Characteristics of International Trade Intermediaries and Their Location in the Supply Chain

Abstract

Wholesale trade firms and their role in international trade are examined using transaction and firm level data sets from Denmark for the period 1998–2006. Compared to internationally trading manufacturing firms, wholesale firms trading internationally are found to focus on fewer countries with more products and lower unit values, and their involvement in international trade transactions differ significantly across industries. Manufacturing industries with more competitive structure, lower firm size, lower capital intensity, higher production fragmentation and lower export/import intensities are found to have higher wholesale share of export. The analysis shows that export and import premia also exist among wholesale trade firms, which is in line with the idea that these premia result from fixed costs of exporting/importing. Systematic differences between wholesale trade firms in intermediate goods markets versus in consumption goods markets are also documented and found critical in understanding the role of intermediaries in international trade. While in intermediate goods export wholesale trade firms’ unit prices are found to be significantly higher than manufacturers unit prices of the same good, the opposite holds true for consumption goods export. Wholesale trade firms that specialize in export of intermediate goods are found to be more skill intensive and pay more in comparison to other exporting wholesale trade firms. The wage premium for exporters of intermediate goods for professional level occupations is robust to controlling for detailed firm and worker characteristics. The results suggest that theories highlighting the potential roles of intermediaries should take the intermediaries’ location in the supply chain into account.

The analysis is conducted while the author visited the Labor Market Dynamics and Growth Center at Aarhus University. The author is grateful to Henning Bunzel and the late Dale Mortensen for facilitating the access to the confidential data bases of the Statistics Denmark and for their support. Support of The Cycles, Adjustment, and Policy research unit, CAP, and the School of Economics and Business, Aarhus University are acknowledged with appreciation.

Appendices

Appendix 1: Additional Tables

See Tables 14, 15, 16, 17, and 18.

Table 14 Summary statistics for WHS firms

Table 15 Share of wholesalers across CN product categories (average between 1993–2006)

Table 16 Concentration in export market by type of firms

Table 17 Concentration in export market by type of goods

Table 18 Concentration in export market by type of firms and type of goods

Appendix 2: Data

2.1 Foreign Trade Data

The foreign trade data sets are compiled from the Danish Customs records. Each shipment record includes the date of the shipment, the value of shipment, the product code (CN-8 digit), and the name of the product, weight of the shipment, type of the weight and sometimes quantity information as well as the unique firm identifier. Statistics Denmark aggregated this data into annual shipments for each product (CN-8 digit), country and firm triplet. As provided by Statistics Denmark, the international transaction data set covers the universe of Danish firms’ transactions for the period 1993–2007. However, only product shipments of 10,000 kr (approx.1800 us $) or above are included in the data set for the transactions with the EU countries.

2.2 Business Statistics Data

Business statistics data are compiled from survey results of firms that take part in a yearly financial survey as well as from tax reports, vat reports, and annual reports from incorporated companies. The general business statistics include only firms that employ at least a 0.5 FTE (full-time equivalent number of employees) and/or have had an estimated earnings of a certain size. Earning sizes are estimated differently for different industries. In the wholesale trade sectors, the limit of earnings is typically over 500,000 Danish Kroner, while in the manufacturing industry, it ranges between 150,000 and 200,000 Danish Kroner. Some of the data for very small firms may be subject to imputation. This data set is available starting from 1995, but only manufacturing, construction and retail sectors are included until 1998. In 1998, the wholesale trade sector is included and starting from 1999 it covers almost all sectors including mining, and all business service sectors.¹⁹ This data set is supplemented with the labor surveys (IDA) that provide information on wages, education and occupation characteristics for each individual in the labor force. In the labor (IDA) data set, for each employed person there is a unique firm identifier provided for the employer. Using this firm identifier, extracted information from IDA is merged with the Firm Accounting Data Set for each year. Only a couple of observations in firm accounting data were left unmatched from this matching.²⁰

Intermediaries are defined as firms with their main economic activity in 2-digit Danish Industrial Classification 51 (wholesale except of motor vehicles) as well as 6-digit industry classifications equal to 501010, 501020, 501030, 503010, 503020, and 504000 which are sale of motor vehicles, parts and accessories.

2.3 Matching Foreign Trade Data with Firm-Level Data Sets

Foreign trade as compiled from the custom records contain firm id’s but not a main business/industry affiliation, so it is not possible to identify the type of firms whether wholesale trade, retailer, manufacturer or service etc. from the foreign trade data alone. The analysis in this paper is carried out by matching the foreign trade data with the business statistics as well as other available data sets from Statistics Denmark such as tax data, and industry sales data. Between 1993 and 2007, most of the foreign data in the import side (94 % of firms) can be matched, less so in export (89 % of firms). A significant part of the transactions cannot be matched in the export side, probably due to reporting errors. Nevertheless during 1998 and 2006 which is the sample period used in the empirical analysis, 91 % of the exporting firms in custom data were matched with their corresponding industry affiliations.

2.4 Product Detail

Products description is based on the Combined Nomenclature (CN) 8 digit categories. The first 6 digits of the CN corresponds to the HS-6 digit classification. For example, 852812 product code refers to color television receivers with built-in picture tubes. In the CN-8 classification there are 19 different kinds of color television receivers depending on different characteristics such as display width, diagonal screen size, and lines of resolution.

2.4.1 Broad Product Classification

Product classification of the products as consumption, intermediate, or industrial good is based on BEC Rev. 3. Consumption goods are defined as (BEC = 112, 122, 522, > = 600). The rest are defined as industrial goods. Intermediate goods definition does not include fuels and lubricants and is defined as (BEC = 111, 121, 210, 220, 420, 530).

CN Chapters are used as broad product classifications and they are listed in Table 15. CN codes are matched with the corresponding manufacturing industries using PRODCOM. Prodcom provides statistics on the production of manufactured goods. Prodcom uses the product codes specified on the Prodcom List, which contains about 4500 different types of manufactured products. Products are identified by an 8-digit code: the first four digits are the classification of the producing enterprise given by the Statistical Classification of Economic Activities in the European Community (NACE). Most product codes correspond to one or more Combined Nomenclature (CN) codes, but some (mostly industrial services) do not. The matching between CN and PRODCOM are provided by EUROSTAT RAMON. The matches are executed for every year separately.

Rauch (1999) classification is used to classify products as homogenous, reference and differentiated goods. Classifications in Rauch (1999) are based on SITC codes. Correspondence tables between CN 8-digit and SITC 4-digit (provided by EUROSTAT RAMON) are used to link the classification with the Danish data.

High tech goods definitions follow OECD nomenclature (Loschky 2008). High-technology classification is based on both direct and indirect R&D intensities in relation to the production output or to the valued added. The indirect R&D intensity is defined as the R&D expenditures embodied in the intermediate products used in the production in another economic sector. See Loschky (2008) for more details.