Determinants of Profitability in the Indian Inorganic Chemical Sector: An ARDL Empirical Analysis

doi:10.21203/rs.3.rs-4890295/v1

This research study into the impact of various factors on the profitability of India's crucial inorganic chemical industry, examining the interplay between a company's profitability and key variables such as Research and Development Intensity (RDI), net fixed asset turnover ratio, leverage, Export Intensity, and net working capital requirement. Utilizing data from the Prowess financial database spanning from 1998 to 2021, using the autoregressive distributed lag (ARDL) method, to assess both the short-term and long-term dynamics between return on assets (ROA) and these independent variables. Findings from the ARDL analysis reveals a short-term negative impact of RDI, net fixed asset turnover ratio, and leverage on ROA, whereas export intensity and net working capital requirement positively influence profitability. Over the long term, RDI continues to exert a negative effect on ROA, whereas positive impacts are observed from export intensity and net working capital requirement on profitability. The empirical investigation within the inorganic chemical sector, this study sheds light on how strategic financial management of factors such as RDI, asset turnover, leverage, export intensity, and working capital can significantly bolster a firm's financial performance, offering valuable insights for companies use to refine their strategies and enhance profitability.

Return on Assets

Cointegration

Working Capital Management

Export Intensity

and Research & Development Intensity

Inorganic chemicals have a substantial role in several industries, including manufacturing, agriculture, pharmaceuticals, and construction. These chemicals are compounds that do not contain carbon-hydrogen bonds and use minerals and non-living substances. Inorganic chemicals include a wide range of compounds, such as acids, bases, salts, metals, and non-metals (Miessler et al., 2013). They are indispensable in the production of numerous consumer and industrial goods. In the manufacturing processes, inorganic chemicals are synthesized through diverse chemical procedures like extraction, precipitation, and electrolysis. Moreover, they serve as crucial raw materials, catalysts, and additives in producing diverse products.

According to the Indian Brand Equity Foundation (IBEF), India is a significant global inorganic chemicals market player. India ranks third globally in polymer consumption, fourth in agrochemical production, and sixth in overall chemical production. The Indian chemicals industry accounts for 3.4% of the global chemicals industry. As of 2019, the Indian chemicals market was valued at US$ 178 billion and is projected to grow at a CAGR of 8%, reaching US$ 383 billion by 2030. Domestic chemicals sector SMEs are expected to achieve 18–23% revenue growth in Financial Year (FY) 2022 due to increased domestic demand and higher chemical prices. The Indian chemical industry has grown substantially and offers a wide range of inorganic chemicals for domestic consumption and exports. Indian specialty chemicals sector is projected to grow at a CAGR of 12.4%, reaching US$ 64 billion by 2025 from US$ 32 billion in 2019 (IBEF, 2023).

The inorganic chemicals market primarily depends on the feedstock supply due to low processing requirements. However, most of the chemicals in this sector are in short supply in India. Nonetheless, the country’s substantial requirement for inorganic chemicals makes it an attractive market. Imports are expected to increase in the inorganic sector, with phosphorus, potassium, and titanium accounting for 70% of total imports. On the other hand, carbon black, sodium, and titanium are expected to increase exports, accounting for 65% of total exports in this segment (Goyal et al., 2023).

As per the twelfth five-year plan (2012–2017) report of the Government of India, the Technology Up-gradation Fund (TUF) scheme aims to facilitate modernization and technological advancements within the Indian chemicals sector. A proposed allocation of Rs. 500 Crore (USD 111 million) has been suggested for the Indian chemical industry during the 12th Five-Year Plan. The proposed annual allocation of Rs. 100 Crore (USD 22 million) implies an average yearly investment. The Indian chemical industry encompasses various sectors, including organic, inorganic, dyes, pesticides, and specialty chemicals, with an estimated value of around USD 22 billion. With an annual allocation of USD 22 million (equivalent to 0.1% of the industry’s value), this represents a modest initial investment. If the scheme proves successful and garners a positive response from the industry, it could be expanded or receive increased funding, like the trend observed in the TUF scheme for textiles.

Ashwin et al., (2016) showed that as board size and the share of independent directors increase, so does R&D expenditure. This means that when these conditions are present, corporations devote more financial resources to research and development.

