Credit Constraints and the Extensive Margins of Exports: First Evidence for German Manufacturing

This paper uses a unique newly constructed data set to investigate for the first time the link between credit constraints and the extensive margins of exports in Germany, one of the leading actors on the international market for goods. In line with theoretical considerations and comparable results reported for a small number of other countries the author reports a negative impact of credit constraints on both the number of goods exported and the number of export destination countries that is both statistically highly significant and large from an economic point of view. (Published in Special Issue Micro-econometric Analyses of International Firm Activities)


Motivation
Insufficient access to credit at reasonable costs can hamper or even prevent exporting. Exporting involves extra costs to enter foreign markets (e.g., for the acquisition of information about a target market, for the adaption of products to foreign legal rules or local tastes, for instruction manuals in a foreign language and for setting up a distribution network) that often have to be paid up front and that to a large extent are sunk costs. Firms need sufficient liquidity to pay for these costs, and constraints in the credit market may be binding. Furthermore, it tends to take considerably more time to complete an export order and to collect payment after shipping compared to a domestic order, and this increases exporters' working capital requirement. The higher risk of export activities (including exchange rate fluctuations and the risk that contracts cannot be as easily enforced in a foreign country) adds to these liquidity requirements. Therefore, whether a firm is financially constrained or not can be considered as one of the characteristics of a firm that are relevant for the decision to export.
While this has been common knowledge for business managers for a long time, economists only recently started to incorporate these arguments in theoretical models of heterogeneous firms and to test the implications of these models econometrically with firm-level data. Chaney (2013), Muuls (2008) and Manova (2013) introduce credit constraints into the seminal model of heterogeneous firms and trade by Melitz (2003) to discuss the role of these frictions for the export decision. 1 Starting with the pioneering study by Greenaway, Guariglia and Kneller (2007) a growing number of empirical papers looked at the links between financial constraints and export activities using data at the level of the firm. Wagner (2014a) surveys 32 empirical studies that cover 14 different countries plus five multi-country studies. 2 While the studies use different measures of financial constraints and apply different econometric methods to investigate the links between these constraints and export activities, the big picture can be summarized as follows: Financial constraints are important for the export decisions of firms -exporting firms are less financially constrained than non-exporting firms. Studies that look at the direction of this link usually report that less constraint firms self-select into exporting, but that exporting does not improve financial health of firms.
Most of these empirical studies focus on the link between credit constraints and export participation or the share of exports in total sales. Only seven studies for four countries deal with the extensive margins of exports -the number of goods exported and the number of countries exported to. Given that the extra costs of exporting often have to be paid for each good that is exported and for each destination country we expect that credit constraints will be negatively related to these extensive margins. Studies for Belgium (Muuls 2008(Muuls , 2015, France (Askenazy et al. 2011), Italy (Forlani 2010, Secchi et al. 2014, Tamagni 2013 and China (Manova et al. 2011) report results that are in line with these hypotheses.
This paper contributes to the literature by reporting first evidence on the link between credit constraints on the one hand and the number of goods exported and the number of destination countries for Germany, one of the leading actors on the world market for goods. 3 To anticipate the most important results, we find that a less favorable credit rating score (that is used to measure the degree of financial constraints) is negatively related to both extensive margins of exports.
The rest of the paper is organized as follows. Section 2 discusses the data and measurement issues. Section 3 presents the results of the econometric investigation. Section 4 concluded.

Data and measurement issues
This paper uses a unique newly constructed data set that merges high-quality data at the enterprise level from various sources. Data are based on information on exports collected for the statistics on foreign trade. These data are merged with a score that measures the credit-worthiness of the firm and that is supplied by the leading They report that none of these five measures identifies firms that behave as if they were constrained. An alternative way to measure credit constraints that has been used in studies for Belgium (Muuls 2008 and, Germany (Wagner 2014b) and Italy (Secchi, Tamagni and Tomasi 2014;Tamagni 2013) is the use of a credit rating score supplied by a credit rating agency. Compared to other widely used measures that are based on balance sheets information or subjective assessments collected in surveys, this score mirrors the credit market experts' view on the creditworthiness of a firm, and it is heavily relied upon by banks and firms in their day-to-day decisions.
Usually a score is based on a number of firm characteristics, including liquidity, turnover, capital structure, information on payment behavior, legal form, industry, firm age, productivity and firm size. Although the score is clearly endogenous to the firm's performance and characteristics, it is not directly affected by its exporting behavior, given that exports are not used in constructing the index. Important advantages are that the score is determined independently by a private firm, is firmspecific, varies over time on an annual basis and allows for a measure of the degree of credit constraints rather than classifying firms as constrained or not (see Muuls (2008Muuls ( , 2015).
In this study we use the credit rating score supplied by Creditreform, the leading credit rating agency in Germany. The score is based on 15 firm characteristics, including liquidity, turnover, capital structure, information on payment behavior, legal form, industry, firm age, productivity and firm size (for details, see Rossen (2012)). The score takes values from 100 to 600, were Creditreform suggests that 100 to 149 should be considered as excellent, 150 to 199 as very good, 200 to 249 as good, 250 to 299 as medium, 300 to 349 as weak, 350 to 419 as high risk of failure, and firms with a score of 420 or more are classified as firms that should not be considered as partners in trade and credit relations.
Data on the credit rating score of manufacturing enterprises were supplied by Creditreform. For several firms the information is updated during a year. The information supplied always refers to the last update during the reporting year. In the empirical models estimated in this study the credit rating score is lagged by one year so that it refers to the creditworthiness of an exporting firm at the start of the year under consideration. These data from Creditreform are used for the first time in this paper to investigate the link between credit constraints and the extensive margins of exports.
In the econometric investigation on the relation between exports and the credit rating score information on a number of firm characteristics that are known to be related to export activities are included as control variables. 4 All control variables are lagged by one year to take care of any problems related to endogeneity. Information on these control variables are based on the report for establishments in manufacturing industries, a survey conducted regularly by the German statistical offices. This survey covers all establishments from manufacturing industries that employ at least twenty persons in the local production unit or in the company that owns the unit. Participation of firms in the survey is mandated in official statistics law.
For this study the information collected at the establishment level has been aggregated at the enterprise level (see Malchin and Voshage (2009) for details). The following control variables are included: Firm size: The positive relationship between exports and firm size qualifies as a stylized fact. Firm size is measured here by the number of employees. To take care of a non-linear relationship the number of employees is included in squares, too.
Productivity: The positive relationship between exports and productivity is another stylized fact that has been documented in a number of recent empirical studies surveyed in Wagner (2012a). Germany is a case in point. Productivity is measured here as labor productivity and defined as total turnover per employee.
Information on the capital stock of the firms is not available in the data, so more elaborate measures of total factor productivity cannot be used in this study.

