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\title{Analyzing the Impact of Advertising and Sales Promotion Costs on Sales Revenues and Profits of DSE Listed Ceramic Companies in Bangladesh}
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             \author[1]{Avik Ranjan  Bhowmik}

             \author[2]{Md. Kamal  Hossain}

             \author[3]{Vongdeuan  Vangpadith}

             \author[4]{Nadia  Chowdury}

             \affil[1]{  Islamic University}

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\date{\small \em Received: 7 December 2019 Accepted: 4 January 2020 Published: 15 January 2020}

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\begin{abstract}
        


The paper aims to analyze the effect of advertising and sales promotion costs on the sales revenues and profits of the ceramic companies listed with DSE in Bangladesh. To attain the objectives of the study, we collected the data from the annual reports of the companies. We organized a panel data of eleven (11) years for the sample companies. This study analyzes the data using the fixed effect and randomeffect model of regression analysis. We find that the sales promotion cost is influential in improving the sales revenues, net profit, and the return on investment.

\end{abstract}


\keywords{advertising cost, sales promotion cost, net profit, }

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he Ceramic-sector industry is one of the growing sectors over the past decades in Bangladesh. With the increased demand for ceramic items both in local and foreign markets, the ceramic companies are putting the highest concentration to produce the quality products, as well as the services for the customers in Bangladesh. After the inception of the journey of this sector in 1958, it experienced snail progress in the initial stage. But nowadays, the ceramic industry is considered a promising industry to earn domestic income as well as foreign income through the export of quality ceramic products. At the beginning stage, the Tajma Ceramic Industry started as the first ceramic firm in Bangladesh in 1958 with a limited amount of capital investment, with the time some other private sector companies came forward and began to invest in this sector  {\ref (BCMEA 2018)}. In the present day, many public and private limited companies are engaging in this sector. As with the advancement of this sector, the competition is becoming faster, and companies are paying attention to increase the advertising cost and the sales promotional expenses to generate more revenues and profits and targeting to expanding the business.\par
Currently, about sixty ceramic companies are running their operations in Bangladesh and making a substantial amount of revenues. The Bangladeshi ceramic companies perform about eighty-five (85\%) of the domestic demands; moreover, the ceramic products are exporting to the international markets. In promoting more local as well as foreign currencies from this sector needs to put more concentration on the sales promotional activities and in advertising. The advertising cost is insignificant in increasing the sales revenue of Food and Beverage Companies in Nigeria, but it has a significant favorable influence on the profits of the companies \hyperref[b0]{(Abdullahi, D. 2015)}. The study also reveals that advertising is one of the tools of communication that affects the performance of companies in multiple ways. The researcher recommended the advertising alone should not pay enough attention when creating strategies related to sales recommendations and profitability promotion policies. The sales promotion, personal sales, sales, etc. are needed to pay attention to increasing the sales revenue and a return to profitability such as. Advertising and publicity have a significant impact on the corporate reputation of individual companies. Other variables, for example, the dividend yield to investors, market value, diversification, and profitability of the firms' dividend to investors, were significantly related to the valuation of the corporate reputation of specific companies. Still, the aspect of the relationship changed from company to company. A firm's value in the current market also affects the valuation of the firm's reputation. More diverse firms gain a less corporate reputation for specific firms. Regarding the relationship between marketing communication and sales earnings, some advertising and promotional companies have a significant impact on sales revenue. The R \& D cost of the firm, the vital point of the firm, and the size of the firm show a significant positive relevance to the sales revenue of individual firms \hyperref[b21]{(Kim, K. 2007)}. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
We present an overview of the literature that relates to the work performed here. We have introduced the literature review in three different sections. Firstly, the advertising, secondly, the promotional costs, and lastly, the profitability of the companies. 