As a result, businesses can access additional resources from their surroundings, leading to increased investment in research and development. Furthermore, this study supports that independent directors serve as essential providers of resources to the organization. The R&D stock replaces the knowledge stock, giving R&D a role similar to physical investment in producing outputs (Nadiri., 1993). This assumption is based on the concept of perpetual inventory, which employs the Constant Returns to Scale (CRS) Cobb-Douglas production function. This framework considers the generation of R&D stock, incorporating an arbitrary depreciation rate. Notably, Indian pharmaceutical companies primarily engaged in process R&D experience high depreciation rates (Suri & Banerjee., 2019).

The industry chosen for the study, namely the essential inorganic chemical industry, holds the distinction of being one of the oldest industries in the world, and this is equally true for its presence in India. This industry includes Research and Development Intensity (RDI), Net Fixed Assets Turnover Ratio (NFAT), Export Intensity (EXI), Leverage (LEV), and Working Capital Management (WCM), with firms primarily objective to maximize their revenue. The products manufactured by this industry are inorganic in nature and serve as significant inputs for various downstream industries such as leather, textiles, paints, rubber, and other chemicals. It can be considered the backbone of many other industries. Given its critical role, substantial development and technological upgradation can be expected in this industry. However, during the implementation of India’s New Economy Policy in 1991, firms in this industry (as well as many other industries) faced domestic and international competition. However, these industry issues were compounded by technical inefficiencies and inefficient productivity.

Profitability is an essential financial metric that measures a firm’s generated revenue from its expenses and investment. In drugs and pharmaceuticals firms, many factors affect profitability, including research and development (R&D) investment, intelligent property rights (IPR) market competitiveness, pricing strategy, and regulatory compliance. Prior research has discussed the effect of variables such as patents and newly introduced drugs on a firm’s profitability (Cockburn & Henderson., 1996). Furthermore, market structure and industry dynamics, such as concentration levels and market share, significantly affect the profitability of pharmaceutical firms (Berndt et al., 2002)

Charitou et al., (2010) investigated the effects of working capital management (WCM) on the financial performance of companies operating in emerging markets. Their dataset included companies listed on the Cyprus Stock Exchange between 1998 and 2007. Regression analysis indicated a substantial relationship between a firm’s profitability and cash conversion cycle (CCC) and its constituent components: inventory duration, accounts receivable duration, and. Accounts Payable Period. Rahman & Nasr., (2007) examined the influence of WCM on industry liquidity and profitability. They revealed a negative relationship between an increase in CCC and the firm’s profitability. In other words, with a raise in CCC, the profitability of the firm was reduced. In order to increase shareholder wealth, managers may aim to reduce the CCC to an optimal and acceptable level. Padachi., (2006) examined WCM policies in small manufacturing companies in Mauritius. The findings showed that increased fixed assets were associated with decreased profitability.

Banerji & Mahor., (2023) observed a long-term relationship between the profitability of India’s leading pharmaceutical industry and variables such as WCM, sales, physical capital intensity, RDI, and R&D spillover affected by physical capital intensity. The dataset was used for the years 1993–2021. The results showed a positive association of NWCR with ROA. Also, ROA has a negative association with other variables like LEV, RDI, NFAT, and Spillover interaction term. The accumulation of fixed assets has had a negative impact on profitability, primarily due to significant investments in active pharmaceutical ingredient (API) manufacturing. According to Cherian et al. (2021), the sector has experienced an adverse impact from Chinese exports in recent years. Furthermore, NFAT and the interaction term are evident in their profitability. It has been proposed that MNCs in developing countries such as India only partially support the "S" curve theory. The internationalization level and demonstrated collaboration follows a U-shaped curve (Kumar & Singh, 2008).

