Keywords

Firm Size, profitability, GLS, ROA, ROE.

Introduction

The firm size is important to indicate the market power of company. Based on signaling theory; the greater the firm size will give a positive signal to the public or market which means the company has better financial performance (Meiryani et al., 2020). Firm size can indicate that the company is experiencing growth so that the market will respond positively.

The most notable feature of larger companies is that affect their performance by economies of scale. According to conventional wisdom; economies of scale stress will increase the output. Therefore larger firms that able to capitalize or economies of scale tend to be more profitable (Lazar, 2016).

That’s interesting to study the impact of size on firm profitability because some authors found a positive relationship; others found a negative relationship and some authors found a non-significant relationship. We employ a methodology of 3 sections. In the first section we will identified the literature review; than in second section the empirical study. We finish by making a conclusion.

Literature Review

There are several articles studied the effect of size on firm profitability. Vintila & Duca (2013) examined the impact of firm size on the return on equity (a measure of firm performance). The research also examined whether large firms are more profitable than other fimr size using data for 100 firms listed in 2010 and the Bucharest stock exchange by using regression analysis method. They found that size has a positive impact on firm profitability Niresh (2014) explored the effect of firm size on profitability for 15 manufacturing active companies in Colombo stock exchange (CSE) for the period form (2008…2012) using multiple regression and correlation method. ROA and net profit are the indicators of firm profitability in the study while total assets and total sales are size indicators. They found a weak positive relationship between firm size and profitability.

Mule et al. (2015) studied 53 listed firms in Nairobi stock exchange during (2010…2014). Firm size proxied by natural log of assets) is found to be positive and significantly only on ROE.

In the US retail industry; Amato & Amato (2004) studied the association between firm size and profitability as measured by return on assets for the period of (1977-1987). They found a cubic relationship between firm size and profits. Dogan (2013) investigate the relationship between firm size indicators represented by total assets; total sales, number of employees and firm performance (ROA). Each measure of size influence positively the firm performance.

Hung et al. (2022) studied a sample of vietnamse private enterprises for the period (2009…2018). They found that size has a positive impact on firm profitability. Isik Studied a sample of manufacturing companies in Turkey for the period (2005…2013). They found that size has a positive impact on firm profitability.

Gaio & Henriques (2018) studied a sample of 54.654 firms form 21 European countries and from 17 industries during the period between (2004…2013). The results suggest that large entrerprises are on average more profitable than small enterprise. SME in Eastern Europe are more profitable than LE and are also more profitable than SME in western Europe.

Renfiana & Ardana (2021) studied a sample of companies listed on the Indonesian stock exchange. They found that firm has a negative and significant impact on firm profitability. Oyelede (2019) studied a sample of firms in Nigeria. He found a positive impact of size on firm profitability.

Based on signaling theory; the greater the firm size will give a positive signal to the public or market which means the company has better financial performance. Meiryani et al. (2020) Firm size can indicate that the company in experiencing growth so that the market will respond positively.

Large enterprise have more advantage to negociating with sellers and suppliers. Big enterprises create market entry barriers for new players (Serrasqueiro & Nunes, 2008); Ramsay et al., 2005). Moreover arge enterprises often exploit capital markets as well as public debt markets with lower capital costs. Large enterprise also have superior resources and capabilities in product development; technology innovation development; and of course better implementation of business; strategy; and e-commerce (Kipesha, 2013).

Dadoo et al. (2020) studied 15 non-financial listed on the Ghana stock exchange for the period (2008…2017). They found that size has a positif effect on firm profitability. Salah & Elwa (2016) studied a sample of 94 firms in Malaysia for the period (2012…2016). They found that firm size is positively and significantly related to profitability. Rahman & Yilman (2021) studied a sample of firm in stock exchange of China for the period (2008…2018). They found that firm size has a negative impact on firm profitability.

Empirical Study

The impact of firm size on profitability has been the object of several researchers. Under this section; we will identify the sample at the beginning and then we specify the variables and the model. After we carry out the necessary econometric tests. Finally we show the estimation results of the model and their interpretations. We used a sample of 30 companies listed in Tunisian stock exchange over the period (2016-2021) Table 1.

