Working Capital Management,Macroeconomic Impacts,and Firm Profitability: Evidence from Indian SMEs

Abstract

The present study investigates the relationship between working capital management and SME profitability. It also analyzes the impact of macroeconomic impulses on firm profitability through efficient management of working capital in the case of Indian small and medium scale enterprises over the time period spanning from 2010 to 2017 using Feasible Generalized Least Square (FGLS) regression models. The study concludes the negative relationship of account receivables together with a positive relationship of inventories and account payables with SME profitability. It implies the firm managers can maximize SME’s profitability by converting the credit sales to cash as early as possible, by increasing the days of accounts payable and following a conservative inventory management strategy. Changes in economic growth and commercial bank advances to small scale industries are the key macroeconomic determinants that are impacting SME profitability. The results from this paper may guide the firm managers to shape their working capital management strategies to maximize profitability. Policymakers may find the study interesting to identify the macroeconomic parameters that significantly influence Indian SMEs.

Keywords

Working capital management macroeconomic effects SME profitability FGLS

Introduction

Smith (1980), emphasizes the role of working capital on firm profitability, risk and consequently on its value. The working capital management has gradually gained importance due to its contribution to shareholder’s value that maintains a tradeoff between profitability and risk (see Banos et al., 2010; Kieschnick et al., 2013; Lazaridis & Tryfonidis, 2006; Nazir & Afza, 2009; Ukaegbu, 2014). According to Aktas et al. (2015) and Yunos et al. (2015), efficient working capital management (WCM) helps in reallocating underutilized resources to generate higher-value that can lead to the enhancement of firm’s performance and can help in increasing profitability without resulting in liquidity problems. These studies have the privilege of analyzing large firms with all the necessary physical resources as well as managerial and intellectual ability to manage their working capital requirements. However, the growth of small and medium scale enterprise (SMEs) is hindered by issues such as poor infrastructure, inadequate market linkages, lack of access to institutional finance, poor management and lack of advanced technology (See Abor & Quartey, 2010; Pansiri & Temtime, 2008; Saleh & Ndubisi, 2006). At the same time some selective studies specific to SMEs like Varaporn and Kiattichai (2018), find a positive impact within industry diversification on the performance of SMEs in emerging markets. Yuan and et al. (2016) find the firm-level features like revenues, profitability, competitive position and future prospects of SMEs play a dominant role in determining the nature of the strategic decisions of SMEs. And according to Alexander et al. (2017), operational and governance improvements are the common value creation measures in all buy-outs of SMEs.

These are some experiences about issues and challenges of small and medium scale enterprise across the globe. The present study will bridge the research gap in WCM literature by providing empirical evidence on the relation between WCM and SME profitability in the case of Indian SMEs and also analyze the impact of macroeconomic inputs on their profitability. So far no such evidence is available in prior literature despite the fact that SMEs are the backbone of not only the Indian economy but also for developing as well as developed countries (Kumar & Rao, 2015).

The present study has two main focuses. Firstly, it aims to investigate the relationship between working capital management and SME profitability. Secondly, it attempts to analyze the impact of macroeconomic impulses on firm profitability through efficient management of working capital. The study contributes to the literature in several ways. Firstly, the study is undertaken on the constituents of the Bombay Stock Exchange (BSE) SME IPO index that are listed as SMEs in the Indian stock market. Secondly, the study tries to capture the impact of all the components of working capital management and macroeconomic impacts on SME profitability. Thirdly, the study uses a feasible generalized least square estimator to handle within sample unobservable heterogeneity.

The paper is structured in the following manner. Section 2 presents the theoretical background of the study. Section 3 presents a methodological approach and research methods, section 4 presents data analysis, section 5 presents findings of the study, section 6 concludes.

Theoretical Background and Review of Earlier Studies

Theoretical Background Between Working Capital Management and Firm Profitability

Looking at the relevance of WCM on firm profitability, all the components of working capital management need to be analyzed independently and the study intends to do that.