Liverpool-Tasie et al., (2017) examined that increased inorganic fertilizer application can result in improved economic returns for maize farmers in Nigeria. Inorganic fertilizer use is associated with higher crop yields, which, in turn, contributes to increased revenue and potentially higher profitability. Li et al. (2019) investigated the performance of inorganic catalysts in energy conversion processes, including carbon dioxide reduction, water splitting, and nitrogen fixation. It emphasizes the significance of developing efficient and sustainable catalysts to address global energy and environmental challenges. Indian technology firms possess impressive technological capabilities and are strongly inclined to engage in mergers and acquisitions (M&A) activities (Gupta & Sharma., 2020). Companies with a track record of innovation and possessing advanced technological capabilities were more inclined to pursue acquisitions to gain access to new technologies and achieve a competitive advantage. Zhai & Ghosal., (2022) study supported the positive relationship between internationalization strategies and firm profitability in the pharmaceutical sector. International expansion enhances financial performance and provides opportunities for diversification, innovation, and competitive advantage. Tyagi & Nauriyal., (2017) Investigated that over the period 2000–2013, higher R&D investment positively affected firm profitability in the Indian pharmaceutical industry. Companies that allocate significant re- sources to research and innovation introduce new and patented drugs, gain a competitive advantage, and capture a large market share. Moreover, larger firms with a significant market share may enjoy economies of scale and bargaining power with suppliers, positively affecting their profitability.

The influence of firm-specific factors on the inorganic growth of technology sector firms in emerging markets, specifically focusing on Indian firms’ mergers and acquisitions (M&A) activities (Das and Kapil., 2015). A dataset comprising 372 Indian firms from 2001–2011, a decade marked by a surge in M&A transactions in the sector. Results suggested the significance of financial performance, technological capabilities, market expansion, and the regulatory environment in shaping firms’ M&A decisions. Understanding these factors is crucial for technology firms to make informed decisions and effectively pursue inorganic growth opportunities amidst the dynamic landscape of emerging markets. Akhmedjonov & Lapatinas., (2017) investigated process innovation, technological advancements, and efficient supply chain management in enhancing the utilization of fixed assets in the pharmaceutical sector. Moreover, the ability to adapt manufacturing processes to changing demand patterns and regulatory requirements can also impact fixed asset efficiency.

The primary data for this research has been obtained from Prowess, a comprehensive financial database that includes information on various financial metrics of firms. The dataset covers the period from 1998 to 2021, providing a substantial period to study the relationship between the dependent and independent variables.

ARDL approaches traditional co-integration methods due to its unique advantages. One of the main strengths is its adaptability in scenarios with restricted sample size or when variables exhibit either order I(0) or order I(1) integration in level. Additionally, the ARDL model allows for concurrently estimating long-run and short-run parameters relationships between ROA (dependent variable) and the independent variables (NFAT, EXI, RDI, LEV, and NWCR).


Variable	Acronym	Formula	Measure
Return on Assets	ROA	$\:\frac{Net\:Profit}{Total\:Assets}$	Measure of Profitability
R&D Intensity	RDI	$\:\frac{R\&D}{Sales}$	Efficiency measure of R&D Intensity.
Net fixed Assets Turnover Ratio	NFAT	$\:\frac{Net\:Fixed\:Assets}{Total\:Assets}$	Efficiency measure of fixed assets to total assets.
Leverage	LEV	$\:\frac{Debt}{Total\:Assets}$	Efficiency measure of Debt,
Export Intensity	EXI	$\:\frac{Export}{sales}$	Measure of Export Intensity
Net Working Capital Requirement	NWCR	$\:\frac{(\text{C}\text{u}\text{r}\text{r}\text{e}\text{n}\text{t}\:\text{A}\text{s}\text{s}\text{e}\text{t}\text{s}-\text{C}\text{u}\text{r}\text{r}\text{e}\text{n}\text{t}\:\text{L}\text{i}\text{a}\text{b}\text{i}\text{l}\text{i}\text{t}\text{i}\text{e}\text{s})}{\left(\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{a}\text{s}\text{s}\text{e}\text{t}\text{s}\right)}$	Measure of Operating Capital

The equation form of the ARDL model is as follows:

$$\:{ROA}_{t}=\:{\beta\:}_{0}+\:{\beta\:}_{1}{ROA}_{t-1}+\:{\beta\:}_{2}{RDI}_{t-1}+\:{\beta\:}_{3}{NFAT}_{t-1}+{\beta\:}_{4}{LEV}_{t-1}+{\beta\:}_{5}{EXI}_{t-1}+{\beta\:}_{6}{NWCR}_{t-1}+ECT\dots\:\dots\:\dots\:\dots\:Model\:1$$

Where, $\:{ROA}_{t}$ is the dependent variable, representing the return on assets for firm, $\:{RDI}_{t-1\:\:},\:{NFAT}_{t-1}\:,\:{LEV}_{t-1\:},\:\:{EXI}_{t-1\:}{,\:NWCR}_{t-1}\:$are the independent variable. $\:{\beta\:}_{0\:}$ is the intercept term. t_− 1 represents the lag value of time. ECT is the error term representing the stochastic component.

