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Does importing more inputs raise exports? Firm-level evidence from France


Does an increase in imported inputs raise exports? We provide empirical evidence on the direct and indirect channels via which importing more varieties of intermediate inputs increases export scope: (1) imported inputs may enhance productivity and thereby help the firm to overcome export fixed costs (the indirect productivity channel); (2) low-priced imported inputs may boost expected export revenue (the direct-cost channel); and (3) importing intermediate inputs may reduce export fixed costs by providing the quality/technology required in demanding export markets (the quality/technology channel). We use firm-level data on imports at the product (HS6) level provided by French Customs for the 1996–2005 period, and distinguish the origin of imported inputs (developing vs. developed countries) in order to disentangle the different productivity channels above. Regarding the indirect effect, imported inputs raise productivity, and thereby exports, both through greater complementarity of inputs and technology/quality transfer. Controlling for productivity, imports of intermediate inputs from developed and developing countries also have a direct impact on the number of exported varieties. Both quality/technology and price channels are at play. These findings are robust to specifications that explicitly deal with potential reverse causality between imported inputs and export scope.

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  1. See, for example, Pavcnik (2002), Schor (2004), Amiti and Konings (2007), Kasahara and Rodrigue (2008), Andersson et al. (2008), Vogel and Wagner (2010), Bas and Ledezma (2010) and Topalova and Khandelwal (2011). For a survey see Wagner (2012).

  2. Among others, Feenstra and Hanson (1996), Hijzen et al. (2005) and Biscourp and Kramarz (2007) evaluate the contribution of imported intermediate goods to the worsening position of unskilled workers (in terms of wages and employment) in developed countries.

  3. As our aim is to illustrate the main mechanisms at play between use of imported inputs, TFP and export scope at the firm level, we present a very simplified model. In contrast, Kasahara and Lapham (2013) derive important results in a more realistic framework.

  4. Feenstra’s (1994) argument goes in the same direction: Varieties of imported inputs are not perfect substitutes. Input cost indices that do not consider variation in the number of input varieties are thus biased, which, for a given level of output, biases the estimation of TFP. Subsequent papers (e.g., Goldberg et al. 2010; Gopinath and Neiman 2011) do account for the role of the number of varieties in estimating the exact price index and then TFP.

  5. In the spirit of Matsuyama (2007), the export fixed cost may also depend on foreign countries’ regulations, business language, consumer culture and network accessibility. Importing inputs may help to reduce the export fixed cost by improving the firm’s knowledge of foreign markets. Our empirical test does not however capture such an effect. We thus leave any test of this mechanism for further research.

  6. We may still encounter some forms of carry-along trade, as revealed by Bernard et al. (2012), where imported inputs are directly re-exported. As shown in Sect. 5, our results are robust to the exclusion, at the firm level, of all goods that are both imported and exported, suggesting that, carry-along trade does not drive our results.

  7. This database is quite exhaustive. Although reporting of firms by trade values below 250,000 euros (within the EU) or 1,000 euros (rest of the world) is not mandatory, we have many observations below these thresholds.

  8. Developing countries correspond to non high-income countries, defined by the World Bank as countries with 2007 per-capita GNIs under $11,456 computed in U.S. dollars using the Atlas conversion factor.

  9. We used different specific deflators at the 2-digit level for added value, materials and capital goods.

  10. For example, Brazilian exports to Argentina may incorporate a technology transfer.

  11. As an alternative definition of intermediate inputs, we also make use of the United Nations Broad Economic Categories (BEC) classification. The results are similar to those found with other classifications and are available upon request.

  12. The work cited above, as well as many others (e.g., Clerides et al. 1998 and Delgado et al. 2002) show that exporters are larger, more productive and more capital intensive. Several studies for European countries (e.g., Andersson et al. 2008 for Sweden, Muûls and Pisu 2007 for Belgium, and Castellani et al. 2010 for Italy) have found that restricting the number of firms (to the largest ones) drastically increases the share of exporters. By contrast, Eaton et al. (2004), who use an exhaustive database of French companies and work with more than 200,000 firms, find that only a small percentage of firms export.

