Financial Constraints across Pakistani Listed Firms

Abstract

Using Q investment model to scrutinize investment-cash flow sensitivity, a measure of financial constraint, has been a subject of controversy in literature. By using an alternative model called Error Correction model, this study aims to check the sensitivity between firm's internal finance and investment level for the case of lower middle income country. Literature has shown that firms' specific characteristics affect the relation between investment and cash flow of the firms. By considering the unique characteristics of Pakistani corporate sector, this study further target to check whether the investment-cash flow sensitivity differs across size of the firms, group affiliation of the firms and dividend policy of the firms. Our findings indicate that Pakistani listed firms are financially constrained. We find a strong relationship between investment and cash flow for small and non-dividend-paying firms; whereas group-affiliation does not affect investment- cash flow sensitivity of firms.

Headnote

Abstract

Using Q investment model to scrutinize investment-cash flow sensitivity, a measure of financial constraint, has been a subject of controversy in literature. By using an alternative model called Error Correction model, this study aims to check the sensitivity between firm's internal finance and investment level for the case of lower middle income country. Literature has shown that firms' specific characteristics affect the relation between investment and cash flow of the firms. By considering the unique characteristics of Pakistani corporate sector, this study further target to check whether the investment-cash flow sensitivity differs across size of the firms, group affiliation of the firms and dividend policy of the firms. Our findings indicate that Pakistani listed firms are financially constrained. We find a strong relationship between investment and cash flow for small and non-dividend-paying firms; whereas group-affiliation does not affect investment- cash flow sensitivity of firms.

Keywords: Financial constraints; Investment; Developing country; Firms

JEL Classifications: G00; G31

(ProQuest: ... denotes formulae omitted.)

Introduction

Economic growth of every country mainly relies on the investment level. Continuous and consistent investment growth is a significant determinant of economic development. An extensive literature check the impact of financial frictions on firm investment and firm behaviour (such as Konings et al., 2003; Guariglia etal., 2010; Hennessy& Whited, 2007; Qasim et al., 2020). However, firm's level of financial constraints is pertinent because of the importance of finance for firm growth and investment activities. An empirical study conducted by (Fazzari, Hubbard & Petersen,1988) (FHP, thereafter) utilized Q investment model, where investment is taken as a function of Tobin's Q and cash flow to estimate firm-level data. This study showed firms tend to be more financially constrained in the case of a bigger effect of cash flow on the investment of firms. The idea behind Q investment model is using equity market participant to capture investment opportunities. However, the relationship between cash flow and investment could be the result of connection between mis-measured or omitted investment opportunities and cash flow (such as Summers, 1981; Erickson & Whited, 2000). The problem may arise if present value of expected future net distribution diverges from the stock market valuation (Cummins et al., 2006). Another challenge to the performance of q theory is given by (Carpenter and Guariglia, 2008) who argued that q model only forecasts outsiders' assessments of investment opportunities. It does not assess insiders' estimation of opportunities. We contribute to the prevailing literature by using a different approach such as error correction model (ECM) to see investmentcash flow sensitivity, whereas the previous studies (such as Riaz et al., 2016; Rashid & Jabeen, 2018) have checked this relationship by using Q investment method.

Our contribution to the literature of effects of financial constraints on the investment with the reference of low and lower-middle income countries is crucial in a number of ways. First, firms' interpretation of cash flow and challenge of financial constraints are considerably less popular in developing countries. Second, under developed financial markets, relatively less regulated equity markets, less developed corporate bond and commercial paper markets trigger the concept of financing constraints much more plausible to developing countries researchers.

We further contribute to the literature by examining the investment-cash flow relationship across small and large firms and among business group firms. The issue of distinct size of the firms is pertinent to investment and financing of firms. Small and medium sized enterprises (SMEs) can suffer more from information asymmetries and market imperfection as compared to large firms. Large firms can have more options to raise funds from external sources (such as credit market) as compared to small firms which rely mainly on internal funds or bank lending. Moreover, this factor is particularly important with the reference of Pakistan as the contribution of SMEs in industrial output and total export of the country is 45 percent and 40 percent respectively (SMEDA, 2009). Besides, Pakistani business groups have their own specific features which make the analysis interesting. First, close banking ties of these business groups. Second, spread of these business groups to an extensive number of industries. The consequences of this range of business groups on investment and firms may be supplementary.