Descriptive analysis

The descriptive statistics reveal that all variables have positive mean values and relatively low volatility within their maximum and minimum ranges. The Jarque-Bera test and corresponding P-values indicate that each variable follows a normal distribution, as shown in Table 2. Additionally, the analysis demonstrates a strong and statistically significant (p>0.01) correlation between ROA and RDI, as well as ROA and EXI. Furthermore, the results indicate positive and significant correlations between NWCR and ROA, and LEV and ROA. These findings suggest that the variables under study exhibit favorable characteristics, and their relationships can be crucial in understanding the factors influencing Return on Assets (ROA).

Table: - 2 Descriptive Statistics

	ROA	RDI	NFAT	LEV	EXI	NWCR
Mean	0.038	0.002	0.433	1.769	0.101	0.033
Median	0.036	0.002	0.426	1.773	0.100	0.029
Maximum	0.084	0.004	0.569	1.818	0.178	0.139
Minimum	0.001	0.001	0.326	1.702	0.033	-0.042
Std. Dev.	0.024	0.001	0.056	0.027	0.045	0.045
Skewness	0.579	0.266	0.725	-0.541	0.151	0.376
Kurtosis	2.268	2.766	3.566	3.272	1.693	2.559
Jarque-Bera	2.657	0.478	3.431	1.764	2.547	1.076
Probability	0.265	0.787	0.180	0.414	0.280	0.584
Correlation
ROA	1
RDI	-0.283	1
NFAT	-0.232	-0.107	1
LEV	0.121	-0.133	0.207	1
EXI	-0.150	0.124	-0.025	-0.399	1
NWCR	0.527	-0.065	0.106	0.448	-0.705	1

The unit root analysis was conducted using the Augmented Dickey-Fuller test (ADF) proposed by Dickey and Fuller (1979) and the Philips Peron (PP) test developed by Phillips and Perron (1988). Table 3 presents the results of the unit root estimations, confirming that all the regressors, as well as the regress and, exhibit non-stationarity at their original levels, displaying trends. However, after taking the first difference of the time series, all variables became stationary. Furthermore, the analysis also reveals that none of the variables show second-order integration or I(2) characteristics. These findings demonstrate the effectiveness of the first differencing approach in achieving stationarity and support the absence of higher order integration among the variables. These results are important in the context of time series analysis as they ensure the appropriateness of subsequent modeling and statistical inferences based on these variables.

Table: - 3 Unit Root Test
Variable	Level		1st Difference		Outcome
	Intercept	Trend & Intercept	Intercept	Trend & Intercept
Augmented Dickey-Fuller test
ROA	-1.428	-1.470	-5.219***	-5.206***	I(1)
RDI	-1.015	-1.253	-6.047***	-6.995***	I(1)
NFAT	-3.352**	-3.708**	-5.246***	-5.149***	I(0)/I(1)
LEV	-2.062	-1.878	-5.970***	-5.964***	I(1)
EXI	-1.573	-1.512	-7.362***	-7.511***	I(1)
NWCR	-2.031	-1.900	-5.617***	-6.552***	I(1)
Phillips-perron Test
ROA	-1.592	-1.615	-5.216***	-5.233***	I(1)
RDI	-1.352	-1.280	-6.390***	-6.203***	I(1)
NFAT	-2.297	-2.586	-5.240***	-5.139***	I(1)
LEV	-2.107	-1.921	-6.090***	-6.077***	I(1)
EXI	-1.470	-1.370	-7.342***	-7.742***	I(1)
NWCR	-2.118	-1.759	-5.618***	-6.711***	I(1)
Note: * and rejected the null hypothesis at 1% and 5% significance levels, respectively.