Table 1 Sample of 30 Companies
SL No.	Name of company
1	Air liquid
2	SIAME
3	Ciment Bizerte
4	Carthage ciment
5	Sotipapier
6	Essoukna
7	Somocer
8	Magasin général
9	STIP
10	SOTETEL
11	SOTEMAIL
12	SITEX
13	SFBT
14	Tawassol
15	Ennakl
16	Adwya
17	SOTUMAG
18	STAG
19	SAH
20	Assad
21	Office plast
22	STEG international services
23	CELLCOM
24	SOPAT
25	SANIMED
26	SOTUVER
27	SIMPAR
28	Poulina
29	Delice
30	SFBT

Estimation Method

We used a model of panel static because panel data can model both the common and individual behaviors of groups. Panel data contains more information; more reliability and more efficiency than pure time series data or cross sectional data (Armitage –Chan ; Jackson 2018)

Panel data can detect and measure statistical effects that pure time series or cross sectional data can’t. Panel data can minimize estimation biases that may arise from aggregating groups into a single time series. Panel data combines both times series and cross sectional techniques. It has a number advantage over both techniques. These include its ability to provide outcomes that could not be projected by either the cross section data or the time series alone (Wafae & Ewa, 2016)

Model Specification

Model 1

ROAi,t= b0+b1 Sizei,t +b2 CAPi,t +b3 CRi,t +b4 Levi,t +b5 Fai,t +b6 ALAi,t +b7 CEAi,t +b8 Pei,t +b9 TPIBi,t +b10 TINFi,t +Ei,t

i= firm t= time

b0 , b1 …..b10= parameters to be estimated

Model 2

ROEi,t =b0+b1 Sizei,t +b2 CAPi,t +b3 Cri,t +b4 Levi,t +b5 Fai,t +b 6 ALAi,t +b7 CEAi,t +b8 Pei,t + b9TPIBi,t +b10 TINFi,t +Ei,t

A high ROA means a high efficiency of the wealth management (assets ) ; which means a high efficiency of management (Hanafi & Halim,2007).

ROE reveals how much profit a company earned in comparison to the total amount of shareholders equity found on the balance sheet. (Ward & Price,2006) Table 2 ,Table 3 & Table 4.

Table 2 Desciprtion of Variables
Variable	Measure
ROA	Net profits / Total assets
ROE	Net profit / total equity
Size	Logarithm of total assets
CAP	Equity / total assets
ALA	Liquidity / total assets
FA	Non current liabilities / Equity
Lev	Total liabilities / total assets
CR	Current assets /current liabilities
CEA	Operating costs / total assets
PE	Operating revenus / total assets
TPIB	Economic growth
TINF	Rate of inflation

Table 3 Descriptive Statistics
Variable	Observations	Mean	Standard deviation	Minimum	Maxiumum
ROA	180	0.042	0.16	-0.64	0.9694
ROE	180	0.071	0.54	-3.004	3.42
ALA	180	0.124	0.521	0.0003	0.678
Size	180	18.44	1.81	11.29	22.024
CAP	180	0.4	0.583	-3.013	3.62
CR	180	2.28	3.52	0.052	32.017
Lev	180	0.85	1.027	0.0021	9.34
FA	180	0.76	2.61	-11.75	14.8
CEA	180	0.37	0.28	0.0019	1.47
PE	180	0.35	0.28	0.00055	0.95
TPIB	180	0.003	0.041	-0.087	0.0333
TINF	180	0.0571	0.011	0.036	0.0731

Table 4 Correlation between Variables
	ROA	ROE	ALA	Size	CAP
ROA	1.000
ROE	0.2520	1.000
ALA	0.0350	0.0088	1.000
Size	0.0251	0.0050	-0.1108	1.000
CAP	0.1436	0.1092	0.3444		1.000
CR	0.0348	0.0194	-0.0407	0.0985	0.1611
Lev	-0.0121	-0.0542	-0.0314	-0.0048	-0.2251
FA	-0.0291	0.11	-0.0187	0.1537	-0.0672
CEA	0.0344	0.015	0.1081	-0.0118	-0.0598
PE	-0.0062	0.0332	0.1371	0.0612	-0.0246
TPIB	0.0357	0.0699	-0.0150	-0.0149	-0.0783
TINF	0.0116	0.0021	-0.0232	0.0329	-0.099

Analysis of Descriptive Statistics

Where:

ROA (mean =0.042). In average net result represent 4.2% of total assets. The standard deviation is high . There is a big difference between firms in term of ROA.

