Examining the Effect of Tax Reform Determinants, Firms’ Characteristics and Demographic Factors on the Financial Performance of Small and Micro Enterprises

Abstract

Taxation is a powerful tool to achieve sustainable development goals (SDG) as tax policies help strengthen economic growth and improve revenue capacity. So, after tax reform, it is vital to analyze their impact on the performance of enterprises. Keeping in mind the SDG, the present study was conducted in India after the major tax overhaul—Goods and Service Tax (GST) on 1 July 2017, to measure the impact on Return on Equity (ROE) and Return on Investment (ROI) as they are the barometers to measure performance (DuPont Analysis). We opted for tax reform determinants, the characteristics of firms, demographic variables, and drivers from DuPont analysis to conduct the research, as all these variables can help identify the different causes of factors impacting ROI and ROE among different types of firms and demographics across countries. An econometric analysis of 546 registered SMEs (small and micro enterprises) was conducted using the regression model, structured equation modeling, and exploratory and confirmatory factor analysis to achieve the objectives. The empirical findings highlighted that a firm’s size, turnover, and DuPont analysis drivers (earnings × asset to sales × asset turnover) positively enhanced the ROI and ROE. Further, the change in the tax system after the tax reforms has enabled the detection of tax fraud and wrong invoices, reducing the missing insolvent traders and increasing the working capital flow of the firms, which in turn has augmented financial performance.

1. Introduction

Tax is primarily linked to economic development as taxation provides revenue to the country. The government needs tax revenues to mobilize resources and to reinforce a country’s structure [1,2]. Tax policies play a significant role in gaining higher revenues, promoting investment innovations, and aligning business models, especially in emerging economies [3]. They also play a key role in achieving the sustainable development goals (SDGs) set by United Nations [4]. One of the goals of the SDGs is to improve economic growth, in which the private sector and its enterprises can be viewed as key drivers. Taxation policies make it feasible to meet and achieve these goals with the support of micro and small enterprises [5,6]. However, in the past few years, a severe decline in the financial performance of small and micro enterprises has been seen, which in turn has led to low tax revenues for the government [7,8]. Therefore, analyzing their financial performance becomes essential, especially after changes to the tax system.

Small and Micro Enterprises (SMEs) are the key players in a conveying economy and constitute the majority of business taxpayers. They are considered the levers for economic development as they generate more production opportunities, entrepreneurial talent, and employment, and accelerate the export of goods and services. They are the firms with the most potential that may eventually grow into larger firms in the future [9,10]. However, due to the presence of internal economies of scale in SMEs, economies which are unique to a firm may vary from other firms [11,12]. Therefore, it is necessary to evaluate the impact of a firm’s characteristics on financial performance after tax reform in a country, as the factors impacting the profitability level vary from firm to firm [13]. Moreover, the association of a firm’s characteristics and financial performance has been the most prominent research topic of Industrial Economic Theory [14] because compared to larger firms, small and micro firms face more administrative and resource allocation difficulties and bear higher costs [15]. Thus, it becomes essential to examine the impact of a firm’s characteristics on the financial performance of SMEs.

Further, after tax reform, firms also experience large fluctuations in performance levels due to demographic variables as well. The variables such as age, gender, race, income, and work experience are greatly associated with the overall performance of the firms [16,17]. These variables strongly motivate and influence business and work efficiency [18]. It has been observed that the age, gender, and income of a person influence the adaptability of the reform and affect the performance in a different way [19]. Thus, it becomes essential to investigate the impact of demographic variables on financial performance, especially after tax reform. These determinants can help identify the different causes of factors impacting the performance among different types of firms and demographics across the countries. Therefore, the present research was conducted with three major objectives:

To examine the impact of change in the tax system (GST) on financial performance.

To examine the impact of firms’ characteristics on financial performance.

To investigate the impact of demographic variables on financial performance.

For the financial performance of firms, we opted for the Return on equity (ROE) and return on investment (ROI) of small and micro firms. ROE and ROI are the barometers of the financial performance of firms, according to DuPont 1912 [20,21]. Moreover, according to Economic Theory, enterprises are driven by ROI and ROE. More profitable and higher-returning firms are eager to make expansion and investment decisions [22]. ROE and ROI provide investors with an insight into how efficiently a business works and measure the profitability margins.

To conduct the present research, we opted for one of the world’s emerging economies, India, which overhauled its indirect tax structure by implementing a GST (Goods and Service Tax) on 1 July 2017. Furthermore, keeping the base of Dupont analysis, its key drivers were used to evaluate the impact on ROI and ROE along with the factors of a change in the tax system (GST), the characteristics of firms, and demographic factors.

The paper makes several contributions to the existing literature—(1) To begin with, the tax system factors provide a comprehensive view of the variables directly associated with businesses that significantly influence the performance. The study highlights that the detection of tax fraud, narrowing down tax evasion, and control of wrong invoices to prevent missing or dummy traders after GST, has contributed effectively to sustaining the ROE and ROI of firms. (2) Along with tax reform variables, drivers from DuPont Analysis (earnings, asset turnover, and tax payments) are also considered, contributing positively to sustain the firms’ ROE and ROI. (3) Finally, the firms’ characteristics (business turnover and size) highlight the importance of growth in businesses and entrepreneurs in formulating strategic decisions after reforms to sustain the performance in the long run.