We use a panel of 337 Pakistani manufacturing listed firms over the period 2008-2017 and check for the investment cash flow sensitivity. Our results of empirical specification indicate investment cash flow sensitivity for the sample firms. Alternatively, it means our sample firms are financially constrained. It highlights the presence of information asymmetry in capital markets which may increase the cost of external funds for the firms, in turn firms rely more on internal funds. Our further empirical testing highlights the stronger effect of cash flow on investment for small and non-dividend paying firms. This shows the presence of lending bias in Pakistani capital market on the basis of size and dividend payment. Moreover, our results highlight the need of financial reforms to reduce lending bias among these firms to provide the easy and equal access of funds to small and non-dividend paying firms. We further find that effect of cash flow on investment is not significant for group-affiliated firms. Our results are in contrast to some studies (such as Hoshi et al., 1991; Kato et al., 2002) but consistent with the findings of some other studies which are conducted for developing countries (such as George et al., 2011). Though, (Saeed& Sameer,2015) highlight that group affiliated firms are more levered, meaning group-affiliated firms borrow more bank loans in Pakistan. It shows the possibility of misuse of loans by the managers of firms.

Our study provides evidence to policy makers to review the process of financial liberalization which took place in the country in 1990s. The results of our empirical study provide evidences to the policy makers to target the particular kinds of firms which are more financially constrained. This further prompts the need to equip financial markets with modern techniques to make them more efficient in the lending process.

Our paper progress as follows: section 2 presents a brief theoretical and empirical review of measures of financial constraints. It further provides background and stylized facts about the Pakistani corporate sector. Section 3 develops the empirical methodology. Section 4 presents the results. The paper will be concluded in section 5.

Literature Review

The literature of financial constraints on investment associate financial constraints with external financial constraints, as firms find it hard to generate funds for particular investment projects because of expensive external funds (such as Whited, 1992; Bond&Meghir, 1994; Love, 2003). Farre-Mensa and Ljungqvist (2016) further explain the characteristics of financially constrained and unconstrained firms in terms of a firm's capital supply curve. The more the firm is financially constrained, the steeper (more inelastic) the supply curve is.

The literature of financial constraints dates back to Modigliani and Miller's (1958) theorem on the irrelevance of capital structure which considers firm's internal and external sources of funds are perfectly substituted; therefore, a firm's investment decision is not stimulated by the method of financing However, capital markets are imperfect in the real world, presence of information asymmetries leading to adverse selection or moral hazard require lenders(investors) be compensated for information cost. In this situation, shadow cost to the manager of uncollaterized external financing surpass that of internal financing; causing financial constraints (Hubard,1998).

Empirically, financial constraint has been gauged by the investment-cash flow sensitivity1. In their influential paper (Fazzari et al.,1988) test the relationship between investment and cash flow (internal finance) by using q model of investment for the case of US manufacturing firms. They find that firms' cash flow plays a significant role in firms' investment decision. Under the Q investment model, Q controls for the market evaluation of the firm's investment opportunities, whereas another variable depicts the condition of internal funds - usually cash flow. Under this model, none of the variables, including cash flow, should be significant. If cash flow appears significant, this means failure of the frictionless q model, which will support the existence of financial constraints. Hoshi et al., (1991) used the same methodology to scrutinize the presence of financial constraints between 24 manufacturing companies that were affiliated with KEIRETSU (industrial group) and 121 independent firms that were not members of KEIRETSU in Japan. Oliner and Rudebusch (1992) also used Q investment model to check investment- cash flow sensitivity between 99 NYSE-listed firms and 21 over-the-counter firms. These studies further supported the findings of (Fazzari et al.,1988).

However, a big concern regarding the use of Q investment to calculate investmet -cash flow sensitivity has been raised in the following years. Though, Hayashi (1982) showed that marginal q and average q are equal in a value maximizing model of investment behaviour in the case of strictly convex adjustment costs and linear homogeneity of the net revenue function. In turn, average q has been used in the literature for the value of q. However, some studies (such as Erickson & Whited 2000; Bond & Cummins, 2001; Cummins et al., 2006) highlight the chances of a measurement error problem in the standard implementation of the investment model if share prices may not depict the true value of the firm. Besides, the relation between investment and conventional measures of average q could not have strong empirical support either2. In this situation, the equality of average q and marginal q become suspicious and validity of the value of q emerges. Guariglia (2008) also argues that q model only forecasts outsiders' assessments of investment opportunities and does not assess insiders' estimation of opportunities. Though, some recent studies have been trying to tackle the measurement error in q (such as Agca & Mozumdar, 2017).