  13. Levinsohn and Petrin (2003) (henceforth LP) build upon the idea of Olley and Pakes using primary input demand instead of the investment decision to control for unobserved productivity shocks. Their rationale lies in the idea that investment data are often missing or lumpy, whereas data on raw inputs are of better quality thus guaranteeing strict monotonicity without efficiency loss. In our French database, 96.5 % of the observations report investment data. We thus rely on the OP method. For robustness, we also estimated production using the LP method. The results are very similar and are available upon request. Note that the OP specification performs better than fixed-effect specifications, as the unobserved individual effect (productivity) is not constrained to be constant over time. Moreover, approaches based on instrumental variables are limited by instrument availability. Finally, the OP methodology does not assume any parameter restrictions.

  14. Ackerberg et al. (2007) provide a detailed description of the OP, LP and ACF methodologies.

  15. De Loecker (2007) studies learning by exporting and includes export status as a state variable in the Olley and Pakes estimation, whereas Kasahara and Rodrigue (2008) add imported inputs status as a state variable in their analysis of the effect of imported inputs on productivity using Chilean plant-level data.

  16. Our results are robust to the use of the value of imported inputs or the import status of the firm (i.e., a dummy that takes a value of one if the firm imports intermediate inputs) instead of the number of imported varieties.

  17. The OP/LP/ACF methodology thus faces the traditional concerns that productivity estimates capture differences in prices and mark-ups instead of actual physical productivity (Erdem and Tybout 2003; Katayama et al. 2009; De Loecker 2007). Following Bernard et al. (2003), we believe however that if price-costs mark-ups correlate with firms efficiency, our measure of TFP is valid.

  18. EU countries include the EU15 member states (i.e., Austria, Belgium, Luxembourg, Denmark, Finland, Germany, Greece, Italy, Ireland, The Netherlands, Portugal, Spain, Sweden and the United Kingdom).

  19. In order to be consistent across estimations, we also use this restricted database in our estimation of firm TFP.

  20. Our results are robust to the use of a weighted average capturing the firm’s relative use of a specific imported input in total imported input: τ jt  = ∑ i α ij τ it , where α ij is the weight of input i in the total input cost of output j and τ it is the output tariff of sector i in t. These results are available upon request.

  21. We run the estimation on the full sample of firms. The OP/ACF semi-parametric estimation requires many lags of the data which reduces substantially the number of observations.

  22. Note that the coefficients on capital stock are small. This might be due to the fact that the OP/ACF semi-parametric estimator for the production function includes the number of imported inputs as an additional persistent variable, which is positively correlated with capital. Hence, it is difficult to assess ex-ante the direction of OLS bias on capital coefficient in this case. Kasahara and Rodrigue (2008) find similar results for Chile.

  23. If \(\ln y = \alpha \ln x\), the explanatory power of variable x that changes by δx with respect to its mean is \(((1+\frac{\delta{x}}{\overline{x}})^{\alpha}-1)*100\) percent, where \(\overline{x}\) is the mean of x. Due to our focus on imported inputs from non-EU countries, the average increase in the number of imported varieties is relatively small. If we include inputs imported from EU countries, the average firm increases its use of imported varieties by 7.3 units over the period.

  24. Within estimates are consistently lower than IV estimates. Good instruments may actually correct for the downward bias caused by measurement errors in the explanatory variables (see Hausman 2001 and Arellano 2003).

  25. Recall that in the IV estimations our sample is restricted to importing firms.

  26. This dataset is based on a firm-level survey of manufacturing firms belonging to groups with at least one affiliate in a foreign country and with international transactions totaling at least one million euros. The survey year is 1998. The data provide a good representation of the activity of international groups located in France. These data cover around 82 % of total trade flows by multinationals, and 55 and 61 % of total French imports and exports respectively.

  27. Tobit models with fixed effects have an incidental parameters problem, and are generally biased.

  28. Consumer preferences are represented by a standard CES utility function \(C^{\frac{\phi -1}{\phi }}=\sum_{k\in \Upomega_{d}}C_{dk}^{\frac{\phi-1}{\phi}}\), where ϕ > 1 is the elasticity of substitution across final consumption goods.

  29. We assume that p iD  = p iN  > p iS , as factors of production are expected to be cheaper in the South.

  30. Note that the price set by an exporting firm is given by \(p_{x}=p_{d}\left(1+\tau \right)\), where τ is the export variable cost.