The results may aid the government, policymakers, and other countries in acknowledging the key factors that might enhance the returns of firms, which ultimately provide more revenues to the economy and help to achieve the sustainable development goals (its economic goal). Further, the result might help investors as they remain interested in the firms that can provide them with large returns. Moreover, the determinants might also help identify the factors which may help sustain the performance and serve as a core to achieve environmental and societal success in the long run, especially in uncertain times (such as COVID-19 or any policy reforms) [23,24].

The paper is organized as follows—Section 2 contains the literature review, and Section 3 states the materials and methods used for the study. Section 4 states the results, followed by the discussion and conclusion in Section 5 of the study.

2. Literature Review and Hypothesis Development

A review of past research lays a strong foundation for any new study. It provides a robust research base that helps identify the research problems, questions, and feasible objectives and constructs for the study. The literature gives an insight into changes in the tax system, the characteristics of firms, demographic variables, and drivers of DuPont analysis. The relationship between financial performance and the above-mentioned determinants helped formulate the hypothesis and reach the study’s objectives.

2.1. Tax Reform (Goods and Service Tax)

Developing countries face challenges in resource mobilization and are unable to raise their revenues which can help achieve sustainable development goals. Therefore, from time to time, the government brings in reforms as they enable the growth in the economy and curtail fiscal imbalance in the country [25]. The core objective of any tax reform is to establish an economically efficient, simple to administer, and transparent tax system for the smooth functioning of the country’s businesses. Theory of Optimal Taxation emphasizes that the change in the tax system should be so that it should reduce distortion in its economy and concerned businesses [26,27].

The tax system strongly influences the business performance of enterprises, as a challenging and complex tax system often proves to be an obstacle in the path of business growth, especially for small firms, due to their limited economies of scale [28,29]. The World Business Environment Survey by World Bank (1998–2000) on 80 countries compromising 10,032 firms of the world emphasized that tax regulations, changes, and taxes themselves are the top constraints on the performance of firms. A change in the tax system is formulated to lower the constraints on firms. The study by Somaya [30] empirically verified that after a change in the tax system, small Egyptian firms observed a decrease in tax obstacles.

Over time, the government has incorporated many tax reforms in India to strengthen the economy. India had a complex indirect tax structure in the past, with a three-tier federal system: at the state level, union government, and the local governmental bodies. The main taxes that the union government is empowered to levy are income tax, customs duties, excise duties, sales tax, and service tax. It is worth noting some peculiar features of this tax bundle. The structure of custom duties is exceptionally intricate, and the burden almost hits the import side of international trade. Moreover, internal indirect taxes are a complicated matter: they are separately levied on goods, services, and intra-state sales. Further, these multiple indirect taxes have cascading effects. In order to eradicate these complications, the GST tax structure was implemented on 1 July 2017 in India [31,32]. Goods and Service Tax (GST) is the latest tax reform that replaced the Value Added Tax (VAT) system as the later led to double taxation [33,34] and affected the productivity of enterprises in terms of equity and revenue collection [35]. GST was first implemented in France in 1954 and has now been adopted in over 160 countries worldwide. GST broadens the tax base, prevents tax evasion, ensures stable government revenue in Canada, Australia, and Europe [31], and reduces the cascading effect, especially among small firms [36,37]. Through the present study, we tried to address the impact of GST on the financial performance of SMEs.

Based on the above literature, we can hypothesize that:

Hypothesis 1 (H1).

A change in the tax system positively influences the financial performance of SMEs.

2.2. Characteristics of Firms

In the area of micro and small business research, the presence of economies of scale strongly implies that the characteristics of firms (size and business turnover) is one of the vital factors in assessing their performance [38]. However, while there has been a vast literature on the determinants of the performance of firms—with family ownership [39], social capital [40], and nature [41], there is scarce literature on the effects of firm size and business turnover.

Firms’ Size: A firm’s size is one of the fundamental factors in determining financial performance due to the basic concept of economies of scale. It has been observed that larger companies produce/manufacture more goods and have enormous capital benefits [42]. Firm size is directly associated with the cost efficiency and profitability of the firms [43]. Lee [44] empirically verified from his research, conducted on more than 700 US public firms, that a firm’s size plays a dominant role in elucidating the firm’s profitability. Large-scale companies have a higher competitiveness than small companies because large companies have a large market, so they have a great opportunity to obtain large profits [45]. A comparative study by Ozgulbas et al. [46], conducted on Istanbul’s firms from 2000 to 2005, demonstrated that firm size directly impacts the firm’s performance. Their study verified that larger firms have a higher operational performance than small firms. Similar implications were observed by Jonsson [47] on Iceland’s firms. A large firm size signifies that the company might have better market value and financial performance compared to smaller ones [48]. In contrast to the stated studies, Becker et al. [49] found a negative association between a firm’s size and the profitability margins of U.S. manufacturing firms from 1987 to 2002. From a strategy perspective, firm size may be an indicator of diversification, which by and large has been found to affect performance negatively [50]. Studies in the literature have shown a mixed firm size and profitability relationship.