Accounts Receivables (AR) and Profitability

The relevance of AR on profitability is analyzed by several studies (Banos–Caballero et al., 2014; Peel et al., 2000) and its nature of relationship depends on aggressive or conservative policy adopted by the firms (Garcia-Teruel & Martinez-Solano, 2007; Nazir & Afza, 2009; Tauringana & Afrifa, 2013). An aggressive policy followed by a lower accounts receivable period may increase profitability via increasing cash flow that ultimately increases the current asset and liquidity level of the firm. It increases their creditworthiness and protects the firms from liquidity crunch and financial distress (Arena & Julio, 2010). Timely payment of current liability also increases stakeholders’ and suppliers’ confidence which translates to larger commercial benefits in the long run. According to the transactional cost theory on the trade credit of Ferris (1981), firms can increase their operational efficiency by reducing transaction costs by decreasing the account receivable period. Hence the reduction of AR will lead to profitability (Bhattacharya, 2008; Nelson, 2002). On the basis of the above discussion, the present study has identified the following hypothesis to test.

Hypothesis (H1): Whether there exists a negative relationship between account receivables and profitability.

Accounts Payable and Profitability

Accounts payable stands to be the second most relevant determinant of WCM. It has both positive and negative effects on firm performance (Banos-Caballero et al., 2014). According to Ferris (1981), transactional cost theory accounts payables increase the operational efficiency of firms through the reduction of transaction costs caused by delaying payments (i.e. increasing of days of accounts payable period). In line with transactional cost theory, Nelson (2002), Bhattacharya (2008), and Sharma and Kumar (2011) find a positive relationship between accounts payables and profitability. It implies that firms are able to increase their profitability through reducing transactional costs of bills payment by delaying payments or by increasing payable days. Whereas, Laziridis and Tryfonidis (2006), Garcia-Jeruel and Martinez-Solano (2007), and Samiloglu and Demrigunes (2008) find a negative relationship between accounts payable and firm profitability. On the basis of the above discussion, the present study sets its hypothesis on the basis of transactional cost theory.

Hypothesis (H2): Whether there exists a positive relationship between account payables and profitability.

Inventory Management and Profitability

Inefficient inventory management could lead to a reduction in firms’ productivity, efficiency and hence profitability (Ching et al., 2011; Eroglu & Hofer, 2011; Gill et al., 2010; Mathuva, 2013). Firm managers can follow either an aggressive or a conservative strategy of inventory management to maximize profit. Holding a lower amount of inventory is considered an aggressive inventory management strategy whereas holding a higher level of inventory is considered to be a conservative strategy. Holding lower inventory through aggressive strategy may increase profitability due to the reduction of holding cost (Garcia-Teruel & Martinez-Solano, 2007; Laziridis & Tryfonidis, 2006; Nazir & Afza, 2009; Tauringana & Afrifa, 2013). It is further justified by the “Just-In-Time” (JIT) theory of inventory management. Accordingly, Banos-Caballero et al. (2010) and Chen et al. (2005) found a significantly negative relationship between inventory and profitability. Contrary to this, the precautionary, speculative and transactional motive theory of holding inventory stands in favor of conservative inventory management strategy and suggests that companies keep inventory as a precaution against the situation of stock out (Bhattacharya, 2008). In a similar line, Blinder and Maccini (1991) and Corsten and Gruen (2004) have favored holding of larger inventory and stated its positive impact on profitability. Nevertheless, it also increases holding and financing costs (Kieschnick et al., 2013), which may lead to high-interest expenses and higher credit risk (Aktas et al., 2015). The above findings of inventory management strategies and their impact on firm profitability will be more appropriate for large firms. But in the case of SMEs, it will be quite preliminary to set the hypothesis for the firms who are suffering from poor infrastructure, inadequate market linkages and lack of access to institutional finance. Therefore, the present study would like to test the following hypothesis by drawing an implication for SMEs and set the hypotheses as follows;

Hypothesis (H3a): Whether an aggressive Inventory management strategy with a minimum stock of inventory will support SME profitability.

Hypothesis (H3b): Whether a conservative Inventory management strategy with more stock of inventory will support SME profitability.

