Abstract
Manufacturing firms have been increasingly focusing on services, a trend that is evident in their composition of bought-in input and in-house production. The services intensity of firms may affect their productivity and thereby their competitiveness abroad; services are also instrumental in connecting firms to foreign markets and can help them to differentiate their offerings from those of other firms. However, the relation between services and manufacturing exports has only been partially analysed in the previous literature. This study contributes to the field by discussing the role of services for firms and empirically testing a set of related conjectures. Export intensity is regressed on two services input parameters, applying a fractional model to a rich panel of firms in Sweden in the period 2001–2007. The microeconometric results suggest that, after controlling for covariates and heterogeneity, service inputs affect a firms’ export capabilities: raising the proportion of services in in-house production yields higher export intensity on average. Furthermore, buying-in more services is associated with higher export intensity for firms in some industries. Overall, the study provides new firm-level evidence of the role of services as inputs in manufacturing.
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Notes
That is, α + β + γ = 1, where (α, β, γ) > 0.
Bought-in services comprise both domestic and imported services.
Moving beyond R&D to capture firm-specific assets, Braunerhjelm (1996) suggests a measure that includes cumulated investment in R&D, software, marketing and education. Regarding organisational capital, Bloom and Van Reenen (2010) use cross-section firm-level data from different countries to find that management practices have an important and significant effect on firm performance.
In 2006, 31 percentage points of the 46 % of people employed in services-related occupations in Swedish manufacturing had post-secondary education (Lodefalk 2013). For 13 OECD-countries, the corresponding mean was 27 percentage points out of 44 % in 2002 (Pilat and Wölfl 2005). Services-related employees constituted less than 30 % of ordinary labor, which does not require any specific education.
R&D services are likely to account for an important but limited share of this effect. R&D services constitute about 7 % of bought-in services in Swedish manufacturing according to Input–Output data. Moreover, according to our own calculations (based on official data from Statistics Sweden), just 6 % of those employed in manufacturing are in R&D activities.
The expression intends to capture key factors for export propensity, as well as export intensity, the latter conditional on being an exporter.
Empirically, the link between firm productivity and export propensity is well established, and ISGEP (2008) verifies a positive and significant link between firm productivity and export intensity in 13 out of 14 OECD-countries studied.
Hulten et al. (2009) also analyse the gap between book and market value in 12 R&D-intensive German firms and find that intangibles explain a large proportion of the gap. At the macro level, empirical studies show that intangibles have accounted for a relatively large share of economic growth in recent years (see e.g., Corrado et al. 2009; Edquist 2009). With respect to innovation and exports, Hirsch and Bijaoui (1985) find a positive correlation between R&D and exports, as do most subsequent studies (Cassiman and Martínez-Ros 2007). Cassiman et al. (2010) contribute by using Spanish firm-level data to analyse the innovation-productivity-export link. Their results suggest that innovation contributes to the noted productivity of exporters-to-be. A largely affirmative empirical study of the innovation-productivity link is done by Griffith et al. (2006), who use micro-level data for four countries. This is in line with the findings of Baldwin and Gu (2004), who show that exporters are more innovative and productive and that their productivity grows faster even before entering the export market.
There are annual observations over the entire period for approximately 2,500 of the 6,000 firms.
The data are not deflated. First, deflation of firm-level data is not a clear-cut issue. Deflators are at best approximations at the industry level, e.g. the producer price index, which still does not take input price developments into account. Second, to consider price changes by deflating with the producer price index at the industry level does not change the results when industry heterogeneity already is controlled for, as in this study.
Having data on cross-border services exports would be valuable but still non-comprehensive in terms of total services exports. For example, a firm may provide services to foreign customers via a commercial presence or through foreign partners. The sale of those services is, at best, indirectly captured in the firm’s own cross-border services exports.
The other occupations included are as follows: drivers and mobile plant workers (830) among plant and machine operators and assemblers and sales and services elementary occupations (910) as well as transport laborers and freight handlers (933) among elementary occupations. The group of services-related employees is dominated by occupations with educational requirements equivalent to upper secondary education or higher.
Results are robust to the use of total input costs as the denominator.
In accordance with EU statistics, an enterprise is not defined to include subsidiary firms.
Nevertheless, the two service measures seem to perform reasonably well in this regard, since regression without multinational firms does not alter key results from subsequent estimation.
The negative minimum value of the share of bought-in services in sales is explained by repurchases of services suppliers, according to Statistics Sweden. The maximum value of the share of in-house services is one; this is recorded for less than 300 firms, which nevertheless are classified as manufacturing firms by Statistics Sweden.
The rise is 14 % for in-house services and the decline 7 % for purchased services, over the period.
Results on export are robust to the inclusion of capital intensities and productivity. Results on productivity and human capital are also robust to the inclusion of capital intensities and physical capital intensity, respectively.
Estimation with an alternative measure of productivity does not alter the main results.
Moreover, we expect only little variation in trade barriers facing most Swedish firms over the sample period.
As a consequence, two years of the seven year panel are lost and we end up with 16,429 observations.