\section[{a) Advertising}]{a) Advertising}\par
Advertising is provided for communication to know or to change to more than one person's behavior". \hyperref[b8]{(Broadbent, S. 2001)}. It is a form of communication mediation from an identifiable source designed to persuade the recipient to take some action at present or in the future \hyperref[b32]{(Richards JI, and Curran CM 2002)}. Advertising defined as "the goal of persuasion people buy" \hyperref[b15]{(Frank J. 2005)}. Advertising includes all activities related to the presentation of a group, including a personal, verbal or visual, publicly sponsored message, and identifying sponsor performs, that is related to the promotion through one or more media \hyperref[b14]{(Etzel MJ. Et al. 2007)}. Advertising is a non-personal introduction and idea of promotion, products, or services paid for by any sponsored sponsor. Various studies have highlighted the importance and benefits of advertising to organizations to sell their products or services \hyperref[b22]{(Kotler P. and Armstrong G. 2010)}. The effect of a negative interaction between pre-launch advertising and promotion which means that advertising is less effective when promoting high-level advertising for the same product. The companies can gain the most sales by performing pre-promotions, and there is little benefit from increasing promotions and advertisements at the same time, which has a consistent effect with negative (pre-launch) and zero (post-launch) interactions \hyperref[b9]{(Burmester, A. B., et al. 2015)}.\par
Advertising creates a place for the products in the consumer mind,  {\ref (Morden AR. 19910)}; it increases buyer feedback, \hyperref[b23]{(Kotler P. 1988)}; it persuades the customer to buy \hyperref[b15]{(Frank J 2005)}; it persuade the customers to buy over the competitors products \hyperref[b40]{(Young CE. 2005;}\hyperref[b32]{Richards JI, and Curran CM. 2002)}; helps raise sales and profits \hyperref[b16]{(Galbraith JK. 1976}); generate positive brand associations \hyperref[b10]{(Cobb-Walgren CA. et al. 1995;} {\ref Moorthy S, Hawkins SA. 2005;}\hyperref[b19]{Keller KL. 2008;}\hyperref[b12]{De Chernatony L. 2010)}; produce selective demand \hyperref[b3]{(Arens WF. 2002)} and the dominance of brand beliefs and preferences.  {\ref (Shimp TA. 2007}). Few studies have indicated that investing more in advertising can increase awareness \hyperref[b39]{(Yoo B et al. 2000;}\hyperref[b37]{Villarejo AF, Sanchez MJ. 2005;}\hyperref[b7]{Bravo R, Fraj E, Martinez E. (2007)}. Well-known advertised respected brands create higher quality perceptions. \hyperref[b39]{(Yoo B. et al. 2000;}\hyperref[b7]{Bravo R)}. There is considerable managed relevance to the stability of this relationship across the country, especially as it is at the center of the debate about centralized versus decentralized control of international advertising. The main distinction is the advertising-and-promotion-tosales ratio of the consumer product. The search seems to contradict general knowledge about higher levels of advertising and promotion costs in the United States of America \hyperref[b41]{(Zif, J., Young, R. F., \& Fenwick, I. 1984)}. 
\section[{b) Sales Promotion}]{b) Sales Promotion}\par
Sales promotion is one of the strategies to convey information about the product \hyperref[b6]{(Blanchard K. et al. 1999}). Short-term promotion activities designed to encourage customers to buy or cooperate from distributors and other business members. \hyperref[b17]{(Griffin RW and Pustay MW. 2007}). Sales promotion is an arrangement of short-term strategies that marketers perform to encourage immediate purchase," \hyperref[b6]{(Blanchard K. et al. 1999)}; sales-based sales promotion is an activity that acts as a direct stimulus, as long as add value or incentives to resellers, salesman or customers for the product. Several research institutes have demonstrated the importance of sales promotion \hyperref[b38]{(William, Ferrel, Pride M, 1994)}. Sales promotion influences the customer decision to buy and brands likes  {\ref (Nijs V. R. et al. 2001)}; maximize sales value, affect sales, profitability and brand equity,  {\ref (Srinivasan SS, Anderson RE.1998;}\hyperref[b29]{Palazon-Vidal M, Delgado-Ballester E. 2005;}\hyperref[b25]{Montaner T, Pina JM. 2008)}; information helps to communicate, \hyperref[b31]{(Pride K. 1987;}\hyperref[b33]{Ricky W, et al. 2005}); increases desire. \hyperref[b17]{(Griffin RW and Pustay MW. 2007}). Thus the above literature is published as a necessary component of the communication mix for companies from sales promotion. There is a controversial concept of sales promotion, as some studies believe it has a significant impact on a company's performance.  {\ref (Boddewyn JJ and Leardi M.1989;}\hyperref[b28]{Odunlami IB, Ogunsiji A. 2011}); Others think it has no significant effect  {\ref (Dekimpe MG, et al.1999;}\hyperref[b35]{Srini S, et al. 2000;}\hyperref[b30]{Pauwels K, 2002)}. 