Business Turnover: Business turnover can be defined as the sales and income growth of a firm. Annual business turnover has been receiving more attention from the government as it directly influences the rate of return in industries and is more valid in actual practice. When the tax system change/tax reform is undertaken in an economy, it directly impacts the revenue, which is linked with the business turnover of the firms in one way. In the past, when tax reform was implemented in Indonesia in 1983 and 1987, the revenue collection rose by 4% [51]. A similar implication was observed after Ontario’s tax reform of 2010, where the effect of policy on after-tax turnover and income increased and gradually improved over time [52]. Further, at the time of reform, it was argued that a comprehensive tax system such as Goods and Service Tax was better for economic growth than multiple indirect tax systems due to its positive effect on encouraging savings, which could lead to increased investment and growth [53]. Therefore, it is essential to evaluate whether the business turnover has any impact on the financial performance of the firm after tax reform, which might contribute to the revenue enhancement of the government. In addition, the empirical study on the relationship between business turnover and profitability received recognition in 1977 from the study conducted by Muller [54]. A higher turnover signifies the ability of the firm to explore more potential markets, which is beneficial in augmenting the firm’s financial performance [55]. In other words, a higher turnover can be described as the best competitive advantage for the firm [56]. Both the size of the firm and the growth rate of sales play an essential role in a firm’s performance [57,58].

Based on the above literature, it can be hypothesized that:

Hypothesis 2a (H2a).

Firm size positively influences the financial performance of SMEs.

Hypothesis 2b (H2b).

Business turnover positively influences the financial performance of SMEs.

2.3. Demographic Variables

Demographic factors in a human being motivate him/her to conduct business and work efficiently towards it [59,60]. These characteristics can be described as age, gender, income, educational qualification, race, and family background, which have a relation to the financial performance of a business [61,62,63].

Age: Age is one of the vital demographic factors that impact the performance of a firm. It was observed by Child [64] that younger entrepreneurs contribute more to the growth of the firm as they expend more physical and mental effort in adapting to changes in economies. Further, past studies have stated that with age comes more experience, which positively impacts a firm’s performance. It was observed that older entrepreneurs/executives/managers have a competitive advantage over younger ones as they have gained more experience with age and work [65,66]. Peni [67] opined that a CEO’s age has a positive association with the return on assets, that is, the firm’s performance. In contrast, Tarus and Aime [68] opined that with the increase in age, a rigidness and resistance to adopt changes increases, which might impact the performance negatively. A similar implication was observed by Ross (2005) on 446 Danish firms. He stated that the board members’ average age had a negative impact on the firm performance.

Income: Firm performance is defined by economic and organizational factors, under which income is one of the influential variables. It has been observed in the developing and developed countries that comprehensive tax reform (GST) broadens the tax base of the economy, that is, it narrows down the tax expenditure, promotes savings and investments, and expands the size of an economy [69]. Mertens and Olea [70] used time-series data from 1946 to 2012 to estimate the impact of marginal tax rates on individual income. They found that marginal rate cuts increased individual income by 0.78 percent by the third year after the tax change. Ljungvist and Smolyansky [71] looked at 250 state corporate tax changes from 1970 to 2010 to assess their impact on employment and income. Comparing nearby counties across states allowed the authors to isolate the impacts of corporate tax changes relative to other policies that might affect economic growth. They found that a one-percentage-point cut in statutory corporate tax rates ledto a 0.2 percent increase in employment and a 0.3 percent increase in income. Therefore, after tax reform, the personal income level is impacted strongly and this is required to evaluate its impact on business performance, as incomes enable managers and entrepreneurs to meet their needs and positively impact the firm’s performance through their positive attitude and satisfaction [72]. Even the expectancy-based theories suggest that a firm’s high performance is directly linked to personal growth and income [73,74]. The study conducted by Dijkhuizen et al. [75] in the Netherlands also observed similar implications that a positive association exists between entrepreneurial income and a firm’s performance.

Based on the above literature, we can hypothesize that:

Hypothesis 3a (H3a).

Age positively influences the financial performance of SMEs.

Hypothesis 3b (H3b).

Income positively influences the financial performance of SMEs.

2.4. DuPont Analysis

DuPont analysis helps determine a firm’s ability to enhance and improve their Return on Equity (ROE). It investigates the main drivers impacting the ROE of the company, which acts as a barometer to measure the performance of the firms [76]. The earlier model is comprised of profit margins, asset turnover, and an equity multiplier as shown in Equation (1) below. Earnings and asset turnover reflect the assets and resources of the business that are utilized to generate revenue. The equity multiplier or financial leverage states the capitalization position the firm. Later, the three step DuPont analysis was revised, with the addition of two crucial factors that impact the ROE of the firms: tax rate and sales. Finally, the five-point analysis states the drivers in detail—comprising net sales, earnings, asset turnover, an equity multiplier, and tax rate as shown in Equation (2) below. There are two versions of DuPont analysis, one utilizing the decomposition of ROE via three steps and another utilizing five steps.

The three-step equation breaks up ROE into three components:

$$ROE=\frac{NI}{S}\times \frac{S}{A}\times \frac{A}{SE}$$

(1)

The five-step version:

$$ROE=\frac{E}{S}\times \frac{S}{A}\times \frac{A}{SE}\times T$$

(2)

where

NI = Net Income

S = Sales

A = Assets

SE = Shareholders’ Equity (share capital + reserves and surplus)

E = Net Earnings

T = Tax Payments

2.5. Conceptual Model

Based on the above stated literature, the following conceptual model was developed to attain the objective of the study (Figure 1).

