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Rethinking the import-productivity nexus for Italian manufacturing


We provide evidence on the firm level productivity effects of imports of intermediates. By exploiting a large panel of Italian manufacturing firms, we are able to separately explore the role of importing from high and low income countries. Importing does not permanently affect the firm productivity growth. This finding holds both when we test for the import entry by means of Propensity Score Matching techniques and when we analyse the import intensity within a dynamic panel data model framework. On the contrary, we confirm the existence of self-selection into importing. Also, our evidence supports the learning-by-exporting effects in Italian manufacturing and we prove that this result is robust to the control of firm import activity.

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  1. 1.

    This strategy is close to the one in Görg et al. (2008) and Forlani (2010).

  2. 2.

    See Halpern et al. (2005) for Hungary et al. (2008) for Paul and Yasar (2009) for Turkey and Burger and Rojec (2011) for Slovenia. Some relevant papers also investigate and confirm the role of trade liberalisation episodes in fostering productivity (Amiti and Konings 2007; Fernandes 2007).

  3. 3.

    Mazzola and Bruni (2000) and Calabrese and Erbetta (2005) focus on firms’ production linkages for a sample of southern firms and for firms in the automotive industry, respectively, finding important effects of outsourcing on the firms’ performance, but they do not deal with international linkages. Finally, Barba Navaretti and Castellani (2004) study the impact of becoming a multinational on a bunch of firm level performance measures between 1993 and 1997. However, our focus is on firm level imports that do not necessarily coincide with foreign direct investments. Furthermore, whereas we distinguish between importing from high and low income economies, they do not dissect the impact of investing abroad according to the income level of the destination country.

  4. 4.

    Details on the sample representativeness are available from the authors upon request.

  5. 5.

    The original number of firms was slightly higher, however, as standard we cleaned the sample removing firms in NACE sectors 16 and 23 (these sectors include a small number of firms and for the nature of the performed activities they may behave differently from the rest of manufacturing sectors) and firms with some anomalous (zero or negative) or missing values for the main variables (output, materials, value added or capital). We have also excluded firms which are considered as outliers for at least one year in the sample period. We consider as outliers those observations from the bottom and top 0.5 % of the distribution of some main ratio (value added on labour and capital on labour).

  6. 6.

    This breakdown has been performed by ISTAT researchers according to source countries’ per capita income level. It is worth to notice that the import measure at our disposal prevents us from disentangling the effects of input purchases from foreign affiliates versus arm’s length purchases. Nevertheless, we believe that our measure mainly captures the latter, as Italian multinationals only account for about 5 % of Italian manufacturing firms and 5 % of importers with at least 10 employees, then the vast majority of importers perform arm’s length transactions. The latter evidence is gathered from the representative EFIGE database for firms having at least 10 employees ( Furthermore, we are not able to identify the goods purchased abroad by Italian manufacturing firms, relabeled—without any production process—and resold. This phenomenon will deserve further investigation, as soon as suitable data will be available.

  7. 7.

    In the empirical analysis below, we will relax this assumption and we will also adopt a TFP index calculated on the basis of an output production technology with material and service inputs too.

  8. 8.

    See “Appendix 1” for the definition and the detailed description of the variables.

  9. 9.

    The inclusion of three digit sector dummies caused convergence problems so we decided to stick to the use of two digit dummies, also not to incur in the inconsistent parameter estimates related to the presence of a large number of fixed effects in short T panels when estimating a model with Maximum Likelihood (see Wooldridge 2002, p. 484).

  10. 10.

    Unfortunately, due to our sample time span, we are not able to test for third order autocorrelation. However, we rest on the Hansen test to evaluate the goodness of the instruments.

  11. 11.