Working Capital Management and Firm Profitability

Earlier studies have analyzed the relationship between overall working capital management (WCM) and profitability under different aspects of the economic environment and also in different industry aspects (Koumanakos, 2008, Panda and Nanda, 2018). Some of the studies such as Karaduman et al. (2011) and Alipour (2011) have found a negative relationship between working capital management and profitability; whereas Lazaridis and Tryfonidis (2006) and Stephen and Elvis (2011) has found positive relationships. These studies leave the firm managers indecisive by providing mixed results and most of the findings are for large firms. Therefore, the present study has attempted to analyze the relevance of WCM on firm profitability for Indian SMEs and set the hypothesis as follows.

Hypothesis (H4a): Whether there is a positive relationship between working capital management and SME profitability.

Hypothesis (H4b): Whether there is a negative relationship between working capital management and SME profitability.

Research Methodology

To establish the relationship between working capital management (WCM), macroeconomic shocks, and firm profitability for Indian SMEs, the present study has estimated a set of models. The basic model in equation–1 explains the above-mentioned three key components, which include various components of working capital management, macroeconomic effects, and firms’ specific control factors for capturing their effects on profitability.

Y_{i t} = α + \sum_{j = 1}^{4} β_{j} W C M_{i j t} + \sum_{l = 1}^{4} ϕ_{l} C_{i l t} + ε_{t}

(1a)

X_{i t} = α + \sum_{k = 1}^{5} γ_{k} M A C R O_{k t - 1} + \sum_{l = 1}^{4} ϕ_{l} C_{i l t} + ε_{t}

(1b)

$Y_{i t}$ implies firm profitability (ROA) and $X_{i t}$ represents WCM for firm i at time t. $\sum_{j = 1}^{4} β_{j} W C M_{i j t}$ is the group of four parameters, which represent working capital management for firm i at time t. $\sum_{k = 1}^{5} γ_{k} M A C R O_{k t - 1}$ is the group of five macroeconomic factors that are purely exogenous to the system of equations. These policy parameters are used with a one-time period lag to avoid the possible effect of simultaneity in econometric modeling. Moreover, the transmission of policy effects on firm performance can be better captured with a time lag. Finally, $\sum_{l = 1}^{4} ϕ_{l} C_{i l t},$ a group of four control variables is used to increase the predictive accuracy of the econometric models. The basic equation has been reframed with the requirements of the objectives of the study and is explained in the subsequent sections.

Working Capital Management and Firm Profitability

The effects of working capital management on SME’s profitability are captured by the following models. The models are primarily developed on the conceptual basis of working capital and are subsequently expanded on the empirical grounds of the existing literature. Each component of working capital management is estimated separately. Following the studies of Aktas et al. (2015), Pais and Gama (2015), Enqvist et al. (2014), Tauringana and Afrifa (2013), Net Working Capital Cycle (NWCC) has been used as the single entity to represent working capital management for capturing its overall impact on profitability.

{ROA}_{it} = α + β_{1} {AR}_{it} + ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t}

(2)

{ROA}_{it} = α + β_{1} {IV}_{it} + ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t}

(3)

{ROA}_{it} = α + β_{1} {AP}_{it} + ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t}

(4)

{ROA}_{it} = α + β_{1} {NWCC}_{it} + ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t}

(5)

Firm size, growth, debt, and liquidity are used as control variables to increase the predictive ability of the model.

Effect of Macroeconomic Impulses on Working Capital Efficiency

The present study has attempted to capture the effects of macroeconomic policy on the profitability of Indian SMEs. We have considered the growth rate of the economy to incorporate the impact of the business cycle. Since the Government of India has taken up many initiatives and special policies to prioritize the sector’s development, the public sector has planned the outlay in the annual budget for industries, which has been considered to capture the impacts of the designed policy on the performance of SME. Similarly, the commercial bank makes advances to small scale industries, and the changes in interest rates capture money market impacts; and the changes in exchange rate addresses the open economy shocks to SME’s profitability. Equations 6 to 9 are designed to capture the impacts of macroeconomic shocks on working capital management of SMEs. All the macroeconomic variables are lagged by a single period to handle the possible issue of simultaneity in econometric modeling. Moreover, policy variables function with a time lag, and the existence of control variables helps in improving the overall level of significance as well as the goodness-of-fit for all the models. The models are explained below.

