consistent with expectations, since r is often in financial analysis used as a surrogate of actual profitability. Table 2. Pearson correlations for the artificial experiment (n = 100). M A g r CFM PRM ROA M 1.000 0.178 0.130 0.531 0.643 0.999 0.589 p-value . 0.077 0.196 <0.001 <0.001 <0.001 <0.001 A 0.178 1.000 0.166 −0.509 −0.424 0.177 −0.523 p-value 0.077 . 0.098 <0.001 <0.001 0.078 <0.001 https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1541 Theoretical Economics Letters Continued g 0.130 0.166 1.000 0.052 −0.456 0.128 0.067 p-value 0.196 0.098 . 0.605 <0.001 0.206 0.508 r 0.531 −0.509 0.052 1.000 0.652 0.533 0.988 p-value <0.001 <0.001 0.605 . <0.001 <0.001 <0.001 CFM 0.643 −0.424 −0.456 0.652 1.000 0.644 0.697 p-value <0.001 <0.001 <0.001 <0.001 . <0.001 <0.001 PRM 0.999 0.177 0.128 0.533 0.644 1.000 0.593 p-value <0.001 0.078 0.206 <0.001 <0.001 . <0.001 ROA 0.589 −0.523 0.067 0.988 0.697 0.593 1.000 p-value <0.001 <0.001 0.508 <0.001 <0.001 <0.001 . Note: p-value refers to 2-tailed significance. Table 3. Spearman rank correlations for the artificial experiment (n = 100). M A g r CFM PRM ROA M 1.000 0.185 0.124 0.721 0.656 1.000 0.724 p-value . 0.066 0.219 <0.001 <0.001 . <0.001 A 0.185 1.000 0.148 −0.483 −0.380 0.185 −0.477 p-value 0.066 . 0.142 <0.001 <.001 0.066 <0.001 g 0.124 0.148 1.000 0.027 −0.446 0.124 0.063 p-value 0.219 0.142 . 0.791 <0.001 0.219 0.534 r 0.721 −0.483 0.027 1.000 0.813 0.721 0.998 p-value <0.001 <0.001 0.791 . <.001 <0.001 <0.001 CFM 0.656 −0.380 −0.446 0.813 1.000 0.656 0.789 p-value <0.001 <0.001 <0.001 <0.001 . <0.001 <0.001 PRM 1.000 0.185 0.124 0.721 0.656 1.000 0.724 p-value . 0.066 0.219 <0.001 <0.001 . <0.001 ROA 0.724 −0.477 0.063 0.998 0.789 0.724 1.000 p-value <0.001 <0.001 0.534 <0.001 <0.001 <0.001 . Note: p-value refers to 2-tailed significance. Figures 1(a)-(c) show graphically the relationship of M to profitability ratios CFM, PRM, and ROA when M is sorted in order of size from smallest (1.00) to largest (1.20). For all ratios, the relationship is increasing as expected. For PRM, the relationship is depicted by almost a straight line. For the ratio CFM, the fluctuations are regular and occur in a limited area. The size of the fluctuations remains approximately constant as M increases. The fluctuations in ROA are however strong and their size increases as M increases. The differences in the fluctuations in ROA and CFM on M are due to the fact that A and g affect the values of these profitability ratios in different ways. For CFM, the effect of A and g is made low by the low values of growth. https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1542 Theoretical Economics Letters Figure 1. Profitability ratios CFM, PRM, and ROA when markup M in rank order goes from 1.00 to 1.20 (artificial experiment data). Table 4 presents descriptive statistics for the empirical data, which is based on the financial statements of 733 Finnish companies. In this data, the average of markup M is even lower than in the artificial material, as the median is only 1.048. The growth of companies is exceptionally slow, often even negative, and https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1543 Theoretical Economics Letters profitability does not rise very high. Thus, the research period is characterized by low growth and low profitability in Finnish firms. The distribution of internal rate of return r is consistent (comparable) with the distribution of the profitabil- ity ratio ROA. The