Detecting financial constraints:firm-level classification--论文代写范文精选

分类允许我们克服通常的批评与财务指标约束。绝对约束的公司是那些无法获得外部融资,有相对的限制。而不受约束的公司是得到新的债务融资,市场上最低的融资成本。下面的paper代写范文进行讲述。

Abstract
Financial constraints are empirically not observable. As there are no specific items on the balance sheets of firms that could tell whether a firm is financially constrained, several avenues have been suggested in the literature, attempting to identify and to measure financial constraints.9 In this paper we follow the literature that gives importance to “a-priori classification” based on firms financial conditions. Notably, we follow and refine the approach of Pal and Ferrando (2010)10 by applying a classification scheme based on information from the balance sheet and profit and loss accounts for the sample of firms we described in the previous section. The advantage of this classification is that it takes into consideration a set of variables and their interrelations within some scenarios, allowing us to attach to firms different degrees of financial constraints accordingly. The classification permits us to overcome the usual criticism related to the choice of single a-priori indicators of financial constraints (Musso and Schiavo, 2008). Table 4 reports the classification revisited from Pal-Ferrando (2010).

We distinguish between absolutely constrained, relatively constrained and unconstrained firms. Absolutely constrained firms are those that cannot get external finance, relatively constrained are those that can access only expensive external sources and unconstrained firms are those that get new debt financing and pay, on average, the lowest financing costs available on the market. We construct our scenarios based on the interrelation of total investment, financing gap (defined as fixed investment plus the change in the net increase in working capital minus cash flow), financial debt and issuance of new shares obtained in the given year, and average interest payments on debt relative to interest rates charged in the local credit market.

The underlying idea is that if firms face financing gaps, they need to find other sources besides their current cash flow. Firms are considered to be unconstrained when they make use of external sources of finance facing favorable conditions, i.e. they can increase their leverage whenever it is needed with low financing costs relative to market conditions (case 2 ). We expect that the demand for financial debt decreases as its cost increases. Those firms that can get only expensive credits tend to use less external finance relative to the unconstrained firms and we consider those firms as constrained in relative sense (case 3 ). And finally, we consider constrained in absolute sense those firms that despite of the financing gap do not get any credit or additional capital from the stock market (cases 6 ).

In the case of liquidation of assets (investment is negative) our classification allows us to distinguish between the case of absolutely constrained firms (case 5 ) from the case when firms are unconstrained (case 1 ), based on their relation to external finance, given from changes in total debt and issuance of new shares of equity. However, it is not certain if their investment is constrained by reimbursement or if they do not invest because of the lack of profitable investment opportunities. Therefore, we choose to include these firms among the constrained ones whenever data on changes in total debt and share issuance are missing. When the financing gap is negative, indicating that the firms’ total investment is lower than the current cash flow, firm are considered financially unconstrained in case they are still increasing their total investment (case 0 ). Under case 4 we include firms that finance their investment not through credit but through the new share issuance, which is more costly due to the presence of asymmetric information.

The second column in Table 2 reports the percentages of firm/year observations according to the classification. Around 21% of observations belong to absolutely financially constrained firms while almost 33% of firm-year observations are classified as unconstrained. The remaining 46% of observations in our sample fall in the category relatively constrained: around 30% are firms that get expensive credits and 16% increase their shareholder funds to finance their investment. Table 3 includes the percentages of firms with different degrees of financial access across countries: based on our classification, in each country a share ranging between 10% and 20% of sampled firms are on average financially constrained in absolute terms. The largest fractions of absolutely constrained firms are in Italy, Spain and France while it is more likely to find Belgian, Finnish and Dutch firms among the least constrained ones.

Financially constraints affect firms persistently over time. In Table 4, we present the transition matrix for the a-priori indicator, obtained by computing the average share of firms flowing each year from one category to the others. Starting form the last row, 33.2% of firms observation that were signaled as absolutely constrained in a given year remained such also the subsequent year; around 40% move to the category relatively constrained while the remaining 26.5% become unconstrained in absolute terms. About 41% of firms that are absolutely unconstrained remain such also in the year after while 36.4% are classified as relatively unconstrained after a year. The transition matrix suggests the following evidence.

On the one hand, about 50% of firms belonging to a certain category at a given point in time, remains in the same category in the next period, signaling the presence of a persistent component in financial constraints at firm-level. On the other hand, access to finance displays a non-negligible time-varying component, as almost 50% of firms is likely to flow to different categories between two consecutive periods. As for firms0 specific characteristics, according to different measures of size, being these either the EC definition or a measure based on the distribution of real total assets, the share of absolutely constrained is around 20% for micro and small firms and around 16% for large firms (Table 7).

This evidence is in line with the literature that shows how smaller firms are more likely to suffer limited access to finance compared to larger business.11 Less clear is the relation between age and financial constraints: while mature firms (larger than 5 year old) are on average more unconstrained compared to younger firms (the share of unconstrained firms among the oldest cohort is equal to 33% of the total, against 27% for the young ones), a much larger share of older firms is also absolutely constrained (around 22%) compared to young enterprises (16%). Finally, as for a sectoral classification, industries like “Information Communication and R&D” and “Retail stands” out as the most financially constrained, with about 22% of absolutely constrained firms out of their total (Table 5), while “Accommodation and Food” displays the highest share of unconstrained firms (42%).

A firm-level measure of financial constraints
As noted by Musso and Schiavo (2008), using a number of different scenarios to classify firms0 ex-ante financial status allows to overcome the weaknesses related to the use of a single variable. The main drawbacks faced to identify financial constraints with a single variable are 1) the fact that most of the chosen criteria are almost time-invariant, whereas it’s likely that firms switch between being constrained or unconstrained depending on the overall credit conditions, on the investment opportunities faced by the firm and on idiosyncratic shocks, and 2) the fact that single proxies span financial constraints on a unique dimension, as it were a phenomenon that is either in place or not, without allowing for heterogeneous degrees in accessing finance. On the other hand, an index relying on information coming from multiple sources is likely to carry out a great deal of mis-measurement errors. We try to address this limitation by refining our proxy of financial constraints as follow. We use the index based on the a-priori classification to estimate an ordered Probit regression and calculate the conditional probability of firms being in one of the three categories.(paper代写)

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