    GMM–SYS estimations are available from the authors upon request. They mimic the findings of GMM–DIFF, and the impact of offshoring to low income countries turns to be non significant when the firm involvement in export markets is accounted for. However, even if the Hansen test often rejects the null in this set of estimates, the Hansen/Sargan test is found to be inclined to some weakness (Roodman 2006). As a matter of fact, Blundell and Bond (2000) observe some tendency for the Sargan/Hansen test statistics to reject a valid null hypothesis too often in their experiments, and this tendency is greater at higher values of the autoregressive parameter. Furthermore, the Hansen test rejection in large firm level samples is not an uncommon feature (Bontempi and Mairesse 2008). Meschi et al. (2011), indeed, discuss that the very large number of observations makes the occurrence of a significant Sargan/Hansen more likely. They report that when in their work they repeat the test over random subsamples the test was not significant most of the times.

  12. 12.

    As a matter of fact, in our sample we observe that if sectors are split into High Tech and Traditional according to Pavitt’s 1984 taxonomy, the largest stock of intangible assets is recorded for firms in the former group while the lowest stock is for firms in the latter.

  13. 13.

    Unfortunately, we are not able to control for the foreign ownership of the firm in this sample. We also lack any information on the firm foreign investments abroad. The inclusion of inward and outward FDI dummies would be desirable here, due to the large intra-firm share of trade that is generally operated by multinationals and to the higher efficiency stemming from being a multinational. To assess whether the omission of such controls may result in a serious misspecification of our empirical model, we made a check on the EFIGE representative database for manufacturing firms with at least 10 employees. This database reports that foreign owned firms (firms with 10 % or more of foreign owned capital) represent in Italy about 5 % of all manufacturing firms. At the same time, only 2.5 % of Italian firms declare to invest abroad. In addition, only 7 % of exporters and 9 % of importers are foreign owned and only 4 % of exporters and 5 % of importers are foreign investors. These figures, concerning the population of firms with at least 10 employees, confirm that multinational activity is not very common within the Italian manufacturing sectors, and that the majority of importers and exporters are not part of a multinational group.

  14. 14.

    Consistently with this view, Lööf and Andersson (2010) find that no import effect on productivity when focusing on persistent exporters.

  15. 15.

    We thank one referee for the suggestion of this line of inquiry.

  16. 16.

    However, it is worth to notice that the validity of GMM instruments is not strongly supported by Hansen tests.

  17. 17.

    Evidence in this line is reported by Lo Turco and Maggioni (2012a). By the same token, evidence of p-substitutability bewteen material inputs and labour in Italian Manufacturing is displayed by Bettin, Lo Turco, and Maggioni (2012).

  18. 18.

    Usually wages are considered as rigid in the Italian labour market.

  19. 19.

    To corroborate this interpretation we tested the impact of importing on the firm average cost, the costs of materials and of labour per unit of output and the ratio of material to labour costs. Importing from low income economies goes with a reduction in total average cost, an increase in the cost of material per unit of output and, a reduction in the cost of labour per unit of output and an increase in the ratio of material to labour cost. Results are not shown for brevity but are available upon request.

  20. 20.

    The choice of this index is motivated by its robustness. Van Biesebroeck (2007) shows that, apart the case of large measurement errors in the data, the index produces consistently accurate productivity growth estimates, even when firms are likely to employ different technologies.

  21. 21.

    Labour is measured as the number of employees in the firm, while capital is proxied by the balance sheet value of material assets.


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Corresponding author

Correspondence to Alessia Lo Turco.

Additional information

The data used in this work are from the ISTAT Annual Report, 2006. All elaborations have been conducted at the ISTAT "Laboratorio per l’Analisi dei Dati ELEmentari” under the respect of the law on the statistic secret and the personal data protection. The results and the opinions expressed in this article are exclusive responsibility of the authors and, by no means, represent official statistics. We are particularly grateful to Dr. Monducci, Dr. Anitori and the ADELE staff for allowing the realization of this research. We are also grateful to participants in the SIE conference 2011 in Rome for useful advice and discussions.