3. Materials and Method Used

At first, Section 3.1 explains the sample size. The second Section 3.2 explains the survey instrument, data, and construct measures used in the study. Finally, the third Section 3.3 explains the methodology adopted to achieve the study’s objectives.

3.1. Sample

The MSME sector constitutes three major categories, i.e., Micro, Small, and Medium enterprises. Due to the diverse nature of the population, i.e., divided into the strata of three types of units—micro, small, and medium, a stratified random sampling technique was adopted. A conscious attempt was made to gather responses across three types of units—Micro, Small, and Medium by applying proportionate random sampling on the number of registered firms, using the base of the MSME Annual Report 2017–18. The size of the firms and turnover are essential features that are needed to determine the profitability of the organizations. Each country has its own definition that determines the size of its firms. In the present research, we opted for the investment threshold limits to determine the sizes of the firms into micro and small-sized enterprises (MSMED Act, 2006). Investments in plant and machinery up to INR 2.5 million falls in the micro-sized firms category and above INR 2.5 million, but up to INR 50 million determines the small-sized firms (MSMED Act, 2006) (where, INR = Indian Rupees and INR 2.5 million = USD 32,000 approx. and INR 50 million = USD 640,000 approx., as the current exchange rate 1 USD = 78 INR).

The sample size was calculated by considering the total number of SMEs retrieved from the report of the Ministry of MSME 2017–18. The sample size was obtained using population standard deviation, which was calculated considering the total number of registered MSMEs in Punjab during the last ten years at a 95% confidence level. The total sample was 546 SME units. The stratified Random Sampling technique was used to collect data from registered small and micro-enterprises.

3.2. Survey Instrument and Data

Survey Instrument: A structured questionnaire was prepared and piloted by 15 academics and 15 practitioners. After their guidance and feedback, alterations (deleting a few repetitive statements, scaling of the factors) were made in the questionnaire’s statements. The questionnaire was filled in by top management personnel, i.e., owners, managers, or tax experts who managed the MSME units’ tax affairs. A self-structured questionnaire was prepared and distributed by post, hand, and e-mails with two to four follow-up reminders among the registered MSMEs of Punjab. The study had a good reliability as the Cronbach’s alpha for all sections was greater than the acceptable range of 0.7 (refer to

Table 1. Dependent and Independent Variables.

Factors	Definition	Cronbach’s Alpha
Financial Performance	The SMEs were often hesitant to publicly reveal their actual financial performance, leading to a poor or non-response. The subjective performance, that is, the perception of business performance on various dimensions rather than actual performance, has been widely collected in earlier studies [77,78]. Further, various studies found performance perception to be more valid and reliable than actual financial figures [79]. Another challenge with considering financial figures was their cross-validation due to privately held information [80,81]. The financial performance parameters used to study the impact were measured on the Likert scale 1–3 (1 = decrease, 2 = no change, and 3 = increase). The performance indicators used were Return on equity (ROE), Return on investment (ROI), and earnings of the firms [82,83,84]	0.878
Firms Characteristics	Size—Micro, Small, and Medium based on the definition provided by MSME Act, 2005 Form (Legal Stature)—proprietorship, partnership, public/private limited Annual business turnover ▪ up to INR 50 million ▪ INR 50–250 million ▪ INR 250–500 million ▪ above INR 500 million	0.727
Demographic Variables	The demographic variables regarding the respondents were asked in relation to their age (25–35 years; 35–45, and above 45 years), gender (male or female) and annual income (Under INR 0.8 million 1; INR 0.8–1 million and above 1 million) [85,86,87]	0.707
GST Reform Determinants	This construct measured the respondents’ perception towards the impact of the new tax system (GST) on their business performance. The different factors of tax reform (GST) asked were detection of tax frauds, reduction in tax evasion, transparency, progressive system, input tax credit mechanism, uniform tax rates, and prevention of stock leakages [88,89,90,91]. A total of 19 statements were asked in the questionnaire (refer to Table 2 for details). The respondents were asked to rate their responses on a five-point Likert scaling (1 = strongly disagree, 5 = strongly agree).	0.898

Source: Authors’ Compilation. ¹ Lakhs = one hundred thousand (in the Indian system of measurement), where 8 lakhs can be written as INR 0.8 million = 10,242 US dollars; INR 1 million = 12,804 USD.

).

Data: The dependent variable for the study was financial performance. The independent variables were the characteristics of firms (size and turnover), demographic variables (age and income), and GST reform (change in tax system), see Table 1.

3.3. Research Methodology

The current study was undertaken to examine the impact of the characteristics of firms, demographic factors, and determinants of the tax system (GST) on the performance of SMEs.

Firstly, we employed explorative factor analysis (EFA). EFA is an explorative technique to ascertain the appropriate number of common factors which are reasonable for the latent variables (based on factor loading values). We applied EFA to reduce the large number of factors of each variable (firm characteristics, demographics, and tax reform determinants) collected through the questionnaire.