distributions of variables M, A and r show clear skewness and kurtosis. Due to the low growth of companies, the values of CFM are mostly positive. However, there are also plenty of negative values (10%), which shows that in several companies the growth rate g exceeds the internal interest rate r as (5) indicates. Table 4. Descriptive statistics of the actual empirical sample (n = 733). Mean Std. Deviation Skewness Kurtosis Quartiles 25 50 75 M 1.073 0.075 1.975 3.880 1.024 1.048 1.093 A 0.867 0.767 2.749 8.235 0.435 0.637 0.979 g 0.025 0.050 0.193 0.096 −0.005 0.025 0.055 r 0.122 0.150 3.291 13.247 0.041 0.079 0.143 CFM 0.059 0.088 0.559 1.736 0.014 0.048 0.099 PRM 0.064 0.058 1.607 2.353 0.024 0.046 0.085 ROA 0.098 0.088 1.683 2.849 0.039 0.072 0.131 Table 5 and Table 6 show Pearson and Spearman correlation coefficients for the empirical data. These correlations show the obvious result that the markup M is almost perfectly or perfectly (Spearman correlation) correlated with the ra- tio PRM with which it has a monotonic relationship. The Pearson correlation coefficient between these variables is 0.981 whereas the rank correlation is 1.000. Markup M is also strongly connected to other profitability ratios CFM (0.597 & 0.587) and especially ROA (0.641 & 0.730). The correlation of M with the inter- nal rate of return r is also strong (0.645 & 0.734), whereas its correlation with growth rate g is low (0.043 & 0.081). The internal rate of return r also has a strong dependence on the ratios of profitability, especially on the ratios ROA and PRM. Markup M also has a significant correlation with average lag A (0.469 & 0.480). Table 5. Pearson correlations for the actual empirical sample (n = 733). M A g r CFM PRM ROA M 1.000 0.469 0.043 0.645 0.597 0.981 0.641 p-value . <0.001 0.241 <0.001 <0.001 0.000 <0.001 A 0.469 1.000 −0.061 −0.177 0.259 0.460 −0.194 p-value <0.001 . 0.100 <0.001 <0.001 <0.001 <0.001 g 0.043 −0.061 1.000 0.098 −0.088 0.054 0.164 p-value 0.241 0.100 . 0.008 0.017 0.147 <0.001 https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1544 Theoretical Economics Letters Continued r 0.645 −0.177 0.098 1.000 0.442 0.658 0.985 p-value <0.001 <0.001 0.008 . <0.001 <0.001 0.000 CFM 0.597 0.259 −0.088 0.442 1.000 0.538 0.429 p-value <0.001 <0.001 0.017 <0.001 . <0.001 <0.001 PRM 0.981 0.460 0.054 0.658 0.538 1.000 0.663 p-value 0.000 <0.001 0.147 <0.001 <0.001 . <0.001 ROA 0.641 −0.194 0.164 0.985 0.429 0.663 1.000 p-value <0.001 <0.001 <0.001 0.000 <0.001 <0.001 . Note: p-value refers to 2-tailed significance. Table 6. Spearman rank correlations for the actual empirical sample (n = 733). M A g r CFM PRM ROA M 1.000 0.480 0.081 0.734 0.587 1.000 0.730 p-value . <0.001 0.029 <0.001 <0.001 . <0.001 A 0.480 1.000 −0.042 −0.154 0.285 0.469 −0.156 p-value <0.001 . 0.252 <0.001 <0.001 <0.001 <0.001 g 0.081 −0.042 1.000 0.141 −0.072 0.092 0.192 p-value 0.029 0.252 . <0.001 0.053 0.013 <0.001 r 0.734 −0.154 0.141 1.000 0.442 0.738 0.998 p-value <0.001 <0.001 <0.001 . <0.001 <0.001 0.000 CFM 0.587 0.285 −0.072 0.442 1.000 0.561 0.433 p-value <0.001 <0.001 0.053 <0.001 . <0.001 <0.001 PRM 1.000 0.469 0.092 0.738 0.561 1.000 0.736 p-value . <0.001 0.013 <0.001 <0.001 . <0.001 ROA 0.730 −0.156 0.192 0.998 0.433 0.736 1.000 p-value <0.001 <0.001 <0.001 0.000 <0.001 <0.001 . Note: p-value refers to 2-tailed significance. Figures 2(a)-(c) show graphically the relationship of M to profitability ratios CFM, PRM, and ROA in the empirical data when M is