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We also thank seminar participants at LSE, NYU, CEPII, EEA (Oslo), EITI (Tokyo), and ETSG (Lausanne) for useful comments. We are responsible for any remaining errors.

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Correspondence to Maria Bas.

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We have benefited from discussions with Matthieu Crozet, Sandra Poncet, Andrew Bernard and Tibor Besedes.


Appendix 1: A simple model

In this Appendix, we present a simple partial equilibrium model which sheds light on the mechanisms via which imported inputs affect firm TFP and export scope. There is a continuum of domestic firms in the economy that supply differentiated final goods under monopolistic competition. Firms differ in their initial productivity draws (\(\varphi\)) which are introduced as in Melitz (2003). In order to produce a variety of final good y, the firm combines three factors of production: labor (L), capital (K) and a range of differentiated intermediate goods (M ij ) produced by industry i, that can be purchased in the domestic or foreign markets. If the firm sources its inputs internationally, it may import intermediate goods from two different sets of countries distinguished by their levels of development. As is traditionally assumed, countries in the North have higher GDP per capita than those in the South. The technology is represented by a Cobb–Douglas production function with factor shares \(\eta +\beta+\sum\nolimits_{i=1}^{I}\alpha_{i}=1\) (for simplicity, we omit the firm subscript):

$$ y=\varphi L^{\eta }K^{\beta }\prod\limits_{j\in \{D,N,S\}}\prod\limits_{i=1}^{I}\left( M_{ij}\right)^{\alpha_{i}} $$

where \(M_{ij}=\left(\sum_{v\in I_{ij}}\chi_{ij}m_{iv}^{\frac{\sigma_{i}-1}{\sigma_{i}}}\right) ^{^{\frac{\sigma_{i}}{\sigma_{i}-1}}}\).

The range of domestic and imported varieties of intermediate goods in industry i are aggregated by CES functions M ij , where i is the industry, j the country region (i.e., domestic, North or South), \(I_{j}=\{1,\ldots.,M_{j}\}\) and σ i  > 1 is the elasticity of substitution across the varieties in industry i. The technology/quality parameter, χ ij , captures the fact that imported inputs may enhance firm efficiency differently depending on their origin. We assume that χ ij is greater than one for inputs sourced from the most developed countries, i.e., j = N, and equal to one otherwise. In this set up, each foreign country may produce one variety of inputs per industry, we thus match our empirical framework where a variety is defined as a product-country pair.

As is common in the literature (e.g., Ethier 1982 and Markusen 1989), we consider that intermediate inputs are symetrically produced at a level \(\overline{m}\). This yields \(M_{iD}=N_{iD}^{\frac{\sigma_{i}}{\sigma_{i}-1}}\overline{m}_{D},\quad M_{iS}=N_{iS}^{\frac{\sigma_{i}}{\sigma_{i}-1}}\overline{m}_{S}\hbox{ and }M_{iN}=\left(N_{iN}\chi_{i}\right)^{\frac{\sigma_{i}}{\sigma_{i}-1}}\overline{m}_{N}\), where N iD N iS and N iN are the number of domestic and imported (from the South or the North) varieties of intermediate goods. The production function for a variety of final good, equation (1), can thus be rewriten as:

$$ y=\varphi L^{\eta }K^{\beta }\prod\limits\limits_{j\in \{D,N,S\}}\prod\limits_{i=1}^{I}\overline{M}_{ij}^{\alpha_{i}} \left(N_{ij}\chi_{ij}\right)^{\frac{\alpha_{i}}{\sigma_{i}-1}} $$

where \(\overline{M}_{ij}=N_{ij}\overline{m_{j}}\). Following Kasahara and Rodrigue (2008), we make the simplifying assumption that firms either source their inputs domestically or internationally (from both the North and the South). Intermediate goods imported from the North have a higher technological content whereas inputs sourced from the South have a lower price, as input prices reflect the assumed relatively lower cost of factors of production in the South. As is standard, the first-order condition is such that prices reflect a constant mark-up, \(\rho =\frac{\phi -1}{\phi }\), over marginal costs, \(p=\frac{MC}{\rho }\), where the marginal cost of production is determined by MC D if the firm sources its inputs domestically and MC F if it does so on foreign markets. Footnote 28