\begin{array}{l} {AR}_{it} = α + γ_{1} Δ {GDP}_{it - 1} + γ_{2} Δ {PSPO}_{it - 1} + γ_{3} Δ {BASSI}_{it - 1} + γ_{4} Δ {EX}_{it - 1} + γ_{5} Δ {INT}_{it - 1} + \\ ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t} \end{array}

(6)

\begin{array}{l} I V_{it} = α + γ_{1} Δ {GDP}_{it - 1} + γ_{2} Δ {PSPO}_{it - 1} + γ_{3} Δ {BASSI}_{it - 1} + γ_{4} Δ {EX}_{it - 1} + γ_{5} Δ {INT}_{it - 1} + \\ ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t} \end{array}

(7)

\begin{array}{l} {AP}_{it} = α + γ_{1} Δ {GDP}_{it - 1} + γ_{2} Δ {PSPO}_{it - 1} + γ_{3} Δ {BASSI}_{it - 1} + γ_{4} Δ {EX}_{it - 1} + γ_{5} Δ {INT}_{it - 1} + \\ ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t} \end{array}

(8)

\begin{array}{l} {NWCC}_{it} = α + γ_{1} Δ {GDP}_{it - 1} + γ_{2} Δ {PSPO}_{it - 1} + γ_{3} Δ {BASSI}_{it - 1} + γ_{4} Δ {EX}_{it - 1} + γ_{5} Δ {INT}_{it - 1} + \\ ϕ_{1} {Size}_{it} + ϕ_{2} {Growth}_{it} + ϕ_{3} {Debt}_{it} + ϕ_{4} {liquidity}_{it} + ε_{t} \end{array}

(9)

The above models are being estimated by using Feasible Generalized Least Squares (FGLS) Estimators. The FGLS estimator is simply the OLS estimator applied to a transformed regression that purges the heteroscedasticity and/or autocorrelation. However, if the model of heteroscedasticity and/or autocorrelation then FGLS is asymptotically unbiased, efficient, and consistent.

Data Analysis

The biggest challenge of the study is the availability of financial data for Indian SMEs. If we look at the potential of the SME sector, the Bombay Stock Exchange (BSE) of India has introduced the BSE SME IPO index on December 14, 2012 with the base date from August 16, 2012 for enabling investors to track the performance of the SME companies listed at BSE. Out of the index constituents, financial information for 49 firms has been collected for the time period spanning from 2010 to 2017 from the Center for Monitoring Indian Economy (CMIE) database. The firms are selected on the basis of availability of data, and the data for the listed firms are collected for three years prior to the launch of SME IPO index keeping in view that the firms must have audited their financial performances for a minimum three years prior to their application for listing. A detail explanation of the variables is presented in Table 1.

Table 1.

Variable Description

Variables	Definition	Sources
Measure of Profitability
ROA	Return on assets, measured as profit before interest and tax to the total assets	CMIE
Measures of Working Capital Management
AR	Account receivable, that is, an average number of day firms takes to collect receivables from customers.	CMIE
IV	Inventories, that is, the average number of day firms that hold their inventory.	CMIE
AP	Account Payables, that is, the average number of days it takes a firm to pay trade credits.	CMIE
NWCC	Networking capital cycle, that is, the time a firm takes to convert its current asset and current liability to cash.	CMIE
The Measure of Macroeconomic Effects
Δ GDP	That is a change in economic growth rate proxy as the change in Gross Domestic Product at factor cost	RBI
Δ PSPO	Stands for changes in Public Sector Plan Outlay in the annual budget for industry and minerals	RBI
Δ BASSI	Changes in commercial bank advance to small scale industries	RBI
Δ Exchange Rate	Changes in real effective exchange rate (REER) index	RBI
Δ Interest Rate	Changes in annual lending rate, a proxy for interest rate	RBI
Control Variables
Size	Firm size, the log of total assets is used as a proxy for firm size;	CMIE
Growth	Firm growth, rate of growth of sale is used as a proxy for the firm’s growth rate	CMIE
Debt	Debt is the debt ratio calculated as debt over total asset	CMIE
Liquidity	Firm’s liquidity, that is, current ratio	CMIE

Source: Authors’s calculation.