sorted in order of size from smallest (1.00) to largest (1.35). The empirical connection between markup M and ratio PRM is, as can be expected from a theoretical relationship, almost linear, and there are no fluctuations, because PRM depends (monotonically) on- ly on M. From the graphs of ratios CFM and ROA, it can be seen that the de- pendence between them and M is anyway positive, but there are very strong fluctuations in the values of both ratios. The oscillations in CFM start at full scale as soon as M starts increasing from unity upwards. They remain large as the values of M increase. In the same way, there are also strong fluctuations in the values of ratio ROA, but not quite at the lowest values of M. The fluctuations of both ratios are clearly stronger than in the artificial data. https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1545 Theoretical Economics Letters Figure 2. Profitability ratios CFM, PRM, and ROA when markup M in rank order goes from 1.00 to 1.35 (empirical data). 5. Conclusion In this study, the focal point is markup, which in this context means the relative difference between the selling price and the unit (full) cost, the relative margin. https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1546 Theoretical Economics Letters Markup-related research has been done from two different perspectives. First of all, econometricians have estimated the markup from the company’s financial statement data and used it to evaluate the competitive situation in the market. The analysis has been based on profit maximization (or cost minimization), where the markup is evaluated using the difference between revenues and mar- ginal costs. Secondly, company managers have used markup as a support for pricing, in which case markup is evaluated as the difference between the selling price and the unit cost of the products. The managerial perspective on the markup is very important, but it has been scientifically studied very little and almost all the research done is based on an econometric perspective. This study concentrated on the managerial perspective of markup. The aim of the study was to evaluate the impact of markup on profitability ratios using a business managerial approach. Expressed more precisely, the goal of the study was to derive a longer-term process model that could be used to evaluate the re- lationship between the markup and key profitability ratios. This process model is a simple mathematical model that does not include optimization. The model is descriptive and its purpose is to describe the markup and its effect on profitabil- ity figures. Using the model, the analysis was simplified. Profitability ratios were derived based on the structure of the model and the importance of the markup on these ratios was evaluated. Mathematical results derived from the model were illustrated using artificial randomized data and empirical data. The results were evaluated using simple statistical methods. The study discussed the effect of markup on three profitability ratios, the cash flow margin, profit margin and return on (investment) assets. For key ratios, the relationship between profit margin and markup is obvious, because profit mar- gin is a simple and monotonous transformation of markup. Markup thus has a direct and immediate effect on the profit margin, although the relationship is not linear. The relationship of the cash flow margin to the markup is also posi- tive, but the ratio is also affected by the lag