$$ MC_{D}=\frac{p_{k}^{\beta}w^{\eta}{\overset{I}{\prod}}_{i=1}p_{iD}^{\alpha_{i}}} {\varphi{\overset{I}{\prod}}_{i=1}N_{iD}^{\frac{\alpha_{i}}{\sigma_{i}-1}}} $$
$$ MC_{F}=\frac{p_{k}^{\beta}w^{\eta}\prod_{j\in \{N,S\}}{\overset{I}{\prod}}_{i=1} p_{ij}^{\alpha_{i}}}{\varphi {\overset{I}{\prod}}_{i=1} \left(N_{iN}\chi_{iN}\right)^{\frac{\alpha_{i}}{\sigma_{i}-1}} \left(N_{iS}\right)^{\frac{\alpha_{i}}{\sigma_{i}-1}}} $$

where w is the wage, p k is the price of capital goods and p ij is the price of inputs from industry i and region j. Footnote 29 Combining the demand faced by each firm, \(q_{j}(\varphi)=\left(\frac{P}{p_{j}(\varphi)}\right)^{\phi}C\)—where P is the aggregate final goods price index and C is aggregate expenditure on varieties of final goods—and the price function, \(p_{j}(\varphi)=\frac{MC_{j}}{\rho}\), revenues are given by \(r_{j}(\varphi)=q_{j}(\varphi)p_{j}(\varphi): r_{j}(\varphi)=\left(\frac{P}{p_{j}}\right)^{\phi-1}R\), where R = PC is the aggregate revenue of the industry, which is considered exogenous to the firm. Firm domestic profits thus simplify to \(\pi_{j}=\frac{r_{j}}{\phi}-F\), where F is the fixed production cost. Firms that import, also incur a fixed import cost, F m . Firm export profits are given by \(\pi_{x}=\frac{r_{x}}{\phi}-F_{x}\), where F x includes the production fixed costs (which include the import fixed cost for importing firms), F, as well as the export fixed costs which, as explained below, fall in the technology/quality parameter, i.e., \(F_{x}=g\left(F, \frac{f_{x}}{\chi_{ij}}\right)\).

Using the price and revenue functions defined in the previous section, we derive the following expression for firms’ export revenues: Footnote 30

$$ r_{x}=\Uppsi\left(\frac{\varphi {\overset{I}{\prod}}_{i=1}\, \left(N_{iN}\chi_{iN}\right)^{\frac{\alpha_{i}}{\sigma_{i}-1}} \left(N_{iS}\right)^{\frac{\alpha_{i}}{\sigma_{i}-1}}}{\prod\nolimits_{j\in \{N,S\}}\prod\nolimits_{i=1} p_{ij}^{\alpha_{i}}}\right)^{\phi-1} $$

where \(\Uppsi=P^{\phi-1}R\left(\rho^{-1}\left(1+\tau\right) p_{k}^{\beta}w^{\eta}\right)_{,}^{1-\phi }\) with τ being the variable export cost, P the aggregate price index of final goods and R aggregate industry revenue, all of which are exogenous to the firm. The corresponding profit can thus be written as

$$ \pi_{x}=\frac{\Uppsi}{\phi}\left(\frac{A}{\prod\nolimits_{j\in \{N,S\}}\prod\nolimits_{i=1}\,p_{ij }^{\alpha_{i}}}\right)^{\phi -1}-F_{x} $$

The tradebility condition for export is given by: \(\pi_{x}\left(\varphi_{x}^{\ast}\right)=0\), where \(\varphi_{x}^{\ast }\) is the Melitz (2003) productivity draw of the marginal firm serving the export market.

Appendix 2: Empirical results

(See Tables 10, 11, 12, 13, 14 and 15)

Table 10 Intermediate inputs
Table 11 Distribution of imported inputs by sector
Table 12 First stage of the IV estimations of Tables 6 and 7
Table 13 Alternative productivity measure
Table 14 Export status and imported inputs
Table 15 Intensive margin and destination of export and imported inputs

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Bas, M., Strauss-Kahn, V. Does importing more inputs raise exports? Firm-level evidence from France. Rev World Econ 150, 241–275 (2014).

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