Financial constraints from from euro area companies--论文代写范文精选

这个指标是基于融资条件的分类方案,考虑到信息来源于资产负债表和损益账户。我们区分绝对约束,相对约束和无约束，根据不同的场景基于总投资的关系。下面的paper代写范文继续展开。

One of the biggest issues facing empirical works in this literature is to objectify financial constraints and to construct a clean measurement, as they are empirically not observable.3 Moreover, because access to finance and productivity are endogenously determined as equilibrium outcomes, a further hurdle is a clear identification of the causal direction of impact. To this regard, we conduct our analysis adopting a novel empirical strategy. First we build a firm-level indicator of financial constraints and second we apply this indicator to a production equation to assess the direct impact of financial constraints on productivity. As first step, we construct a semi-parametric index of firm-specific financial constraints, as originally developed by Pal and Ferrando (2010). 

This indicator is based on a classification scheme of firms0 financing conditions, taking into account information derived from balance sheets and profit and loss accounts. We distinguish between absolutely constrained, relatively constrained and unconstrained firms according to different scenarios based on the relation among total investment, financing gap, financial debt, equity issuance and average interest payment on debt compared to the rate charged in the local credit market. The index gives us some hints on the heterogeneity in financial constraints across firms and euro area countries. 

To obtain a synthetic value, we relate our indicator to a number of specific firm-level characteristics, like age, size and cash holding, which are extensively used in the literature to proxy financial constraints, and we use a ordered probit estimation to predict the probability of belonging to one of the aforementioned groups for each firm in the sample. The resulting predicted index, i.e. a continuous variable with higher values associated with more constrained firms, will represent our core measure of financial constraints: differently from the existing literature, this index takes into consideration a broader set of firm-level factors affecting access to external source of finance, rather than a single proxy. 

In the second part of the analysis, we estimate the reaction of companies0 labour productivity to financial constraints. Acknowledging the presence of endogeneity in assessing the causal impact, we exploit the nature of our index of financial constraints, which by construction is an additional state variable in the firm-level production function (together with capital stock) and we modify the Wooldridge-Levinsohn-Petrin methodology to accordingly account for that.4 We use panel generalized method of moments of Arellano and Bond (1991) and Blundell and Bond (1998) to estimate a firm-level production function equation which directly includes our index of financial constraints as one of the regressors, assuming productivity to evolve as a first-order autoregressive process. To provide robustness, we carry out this estimation for each country and sector separately while controlling for time-effects.

Our main findings are the following ones. Financial constraints do lower productivity in most sectors across countries: in the great majority of the estimations, the direct impact of financial constraints is statistically and economically significant. The coefficient estimates are significantly higher in industries that innovate the most, like “Energy, Gas and Water Supply” and “R&D, Communication and Information”, while turn to be lower in “Construction and Real Estate”, a sector that have benefited more than others from low interest rates along the period 2001-2007. From a cross-country perspective, Italy and Portugal are the most affected by financial constraints, with an estimated counter-factual loss in their average labor productivity of about 21% due to limited access to finance; Germany and Netherlands are the most immune countries, with counter-factual losses of around 11 and 15 percent. 

In addition, each country would gain on average between one and two percent of their labor productivity by expanding the access to finance of small firms to that of the average large firm. All these results are robust to a number of robustness checks, including alternative econometric specifications, and to several sub-samples. This paper relates to a number of literature. First, it contributes to the literature that tries to detect and measure the degree of financial constraints from a firm-level perspective. Since the ICFS (investment cash-flow sensitivity) measure proposed by Fazzari et al. (1988), the debate over the consistency in measuring financial constraints has been vivid and has resulted to an extensive empirical work related to this topic. 

Among the others, the Kaplan and Zingales (KZ) index of financial constraints (Lamont et al., 2001), the CCFS (cash flow sensitivity of cash) index (Almeida et al. 2004), the Whited and Wu (WW) index of constraints (Whited and Wu, 2006), the size-age (SA) index recently advanced by Hadlock and Pierce (2010) and a variety of different criteria based on firm characteristics have been proposed and tested. Differently from the majority of the existing contributions, and in line with Musso and Schiavo (2008), we do not focus on single proxies but we build our indicator upon an a-priori discreterange firm classification and obtain a synthetic value using a ordered probit estimation. Thus, we attempt to estimate the response of firm-level productivity to the likelihood of accessing external finance, as measured by our index. To this extent, this paper relates to the empirical literature that looks explicitly at the impact of financial constraints on firm behavior and measures of performance. 

A number of contributions have shown that financial constraints and liquidity constraints affect the decision to engage in R&D investment (Bond et al., 2005, and Mancusi and Vezzulli, 2010); that financing frictions have an impact on corporate investment and that the inability to access external source of funding can cause firms to bypass profitable investment opportunities (Almeida and Campello, 2007); that more constrained firms during the global financial crisis of 2008 planned deeper cuts in tech spending, employment, and capital spending (Campello et al., 2010); that financial constraints act as a barrier to export participation (Bellone et al., 2010, Silva and Carreira, 2011). 

We collocate our paper within this literature by focusing on the effect of financial constraints on labor productivity, and we show that, everything else equal, limited access to finance significantly dampens firm-level real value added in most of the countries and sectors. Finally, our paper contributes to the policy debate on the spillover effects from the financial sector on the real economy and on the implications for policy makers to foster long-term investment and growth in the economy.5 The remainder of the paper is organized as follows. In Section 2 we describes the dataset. In Section 3 we introduce the classification scheme used to detect financial constraints and we derive a synthetic indicator that will be included in the production function equation. In Sections 4 we describe the empirical strategy used to estimate the impact of financial constraints on productivity. In Section 5 we report the core results of the paper and we discuss a number of robustness checks. We conclude in Section 6.(paper代写)

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