Note: CMIE: Center for Monitoring Indian Economy; RBI: Reserve Bank of India.

Summary Statistics

Table 2 reports the pair-wise correlation coefficients with their respective level of significance. Among the firm-specific factors, profit is negatively and significantly correlated with account receivable, NWCC, size, and growth. Among the macroeconomic indicators, profit is positively and significantly correlated with economic growth. The parameters of working capital management are experiencing an insignificant correlation with the changes in macroeconomic impulses, whereas the firm-specific control variables like size, growing debt, and liquidity have shown some significant correlation. The size of SMEs shows a significant negative correlation with the changes in the advances made by a commercial bank to small scale industries and their positive correlation with the changes in the exchange rate. The growth of Indian SMEs is significantly and positively correlated with the changes in public sector plan outlay in the annual budget for industry and by the changes in the exchange rate. Similarly, debt and liquidity positions are positively correlated with the changes in the advances made by the bank to small-scale industries. The study finds evidence about the significant impacts of macroeconomic policy variables on the key performance parameters of Indian SMEs, particularly those that are expected to influence profitability and working capital management cycles of the firms.

Table 3 reports the summary statistics of firm characteristics and macroeconomic indicators. Among the firm-specific variables, inventories, firm size, and debt are more close to a normal distribution with skewness closer to zero and kurtosis at around 3. Similarly, the probability density function of all the macroeconomic variables is also close to normal distribution. Variables like account receivable, account payables, NWCC, growth, and liquidity level of Indian SMEs are mostly non-normally distributed and largely positively skewed. The probability density function of these variables shows the heavy tail with high outliers within the distribution. Other factors like account receivable, account payables, and NWCC are highly volatile. This provides primary evidence about the relevance of working capital management for Indian SMEs. Further, the higher standard deviation of changes in Public Sector Plan Outlay in the annual budget for Industry (ΔPSPO) may increase policy uncertainty and discourage the SME managers to take long-term strategic decisions. Interestingly, all the variables in the study are positively skewed.

Table 2

. Correlation Coefficients of Firm-specific Variables Used in the Study

ROA

NWCC

Size

Growth

Debt

Liquidity

ΔGDP

ΔPSPO

ΔBASSI

ΔExrate

ΔINTR

ROA

–0.12^**

0.106

–0.12^**

–0.08

0.42^***

–0.11

NWCC

–0.14^**

–0.01

0.15^**

0.02

Size

–0.08^**

0.41^***

–0.21^***

0.42^***

–0.03

Growth

–0.01^*

0.07^*

0.03

0.04

–0.005

0.05

Debt

–0.09

0.08

–0.02

–0.03

0.02

0.11^**

0.01

Liquidity

0.021

–0.15^***

0.02

–0.07

–0.24^***

–0.04

–0.08

ΔGDP

0.003^*

0.02

–0.04

0.03

–0.007

–0.02

0.001

–0.02

0.008

ΔPSPO

–0.013

–0.001

0.005

0.007

0.06

–0.01

0.07^***

–0.04

0.01

0.77^***

ΔBASSI

–0.035

–0.03

0.006

0.07

–0.18^***

–0.04

0.003^*

0.11^***

0.26^***

–0.07^**

ΔExrate

–0.08

0.01

–0.001

0.004

0.08

0.05^**

0.11^***

–0.05

0.03

–0.06

0.03

–0.11^***

ΔINTR

0.04

0.02

–0.07

0.03

–0.05

–0.001

–0.05

0.04

0.12^***

0.14^**

0.41^*

–0.18^***

Source: Authors’s calculation.

Notes: ROA = return on assets, indicator of firm’s profitability; AR = accounts receivable; IV = inventories; AP = accounts payables; NWCC = net working capital cycle; Size = firm size considered as proxy of log of total assets; Growth = rate of growth of sales, a proxy for firm’s growth rate; Debt = the debt ratio calculated as debt over total asset; Liquidity = represents (current ratio) firm’s liquidity; ΔGDP = change in economic growth rate (Gross Domestic Product at factor cost); ΔPSPO = Public Sector Plan Outlay (in annual budget) for industry and minerals; ΔBASSI = commercial bank advances to small scale industries; ΔExrate = change in real effective exchange rate; ΔINTR = change in annual lending rate, proxy for interest rate. The superscripts ***, ** and * indicate the level of significance at 1%, 5%, and 10%, respectively.