Secondly, we employed confirmatory factor analysis (CFA). CFA helps to allocate the specific number of factors and the appropriate constructs (based on factor loadings, multiple correlations, and covariance values). The pre-specified factor solution is evaluated in terms of how well it reproduces the sample covariance matrix of the measured variables. EFA is often used early in the process of scale development and construct validation, whereas CFA is used in the later phases when the underlying structure has been established on prior empirical and theoretical grounds. Here, in this study, CFA was applied after EFA to allocate the appropriate constructs to evaluate their impact on performance.

Thirdly, we applied Stepwise Regression and AMOS-SEM to measure the impact of the independent variables on the’ performance of firms. Stepwise regression is applied when there is a large number of predictor variables. Stepwise regression, enters and removes predictors in a stepwise manner with justifiable reasons. Its primary objective is to choose a small subset from the large one to form a good regression model with a good predictive ability. AMOS-SEM is a multivariate analysis method and has higher levels of statistical power. The measurement model helps to decide the scales’ properties, and the structural model establishes the relationships among the variables. AMOS enables a large number of underlying variables to be handled carefully and predicts more practical results as it uses the covariance-based algorithm. AMOS-SEM supports large sample size data and helps to manage the complex conceptual framework as stated in the present study. Further, it enables an analysis of the cause–effect relationship [92]. Moreover, it supports a non-normal set of data and helps to obtain an accurate prediction. SEM is broadly used in social science research as it can build a model using multiple latent variables by considering various measurement errors [92].

As the present research deals with complex variables, that is, business performance, we adopted two methods—stepwise regression and SEM to evaluate the impact of the characteristics of firms, demographic factors, and tax reform determinants, which is depicted in the functional Equation (3) below (based on the literature review in Section 2):

$$Financial performance=\alpha +{\beta }_1\left(TS\right)+{\beta }_2\left(FC\right)+{\beta }_3\left(DV\right)+{\beta }_4\left(D\_Dp\right)+\epsilon$$

(3)

where:

TS = determinants of change in Tax System

FC = Firms’ Characteristics

DV = Demographic Variables

D_Dp = Drivers from DuPont Analysis

4. Results

This section elaborates on the findings as stated in the research methodology adopted for the study (Section 3.3). Firstly, the EFA results are demonstrated (Section 4.1), followed by the CFA model, and the model fit results (Section 4.2). Then, the final results from both the stepwise regression and SEM path model are elaborated on in relation to the study’s objectives (Section 4.3).

4.1. EFA Results

Explorative factor analysis explored the underlying factors from all the constructs included in the questionnaire during the survey. The EFA was determined with the Kaiser–Meyer–Olkin and Bartlett’s tests which are significant at 1%.

Table 2. Explorative Factor Analysis.

Factors	Component						KMO
Change in tax system	1	2	3	4	5	6	0.791 ***
Tax_Evasion	0.911
Tax_Frauds	0.892
Wrong_Invoice	0.851
Tax_Credits	0.585
Smooth flow		0.724
E-way bills		0.708
Progressive tax		0.633
Liquidity		0.618
Unfair demands			0.803
No central jurisdiction			0.729
Tax structure simpler			0.660
Biggest reform			0.537
Efficiency				0.891
Transparency				0.857
Cascading effect				0.600
Insolvent trader					0.850
Illegal refunds					0.710
Expand tax base						0.854
Rational rates						0.767

Source: Authors’ Compilation via SPSS. *** significant at 1%.

shows all the factors and related constructs identified for the study. Initial factors were measured using the principal component analysis and rotated with varimax rotation, and the constructs were determined based on the KMO criteria and an Eigenvalue of more than one. We applied the EFA for one of the variables with the highest number of statements asked in the questionnaire, that is, the tax system (Table 2).

4.2. CFA Results

CFA is a statistical technique that helps the researcher to test the hypothesis in observed variables and their underlying constructs used in the study. Following the recommendation of Muller [93] and Hu and Bentler [94], multiple indices of model fit were taken into consideration. They recommended using chi-square statistics, CFI (comparative fit index), and NFI (the normed fit index). The accepted model fit value for CFI and NFI is 0.90 and above, the root means square error (RMSEA) value should be 0.08 or below, and CMIN/df < 5 indicates an acceptable and reasonable fit for the model [95,96].

Figure 2 and

Table 3. CFA Model Fit—Goodness of fit results.

Model	χ2	Df	CMIN/DF	CFI	NFI	RMSEA	p-Value
CFA	121.683	44	2.766	0.982	0.973	0.057	0.000

Source: Authors’ compilation via SPSS AMOS.

demonstrate that the CFA model fit and the overall chi-square were significant, which were χ² = 121.683, df = 44, and p < 0.000. The CFI and NFI were both above 0.90, the basic criteria which indicates a good fit model. In addition, as suggested by Arbuckle and Wothke [97] the root square mean error (RMESA) was 0.057 (below 0.08), which suggests the model represented a good approximation [98,99]. Figure 1 shows the CFA model.

4.3. SEM Model and Regression Model Results

The empirical findings are stated in this section: (A) First, a model was developed using IBM AMOS 24, and its results are shown. (B) Secondly, multiple regression was used to achieve the objective using IBM SPSS 21, and its results are depicted.

(A)

AMOS Model Results

The measurement model results in

Table 4. Measurement Model Results.