Appendix 1: Variables definition and description

  • tfp: total factor productivity. Throughout the paper the latter is computed following Caves et al. (1982) Footnote 20 as:

    $$ \begin{aligned} ln TFP_{ft} &=ln Y_{ft} -\bar{ln Y_{t}}+\sum_{s=2}^{t} (\bar{lnY_{s}}-\bar{lnY_{s-1}})\\ & \quad -\frac{1}{2}\sum_{i=1}^{n}(S_{fit}+\bar{S_{it}})(lnX_{fit}-\bar{lnX_{it}}) + \frac{1}{2}\sum_{s=2}^{t}\sum_{i=1}^{n}(\bar{S_{is}}+\bar{S}{_{is-1}})(\bar{lnX_{is}}-\bar{lnX_{is-1}}) \end{aligned} $$

    with Y and X respectively measuring real value added and the quantities of the n = 2 primary factors of production, i.e. labour and capital.Footnote 21 S refers to the expenditure share of each factor and the bar indicates the average over the relevant quantity. We define a hypothetical firm having input cost shares equal to the arithmetic mean cost shares over all observations, and with input and output levels equal to the geometric mean of inputs and output over all observations. The terms in the first sum describe the difference between the firm f and the hypothetical firm at time t, while the terms in the second sums chain together the hypothetical firms back to the base period. The index measure the productivity in each year relative to a hypothetical firm that represents the average firm in the sector in the first year of our sample time span.

  • tfp s: total factor productivity based on real sales. Throughout the paper the latter is computed following Caves et al. (1982) as:

    $$ \begin{aligned} ln TFP^{s}_{ft} &= ln Y_{ft} -\bar{ln Y_{t}}+\sum_{s=2}^{t} (\bar{lnY_{s}}-\bar{lnY_{s-1}})\\ & \quad -\frac{1}{2}\sum_{i=1}^{n}(S_{fit}+\bar{S_{it}})(lnX_{fit}-\bar{lnX_{it}}) + \frac{1}{2}\sum_{s=2}^{t}\sum_{i=1}^{n}(\bar{S_{is}}+\bar{S}{_{is-1}})(\bar{lnX_{is}}-\bar{lnX_{is-1}}) \end{aligned} $$

    This index depart from the one above since it rests on the output specification of the production function, where Y measures the real output and X denotes the quantities of the n = 3 primary factors of production, i.e. labour, capital and the sum of intermediate material and service purchases. S refers to the expenditure share of each factor and the bar indicates the average over the relevant quantity.

  • va: logarithm of the firm real value added;

  • lp: labour productivity, measured as the logarithm of the firm real value added over firm total employment;

  • Imp LI: import status from low income economies, measured as a dummy variable taking value 1 if the firm imports from low income countries and 0 otherwise;

  • Imp HI: import status from high income economies, measured as a dummy variable taking value 1 if the firm imports from high income countries and 0 otherwise;

  • ImpSh LI: import intensity from low income economies, measured as the share of imported inputs from low income countries over total output;

  • ImpSh HI: import intensity from high income economies, measured as the share of imported inputs from high income countries over total output;

  • Exp: export status, measured as a dummy variable taking value 1 if the firm exports;

  • ExpSh: export intensity, measured as the value of total exports over total output;

  • wage: average wage, logarithm of total labour cost over total employment;

  • kl: capital labour ratio, measured as the logarithm of the ratio between the firm real material assets and the firm total employment;

  • MatSh dom : firm level intensity in domestic materials, measured as the share of material inputs purchased domestically over total material purchases;

  • lab: size, measured as the logarithm of firm employment;

  • k int : intangible capital stock, measured as the logarithm of the firm real intangible assets;

  • imp_pen sect : sector level import penetration, measured as the three digit level sector imports over the summation of the total three digit level sector output and imports minus exports;

  • exp_open sect : sector level export openness, measured as the three digit level sector exports over total sectoral output;

  • skill sect : sector level skill ratio, measured as the ratio between the three digit level sector share of white collars over total sectoral employment.

Appendix 2: Additional graphs and tables

Fig. 1

Productivity—Kernel density

Fig. 2

Propensity score—Kernel density

Table 9 Descriptive statistics
Table 10 Balancing tests

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Conti, G., Lo Turco, A. & Maggioni, D. Rethinking the import-productivity nexus for Italian manufacturing. Empirica 41, 589–617 (2014).

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  • Imports
  • Exports
  • Productivity

JEL Classification

  • F14
  • D24