Table 3.

Summary Statistics

Variable	Mean	Median	SD	Skewness	Kurtosis
ROA	3.86	2.23	6.33	3.37	17.7
AR	97.2	34.5	170.36	4.45	33.64
IV	31.12	27.54	22.61	0.73	2.86
AP	60.29	28.7	124.87	5.33	39.04
NWCC	326.18	59.61	1975.3	6.43	52.69
Size	4.94	5.47	1.89	1.4	5.21
Growth	3.44	0.11	15.34	8.10	71.34
Debt	0.45	0.45	0.21	0.39	2.57
Liquidity	3.7	1.42	7.91	5.42	36.79
ΔGDP	6.95	6.88	1.20	0.34	1.91
ΔPSPO	13.32	15.18	24.06	–0.39	3.37
ΔBASSI	21.79	21.89	11.81	0.078	1.92
ΔExrate	0.59	–0.35	5.02	0.56	2.53
ΔINTR	–3.71	–1.93	13.20	–0.97	4.06

Source: Authors’s calculation.

Notes: ROA = return on Assets, indicator of firm’s profitability; AR = accounts receivable; IV = inventories; AP = accounts payables; NWCC = net working capital cycle; Size = firm size with a proxy of log of total assets; Growth = rate of growth of sales, a proxy for firm’s growth rate; Debt = debt ratio calculated as debt over total asset; Liquidity = (current ratio) firm’s liquidity; ΔGDP = change in economic growth rate (Gross Domestic Product at factor cost); ΔPSPO = Public Sector Plan Outlay (in annual budget) for industry and minerals; ΔBASSI = commercial bank advances to small scale industries; ΔExrate = changes in real effective exchange rate; ΔINTR = change in annual lending rate, proxy for interest rate.

Findings of the Study

Working Capital Management and SME Profitability

Table 4 presents the estimated statistics of Equations 2, 3, 4 and 5 that reports the relationship between working capital management and profitability of Indian SMEs. The study finds a negative and significant relationship between profit and account receivables (AR) and a positive relationship between profit and account payables, where we accept the hypothesis H1 and H2. This implies profitability would increase through the reduction of the length of the account receivable time period and NWCC. This is in line with some of the previous studies (Enqvist, 2014; Garcia & Solano, 2007; Gill et al., 2010; Lazaridis & Tryfonidis, 2006; Yunos et al., 2015). But the present study deviates from these studies in two points, as we find a significant and positive relationship between profit and days of inventories as well as profit and account payables. In the case of inventory, the study accepts the hypothesis H3b that advocates that a conservative inventory management strategy could help Indian SMEs to maximize profitability. The advantage of handling larger inventories is supported by several researchers such as Blinder and Maccini (1991) and Corsten and Gruen (2004) from the point of view that holding larger inventory protects a firm from the adversities of input price fluctuations and minimizes the loss of sales owing to potential stock-outs. Moreover, the negative relationship of account receivables together with a positive relationship of inventories and account payables with profitability leads to the generation of a beautiful insight into working capital management for Indian SMEs. Firm managers may maximize SME’s profitability by reducing average account receivables and increasing the account payables as well as by maintaining an adequate inventory to maintain an uninterrupted operating cycle. However, an over investment in working capital may increase the holding and financing costs (Kieschnick et al., 2013), which in turn may lead to a situation of high-interest expenses and high credit risk (Aktas et al., 2015) scenario. Table 4 also presents the test statistics that enlighten the model’s stability and robustness. A significant Wald Chi² validates the overall level of significance of the models with an alternate hypothesis, which states that all the estimated coefficients are significantly other than zero. Similarly, Breusch-Pagan and White’s test measures the nature of heterogeneity within the panels. These pre and post estimation test statistics are presented to ensure the accuracy of the models.