Factors	Regression Weights Estimate	S.E.	C.R.	p-Value	Standardized Regression Weights	Squared Multiple Correlations
Financial performance ← firm characteristics	0.049	0.023	2.089	***	0.039	0.945
financial_performance ← tax_system	0.050	0.016	3.165	***	0.053
financial_performance ← DuPont Analysis	0.900	0.032	27.754	***	0.970
NATURE ← firm_characteristics	1.000			***	0.638	0.407
SIZE ← firm_characteristics	0.762	0.059	12.821	***	0.786	0.618
TURNOVER ← firm_characteristics	1.263	0.099	12.723	***	0.787	0.620
Wrong_Invoice ← tax_system	1.000				0.869	0.754
Tax_Frauds ← tax_system	1.022	0.035	29.413	***	0.878	0.771
Tax_Evasion ← tax_system	1.126	0.032	35.365	***	0.994	0.988
Earnings ← DuPont Drivers	1.000				0.927	0.860
Assets to sales← DuPont Drivers	1.115	0.022	51.210	***	0.990	0.981
ROE ← financial_performance	1.000				0.830	0.690
ROI ← financial_performance	1.155	0.037	31.301	***	0.964	0.930

Source: Authors’ compilation via AMOS. *** p < 0.01, ** p < 0.05, * p < 0.10. Note: CMIN/Df 4.134; CFI: 0.979; NFI: 0.973; RMSEA: 0.076.

highlight the impact of the characteristics of firms, the determinants of the tax system, and demographic factors on the financial performance of SMEs. The SEM model (Figure 3) and Table 4 demonstrate the overall positive impact of sustaining the financial performance at a 1% significant level. AMOS-SEM displayed squared multiple correlations for all the constructs and its respective variables opted for in the study (Arbuckle and Wonthke, 2001). All the factors could explain 94.5% percent of the variance in the financial performance of the MSMEs. The absolute fit of the model was attained for the measurement model (CMIN/df < 5; CFI > 0.9; NFI > 0.9 and RMSEA < 0.08). The notable findings of the study were as follows.

Tax system: The first notable finding was the impact of a change in the tax system on the performance at a 1% significance level. It had a positive impact on the financial performance (p-value < 0.000; loading value 0.049 and c.r = 2.089). It stated that a change in tax system enabled the detection of tax fraud (p < 0.000; loading value 1.022; c.r: 29.413; R² 0.771 ), a drop in tax evasion (p < 0.000; loading value 1.126; R² 0.988; c.r = 35.365), and the issuance of wrong invoices which were detected at a much faster rate (p < 0.000; R² 0.754). This made it easier to avail the input tax credits and reduced the blockage of working capital and business funds, leading to the achievement of Hypothesis 1: A change in the tax system positively influenced the financial performance of SMEs.

Characteristics of firms: The second notable finding was the impact of the characteristics of firms: their nature of business, size, and business turnover. The findings stated that the characteristics of firms were significant at a 1% level (p-value < 0.000). In the area of micro and small business research, the presence of economies of scale strongly implies that a firm’s characteristics (size and business turnover) are important factors in assessing its performance. The results highlighted that the nature of the business (manufacturing), size (small and micro), and business turnover were positively associated with financial performance. They were significant at a 1% level (p-value < 0.000) with high squared multiple correlations—Size (p-value < 0.000; c.r:12.821; R² 0.618); Turnover (p-value < 0.000; c.r: 12.723; R² 0.620), leading to the achievement of Hypothesis 2a: The size of firms positively influences the financial performance of SMEs and Hypothesis 2b: Business turnover positively influences the financial performance of SMEs.

DuPont Drivers: The third notable finding was the impact of drivers from the DuPont analysis impacting the ROE and ROI after tax reform. The results highlighted that the drivers contributed significantly at a 1% level (p-value < 0.000). Assets to sales and the earnings of the firms after GST showed a high variance in influencing the financial performance with R² 0.981 and 0.860, respectively.

Demographic variables (age and income) had no impact in the present study on the financial parameters of micro and small enterprises after tax reform. The insignificant results at a higher p-value > 0.10 led to the removal of the variables from the measurement model and the rejection of Hypothesis 3a: Age positively influences the financial performance of SMEs and Hypothesis 3b: Income positively influences the financial performance of SMEs.

(B)

Stepwise Regression Results

After the results of EFA and CFA (Section 4.1 and Section 4.2), the overall impact of the characteristics of firms, demographic variables, determinants of tax reform, and the drivers of DuPont analysis on financial performance were measured.

Table 5. Impact of the characteristics of firms, demographic variables, and GST determinants on the performance of MSMEs.