ROE (mean = 0.071). In average net result represent 7.1% of total equity . The standard deviation is high . There is a big difference between firms in term of ROE

ALA (mean = 0.124) . In average liquid assets represent 12.4% of total assets. Standard deviation is ghigh . There is a big difference between firms in term of ALA

Size (mean = 18.44) . There are a big firms and small firms in this sample

CAP (mean = 0.40) . In average the cap represent 40% of total assets. There is a big difference between firms in term of CAP

CR (mean = 2.28) . In average current assets represent 2.28 of current liabilities . The standard deviation is low

Lev (mean = 0.85) . In average total liabilities represent 85% of total assets . Standard deviation is high . There is big differences between firms in term of leverage .

FA (mean = 0.76) . In average non current liabilities represent 76% of total equity. Standard deviation is high .

CEA (mean =0.37) . In average operating costs represent 37% of total assets . Standard deviation is low . There is non big differences between firms in term of CEA

PE (mean = 0.35) . In average operating revenus represent 35% of total assets . Standard deviation is low.

TPIB (mean = 0.003) . In average economic growth is low for the period (2016…2021) . There is due a negative effects of Tunisian revolution and period de Covid19

TINF (mean = 0.0571) . In average rate of inflation is 5.71% for the period (2016..2021) . Standard deviation is low

Multicolinearity Test

The all coefficients are inferior to 80% there is no problem of multicolinearity

A variance inflation factor is a measure of the amount of multicollinearity in regression analysis. Multicollinearity exists when there is a correlation between multiple independent variables in a multiple regression model. This can adversely affect the regression results. Thus the variance inflation factor can estimate how much the variance of regression coefficients is inflated due to multicolinearity Table 5 & 6.

Table 5 Suite of Correlation Between Variables
	CR	Lev	FA	CEA	PE	TPIB	TINF
CR	1.000
Lev	-0.1725	1.000
FA	-0.0573	-0.225	1.000
CEA	-0.0461	0.0343	-0.0052	1.000
PE	-0.0872	0.2918	0.00310	0.5745	1.000
TPIB	-0.0394	0.0797	0.0744	0.0114	-0.0844	1.000
TINF	0.0665	-0.0152	-0.0583	-0.0510	-0.0426	0.1048	1.000

Table 6 VIF of Variables
Variable	VIF	1/VIF
PE	1.76	0.56
CEA	1.58	0.63
CAP	1.27	0.78
Lev	1.23	0.80
ALA	1.19	0.92
CR	1.08	0.93
Size	1.07	0.95
TPIB	1.05	0.93
TINF	1.5	0.96
FA	1.4	0.97

In general terms,

1. VIF equal to 1 = variables are not correlated

2. VIF between 1 and 5 = variables are moderately correlated

3. VIF greater than 5 = variables are highly correlated

The higher the VIF, the higher the possibility that multi-collinearity exists, and further research is required. When VIF is higher than 10, there is significant multi-collinearity that needs to be corrected Table 7.