Table 4.

Working Capital Management and Firm Profitability

Independent Variables	Equation 2	Equation 3	Equation 4	Equation 5
AR	–0.003^***
IV		0.007^***
AP			0.003^***
NWCC				–0.028^**
Size	–0.295^***	–0.421^***	–0.427^***	–0.667^***
Growth	–0.014^*	–0.047^***	–0.076^***	–0.059^***
Debt	–0.694	–0.706	–2.126^***	–0.626
Liquidity	0.019	0.059	0.436^***	–0.003
Constant	5.908^***	5.993^***	6.698^***	7.887^***
Wald Chi2	29.95***	27.65***	34.08***	25.19***
B & P Test	111.9***	105.76***	105.7***	99.58***
White’s Test	78.47^***	69.03***	71.04***	93.22***

Source: Authors’s calculation.

Notes: The dependent variable is ROA used as a proxy for firm profitability. AR = accounts receivable; IV = inventories; AP = accounts payables; NWCC = net working capital cycle; Size = firm size (i.e., log of total assets); Growth = rate of growth of sales (a proxy for firm’s growth rate); Debt = debt ratio calculated as debt over total asset; Liquidity = (current ratio) firm’s liquidity. Wald Chi² statistics present an overall significance level of the model by rejecting the H0: that all the coefficients of the model are not significant other than zero. B & P Test indicates the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity, White’s Test tests the H₀: Homoscedasticity against H₁: unrestricted heteroskedasticity. The superscripts ***, ** and * indicate the level of significance at 1%, 5%, and 10%, respectively.

Table 5.

Effects of Macroeconomic Variables on Working Capital Efficiency

Independent Variables	Equation 6	Equation 7	Equation 8	Equation 9
ΔGDP	8.98^***	–0.772	5.638^**	–6.721^***
ΔPSPO	–1.734^***	0.011	–0.135	–1.66
ΔBASSI	0.307	–0.029	0.956^***	–1.99^**
ΔExrate	3.109^***	0.001	–0.283	0.060
ΔINTR	–1.345^***	–0.042	–0.533	0.286
Size	32.96^***	–3.183^***	8.971^***	–3.013^**
Growth	–0.200	–0.016	–0.028^***	–0.364^*
Debt	7.28^***	6.466^***	–11.01^***	9.748
Liquidity	–1.742^*	–0.118	–2.073^*	–0.355
Constant	–59.29^***	51.084	–15.31^***	13.81^***
Wald Chi2	86.34***	17.69**	137.32***	22.95***
B & P Test	58.75***	34.33***	183.39***	181.54***
White’s Test	58.26***	90.44***	86.95***	17.46

Source: Authors’s calculation.

Notes: The dependent variable for Equations 6, 7, 8 and 9 are account receivable (AR), inventories (IV), account payable (AP) and net working capital cycle (NWCC), respectively. ΔGDP = change in economic growth; ΔPSPO = Public Sector Plan Outlay for industry and minerals; ΔBASSI = commercial bank advances to small scale industries; ΔExrate = change in real effective exchange rate; ΔINTR = change in annual lending rate; Firm Size = firm’s growth rate; Debt = debt ratio calculated as debt over total asset; Liquidity = (current ratio) firm’s liquidity. Wald Chi² statistics present an overall significance level of the model by rejecting the H₀: that all the coefficients of the model are not significant other than zero. B & P Test indicates Breusch–Pagan/Cook–Weisberg test for heteroskedasticity, White’s Test tests the H₀: Homoskedasticity against H₁: unrestricted heteroscedasticity. The superscripts ***, ** and * indicate the level of significance at 1%, 5%, and 10%, respectively.