between expenditures and revenues (negatively) and growth (negatively). In the same way, the relationship between markup and return on assets is broadly positive, but the relationship is also af- fected by the lag (negatively) and growth (positively). For the sake of the model, the connection between the markup and key profitability ratios is simple and easy to interpret. The relationship between markup and profitability ratios was also investigated using artificial data (100 randomized observations) and empirical data (733 Finnish companies). The results show that there is a statistically significant cor- relation between the ratios and the markup. As indicated by the theoretical model, the relationship between markup and profit margin is monotonic, so there was a perfect correlation between the variables in both datasets as evaluat- ed by the rank correlation. The ratios return on assets and cash flow margin have a strong correlation with the markup. In particular, return on assets has a high correlation with the markup and also with the internal rate of return, which re- https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1547 Theoretical Economics Letters flects true profitability. Finally, graphical examinations showed that return on assets and cash flow margin have a positive dependence on the markup. Howev- er, both ratios have considerable fluctuations due to the influence of other fac- tors. The relationship between profit margin and markup is described by an al- most linear straight line. The study produced interesting results on the relationship between profitabil- ity ratios and markup. However, several limitations have been made in the study, which weaken the generalizability of the results. The mathematical model de- veloped in the research is simple and assumes that the company lives in a steady state situation. The model describes the lag structure between expenditures and revenues simply by means of an infinite geometric series. In the future, the mod- el can be more complicated and made more realistic, for example by abandoning the steady state assumption and using other lag structures. Artificial data and empirical data consisting of Finnish companies were used as materials in this study. In the future, in the creation of artificial data, an advanced methodology related to experiments can be used. Moreover, the empirical data can be ex- panded to data from other countries than Finland. In addition to that, more ad- vanced empirical methods can be used to analyze the data instead of the simple methods (correlations) used in this work. Conflicts of Interest The author declares no conflicts of interest regarding the publication of this pa- per. References Ahmed, M. N., & Scapens, R. W. (2000). Cost Allocation in Britain: Towards an Institu- tional Analysis. European Accounting Review, 9, 159-204. https://doi.org/10.1080/09638180050129864 Amoroso, L. (1930). La curva statica di offerta. Giornale Degli Economisti, 70, 10-46. Avlonitis, G. J., & Indounas, K. A. (2006). Pricing Practices of Service Organizations. Journal of Services Marketing, 20, 346-356. https://doi.org/10.1108/08876040610679954 Bilyk, O., Grieder, T., & Khan, M. (2023). Markups and Inflation during the COVID-19 Pandemic. Bank of Canada, Staff Analytical Note/Note Analytique du Personnel-2023-8. Bureau van Dijk (2023). Orbis Is a Growing Database of Companies and Other Entities. https://www.bvdinfo.com/en-gb/our-products/data/international/orbis