Table 7 Estimation Results of Model (1)
	Fixed effect		Random effect
ROA	Coefficient	Tstatistic	Coefficient	T statistic
Size	0.13	-6.26(***)	0.0086	-0.90
CAP	0.063	-2.94(***)	0.0031	0.14
CR	0.0022	-0.69	0.00077	-0.22
Lev	0.017	2.50(**)	0.017	2.38
ALA	0.0086	0.40	0.015	0.66
FA	-0.00037	-0.09	-0.0032	-0.71
CEA	-0.012	0.20	-0.044	0.075
PE	0.085	-2.68(***)	0.057	-2.04
TPIB	0.12	-2.53(**)	0.076	2.29
TINF	0.56	0.69	0.20	0.21
Constant	2.47	6.32	0.18	0.98

Estimation Results

Test of Hausman

Hausman tests (Hausman 1978) are tests for econometric model misspecification based on a comparison of two different estimators of the model parameters. The estimators compared should have the properties that (1) under the null hypothesis of correct model specification both estimators are consistent for the “true parameters” of the model (those corresponding to the data generating process), whereas (2) under misspecification (the alternative hypothesis) the estimators should have differing probability limits.

The former property ensures that the size of the test can be controlled asymptotically, and the latter property gives the test its power. Heuristically, the key idea is that when the model is correctly specified, the compared estimators will be close to one another, but when the model is misspecified, the compared estimators will be far apart. In our model Pv = - 0.152 inferior to 0.05 we choose a model of fixe effect in Table 8;

Table 8 Results of Estimation of Model 2
	Fixed effect		Random effect
ROE	Coefficient	Tstatistic	Coefficient	T statistic
Size	0.027	-0.33	0.00496	-2.16(**)
CAP	0.0042	0.05	0.079	2.01(*)
CR	-0.0076	-0.59	-0.0035	-0.29
Lev	-0.010	-0.22	-0.023	-2.53(**)
ALA	-0.045	-0.52	-0.042	-0.52
FA	0.046	2.74	0.033	2.12(**)
CEA	-0.53	-2.09	-0.163	-2.82(***)
PE	0.062	0.31	0.111	0.59
TPIB	0.75	0.80	0.93	2.06(*)
TINF	0.087	0.03	0.52	0.16
Constant	0.74	0.47	0.12	0.21

Pv value = 0.28 (Test of Hausman)

Therefore we choose random effect

4- Interpretations of estimation Model (1)

There is a positive relationship between ROA and Size (if size increase by 1% ROA increase by 0.13%) . The increase of size has a positive effect on return on assets. This relationship is statistically significant at 1%.

This result is similar to result found by (Odusanya and al (2018)); Akinlo (2012) ; Kant (2017); Dahmesh and al (2021) ; Cheng and Haong (2021) ; Susolo and Wdystiuti (2020)), Dodo and Donkor (2020), Mwenda and al (2021) but contrary to result found by (Aldrish and Auster (1986) ; Salaman ; Yazdafar (2012), Lazar (2016); Margaretha and Supritha (2016) , Kariskasari and Merianti (2016).

Thus large firms tend to be more profitable than smaller firms . The large firms have more advantage in negotiating their inputs ; reducing their costs (Asimalopolus (2009)).

Large benefits from economies of scale and economies of scope ; low level of information asymmetries (Dogan (2013); Jhonson (2007)).

Also the relationship between CAP and ROA is positive (if CAP increase by 1% ROA increase by 0.063%) . The increase of capital has a positive effect on ROA

This relationship is statistically significant at 1%

The relationship between CR and ROA is positive (if CR increase by 1%) ROA increase by 0.0022%. The relationship is not statically significant . This result is similar to result found by (Pervan ; al (2019); Kant (2017); Nazir ; Afra (2009) ; Chong and Haong (2021) ; Susolo and Wdysudi (2020) ; Taileb (2014). Which suggested that firms with a greater current ratio tend to be more profitable.

The relationship between ROA and Lev is positive (if Lev increase by 1% ROA increase by 0.017%) . The increase of leverage has a positive effect on return on assets. This relationship is statistically significant at 5%. The result is similar to result found by Karisasari and Merianti (2016)), Mwenda and al (2021)

There is positive relationship between ALA and ROA (if ALA increase by 1% ; ROA increase by 0.0086%) . The increase of liquidity has a positive effect on return on assets. This relationship is not statically significant .

There is negative relationship between FA and ROA (if FA increase by 1% , ROA decrease by 0.00037%) . The increase of (non current liabilities /equity ) has a negative effect on return on assets .