In order to overcome the shortcomings of q model to test the hypothesis of financial constraint, literature has used another approach called Error Correction model (such as Bond et al.,2003; Guariglia 2008). Bean (1981) initially applied this model in the investment literature. The model specifies a long- run level of capital stock and allows a flexible specification of the adjustment dynamics to be estimated from the data. Some studies have used ECM to check the changes in financial conditions on the level of investment. Bond et al., (2003) used ECM to check the effects of financial constraint on investment under different financial systems including Belgium, France, Germany and UK. Chen (2008) used ECM to see the role of cash flow to determine firms' levels of investment in the transition economy of China. There is still a gap in literature to use ECM in the context of investment-cash flow sensitivity for developing countries.

Corporate Sector in Pakistan

Size and group affiliations are two prominent features of Pakistani corporate sector in the context of lending bias. It is worth mentioning here that government of Pakistan (GOP) took a number of steps, such as the establishment of Small and Medium Enterprise Development Authority (SMEDA) in 1998, the formation of SMEs and micro finance banks and creation of specialized departments for the SME sector in most commercial banks to overcome the problems related to their financial needs. Regardless of all these steps, SMEs have been suffering from a number of hindrances that are affecting their performance level. These obstacles include a low level of financial literacy, lending strategies by the banks, and absence of reliable business information structure (Khwaja & Mian, 2005; Dar et al., 2017). Another significant feature of Pakistani industrial sector is group-affiliation of firms. These groups do not only own manufacturing industries but also private banks, insurance companies, leasing companies, modarabas (financial contracts), and power plants. In addition to listed companies, the number of unlisted public firms and private limited companies is also innumerable. These groups also have companies that are incorporated abroad. They have ties to each other via formal and informal relations (Rehman, 2006). These features of Pakistani corporate sector make it interesting to analyse the effects of cash flow on investment under this particular setting.

Research and Methodology

Error correction model (ECM)

By following (Bond et al., 2003; Bond and Lombardi 2006; Guariglia 2008), the version of ECM that we derive will take the following form:

... (1)

Where Iit denotes investment for firm i in period t, Kit is the value of capital stock. a is a constant term. Sit logarithm of real sales showing output, CFit is cash flow, vt shows time-specific effect, vi gives firm-specific effect, vr controls for region effects, and eit gives idiosyncratic shock. To investigate the role of financial factors in investment, we include current cash flow term in the estimation equation. A significant coefficient of cash flow refers to the presence of financial constraints on investment. We would expect a positive and significant coefficient on cash flow, if firms' internal funds affected firms' investment.

Role of size, group membership, and dividend ratio

To test the impact of firm size on investment-cash flow sensitivity, we divide the sample on the basis of total assets. We sort the data by size. The firms with total assets above the sample median are in the group of large firms. The firms with total assets less than the sample median comprise the group of small firms. We generate a dummy variable, size dummyit, which is equal to 1 if firm i has total assets larger than median in year t, and 0 otherwise. We estimate our investment equation where cash flow variables are interacted to size dummy. We will use error correction model for estimation. The equation (2) will become:

... (2)

To test the impact of group membership, we will introduce the dummy of group affiliation in to the main empirical specification. The group affiliation dummy is equal to 1 if firms belong to the top 30 industrial groups in Pakistan.

... (3)

To test the impact of dividend ratio, we will generate the dummy variable. If the firms pay a dividend to their shareholders, it will be equal to 1; otherwise, it is 0.

... (4)

Estimators

It is expected that some estimators may lead to bias because some regressors may be endogenously determined in our specifications3. In the presence of endogeneity, ordinary least square (OLS) estimator tend to upward bias the estimation of lagged dependent variable. Fixed effect estimator may lead to a downward bias of lagged dependent variable. The useful estimator, which can control for unobserved heterogeneity and endogeneity problems at the same time, is generalized method of moments4 (GMM). We will estimate the empirical specification with system GMM given by (Arellano & Bover,1995; Blundell & Bond, 1998). By accumulating an additional assumption of uncorrelation between the first differences of instrument variables and fixed effects, Arellano and Bover (1995) and Blundell and Bond (1998) improve the efficiency of original difference GMM (Roodman, 2009). We further choose two-step estimation as finite-sample correction to the reported standard errors is performed in it (Windmeijer, 2005). In order to evaluate the model and instruments, we will rely on the AR (2) test for second-order serial correlation of the residuals, and the Sargan test for over- identifying restrictions in the differenced equations.

Results and Discussion

Sample selection and summary statistics

Financial data at firm-level is taken from the ORBIS database, this includes Profit and Loss and balance sheet data. We use US two-digit standard industry classification (SIC) and select Pakistani listed nonfinancial firms. By following (Aharony et al., 2010 & Saeed et al., 2014), we re-distribute two-digit SIC into eight-industry categories. Appendix A.1 gives the detailed industry classification.