Chavas, J., & Pagani, E. (2020). On Nonlinear Pricing. Theoretical Economics Letters, 10, 1213-1226. https://doi.org/10.4236/tel.2020.106073 Cunningham, D., & Hornby, W. (1993). Pricing Decision in Small Firms: Theory and Practice. Management Decision, 31, 46-55. https://doi.org/10.1108/00251749310046765 Drury, C., & Tayles, M. (2000). Cost System Design and Profitability Analysis in UK Companies. The Chartered Institute of Management Accountants. Fitzpatrick, A. (1964). Pricing Methods of Industry. Pruett Press. Govindarajan, V., & Anthony, R. N. (1983). How Firms Use Cost Data in Price Decisions. https://doi.org/10.4236/tel.2024.144077 https://doi.org/10.1080/09638180050129864 https://doi.org/10.1108/08876040610679954 https://www.bvdinfo.com/en-gb/our-products/data/international/orbis https://doi.org/10.4236/tel.2020.106073 https://doi.org/10.1108/00251749310046765 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1548 Theoretical Economics Letters Management Accounting, 65 (July), 30-31, 34-36. Howard Finch, J., Becherer, R. C., & Casavant, R. (1998). An Option-Based Approach for Pricing Perishable Services Assets. Journal of Services Marketing, 12, 473-483. https://doi.org/10.1108/08876049810242759 Konzcal, M., & Lusiani, N. (2022). Prices, Profits, and Power: An Analysis of 2021 Firm-Level Markups. Roosevelt Institute. Laitinen, E. K. (2009). From Complexities to the Rules of Thumb: Towards Optimisation in Pricing Decisions. International Journal of Applied Management Science, 1, 340-366. https://doi.org/10.1504/ijams.2009.026197 Laitinen, E. K. (2013). The Sophisticated Survey as a Research Method to Explore Man- agement Accounting Practices: The Reality Gap in Pricing. International Journal of Management Accounting Research (IJMAR), 2, 1-52. Laitinen, E. K. (2024). Effect of Expenditure Structure on Profitability Ratios. Theoretical Economics Letters, 14, 576-596. https://doi.org/10.4236/tel.2024.142031 Laitinen, E. K., & Laitinen, T. (2022). Timing of Revenues and Expenses: Evidence from Finland. Theoretical Economics Letters, 12, 712-741. https://doi.org/10.4236/tel.2022.123040 Lucas, M. R. (2003). Pricing Decisions and the Neoclassical Theory of the Firm. Man- agement Accounting Research, 14, 201-217. https://doi.org/10.1016/s1044-5005(03)00044-1 Lucas, M., & Rafferty, J. (2008). Cost Analysis for Pricing: Exploring the Gap between Theory and Practice. The British Accounting Review, 40, 148-160. https://doi.org/10.1016/j.bar.2007.11.002 Nagle, T. T., & Holden, R. K. (1995). The Strategy and Tactics of Pricing. Prentice-Hall. O’Connor, P. (2003). Online Pricing: An Analysis of Hotel-Company Practices. The Cornell Hotel and Restaurant Administration Quarterly, 44, 88-96. https://doi.org/10.1016/s0010-8804(03)90049-8 Potter, D. V. (2000). Discovering Hidden Pricing Power. Business Horizons, 43, 41-48. https://doi.org/10.1016/s0007-6813(00)80021-x Robinson, J. (1933). The Economics of Imperfect Competition. MacMillan. Shim, E., & Sudit, E. F. (1995). How Manufacturers Price Products. Management Ac- counting, 8, 37-39. Smyth, R. L. (1967). A Price-Minus Theory of Cost? Scottish Journal of Political Econo- my, 14, 110-117. https://doi.org/10.1111/j.1467-9485.1967.tb00761.x Urbany, J. E. (2001). Justifying Profitable Pricing. Journal of Product & Brand Manage- ment, 10, 141-159. https://doi.org/10.1108/10610420110395386 https://doi.org/10.4236/tel.2024.144077 https://doi.org/10.1108/08876049810242759 https://doi.org/10.1504/ijams.2009.026197 