For an overview of firm characteristics associated with export performance, see for example Bernard et al. (2007); Greenaway and Kneller (2005, 2007); Hiep and Nishijima (2009). As regards size, the effect on export performance is not entirely clear. Fryges and Wagner (2010) as well as Sterlacchini (2001) find a positive effect, but there is also a level effect, which might be the result of larger exporters moving on to serve foreign markets mainly through foreign direct investments rather than exports. In the sensitivity analysis, we therefore include the square of size. However, the coefficient is insignificant and does not change results. It can be added that the share of services sales in total sales is included in the services intensity vector to control for any potential influence of services output on merchandise export.
This is implemented by using the built-in lag operators of the Stata software program rather than by generating lagged variables, thereby assisting the software in treating the model as a dynamic one.
The main results are robust to a more fully dynamic specification, a specification which, however, is less in line with our conceptual framework.
17 % of the 27,871 observations have zero export values.
Throughout the paper, average marginal effects are displayed in elasticity form, while taking any non-linearities and interactions of specifications into account. Technically, these elasticites are mainly based on output from the built-in margins feature of the Stata software, a feature which facilitates computing the average of predicted marginal effects for individuals.
This result indicates the importance of considering the fractional nature of export intensity in estimation.
A simple calculation shows that the turning point for the concave services-export intensity relation is 75 % for in-house services and 29 % for bought-in services.
A caveat is that the estimated coefficients on services in this regression are likely to be somewhat biased, due to correlation with current period labor productivity.
High-skill human capital is an inappropriate proxy for R&D expenditures, though, which also include machinery, laboratory equipment, etc.
Moreover, only 7 % of bought-in services in manufacturing are R&D services, and an even smaller share of employees do R&D, according to the our calculations based on official data from Statistics Sweden.
In Sweden, the overwhelming majority of manufacturing firms participate in foreign trade (Table 1), thereby making sample selection in total firm trade less of a problem than in large countries where most firms do not participate in foreign trade. Moreover, disregarding sample selection is expected to lead to underestimation of impacts, making the fractional Tobit results lower limits of services impacts.
Generally, the effects of most explanatory variables are smaller. The diminishing return to in-house services is now both statistically and practically much less important.
For 2006, 55 “composite firms” enclosed 1,071 other legal entities.
In 2006 about 70 % of firms in the EGR were in Swedish-only groups, 17 % in foreign firms and 13 % in Swedish multinationals.
The earlier limit for exports and imports being covered was 1.5 mn SEK (1998–2004). For trade via another EU member, information on the actual sender or receiver is unavailable.
Data for travel funds and some government authorities are reported separately by the Central Bank to Statistics Sweden.
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Acknowledgments
The author thanks Fredrik Sjöholm and Pär Hansson for encouragement and helpful suggestions. Thanks also go to Hildegunn Kyvik-Nordås, Sune Karlsson, the editor, two anonymous referees, participants at the European Trade Study Group conference in 2011 and seminar participants at the universities in Örebro, Lund and Karlstad for valuable comments. The opinions expressed herein are solely those of the author.
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Appendices
Appendix 1
Appendix 2: Data
Data for the study come from Statistics Sweden and cover 2001–2007. The resulting unbalanced micro panel database encompasses all manufacturing firms in Sweden (ISIC 10–37).
Financial information comes from the Swedish Structural Business Statistics (SBS). The SBS is based on data from the Swedish Tax Authority, but is supplemented by survey data for some variables as well as for the largest firms. A firm is generally defined as the smallest legal entity. However, there are some 50 “composite firms” who report for more than one legal entity within the same enterprise group.Footnote 37 Firms and entities’ industry affiliations are from the Business Register and are defined using the Swedish Standard Industrial Classification (SNI 2002). SNI 2002 corresponds to NACE (rev. 1.1) up to the 4-digit level and to ISIC (rev. 3) up to the 2-digit level.
Information on enterprise affiliation comes from the Swedish Enterprise Group Register (EGR). Data have been collected by Statistics Sweden and PAR AB. An enterprise group is defined as a group consisting of a mother firm and at least one more firm, where the mother holds the absolute and therefore controlling majority (>50 %) of the stocks.Footnote 38
Statistics on the highest level of education attained for each resident aged 16–74 comes from the register based labor market statistics (RAMS). Since 2001 RAMS has also contained information on the number of employees in a firm, their occupation and remuneration.
The foreign trade data are from the Swedish Foreign Trade Statistics (FTS). It includes value (SEK) and country of origin or destination. With respect to merchandise trade with non-EU countries, data comes from compulsory registration information collected by Swedish Customs. Regarding intra-EU merchandise trade, the data cover the trade of all firms with an annual import or export of 2.2 and 4.5 million SEK, respectively.Footnote 39 For services trade, all collated bank transactions larger than 150,000 SEK crossing the Swedish border is included before 2003; data gathered after 2003 is based on a quarterly survey. A representative sample of some 5,000 services traders—10 % of the population—is included in the survey, with a third of the sample being replaced each year.Footnote 40
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Lodefalk, M. The role of services for manufacturing firm exports. Rev World Econ 150, 59–82 (2014). https://doi.org/10.1007/s10290-013-0171-4
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DOI: https://doi.org/10.1007/s10290-013-0171-4