ORBIS database comprises 349 non-financial listed firms which accounts for 87 per cent of all non-financial listed firms in Pakistan. We only concentrate on positive values of capital, total assets and sales. To clean our data we also remove those firms which have missing values for the essential variables. Our data further includes those firms which have observations for a minimum of two consecutive years. To further eradicate the potential influence of outliers, we exclude observations in the 1% tails for each of the regression variables. After meeting these conditions, the final sample consists of an unbalanced panel of 2190 firm year observations of 337 firms for the period 2008-2017. As the firms in the sample consist of different number of time-series observations, therefore, it is an unbalanced panel data.

GDP Price Deflator and Wholesale Price Index have been used to get real values of the variables for this study. Data on the GDP Price deflator and Wholesale Price Index have been extracted from the World Bank database. We use Wholesale Price Index to deflate Capital stock and investment. We use GDP Price Deflator to deflate other variables. By following (Masulis et al., 2011; Ashraf & Ghani, 2005) we collect data on group affiliated firms by (Rehman,2006). Rehman (2006) gives list of top 38 business groups (based on their size) and names of their affiliated firms. However, 8 out of 38 groups consist of non-listed and financial firms only. We find 101 firms are affiliated to groups, whilst 236 firms are stand-alone by matching the affiliated firms to those 30 business groups in our total sample of 337 firms.

Summary statistics

Summary statistics for the key regression variables for the total sample of the firms is provided in table 1. The second row in table 1 reports the investment to tangible fixed assets ratio which is used to see the intensity of investment for the firm. Based on this ratio, the investing intensity of overall listed firms in Pakistan is 6 per cent. The cash flow to capital ratio shows how much capital expenditure requirements are fulfilled by internal cash flow. Our statistics show it is 17 per cent for the whole sample of listed firms. Sales to capital ratio gauges operational efficiency of the firm which is 2.93 for the case of our sample firms.

Table 2 gives summary statistics for the key regression variables for the firms sorted by size, group affiliation, and dividend. The large and small firms are investing 10 and 2 per cent of their tangible assets each year respectively. The difference is also highly significant at 1% level. The group-affiliated and stand-alone firms are investing 5 and 6 per cent of their total tangible fixed assets respectively. However, the difference is statistically not significant. The firms that pay dividends are investing 8 per cent as compared to the 3 per cent of investment of those firms that do not pay dividends. The difference is also statistically highly significant. The differences in cash flow and sales ratios are statistically insignificant between large and small firms. Cash flow ratio is higher for non-affiliated and dividend paying firms as compared to affiliated and non-dividend paying firms. This difference is highly significant. Non-affiliated and dividend paying firms have higher sales to capital ratio as compared to affiliated and non-dividend paying firms. This difference is statistically significant too. Overall, summary statistics show that mostly differences are statistically significant across the firms on the basis of group-affiliation and dividend-payment.

We use ECM to check investment cash flow sensitivity. Equation (1) is estimated for the whole sample of the firms and reported in table 3. The error correction term is estimated by 2-step System GMM. Current sales growth affects investment positively. Lagged sales growth affects the investment too. The coefficient of cash flow is positive and significant which shows a positive relationship between firms' investment and cash flow. The estimated coefficient of cash flow shows that, on average, investment of Pakistani non-financial firms increased 0.27 units for each additional unit of cash flow. Table 3 also reports Sargan test and AR(2) test for GMM. The regression estimates pass both tests which shows second-order serial correlation does not exist in residual. This further proves the validity of instruments. Our results support the findings of vast literature such as (Fazzari et al,.1988) for US firms, (Carpenter & Guariglia 2008) for UK firms and (Riaz et al.,2016) for Pakistani firms.

Robustness Check

We also use Q model to check investment cash flow sensitivity for the robustness check. Table 4 gives the estimates of empirical specification for a full sample which is given below

...

where I/K is investment to fixed assets ratio. This ratio is used to see the intensity of investment for the firms. We define investment as change in real tangible fixed assets plus depreciation. In order to control possible heteroscedasticity due to size scale effect and for comparison purposes, we will scale the key variables with lagged fixed assets. K is capital, which is measured by fixed assets. Q is Tobin's q. CF is cash flow, as given by the ORBIS database. ORBIS defines it as profit for period plus depreciation. A significant and positive coefficient of cash flow after controlling the investment opportunities via Tobin's q, will highlight the existence of financial constraints. vt shows time-specific effect, vi gives firm-specific effect, vr controls for region effects, and eit gives idiosyncratic shock.