Independent Factors	ROE				ROI
	Coeff.	t-Stats	Collinearity Stats.		Coeff.	t-Stats	Collinearity Stats.
			Tolerance	VIF			Tolerance	VIF
Firms’ characteristics
Size	0.100	2.710 ***	0.613	1.630	--	--	--	--
Turnover	0.080	3.460 ***	0.584	1.713	--	--	--	--
Demographic Variables
Age	--	--	--	--	--	--	--	--
Income	--	--	--	--	--	--	--	--
Change in tax system
Tax Fraud	0.083	4.379 **	0.903	1.107	0.073	2.801 ***	0.956	1.046
Tax evasion	--	--	--	--	--	--	--	--
Wrong Invoice	--	--	--	--	--	--	--	--
DuPont Analysis Drivers
Earnings	0.734	22.328 ***	0.293	3.413	0.466	10.235 ***	0.302	3.310
Sales to assets	0.103	2.897 ***	0.289	3.464	0.160	3.308 ***	0.310	3.229
Asset Turnover	−0.087	−3.267 ***	0.725	1.380	--	--	--	--
Constant	0.215	0.068 *	--	--	0.434	4.138 ***	--	--
R	0.905				0.724
R-square	0.818				0.525
Adjusted R-square	0.816				0.522
Durbin Watson Ratio	1.098				1.798
F-value	404.948				199.359
p-value	0.000 ***				0.000 ***

*** Statistically significant at 1% level (p-value < 0.01); ** at 5% level (p-value < 0.05); * at 10% level (p-value < 0.10). Source: Authors’ compilation from SPSS.

demonstrates the significance of the above-stated determinants impacting the ROI and ROE of the firms. The results represent unstandardized beta coefficients, the value of t-statistics of all the significant variables are shown as parentheses. The t-statistic measures how many standard errors the coefficient is away from zero. Generally, any t-value greater than +2 or less than −2 is acceptable. The higher the t-value, the greater our confidence in the coefficient as a predictor. The three major outcomes from the regression model in the present research are as follows.

Firstly, a change in the tax system was the most dominant factor. It affected ROI and ROE at the highest significant level of 1% (p-value < 0.000). The stepwise regression results depicted a positive impact of a change in tax system through the detection of tax frauds on ROE (β: 0.083; t-stats: 4.379) and ROI (β: 0.073; t-stats: 2.801). The results signified that a change in the tax system enhanced the profit margins of SMEs.

Secondly, the characteristics of firms influenced the performance of SMEs. With the change in the size of the firm (micro or small), the ROE was impacted (β: 1.000; t-stats: 2.710; p-value < 0.000). The annual business turnover also impacted the ROE of SMEs (β: 0.080; t-stats: 3.460; p-value < 0.000).

Thirdly, the drivers from DuPont analysis were evaluated both on ROE and ROI. The firm’s earnings positively enhanced the ROE (β: 0.734; t-stats: 22.328; p-value < 0.000) and ROI (β: 0.466; t-stats: 10.235; p-value < 0.000). Further, after tax reform, sales to assets positively impacted the ROE (β: 0.103; t-stats: 2.897; p-value < 0.000) and ROI (β: 0.160; t-stats: 3.308; p-value < 0.000). The asset to sales ratio helped to show the company how much revenue a company can generate using their assets, especially after tax reform. Demographic variables (age and income) had no impact on the ROE and ROI of the firms.

The R-square in the stepwise regression for ROE 0.905 and ROI was 0.724. This implies a variance of 90.5% for ROE and 72.4% for ROI which can be explained by all the independent variables for the business performance parameters. The difference between the r-square and the adjusted r-square for all the business performance parameters is less than 0.05. The F-value (ANOVA) was more than ten and the Durbin Watson ratio was near to two for the ROE (1.098) and ROI (1.798) of business performance. Further, the present study indicates no multicollinearity problem as the VIF value < 5. Several researchers [100,101] have suggested typical cutoff values (rules of thumb) for large VIFs of 5 or 10. A VIF value greater than five demonstrates the existence of multicollinearity in the model. A value larger than 10 indicates a severe problem of multicollinearity. Therefore, the present model had no autocorrelation problem [102]. Overall, the results of the stepwise regression indicated that all the criteria for the different business performance parameters for a good fit model were met.

Table 6. Pearson Correlation.