\section[{c) Profitability}]{c) Profitability}\par
The impact of sales and profits on advertising spending was significant, supporting their dynamic relationship in the context of restaurants. Restaurant managers plan their advertising budget by considering their various performance goals and changing the effectiveness of advertising (Kim, J., Jun, J., \& Tang, L. (Rebecca). 2019). There is a significant relationship between marketing costs and the firm's profitability and also an essential link between the firm's turnover and marketing costs. The organization should maintain a cost-effective system of advertising so that high-quality employees are a vital component \hyperref[b1]{(Agbeja, O. et al. 2015)}. If managers want to measure the success of their business, they need to measure profitability. Because recent researches depict that profit is something that can help managers to understand where their company's progress stays. It ensures the fact that managers need to take measured to ensure profitability in terms of returns. Scholars termed this theory as the theory of profit, monopoly theory of profit, etc. When there is a change in the economy, which causes cost reduction resulting in the difference between price and cost, alternately named residue defines the Dynamic theory of profit. Two types of profits, called accounting profits and economic profit, explains profitability where net income refers to amounting profit, and net worth relates to economic-profit. Therefore, this is something that designs the future of the organization or the project. However, some scholars define this in terms of income and expense. If a person produces and sells something that generates income, which is not similar to borrowing some money from other sources. The income statement shows the income of the entity, and it ascertains the actual profitability of the business. The profitability outlines the success and the sustainability of the business concern. Therefore, the managers or the businessmen should focus on ensuring the profitability of the firm. 
\section[{III.}]{III.} 
\section[{Statement of the Problem}]{Statement of the Problem}\par
As the manufacturing organization is concerned about earning revenues and profit to recover the cost and expenses to be survived in the competitive business environment. Most of the manufacturing and merchandising organizations are needed to forecast their budget for production, costs, and sales. The manufacturing concerns are engaged in purchasing the materials for production, which provides them to prepare budgeted sales and expenses. Thus the sales decision is a complex one for the manufacturing concern as it does not remain the same with different periods because the demand for the products is not the same throughout the different periods. Since the sales revenue depends upon the consumers' demand, so, it is a challenging task to forecast and estimate it by the management of the company. The advertising and the promotional efforts of the companies influence the demands of consumers for the products. The recent financial growth in this sector is the result of advertising and sales promotion efforts. Since it conveys the messages about the products of the companies and creates awareness about the product among the customers. And add value for the users. Therefore, advertising and sales promotion expenses are considered to be the essential factors to increase sales revenues like other mixes of marketing, namely product, price, place, and promotion. Since no related study has been conducted in the field of ceramic companies in Bangladesh, so we in this study attempt to assess the impact of advertising and sales promotional expenses on sales revenues and profitability of the ceramic companies.\par
IV. 
\section[{Research Hypotheses}]{Research Hypotheses}\par
The aims of the present study were to test the following null hypotheses: i) There exist no significant impact of advertising and sales promotion cost on the sales revenue of the companies under the study. ii) There exist no significant impact of advertising and sales promotion cost on the net profit of the companies under the study. iii) There exist no significant impact of advertising and sales promotion cost on the return on investment (ROI) of the companies under the study. iv) There exist no significant impact of advertising and sales promotion cost on the return on equity (ROE) of the companies under the study.\par
V. 