https://doi.org/10.4236/tel.2024.142031 https://doi.org/10.4236/tel.2022.123040 https://doi.org/10.1016/s1044-5005(03)00044-1 https://doi.org/10.1016/j.bar.2007.11.002 https://doi.org/10.1016/s0010-8804(03)90049-8 https://doi.org/10.1016/s0007-6813(00)80021-x https://doi.org/10.1111/j.1467-9485.1967.tb00761.x https://doi.org/10.1108/10610420110395386 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1549 Theoretical Economics Letters Appendices Appendix 1. Artificial experiment data used in the study. Case M A g r CFM PRM ROI 1 1.130 0.590 0.010 0.283 0.110 0.115 0.223 2 1.190 1.090 0.000 0.211 0.160 0.160 0.174 3 1.110 0.500 0.000 0.282 0.099 0.099 0.220 4 1.110 0.920 0.050 0.136 0.060 0.099 0.126 5 1.060 0.780 0.010 0.083 0.049 0.057 0.078 6 1.000 1.260 0.090 0.000 −0.104 0.000 0.000 7 1.110 1.450 0.030 0.082 0.061 0.099 0.078 8 1.040 0.520 0.000 0.083 0.038 0.038 0.077 9 1.020 1.120 0.050 0.018 −0.033 0.020 0.019 10 1.150 0.680 0.020 0.283 0.119 0.130 0.225 11 1.090 1.360 0.090 0.071 −0.020 0.083 0.072 12 1.070 1.440 0.040 0.051 0.014 0.065 0.051 13 1.020 1.710 0.070 0.012 −0.090 0.020 0.013 14 1.080 1.850 0.070 0.045 −0.038 0.074 0.046 15 1.070 0.950 0.020 0.080 0.048 0.065 0.075 16 1.070 0.970 0.070 0.078 0.006 0.065 0.077 17 1.000 0.880 0.000 0.000 0.000 0.000 0.000 18 1.170 1.110 0.020 0.181 0.127 0.145 0.156 19 1.080 0.490 0.010 0.195 0.070 0.074 0.165 20 1.030 1.870 0.000 0.016 0.029 0.029 0.016 21 1.110 2.200 0.080 0.053 −0.048 0.099 0.054 22 1.140 1.920 0.020 0.079 0.090 0.123 0.074 23 1.170 2.110 0.060 0.088 0.043 0.145 0.085 24 1.170 0.740 0.090 0.298 0.093 0.145 0.250 25 1.080 0.570 0.050 0.163 0.049 0.074 0.147 26 1.200 0.900 0.070 0.286 0.118 0.167 0.238 27 1.140 0.320 0.090 0.778 0.100 0.123 0.477 28 1.050 2.300 0.010 0.022 0.026 0.048 0.022 29 1.040 0.890 0.000 0.047 0.038 0.038 0.045 30 1.120 2.360 0.050 0.054 0.007 0.107 0.053 31 1.170 0.720 0.090 0.309 0.094 0.145 0.257 32 1.200 0.610 0.070 0.488 0.133 0.167 0.351 33 1.160 0.660 0.070 0.320 0.101 0.138 0.259 34 1.120 1.960 0.060 0.065 0.008 0.107 0.065 35 1.190 2.280 0.060 0.091 0.051 0.160 0.088 36 1.080 0.480 0.020 0.200 0.065 0.074 0.170 37 1.030 0.640 0.040 0.049 0.005 0.029 0.049 https://doi.org/10.4236/tel.2024.144077 E. 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Laitinen DOI: 10.4236/tel.2024.144077 1550 Theoretical Economics Letters Continued 38 1.090 1.110 0.080 0.088 0.007 0.083 0.088 39 1.070 1.510 0.090 0.049 −0.051 0.065 0.051 40 1.020 1.960 0.050 0.010 −0.072 0.020 0.011 41 1.080 2.040 0.050 0.041 −0.016 0.074 0.041 42 1.180 1.080 0.040 0.200 0.117 0.153 0.173 43 1.020 1.460 0.090 0.014 −0.099 0.020 0.015 44 1.050 0.780 0.000 0.068 0.048 0.048 0.064 45 1.100 0.710 0.000 0.164 0.091 0.091 0.141 46 1.120 0.390 0.020 0.444 0.100 0.107 0.314 47 1.200 2.100 0.080 0.105 0.037 0.167 0.103 48 1.100 1.360 0.060 0.079 0.021 0.091 0.078 49 1.000 0.420 0.080 0.000 −0.031 0.000 0.000 50 1.200 2.060 0.030 0.108 0.117 0.167 0.100 51 1.160 1.610 0.060 0.110 0.059 0.138 0.105 52 1.040 2.340 0.040 0.017 −0.048 0.038 0.018 53 1.020 0.290 0.100 0.074 −0.006 0.020 0.076 54 1.140 1.140 0.030 0.140 0.094 0.123 0.126 55 1.060 0.250 0.100 0.316 0.035 0.057 0.264 56 1.120 2.500 0.100 0.050 −0.096 0.107 0.053 57 1.040 0.350 0.080 0.129 0.014 0.038 0.123 58 1.110 1.580 0.030 0.075 0.058 0.099 0.072 59 1.050 1.510 0.090 0.034 −0.071 0.048 0.036 60 1.140 1.200 0.070 0.132 0.054 0.123 0.125 61 1.180 0.550 0.080 0.486 0.118 0.153 0.353 62 1.110 2.030 0.030 0.057 