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Small Business Economics

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05.05.2021

Short-term financial constraints and SMEs’ investment decision: evidence from the working capital channel

verfasst von: Théo Nicolas

Erschienen in: Small Business Economics | Ausgabe 4/2022

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Abstract

This paper investigates the real effect of short-term financial constraints on small and medium-sized enterprises (SMEs). Under the working capital channel, cash credit constraints force entrepreneurs to forgo investment opportunities in order to finance their working capital needs. Building on survey data, I find that short-term credit constraints are as important as long-term ones in SMEs’ investment decisions. Besides, the detrimental effect of cash credit constraints on corporate investment is stronger for SMEs with higher increase in working capital needs but lower for liquid ones that are able to adjust their accounts receivable and inventories. My results suggest that short-term finance is a major issue for SMEs.

Plain English Summary

Short-term credit constraints turn out to be as important as long-term ones in SMEs’ investment decisions. Building on survey data, this paper investigates the real effects of short-term financial constraints on small businesses through an under-explored transmission mechanism. While the bulk of research on the effects of financial constraints focuses on long-term liabilities, I stress the importance of short-term finance. Owing to the competition between working and fixed capital in cash-flow uses, cash credit constraints force entrepreneurs to allocate additional cash-flow to finance the increase in their working capital needs to the detriment of long-term assets.The main implication of this work is that supports for short-term financing would provide a significant boost to long-term investment and economic growth.

Vorheriger Artikel Evolutionary entrepreneurial ecosystems: a research pathway

Nächster Artikel Are SMEs less efficient? A Bayesian approach to addressing heterogeneity across firms

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Following the work of the physiocrats, Smith and Todd (1776) made the first explicit distinction between fixed and circulating capital.

In line with the European Commission, my definition of SMEs is based on number of employees (less than 250), turnover (less than EUR 50 million) and total assets (less than EUR 43 million).

Computations are made using the Fiben database described below.

Note that I rule out from the analysis firms that were simultaneously credit constrained regarding both cash and investment credit in order to separately identify the causal effect of each credit constraint.

In addition, the potential importance of these working capital effects has also been stresses in studies of monetary policy shocks, as interest rates changes affect supply conditions (marginal costs) through nominal rigidities in the presence of sticky wages (Christiano et al. 2005).

As marginal adjustment costs of acquiring and installing capital rise as the rate of investment increases, firms are willing to maintain a stable fixed investment path (Lucas 1967).

One should keep in mind that the manufacturing sector is over-represented in the sample (50%) so that the survey cannot be interpreted as a representative estimate of the opinion French firms have on their credit conditions.

Given the lack of market data available for unquoted SMEs, traditional variables such as Tobin’s Q or Fundamental Q cannot be computed. Following Honda and Suzuki (2000), D’Espallier and Guariglia (2015) developed an accounting proxy for marginal Q to control for investment opportunities. Their marginal Q is defined as the ratio of profit per unit of capital over the cost of capital.

This credit ratings belongs to the in-house credit assessment systems (ICAS) validated by the Eurosystem, which means that the Eurosystem can rely on it when assessing the credit quality of eligible credit claims within its collateral framework. The information gathered and analyzed by the Banque de France is used to conduct a comprehensive assessment of a company’s credit risk. The data are based on hard information such as balance sheet data, payment incidents data etc., as well as soft information gathered from interviews with company managers. Low, speculative-grade, BdF ratings rank from 9 to 5+.

In this regard, note that in 2015 the Banque de France included questions about the reasons why firms do not ask for bank credit in order to gauge the level of “discouraged borrowers”: only 2% of French SMEs are associated with such a phenomenon.

To the extent that depreciation is likely to be somewhat arbitrary, I assume that gross investment better captures a firm’s investment decision.

One should note that the effect of trade credit on loan demand is ambiguous as trade credit can be seen as a substitute for bank credit.

The interpretation of cash flow is controversial in the literature. See the work of Fazzari et al. (1988) and Kaplan and Zingales (1997) for more details.

Yet, as these types of information are not available in the Banque de France datasets, I’m not able to assess the differential effect of financial constraints according to these characteristics.

Note that the sector classification builds on the Nace Rev. 2 classification of the European Community.

In dynamic panel data where the observations are highly autoregressive an the number of time series is small, the standard GMM estimator has been found to have large finite sample bias and poor precision in simulation studies. The weak performance of the standard GMM panel data estimator is also frequent in relatively short panels with highly persistent data where lagged endogenous variables are weak instruments. Hence, the system-GMM estimator improves the performances of the standard GMM (Blundell et al. 2001).

Estimation is implemented in Stata using Roodman’s xtabond2 package in which I use two lags of instruments and collapse the instrument matrix, see Roodman (2009).

Consistent with Bond (2002), note that the coefficient on lagged investment is in the range between the OLS and FE estimates (results not reported but available upon request).

Note that I also use all current assets as an alternative measure of liquidity and find similar results.

The procedure takes into account this limitation and applies a bias-correction component to the estimates of interest. In addition, I use the estimator of Abadie et al. (2004) which produces heteroskedastic-robust standard errors

Yet, as I do not know all of the factors that determine treatment assignment (i.e. financial constraints), I cannot make sure that there is no hidden bias that could undermine my inference.

Based on overlapping tests, I select the best match using a caliper (i.e the maximum permitted difference between matched subjects) of 0.45 standard deviations.

Yet, as outlined previously, all measures of investment, along with all other continuous variables are winsorized at the top and bottom first percentile.

Results are not presented but available upon request.

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Titel: Short-term financial constraints and SMEs’ investment decision: evidence from the working capital channel
verfasst von: Théo Nicolas
Publikationsdatum: 05.05.2021
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 4/2022
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-021-00488-3

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