Effects of Macroeconomic Variables on Profitability via Working Capital Efficiency

In this part of the study we are trying to analyze the effects of macroeconomic policies on SMEs profitability through the channels of WCM by estimating equations 6, 7, 8 and 9 and the estimated results are presented in Table 5. In this section, the study aims to explore the relationship of macroeconomic parameters with the components of WCM and then using the finding of our 1^st objective discussed in section 5.1, we will analyze the implied relationships of macroeconomic parameters with SME profitability and will validate the hypotheses. The relation between macroeconomics variable is WCM are analyzed through the channels of account receivable, stock of inventory accounts payable and networking capital cycle. Economic growth is expected to have a positive impact on SME profitability. An increase in GDP is significantly increasing AR which intern decreases profitability and hence we fail to accept the hypothesis. Change in GDP is positively impacting AP and hence can boost SME profitability. Similarly, NWCC is negatively related to GDP and hence justifies the positive impact of GDP on profitability. Altogether, we accept the hypothesis that economic growth supports SME profitability through account payable and NWCC. Similarly, Government planned allocation (PSPO) is expected to increase SME profitability and found significant only with AR. An increase in PSPO decreases accounts receivable and hence increases profitability. Coming to commercial bank advances to small scale industries (BASSI), it is significantly impacting accounts payable (AP) and NWCC. An increase in BASSI increases AP and hence increases SME profitability, whereas an increase in BASSI decreases NWCC and increases profitability. Similarly, AR is the only component of WCM that is significantly explained by the exchange rate and interest rate. Overall, changes in economic growth (GDP) and commercial bank advances to small scale industries (BASSI) are the macroeconomic parameter that is explaining SME profitability significantly better than others.

Conclusion

The present study analyzes the relationship between working capital management, macroeconomic impulses, and profitability of Indian SMEs from 2010 to 2017. The study finds a negative relationship between AR (like Enqvist, 2014; Garcia & Solano, 2007; Gill et al., 2010; Lazaridis & Tryfonidis, 2006; Yunos et al., 2015) and positive coefficient of AP with ROA. It implies the firm managers may maximize SME’s profitability by converting the credit sales to cash as early as possible and by increasing the days of AP. In spite of the risk of increasing financing cost and increasing the sensitivity of high credit risk (see Aktas et al., 2015; Kieschnick et al., 2013), the study finds evidence that high inventories may support the profitability of Indian SMEs. With the drawback of poor management capability of the SME owners or managers (Pansiri & Temtime, 2008), it is certainly not easy for SMEs to predict the accurate inventory level. Holding larger inventory may increase carrying cost, but for Indian SMEs, it is relatively favored. For ordering inputs and selling outputs, SMEs are largely dependent on local markets and there is always a risk of inventory dry out. The operational supply chain may any time be disturbed. Hence, holding higher inventory seems to be increasing SME’s profitability for Indian SMEs. While SMEs are struggling to obtain finance for their operation and investment (Allen et al., 2012; Thampy, 2010), it is highly essential for Indian SMEs to convert their credit sales and inventories to cash. Finally, for the overall impact of working capital management on SME profitability, the paper concludes that minimizing NWCC can maximize the profitability of Indian SMEs.

The study further finds the strong impact of macroeconomic policies on SMEs performance and conclude that changes in economic growth (GDP) and commercial bank advances to small scale industries (BASSI) are the most influential macroeconomic parameter to explain SME profitability significantly better than others. The study further undertakes a sensitivity analysis of SME profitability and finds the impact of economic growth is clear and dominant.

The major limitation of the study is the unavailability of secondary financial data for Indian SMEs, which makes it difficult to undertake research based on financial aspects. Therefore, the study has used the financial information of the constituents of the SME IPO index of the Bombay Stock Exchange (BSE). Furthermore, the findings of the study might have limited implications, as there are hundreds of small scale enterprises that are not listed in the BSE SME IPO index. This study makes a potential contribution to the existing literature on corporate finance of SMEs and is unique in its kind because it explores the nexus between working capital efficiency, macroeconomics impulses, and profitability of the Indian SME. The findings of the study will definitely guide the firm managers to shape their working capital management strategies. Policymakers may find the study interesting to identify the macroeconomic parameters that significantly influence Indian SMEs.

The present research has lots of scope for future studies. The working capital management practices of Indian SMEs can be integrated with their financing practices. The study can be extended to relate the existing practices of working capital management with capital structure and working capital financing practices. Integrating the best practices of working capital management with its financing structure can help in maximizing firm value is the issue to be explored.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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