	ROI	Earnings	Asset Turnover	Sales to Assets	Wrong Invoice	Tax Evasion	Tax Fraud	Size	Turnover	Age	Income
ROI	1.000	0.714	−0.405	0.643	0.133	0.187	0.210	0.009	0.118	0.005	0.128
EARNINGS	0.714	1.000	−0.493	0.829	0.140	0.167	0.192	0.047	0.088	0.030	0.137
ASSET TURNOVER	−0.405	−0.493	1.000	−0.495	−0.131	−0.136	−0.168	−0.021	−0.057	0.031	−0.039
SALES TOASSET	0.643	0.829	−0.495	1.000	0.056	0.089	0.112	0.128	0.181	0.103	0.172
Wrong_Invoice	0.133	0.140	−0.131	0.056	1.000	0.864	0.762	0.107	0.162	0.146	0.058
Tax_Evasion	0.187	0.167	−0.136	0.089	0.864	1.000	0.873	0.122	0.179	0.179	0.075
Tax_Frauds	0.210	0.192	−0.168	0.112	0.762	0.873	1.000	0.121	0.203	0.125	0.089
SIZE	0.009	0.047	−0.021	0.128	0.107	0.122	0.121	1.000	0.620	0.163	0.503
TURNOVER	0.118	0.088	−0.057	0.181	0.162	−0.179	−0.203	0.620	1.000	0.154	0.499
AGE	0.005	0.030	0.031	0.103	0.146	0.179	0.125	0.163	0.154	1.000	0.205
INCOME	0.128	0.137	−0.039	0.172	0.058	0.075	0.089	0.503	0.499	0.205	1.000
	ROE	Earnings	Asset Turnover	Sales to Assets	Wrong Invoice	Tax Evasion	Tax Frauds	Size	Turnover	Age	Income
ROE	1.000	0.894	−0.510	0.778	0.180	0.205	0.244	0.028	0.115	0.041	0.139
EARNINGS	0.894	1.000	−0.493	0.829	0.140	0.167	0.192	0.047	0.088	0.030	0.137
ASSET_TURNOVER	−0.510	−0.493	1.000	−0.495	−0.131	−0.136	−0.168	−0.021	−0.057	0.031	−0.039
SALES toASSET	0.778	0.829	−0.495	1.000	0.056	0.089	0.112	0.128	0.181	0.103	0.172
Wrong_Invoice	0.180	0.140	−0.131	0.056	1.000	0.864	0.762	−0.107	−0.162	−0.146	−0.058
Tax_Evasion	0.205	0.167	−0.136	0.089	0.864	1.000	0.873	−0.122	−0.179	−0.179	−0.075
Tax_Frauds	0.244	0.192	−0.168	0.112	0.762	0.873	1.000	−0.121	−0.203	−0.125	−0.089
SIZE	0.028	0.047	−0.021	0.128	−0.107	−0.122	−0.121	1.000	0.620	0.163	0.503
TURNOVER	0.115	0.088	−0.057	0.181	−0.162	−0.179	−0.203	0.620	1.000	0.154	0.499
AGE	0.041	0.030	0.031	0.103	−0.146	−0.179	−0.125	0.163	0.154	1.000	0.205
INCOME	0.139	0.137	−0.039	0.172	−0.058	−0.075	−0.089	0.503	0.499	0.205	1.000

Source: Authors’ Compilation via SPSS.

states the Pearson correlation matrix. The Pearson correlation measures the strength of the linear relationship between two variables. It had a value between −1and 1, with a value of −1 meaning a total negative linear correlation, 0 being no correlation, and +1 meaning a total positive correlation. All the variables were positively correlated except asset turnover which was negatively related and which was accepted as per the DuPont Analysis Theory.

5. Discussion and Conclusions

Tax policies play a crucial role in achieving sustainable development goals. One of the aims of SDG is to improve economic growth, and tax policies and small and micro enterprises can be viewed as key drivers. As better-performing firms can attract investment and support economic growth in the long run, tax policies help them achieve it. So, it becomes essential to evaluate the performance of small and micro enterprises, especially after tax reform or changes in any country. Therefore, along with tax determinants, it is essential to evaluate the impact of the characteristics of firms and demographic variables on the performance of small and micro firms, that is, Return on equity (ROE) and Return on Investment (ROI)—the major barometers of firm performance (as defined in DuPont Analysis). So, in the present study, we utilized the drivers from DuPont analysis, the characteristics of firms (size and business turnover), and tax reform determinants cumulatively to evaluate the impact on the ROI and ROE of the firms. ROI and ROE are the ratios that provide investors with insights into how efficiently a company (more specifically, its management team) is handling the money that shareholders have contributed to it. In other words, it measures a corporation’s profitability in relation to stockholders’ equity.

The study conducted a primary survey on 546 SMEs in the northern region of India and applied SEM via SPSS AMOS. There were three significant findings of the study.

Firstly, the change in the tax system has eradicated the shortcomings in the previous tax structures. The detection of tax frauds, issuance of wrong invoices, and the availment of input tax credits have supported the MSMEs. Moreover, GST has provided a uniform tax structure by merging multiple tax systems that prevailed earlier (sales tax, custom tax, excise, and value-added tax). This has reduced the working capital blockage of the funds and enhanced production, which is clearly evident from the firms’ increased net sales and net profits. A similar opinion was posed by the authors of [103] that changing the tax system increased production efficiency.

Secondly, the size and turnover of the firms positively enhanced the ROI and ROE. In the area of micro and small business research, the presence of economies of scale strongly implies that the characteristics of firms (size and business turnover) are essential factors in assessing its performance. The results highlighted that the nature of the business (manufacturing), size (small and micro), and business turnover are positively associated with financial performance.

Thirdly, the drivers from DuPont analysis after tax reform indicate that change in tax system has influenced the earnings and sales to asset ratio of the firm, which has influenced the ROE and ROE of SMEs. Both factors contribute to enhance financial performance. It is important to evaluate these factors as tax burdens reduce the profitability and sales margin of the firms, which in turn impacts the ROI and ROE.

Practical implications:

Return on equity and Return on investment represent the income measures as they imply the returns of the firms’ stakeholders. The impact of drivers such as sales to assets, earnings, and tax reform determinants enables the establishment of a strong base to measure operational efficiency and capital intensity for the governments, policymakers, and businesses to achieve their target with enhanced performance. Since Return on equity is highly correlated with market value/equity and other market value measures, it can benefit investors. Investors remain interested in the firms that can provide them with large returns and the results may help them analyze the same. Therefore, the results may aid investors at large and the economy as the firms can fetch high investments. Further, the results may aid the government, policymakers, and other countries in acknowledging the key factors that might enhance the returns of firms, which ultimately provide more revenue to the economy and help achieve the economic goals of the sustainable development goals.