Abstract
This chapter tests the role of agglomeration effects on the export decision of services firms. Recent theories on trade with heterogeneous firms predict that export to foreign markets goes along with sunk market-entry costs. Only the more productive firms will be able to absorb such sunk costs, so that ex ante self selection on the basis of productivity may be expected. Recent research by spatial economist suggests however that productivity sorting may also be the result of operating in large-city areas. In this chapter I find strong evidence that the productivity differences between exporting and non-exporting services firms depend on both agglomeration effects and on anticipated market-entry costs in foreign markets. The research is based on a large set of micro-data for Dutch services establishments. Productivity sorting is strongest for markets with heterogeneous services. Productivity self-selection is found to be strongest for services exporters in rural areas and small agglomerations. This is consistent with the finding that urban services firms on average already have a higher productivity.
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Notes
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- 2.
Allowing only the most productive ones to survive and thus generating higher average productivity compared to rural areas (like in Melitz & Ottaviano, 2008).
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Although productivity premiums will be shown as a form of robustness test.
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- 7.
The sampling data are provided in the form of an expansion factor that says for each sampled establishment how much other establishments it represents in its stratum. This expansion factor is used as a weight in regressions.
- 8.
The problem of a long under-represented tail has been reduced by adopting a cut-off size of at least ten employed workers for services establishments. Export participation and association with multinational firm are less important for these small establishments. Data entries holding imputations by Statistics Netherlands were removed from our sample, keeping only questionnaire-based establishment data.
- 9.
The agglomeration density variable measures the average number of addresses per square kilometre within a circle of a 1-km ray, measured at the beginning of each year. The five urbanisation classes are: (1) very strong urbanisation (≥2,500 addresses per km2); (2) strong urbanisation (1,500–2,500 addresses per km2); (3) moderate urbanisation (1,000–1,500 addresses per km2); (4) weak urbanisation (500–1,000 addresses per km2); (5) non-urban area (<500 addresses per km2). In the regressions I have taken the log of the urbanisation-class indicator.
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The size classes are based on the number of employed persons and cover the following intervals: (1) 10–19 employed persons; (2) 20–39 employed persons; (3) 40–59 employed persons; (4) 60–80 employed persons; (5) 80–124 employed persons; (6) 125–249 employed persons; (7) 250–499 employed persons; (8) 500–999 employed persons; (9) 1,000–1,999 employed persons; (10) >2,000 employed persons. This size class definition is consistently applied in the remainder of the paper.
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The data do not allow a reliable identification of establishments that are associated with foreign multinational firms.
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The distinction between rural and non-rural areas is made on the basis of the rural area dummy. The “rural area dummy” has been set to 1 if the number of addresses per postal code amounted to <1,501 per km2, and zero otherwise.
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Given that for this regression international transaction data were not available, the following assumptions are imposed to test the predictions of the heterogeneous-firms trade model: (a) establishments in each sector (4-digit) have the same information about market size, variable and sunk trade barriers, covering all relevant countries; (b) establishments share a common country set as potential export markets and (giving assumption a) have an identical ranking within their set of preferred export countries; (c) establishments in a (4-digit) sector enter potential export countries according to an identical country sequence based on market size, distance and sunk market-entry costs. In other research (Smeets, Creusen, Lejour, & Kox, 2010) we have estimated the country-specific sunk market entry costs for Dutch manufacturing firms.
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The threshold value can be set at zero without loss of generality.
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Usually this means that its variance is fixed at a given value (Verbeek 2004). Since \( \mathrm{ F}(\beta \mathrm{ x}\_ \mathrm{ it}\ ) \) is also bounded between 0 and 1, it is plausible to choose a standard normal distribution \( \varphi (\beta \mathrm{ x}\_ \mathrm{ it}). \) There is no reason to expect that the standard normal distribution does not apply.
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Impact elasticities instead of marginal effects are presented, because the intuitive interpretation of elasticities is easier. I evaluated point elasticities at the mean and at the median values of ln \( \beta \mathrm{ x}\_ \mathrm{ it} \). Since differences between both were very small, I only report point elasticities at the mean.
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The size class is measured on a 10-point Likert scale {1, … ,10} that increases in employment size. We took the median size category for the firm over the full observation period. The result is expressed as a natural logarithm.
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The population size and the agglomeration density scale are expressed in logs.
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Andersson and Lööf (2009) conclude that firms located in larger regions are more productive, even when controlling for size, human capital, physical capital, ownership structure, import and export, industry classification and time trend. Second, they find that results from dynamic panel estimations suggest a learning effect in that agglomeration enhances firms’ productivity. Third, the role of agglomeration phenomena does not seem to have a clear coupling to firm size.
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The caveat raised at the end of Sect. 2 about the unreliability of summary statistics for measuring self selection (in the presence of agglomeration factors) remains valid.
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Cf. Békés and Muraközy (2008) for Hungary. Besedes and Prusa (2006) established for the USA that trade relationships typically start small and that almost half of the “small relationships” end within a year, while larger initial purchases result in longer, stable relationships. From this they advance a matching model of international trade in which uncertainty and the costs of searching reliable trade partners play important roles. The search cost idea can be reconciled with the Melitz model, because search costs are in fact country-specific sunk entry costs. Albornoz, Calvo Pardo, Corcos, and Ornelas (2009) go one step further in evaluating the role of uncertainty and learning. In their view a strategy of sequential exporting to different country markets is a rational firm strategy to discover their own competitive advantage.
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Think of taxes that are linked to urban land and property prices, such taxes embody the agglomeration mark-up linked with being in an attractive business and living agglomeration.
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Kox, H.L.M. (2013). Export Decisions of Services Firms Between Agglomeration Effects and Market-Entry Costs. In: Cuadrado-Roura, J. (eds) Service Industries and Regions. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35801-2_8
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