\section[{Objectives of the Study}]{Objectives of the Study}\par
In order to address the hypotheses outlined above, we aim to analyze the impact of advertising on the sales revenues of the ceramic sector companies listed in Dhaka Stock Exchange. The current study also intends to find the effect of the same variables on the profitability of the sector under the study. Therefore, the researchers will attempt to give suggestions on which factor is more influential and should pay more attention to generate more revenues and profits by the companies of the designated sector. 
\section[{VI.}]{VI.} 
\section[{The Methodology of the Study}]{The Methodology of the Study}\par
In the current study, weused secondary data. We collected the data from the annual report of the companies under the study. We received the data from all of the five companies listed with Dhaka Stock Exchange (DSE). During the collection of data, we gathered panel data of eleven (11) years of the sample companies. The population consists the five (05) listed ceramic sector companies of DSE. We selected the return on investment (ROI) and the return on equity (ROE) as the profitability measures in this study. We converted the quantitative figures into the natural log to measure the different variables in a common parameter. In this study we have used the quantitative data and approached two types regression analysis namely, the fixed-effect regression, and the random-effect GLS regression model where it suits; for this purpose, we test first the Hausman-effect model and observe that for the regression of advertising expense and sales promotion expense on the sales revenue, net profit and return on investment (ROI) the random-effect GLS regression model is fit, whereas, for the impact of advertising expense and sales promotion expense on the return on equity (ROE) the fixed-effect regression model is fit. Based on this justification, we analyzed the data by the respective models as it fits. With the help of software STATA 15 we performed the statistical tools and obtained the results. 
\section[{VII.}]{VII.} 
\section[{Findings and Analysis}]{Findings and Analysis}\par
This study has analyzed the data rigorously, and after this procedure, the following tables are prepared to present the results discovered from the analysis. As shown in the above Table \# 1, the randomeffect GLS model results that the sales promotion costs have a P-value of 0.000, which indicates for the hypothesis 1, the sales revenues are positively influenced by the increase of sales promotion expense, i.e. if 1\% increase in the sales promotion cost will lead to a 24.13\% increase in the sales revenues of the companies, but the advertising cost has no significant influence on the sales revenues of the ceramic companies in Bangladesh under the study; since, the p-value is more than 0.05 in this case. For the hypothesis 1, the R-square value is 0.5653, which indicates that 56.53\% of the variation in sales revenues is explained by the advertising costs and sales promotion costs, the remainder is not explained by these variables, and thus, the sales revenue may be changed by some other factors beyond these two variables. The random-effect model here implies that the output-variable, sales will be changed in different periods due to the changes in sales promotion cost specifically. The Table \# 2 shows the random-effect GLS model that both the advertising cost and the sales promotion costs have a P-value of 0.000; thus the results of the p-value are significant, and therefore, hypothesis 2 is rejected, the coefficient of the regression indicates that the net profits are negatively influenced by the increase of advertising cost, but net profits are positively impacted by sales promotion expense, i.e., if 1\% increase of the advertising cost will lead to a 59.55\% decrease in the net profit of the companies, whereas 1\% increase of the sales promotion cost will lead to a 95.16\% increase in the net profit of the companies in Bangladesh under the study. Besides this, the regression model shows that the R-square value is 0.4366 which indicates that 43.66\% of the variation in