Human capital intensity:
The quality of the workforce of a firm is positively related to the quality and innovativeness of the products produced. Firms that produce high-quality innovative products can be expected to export more often and to a larger extent to more foreign markets, too. Therefore, human capital intensity and export activities are positively related. Human capital intensity is measured here by the average wage per employee. Information on the qualification of the employees is not available in the data, but Wagner (2012b) demonstrates that the average wage is indeed a good proxy variable for the qualification of the workforce in German manufacturing firms.

Credit rating score and extensive margins of export: Econometric investigation
Export activities involve extra costs related to the entry into foreign markets that often have to be paid in advance, and firms have to have (access to) sufficient liquidity to cover these costs. Given that the extra costs of exporting often have to be paid for each good that is exported and for each destination country we expect that credit constraints will be negatively related to these extensive margins. Therefore, a better credit rating score of the type used here (described in detail in section 2) can be expected to be positively related to export activities for three reasons: First, by construction, liquidity of the firm is used to compute the value of the credit rating score. Second, the score mirrors the credit market experts' view of the creditworthiness of the firm. Therefore, the score value plays a role in the decision over a credit application, and it influences the rate of interest a firm has to pay. Third, 5 There are still large differences between enterprises from manufacturing industries in West Germany and in former communist East Germany even some 20 years after the unification back in 1990, and this holds especially for international trade (see Wagner (2014c)). Both parts of Germany have to be investigated separately. Given the small number of firms from East Germany in the sample we focus on West German firms in this study only.
the score value can be used by potential trading partners in foreign countries to decide whether and to which conditions they would be willing to do business with a firm.
These considerations about the link between the credit rating score -a higher value of which by construction indicates a lower degree of creditworthiness and a higher degree of credit constraints -and the extensive margins of export lead to two empirically testable hypotheses: H1: Firms with a higher credit rating score will export a smaller number of goods.
H2: Firms with a higher credit rating score will export to a smaller number of countries.
Descriptive statistics for the credit rating score, the number of goods exported and the number of destination countries of exports are reported in Table 1 Results of the econometric test of the two hypotheses H1 and H2 are reported in Table 2. A higher credit rating score that by construction indicates a higher degree of credit constraints goes hand in hand with a smaller number of exported goods and a smaller number of destination countries. Note that the inclusion of control variables for firm size, labor productivity , and human capital intensity reduces the estimated coefficient of the credit rating score (in absolute terms); this effect, however, is small in the case of the number of destination countries. The estimated regression coefficients are statistically significantly different from zero (and the prob-value is 0.000 in seven out of eight cases reported in Table 2). The estimated effects are large from an economic point of view. An increase in the credit rating score by one standard deviation leads to an estimated decrease in the number of goods exported by 3.8 to 13.7 (which corresponds to 6.6 percent to 26.4 percent of the mean number of exported goods). The corresponding value for the estimated decrease in the number of destination countries is between 3.4 and 4.3., and this corresponds to 10.2 to 13.9 percent of the mean number of destination countries.
[ Table 2 near here] The bottom line, then, is that the results of the empirical investigation are fully in line with the two hypotheses H1 and H2. Firms with a higher degree of financial constraints export a smaller number of goods and they export to a smaller number of destination countries.

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between credit constraints and the extensive margins of exports in Germany. In line with theoretical considerations and comparable results reported for a small number of other countries we report a negative impact of credit constraints on both the number of goods exported and the number of export destination countries that is both statistically highly significant and large from an economic point of view. Access to finance and credit costs do matter for the extensive margins of exports.    Estimated change for -9.6 -3.8 -4.3 -3.4 increase of score by one standard deviation (in percent of mean) (-16.7) (-6.6) (-12.9) (-10.2) Note: ß is the estimated coefficient from an OLS regression, p is the prob-value which is based on heteroscedasticity-robust standard errors. Model 1 controls for industry affiliation at the 2-digit level, Model 2 includes the following control variables: labor productivity, number of employees (also included in squares), wage per employee and industry dummies at the 2-digit level. All models include a constant, too. For number of cases and descriptive statistics see Table 1.