Table 4 presents the results of the Granger Causality (GC) Test conducted to assess the causality relationship between the variables in the study. This test was given by Granger., (1969), test is constructed on the null hypothesis that one variable does not Granger cause the other variable. The test is performed with a lag of 2 periods. The long-run bidirectional causality is only determined between ROA and NFAT with the F-statistic value 3.604 and 3.762, failing to reject the null hypothesis. The causality between ROA to LEV has shown the unidirectional. The null hypotheses are rejected in both cases, suggesting a significant Granger-causality relationship. The long-run unidirectional causality between NWCR to ROA and EXI to ROA, where the null hypothesis is not rejected, indicates a significant GC relationship between the variables.

Overall, the GC Test results provide insights into the directional and significance of causal relationships between variables in the study, helping to understand the dynamics and interactions among them.

Table: - 4 Granger Causality Test

The ARDL approach (Table 5), indicates that the F-statistics for the model are 5.894, and they are statistically significant, exceeding the upper limit in both models. As a result, null hypothesis is rejected, providing evidence of co-integration between the determinants. Next is the lag selection criterion (AIC) criterion to determine the optimal lag length for the model.

To ensure appropriate lag length specification, determine the lag selected is 2. The VAR Granger Causality/Block Exogeneity Wald test reveals causation from the model with a lag of 2. To comprehensively examine the relationships, we performed tests for both short-term and long-term associations (error correction models) in the ARDL model (Tables 6 and 7).

Table: - 5 Estimates based on the Bounds test
F-Bounds Test	Value	Signif.	I(0)	I(1)	Decision
F-statistic	5.894	1%	3.79	4.85	Cointegration
K	5				Cointegration

The short-run analysis reveals that the RDI and RDIt-2 have a significant negative influence on ROA. Specifically, a 1% growth in RDI and RDIt-2 leads to a decrease in profitability by -7.572% and -6.893%, respectively. Furthermore, the variable Leverage (LEV) exhibits a negative and significant association with ROA, implying that a 1% increase in LEV results in a -0.194% decrease in profitability.

Conversely, EXI, NWCR, and its lagged term of NWCR demonstrate significant positive associations with ROA. A 1% increase in EXI, NWCR, and NWCRt-1 leads to consecutive rises in firm profitability by 0.189%, 0.266%, and 0.304%, respectively. The variable NFAT exhibits a negative impact on profitability, although the association is statistically insignificant, as shown in table 6.

Table: - 6 Short-Run ARDL Estimates
Lags	(1, 2, 0, 0, 0, 2)
Variable	Coefficient	Std. Error	t-Statistic	Prob.*
ECT	-0.612	-0.219	-2.794	0.011
ROA t-1	0.388	0.128	3.020	0.007
RDI	-7.572	2.357	-3.213	0.004
RDI t-1	-0.565	4.466	-0.127	0.901
RDI t-2	-6.893	3.077	-2.240	0.036
NFAT	-0.020	0.057	-0.356	0.725
LEV	-0.194	0.101	-1.931	0.067
EXI	0.189	0.066	2.849	0.010
NWCR	0.266	0.144	1.847	0.078
NWCR t-1	0.304	0.136	2.231	0.037
NWCR t-2	-0.129	0.118	-1.089	0.288
C	0.376	0.160	2.354	0.028
R²	0.803
Adj. R²	0.749
F-statistic (Prob.)	10.29 0.000
Durbin-Watson stat	2.147
Source: Authors’ compilation

The ARDL (1, 2, 0, 0, 0, 2) presented in table, model exhibits a good fit with an R-squared value of 74.79%, demonstrating no autocorrelation. The cointegrating equation as Eq. 1: -

ROA = 0.388*ROA (-1) - 7.572*RDI - 0.565*RDI (-1) - 6.893*RDI (-2) - 0.020*NFAT - 0.194*LEV + 0.189*EXI + 0.266*NWCR + 0.304*NWCR (-1) -0.129 NWCR (-2) + 0.376………Eq. 1

Cointegrating Equation: -

D(ROA) = 0.376 -0.612*ROA (-1) -13.796*RDI(-1) -0.398*NFAT(-1) -13.796*RDI(-1) -0.398*NFAT(-1) + 0.446*LEV(-1) -0.261*EXI(-1) + 0.166*NWCR(-1) -0.011*D(ROA(-1)) -7.571*D(RDI) + 5.573*D(RDI(-1)) -0.138*D(NFAT) + 0.076*D(NFAT(-1)) -0.011*D(LEV) -0.346*D(LEV(-1)) + 0.058*D(EXI) + 0.210*D(EXI(-1)) + 0.065*(ROA - (-24.541*RDI(-1) – 0.033*NFAT(-1) -0.317*LEV(-1) + 0.309*EXI(-1) + 0.720*NWCR(-1) ) + 0.376*D(RDI) ) ………Eq. 2

In the long run, approximately 61% of the deviation in ROA is corrected in nineteen months, returning the variable to its long-run equilibrium, as indicated by Eq. 2.