net profits are explained by the advertising costs and sales promotion costs, the remainder is not explained by these variables, and thus, the net profits may be changed by some other factors beyond these two variables. The random-effect model here implies that the output-variable, sales will be changed in different periods due to the changes in both advertising cost and sales promotion costs. As shown in the above Table \hyperref[tab_0]{\#} 3, the randomeffect GLS model results that both the advertising costs and the sales promotion costs have a P-value of less than 0.05, which indicates the hypothesis 3 can be rejected, hence the results of regression portraits that the returns on investment are influenced by the increase of both advertising costs and sales promotion expense, i.e., if 1\% increase in the sales promotion cost will lead to a 68.77\% increase in the return on investment of the companies, however, the increase of advertising costs will lead an adverse return on investment, i.e. i.e., if 1\% increase in the advertising cost will lead to a 56.06\% decrease in the return on investment of the companies. For the hypothesis 3, the R-square value is 0.1442 which indicates that only 14.42\% of the variation in return on investments are explained by the advertising costs and sales promotion costs, the remainder is not explained by these variables, and thus, the sales revenue may be changed by some other factors beyond these two variables. The random-effect model here implies that the output-variable, return on investment will be changed in different periods due to the changes in sales promotion cost specifically. The Table \# 4, generates results using the fixed-effect model and reveals the sales promotion costs has a P-value of less than 0.05, thus the results of the pvalue are significant, and therefore, the coefficient of the regression indicates that the return on equity is negatively influenced by the increase of advertising cost, i.e., if 1\% increase of the advertising cost will lead to a 61.31\% decrease in the return on equity of the companies, but the sales promotion cost is insignificant to impact return on equity, as the p-value for this case is more than 0.05, so the null hypothesis, in this case, cannot be rejected here. The regression model shows that the R-square value is 0.1309 which indicates that 13.09\% of the variation in return on equity is explained by the advertising costs and sales promotion costs, the remainder is not explained by these variables, and thus, the net profits may be changed by some other factors beyond these two variables. 
\section[{VIII.}]{VIII.} 
\section[{Conclusion}]{Conclusion}\par
The main conclusions of this work tell us that in most circumstances, it is found that the sales promotion cost has a significant impact on the sales revenues, net profits, and return on investments, only in the case of return on equity the sales promotion costs are insignificant to influence. Whereas the advertising cost has no significant influence on sales revenues, but it has a significant adverse effect on the net profits, return on investment, and return on equity of the listed ceramic companies in Bangladesh. We suggest from the findings of this study that the companies should reduce their advertising costs to increase the net profits, ROI and ROE of the companies. On the other hand the companies' should put more promotional investments to increase the earnings and profitability of the companies drawn together and presented in this section\begin{figure}[htbp]
\noindent\textbf{\#} \par 
\begin{longtable}{P{0.3257648953301127\textwidth}P{0.19710144927536233\textwidth}P{0.043800322061191624\textwidth}P{0.020531400966183572\textwidth}P{0.14098228663446052\textwidth}P{0.07665056360708535\textwidth}P{0.04516908212560387\textwidth}}
\multicolumn{2}{l}{Random-effects GLS regression}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Group variable:\tabcellsep Companynum\tabcellsep \tabcellsep \tabcellsep Number of obs\tabcellsep 55\tabcellsep \\
R-sq:\tabcellsep \tabcellsep \tabcellsep \tabcellsep Number of groups\tabcellsep 5\tabcellsep \\