Also there is negative relationship between CEA and ROA (if CEA increase by 1% ; ROA decrease by 0.012%) . The increase of operating costs has a negative impact on return on assets . This relationship is not statically significant .

There is a positive relationship between ROA and PE (if PE increase by 1% ; ROA increase by 0.085%) . The increase of operating revenues has a positive effect on return on assets .

There is a positive relationship between TPIB and ROA (if TPIB increase by 1% , ROA will increase by 0.12%) . This relationship is statistically significant at 5%

The increase of economic growth has a positive effect on return on assets .

During periods of economic growth demand for the firm’s goods and services is potentially increasing and consequently it is expected that the firm will increase its sales and achieve higher profitability . (Pervan and al 2021)

This result is similar to result found by (Pervan and al (2019); Isik and Duski (2017) ; Chong and Haong (2021)), Matar and al (2018). The improvements of economic conditions enhances the firm profitability .

There is a positive relationship between ROA and Inf (if TINF increase by 1% ; ROA will increase by 0.56%) . The increase of inflation has positive effect on return on assets . This relationship is not statically significant . According to Perry (1992) the effect of inflation on profitability depends on whether inflation is anticipated or unanticipated . In the case of anticipated inflation ; firms are able to timely adjust the prices of goods at a level which ensure higher revenues and the adequate cost management measures ; ensuring that operating costs do not exceed revenues .

Interpretations of estimation (Model 2)

-There is a positive relationship between ROE and Size (if Size increase by 1% ; ROE will increase by 0.0046%) . The increase of size has a positive effect on return on equity.

This relationship is statistically significant . This result is contrary to result found by Nguyen and Nguyen (2020)

-There is a positive relationship between ROE and CAP (if CAP increase by 1% ; ROE will increase by 0.079%) . This relationship is statistically significant at 10% . The increase of capital has a positive effect on return on equity .

-There is a negative relationship between ROE and CR (if CR increase by 1% ; ROE will decrease by 0.0035%) . The increase of (current assets / current liabilities ) has a negative effect on ROE . This relationship is not statically significant .

There is a negative relationship between ROE and Lev (if Lev increase by 1% ; ROE decrease by 0.023%) . The increase of leverage has a negative impact on ROE . This result is similar to result found by Nguyen and Nugyen (2020)

There is a negative relationship between ROE and ALA (if ALA increase by 1% ROE decrease by 0.042%) . The increase of liquidity has a negative impact on ROE

There is a positive relationship between FA and ROE (if FA increase by 1% ; ROE increase by 0.033%) . The increase of (non current liabilities / equity ) has a positive impact on return on equity . between CEA and ROE (if CEA increase by 1% ROE will decrease by 0.163%) . The increase of operating costs has a negative impact on return on equity . This relationship is statistically significant at 1%

There is a positive relationship between PE and ROE (if PE increase by 1% ROE will increase by 0.111%). The increase of operating revenues has a positive impact on return on equity .

There is a positive relationship between TPIB and ROE (if TPIB increase by 1% ROE will increase by 0.93%) . The increase of economic growth has a positive effect on return on equity .

There is positive relationship between TINF and ROE (if TINF increase by 1% ROE will increase by 0.52%). The increase of inflation has a positive impact on ROE .

Conclusion

Profitability is the company’s ability to generate profit in the future and it is an indicator of the company’s operational success . Companies that are considered big in size ; seek to obtain ; acquire ; develop ; utilize ; maintain ; and disclose strategic resources to the market .(Triwalan and al (2019).

The objectif of this article is to indentify the impact of size on firm profitability . We used a sample of 30 firms quoted in Tunisian stock exchange for the period (2016-2021) . By applying 2 models of panel static we found that size has a positive effect on firm profitability

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Received: 26-May-2023 Manuscript No. AAFSJ-23-13634 ; Editor assigned: 29-May-2023, PreQC No. AAFSJ-23-13914 (PQ); Reviewed: 03- June-2023, QC No. AAFSJ-23- 13914 ; Revised: 07-June-2023, Manuscript No. AAFSJ-23- 13914 (R); Published: 15-June-2023