0.046 0.099 0.056 63 1.000 2.420 0.100 0.000 −0.220 0.000 0.000 64 1.160 1.710 0.080 0.103 0.029 0.138 0.101 65 1.130 0.570 0.050 0.295 0.091 0.115 0.239 66 1.070 2.340 0.050 0.031 −0.039 0.065 0.031 67 1.090 1.800 0.000 0.053 0.083 0.083 0.050 68 1.000 0.870 0.060 0.000 −0.049 0.000 0.000 69 1.170 2.190 0.050 0.084 0.056 0.145 0.082 70 1.130 1.370 0.080 0.105 0.025 0.115 0.102 71 1.010 1.050 0.000 0.010 0.010 0.010 0.010 72 1.060 1.000 0.000 0.064 0.057 0.057 0.060 73 1.150 0.480 0.020 0.455 0.122 0.130 0.319 74 1.090 1.220 0.100 0.080 −0.019 0.083 0.081 75 1.070 2.420 0.050 0.030 −0.042 0.065 0.030 76 1.070 0.430 0.030 0.194 0.054 0.065 0.168 77 1.010 1.660 0.000 0.006 0.010 0.010 0.006 https://doi.org/10.4236/tel.2024.144077 E. 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Laitinen DOI: 10.4236/tel.2024.144077 1551 Theoretical Economics Letters Continued 78 1.170 0.840 0.040 0.254 0.118 0.145 0.210 79 1.030 1.860 0.060 0.016 −0.073 0.029 0.017 80 1.190 1.750 0.090 0.122 0.038 0.160 0.118 81 1.050 0.550 0.100 0.100 0.000 0.048 0.100 82 1.020 0.290 0.010 0.074 0.017 0.020 0.070 83 1.070 1.940 0.090 0.037 −0.084 0.065 0.039 84 1.020 0.440 0.040 0.048 0.003 0.020 0.047 85 1.140 1.830 0.070 0.083 0.018 0.123 0.082 86 1.190 1.930 0.020 0.109 0.128 0.160 0.100 87 1.080 1.390 0.060 0.061 0.001 0.074 0.061 88 1.100 0.650 0.050 0.182 0.063 0.091 0.162 89 1.090 1.740 0.030 0.055 0.036 0.083 0.053 90 1.070 1.340 0.040 0.055 0.017 0.065 0.054 91 1.010 1.050 0.010 0.010 0.000 0.010 0.010 92 1.060 0.840 0.070 0.077 0.005 0.057 0.076 93 1.040 0.740 0.010 0.057 0.031 0.038 0.055 94 1.160 2.050 0.100 0.085 −0.023 0.138 0.086 95 1.020 0.260 0.080 0.083 0.001 0.020 0.083 96 1.100 1.430 0.090 0.075 −0.016 0.091 0.076 97 1.120 2.340 0.090 0.054 −0.065 0.107 0.056 98 1.180 2.430 0.070 0.080 0.018 0.153 0.079 99 1.080 0.410 0.050 0.242 0.056 0.074 0.205 100 1.040 0.760 0.020 0.056 0.024 0.038 0.054 Appendix 2. Industrial distribution of the sample firms (n = 733). Frequency Percent Industry 6 0.80 A Agriculture, forestry and fishing 01 - 03 3 0.40 B Mining and quarrying 05 - 09 267 36.40 C Manufacturing 10 - 33 19 2.60 D Electricity, gas, steam and air conditioning supply 35 4 0.50 E Water supply; sewerage, waste management and remediation activities 36 - 39 39 5.30 F Construction 41 - 43 126 17.20 G Wholesale and retail trade; repair of motor vehicles and motorcycles 45 - 47 41 5.60 H Transportation and storage 49 - 53 19 2.60 I Accommodation and food service activities 55 - 56 52 7.10 J Information and communication 58 - 63 39 5.30 K Financial and insurance activities 64 - 66 7 1.00 L Real estate activities 68 https://doi.org/10.4236/tel.2024.144077 E. K. Laitinen DOI: 10.4236/tel.2024.144077 1552 Theoretical Economics Letters Continued 57 7.80 M Professional, scientific and technical activities 69 - 75 27 3.70 N Administrative and support service activities 77 - 82 6 0.80 P Education 85 10 1.40 Q Human health and social work activities 86 - 88 4 0.50 R Arts, entertainment and recreation 90 - 93 7 1.00 S Other service activities 94 - 96 733 100.00 In all https://doi.org/10.4236/tel.2024.144077 Impact of Markup on Profitability Ratios: Evidence from Finland Abstract Keywords 1. Introduction 2. Framework for the Analysis 3. Data, Methods and Variables 4. Empirical Results 5. Conclusion Conflicts of Interest References Appendices Appendix 1. Artificial experiment data used in the study. Appendix 2. Industrial distribution of the sample firms (n = 733).