Empirical specification is estimated by 2-step System GMM estimator. The focus of the estimation is the coefficient of cash flow, which is statistically significant. This evidence shows that listed firms in Pakistan are financially constrained as investment is positively and significantly influenced by cash flow of the firms.

Investment-cashflow sensitivity across different types of firms

We now estimate empirical specification given in equation (2) by System GMM. Column 1 of table 5 gives the results of the firms with size dummy. Our results in table 5 predict that small firms suffer from tighter financial constraints as compared to large firms. The coefficient of the interaction term of cash flow and dummy of firm size is negative and significant (-0.116). Our results pass j and m2 tests. Our results support the findings of previous studies such as (Carpenter & Guariglia,2008 ; Guariglia, 2008) which also highlight that cash flow of small firms are more sensitive to investment as compared to large firms. These findings show the presence of cost effect for small firms. It further highlights that small firms are more sensitive to asymmetric information as compared to large firms.

Equation (3) which includes the dummy of affiliation with industrial groups is estimated by System GMM. The results are reported in column 2 of table 5. The coefficient of interaction term (CF·Group-aff) is insignificant. On the basis of insignificant coefficient of interaction term, we can predict group affiliation of firms do not affect financial status of listed firms in Pakistan. Our results are in contrast of other studies in literature. Such as our results do not support Hoshi et al.,1991 who find lower investment -cash flow sensitivity among groupaffiliated firms. Our results are in contrast of some other studies which are conducted for developing countries such as (George et al., 2011). George et al., find that both affiliated and group-affiliated firms have positive investment-cash flow sensitivities. Since our results show that group affiliation does not affect investmentcash flow sensitivities for Pakistani listed non-financial firms exhibiting investment-cash flow sensitivity is not a characteristic of Pakistani business group.

Column 3 of table 5 gives the result of equation (5), when firms are sorted on the basis of dividend payments estimated by System GMM. The coefficient of the interaction term of cash flow and dummy of dividend is significant and negative (-0.220). It implies a firm's dividend policy affects investment-cash flow sensitivity for Pakistani listed non-financial firms. In other words, dividend ratio is associated with Pakistani firms' financial status. The results are in line with the findings of previous studies (such as Fazzari et al., 1988; Hubbard, et al., 1995), which find low investment-cash flow sensitivity among dividend-paying firms. ·-1

Conclusion

This paper inspects investment-cash flow sensitivity for Pakistani listed firms over the period 2008-2017 by using Error Correction Model (ECM). The process of financial liberalization which took place in the country in 1990s makes it interesting to investigate this issue. The process was initiated to reduce the financial limitations to corporate sector. The empirical results of this study confirm Pakistani firms are financially constrained. It is substantially important for Pakistan to develop its financial markets to facilitate the financial demand of the firms. Failure to do so may further slowdown the process of economic growth that the economy has been encountering for almost the last two decades. Financially constrained listed firms further highlight the underdeveloped stock markets in Pakistan. These stock markets are not fully equipped to fulfil the financial demands of listed firms.

Moreover, our findings suggest a different investment cash flow relationship across firms' size and dividend policy. Our results lend support to the presence of lending bias among large and dividend paying firms. As large and dividend-paying firms show less investment -cash flow sensitivity as compared to small and nondividend paying firms. We do not find the evidence to claim that group affiliation of firms affect firms' investment-cash flow sensitivity. These results could be the result of strict borrowing conditions and transparency requirements of external funds among small and non-dividend paying firms.

Footnote

1 For instance, see Fazzari etal.,1988; Kaplan ,1997 Guariglia (2008). Cash flow is normally measured by the level of internal funds in the empirical literature.

2 Such as see Abel and Blanchard (1986), Hayashi & Inoue (1991)

3 Cash flow and sales are taken as endogeneous variables in the investment equations, as it is understandable that higher investment leads to higher sales and cash flow.

4 GMM hereafter.

References

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Appendix

A1: Industry classification

A2: Definition of variables

Iit: Investment is measured as the change in real tangible fixed assets plus depreciation.

CFit: Cash flow is defined by ORBIS as net income for the year plus depreciation.

Kit: Capital is measured by real tangible fixed assets.

Q: Q is defined by ORBIS as ratio of market value of the firm over total assets.

Sit: Sales is the real net sales defined by ORBIS.

Total assets: Book value of total assets.

Dividend: Dividend is proxied by cash dividend paid by the firm.

Availability of data and materials: The datasets generated and analysed during the current study are available in the ORBIS repository https://www.bvdinfo.com/engb/our-products/data/international/orbis