within =\tabcellsep 0.2102\tabcellsep \tabcellsep \tabcellsep Obs per group:\tabcellsep \tabcellsep \\
between =\tabcellsep 0.5978\tabcellsep \tabcellsep \tabcellsep min =\tabcellsep 11\tabcellsep \\
overall =\tabcellsep 0.5653\tabcellsep \tabcellsep \tabcellsep avg =\tabcellsep \multicolumn{2}{l}{11.0}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep max =\tabcellsep 11\tabcellsep \\
corr(u\textunderscore i, X) =\tabcellsep 0 (assumed)\tabcellsep \tabcellsep \tabcellsep Wald chi2(2)=\tabcellsep \multicolumn{2}{l}{15.84}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Prob > chi2=\tabcellsep \multicolumn{2}{l}{0.0004}\\
Sales Revenue Ln\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep Z\tabcellsep P>|z|\tabcellsep {}[95\% Conf.\tabcellsep Interval]\\
Advertexpln\tabcellsep -.0135606\tabcellsep .0260884\tabcellsep -0.52\tabcellsep 0.603\tabcellsep -.064693\tabcellsep .0375718\\
Salespromoln\tabcellsep .2413625\tabcellsep .060728\tabcellsep 3.97\tabcellsep 0.000\tabcellsep .1223378\tabcellsep .3603872\\
\textunderscore cons\tabcellsep 17.34054\tabcellsep 1.026009\tabcellsep 16.90\tabcellsep 0.000\tabcellsep 15.3296\tabcellsep 19.35148\\
sigma\textunderscore u\tabcellsep 1.0256188\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
sigma\textunderscore e\tabcellsep .26864087\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rho\tabcellsep \multicolumn{4}{l}{.9357972 (fraction of variance due to u\textunderscore i)}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Source: Authors' Computation}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table \# 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{\#} \par 
\begin{longtable}{P{0.2958404074702886\textwidth}P{0.2063667232597623\textwidth}P{0.047623089983022074\textwidth}P{0.020203735144312392\textwidth}P{0.14864176570458404\textwidth}P{0.08225806451612903\textwidth}P{0.049066213921901525\textwidth}}
\multicolumn{2}{l}{Random-effects GLS regression}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Group variable:\tabcellsep Companynum\tabcellsep \tabcellsep \tabcellsep Number of obs\tabcellsep 55\tabcellsep \\
R-sq:\tabcellsep \tabcellsep \tabcellsep \tabcellsep Number of groups\tabcellsep 5\tabcellsep \\
within =\tabcellsep 0.2768\tabcellsep \tabcellsep \tabcellsep Obs per group:\tabcellsep \tabcellsep \\
between =\tabcellsep 0.5886\tabcellsep \tabcellsep \tabcellsep min =\tabcellsep 11\tabcellsep \\
overall =\tabcellsep 0.4366\tabcellsep \tabcellsep \tabcellsep avg =\tabcellsep 11.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep max =\tabcellsep 11\tabcellsep \\
corr(u\textunderscore i, X) =\tabcellsep 0 (assumed)\tabcellsep \tabcellsep \tabcellsep Wald chi2(2)=\tabcellsep 22.66\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Prob > chi2=\tabcellsep \multicolumn{2}{l}{0.0000}\\
Netprofitln\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep Z\tabcellsep P>|z|\tabcellsep {}[95\% Conf.\tabcellsep Interval]\\
Advertexpln\tabcellsep -.5955139\tabcellsep .1521932\tabcellsep -3.91\tabcellsep 0.000\tabcellsep -.8938071\tabcellsep -.2972207\\
salespromoln\tabcellsep .9516652\tabcellsep .2631064\tabcellsep 3.62\tabcellsep 0.000\tabcellsep .4359861\tabcellsep 1.467344\\
\textunderscore cons\tabcellsep 11.3295\tabcellsep 3.899859\tabcellsep 2.91\tabcellsep 0.004\tabcellsep 3.68592\tabcellsep 18.97309\\
sigma\textunderscore u\tabcellsep 1.571504\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
sigma\textunderscore e\tabcellsep 1.6310388\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rho\tabcellsep \multicolumn{4}{l}{.48141657 (fraction of variance due to u\textunderscore i)}\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: Source: Authors' Computation]} 
\caption{\label{tab_1}Table \# 2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{\#} \par 
\begin{longtable}{P{0.28767006802721085\textwidth}P{0.20960884353741496\textwidth}P{0.04625850340136054\textwidth}P{0.021683673469387755\textwidth}P{0.14889455782312924\textwidth}P{0.0852891156462585\textwidth}P{0.05059523809523809\textwidth}}
\multicolumn{2}{l}{Random-effects GLS regression}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Group variable:\tabcellsep companynum\tabcellsep \tabcellsep \tabcellsep Number of obs\tabcellsep 55\tabcellsep \\