Table: - 7 Long-Run ARDL Estimates
Lags	(1, 2, 0, 0, 0, 2)
Variable	Coefficient	Std. Error	t-Statistic	Prob.*
RDI	-24.541	8.930	-2.748	0.012
NFAT	-0.033	0.089	-0.375	0.712
LEV	-0.317	0.186	-1.703	0.103
EXI	0.309	0.107	2.872	0.009
NWCR	0.720	0.197	3.664	0.001
Source: Authors’ compilation

The long-run analysis reveals that the RDI significantly negatively influences ROA. Specifically, a 1% growth in RDI leads to a decrease in profitability by -24.541. Excessive investment in research and development (R&D) can negatively impact the profitability of Indian inorganic chemical companies due to high initial costs and uncertain results. EXI and NWCR demonstrate significant positive associations with ROA. A 1% increase in EXI and NWCR leads to rises in firm profitability by 0.309% and 0.720%, respectively. Firms can reduce their dependence on the domestic market and mitigate risks associated with fluctuations in local demand and economic conditions. Moreover, fluctuations in exchange rates can create opportunities for these firms to achieve price competitiveness in foreign markets. Effective operations management in inorganic firms, as reflected in the NWCR, is instrumental in attaining improved financial stability. Moreover, a positive NWCR enhances the firm's creditworthiness, leading to better credit terms and lower interest rates when accessing capital. These favourable financial conditions contribute to the overall profitability of the inorganic firm. The variables NFAT and LEV negatively impact profitability, although the association is statistically insignificant, as shown in table 7.

Table: - 8 Residual Diagnostics
LM (Prob.)	0.210 (0.812)
Heteroskedasticity Test (Prob.)	0.901 (0.548)
Jarque-Bera (Prob.)	0.771 (0.679)
CUSUM	Stable
CUSUM - Sq.	Stable
Source: Authors’ compilation

In the diagnostic phase in table 8, tests are conducted to ensure the adequacy and stability of the model. Firstly, an examination of residual normality is performed, along with the presence of serial correlation and the homoscedasticity of residuals. Secondly, the model's stability is verified using the Ramsey RESET test. Additionally, two tests, namely the Cumulative Sum of Recursive Residuals (CUSUM) and Cumulative Sum of Square of Recursive Residuals (CUSUMSQ) as seen in figure 1, are utilized to assess the model's stability further.

The Indian inorganic chemicals industry a significant global player in the inorganic chemicals market, the industry's growth and potential are evident. The Indian chemicals market has witnessed substantial growth, with projections indicating further expansion in the coming years. The inorganic chemicals market in India heavily relies on feedstock supply, which poses challenges due to short supply of certain chemicals. However, the substantial demand for inorganic chemicals in the country makes it an attractive market for imports and exports.

The GC Test explored the causal relationships between the variables. The results revealed a long-run bidirectional causality between ROA and NFAT and a unidirectional causality from ROA to LEV. Moreover, long-run unidirectional causality was observed from NWCR to ROA and from EXI to ROA. The ARDL approach indicates that RDI and its lagged term negatively impact profitability in the short-run, while EXI and NWCR have positive significant effects. In the long run, excessive investment in R&D negatively affects profitability, while higher export intensity and effective working capital management positively influence firm profitability.

Analysis confirmed the long-run relationships among the variables, implying that adjustments to deviations from equilibrium would eventually lead to the long-term stability of profitability. With the right policies, investments, and efficient management of factors influencing profitability, the industry can contribute substantially to India's economic progress.

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Determinants of Profitability in the Indian Inorganic Chemical Sector: An ARDL Empirical Analysis

Status:

Version 1

Abstract

Figures

Introduction

Review of Literature

Research Methodology

Results and discussion

Conclusion

References

Status:

Version 1