R-sq:\tabcellsep \tabcellsep \tabcellsep \tabcellsep Number of groups\tabcellsep 5\tabcellsep \\
within =\tabcellsep 0.2928\tabcellsep \tabcellsep \tabcellsep Obs per group:\tabcellsep \tabcellsep \\
between =\tabcellsep 0.0370\tabcellsep \tabcellsep \tabcellsep min =\tabcellsep 11\tabcellsep \\
overall =\tabcellsep 0.1442\tabcellsep \tabcellsep \tabcellsep avg =\tabcellsep 11.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep max =\tabcellsep 11\tabcellsep \\
corr(u\textunderscore i, X) =\tabcellsep 0 (assumed)\tabcellsep \tabcellsep \tabcellsep Wald chi2(2)=\tabcellsep 17.72\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Prob > chi2=\tabcellsep \multicolumn{2}{l}{0.0001}\\
Roiln\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep Z\tabcellsep P>|z|\tabcellsep {}[95\% Conf.\tabcellsep Interval]\\
Advertexpln\tabcellsep -.5605998\tabcellsep .1482083\tabcellsep -3.78\tabcellsep 0.000\tabcellsep -.8510827\tabcellsep -.2701169\\
Salespromoln\tabcellsep .6876987\tabcellsep .234957\tabcellsep 2.93\tabcellsep 0.003\tabcellsep .2271915\tabcellsep 1.148206\\
\textunderscore cons\tabcellsep -6.778988\tabcellsep 3.413298\tabcellsep -1.99\tabcellsep 0.047\tabcellsep -13.46893\tabcellsep -.0890482\\
sigma\textunderscore u\tabcellsep 1.2136483\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
sigma\textunderscore e\tabcellsep 1.572872\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rho\tabcellsep \multicolumn{4}{l}{.37319263 (fraction of variance due to u\textunderscore i)}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table \# 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{\#} \par 
\begin{longtable}{P{0.33663366336633666\textwidth}P{0.2091937765205092\textwidth}P{0.03847241867043847\textwidth}P{0.018033946251768036\textwidth}P{0.13826025459688826\textwidth}P{0.06973125884016973\textwidth}P{0.039674681753889676\textwidth}}
roeln\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep Z\tabcellsep P>|z|\tabcellsep {}[95\% Conf.\tabcellsep Interval]\\
advertexpln\tabcellsep -.6130724\tabcellsep .1516663\tabcellsep -4.04\tabcellsep 0.000\tabcellsep -.918018\tabcellsep -.3081269\\
salespromoln\tabcellsep .6961638\tabcellsep .3662804\tabcellsep 1.90\tabcellsep 0.063\tabcellsep -.0402923\tabcellsep 1.43262\\
\textunderscore cons\tabcellsep -5.788814\tabcellsep 5.57034\tabcellsep -1.04\tabcellsep 0.304\tabcellsep -16.98873\tabcellsep 5.411105\\
sigma\textunderscore u\tabcellsep 1.563046\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
sigma\textunderscore e\tabcellsep 1.5358726\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
rho\tabcellsep \multicolumn{4}{l}{.50876798 (fraction of variance due to u\textunderscore i)}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{F test that all u\textunderscore i=0: F(4, 48) = 7.93}\tabcellsep \tabcellsep \tabcellsep Prob > F = 0.0001\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Source: Authors' Computation}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Source: Authors' Computation}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{3}{l}{Fixed-effects (within) regression}\tabcellsep \tabcellsep \tabcellsep \\
Group variable:\tabcellsep companynum\tabcellsep \tabcellsep \tabcellsep Number of obs\tabcellsep 55\tabcellsep \\
R-sq:\tabcellsep \tabcellsep \tabcellsep \tabcellsep Number of groups\tabcellsep 5\tabcellsep \\
within =\tabcellsep 0.2851\tabcellsep \tabcellsep \tabcellsep Obs per group:\tabcellsep \tabcellsep \\
between =\tabcellsep 0.0200\tabcellsep \tabcellsep \tabcellsep min =\tabcellsep 11\tabcellsep \\
overall =\tabcellsep 0.1309\tabcellsep \tabcellsep \tabcellsep avg =\tabcellsep 11.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep max =\tabcellsep 11\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep F(2,48)=\tabcellsep 9.57\tabcellsep \\
corr(u\textunderscore i, Xb) =\tabcellsep -0.4052\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Prob > chi2=\tabcellsep 0.0003\tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table \# 4}\end{figure}
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\end{document}
