Abstract
This chapter studies the effect of FDI in business services on total factor productivity (TFP) of Italian manufacturing firms, over the period 2003–2008. More precisely, the chapter tests the presence of vertical linkages between foreign business professionals and domestic manufacturing firms. Our results, robust to different specifications, show that foreign capital inflows in business services improve the performance of domestic manufacturing firms. This relationship is particularly strong in the case of high-tech sectors, such as mechanics and machinery. Traditional sectors, on the other hand, seem to be less sensitive to the availability of foreign business services in the same location.
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Notes
- 1.
- 2.
Business services include services to other businesses ranging from accounting and legal services to industrial cleaning. For the purposes of this chapter, the business services sector is statistically defined as a subset of Section K in the national accounts, including computer and related activities, research and development, and other business activities’ Standard Industrial Classification (sic) codes 72–74—it also includes elements of telecommunications and services classified in sections I and J.
- 3.
According to the ISTAT (2010b): “between 2000 and 2009, total factor productivity (TFP) has declined ( − 0. 9 % per year on average), due to a negative trend in the value added ( − 0. 2 %) and a positive development of the productive inputs (average annual growth of 0. 8 %). In particular, since 2000 it is possible to recognize three stages corresponding to different trends: a negative trend in 2000–2003 ( − 1. 3 % annual average), a moderately positive dynamic in the years 2003–2007 (0. 6 % annual average) and a marked reduction in the period 2007–2009 ( − 3. 4 % annual average)”.
- 4.
According to UNCTAD (2004), FDI in services has been increasing at high rates from the end of the 1990s. Different subsectors, however, had different developments. Business sectors have had the highest rate of growth.
- 5.
To see better this point, think of a country with inadequate services that negatively affect firms’ performance. Arnold et al. (2008) provide several examples of dysfunctional services and their impact on African firms. Unstable telecommunication services affect coordination with clients and suppliers; inadequacies of banking services may prevent a firm from investing; power cuts can disrupt production etc.
- 6.
By knowledge spillovers, we mean “knowledge” created by a multinational, and used by the domestic firm and not necessarily entailing full compensation to the MNC. We include managerial skills, organization of production; know how, better marketing and distribution, transfer of technical skills etc.
- 7.
By pecuniary spillover, we mean nominal gains resulting from quality increases not necessarily reflected in prices.
- 8.
It has also been suggested that FDI spillovers (both positive and negative) have a limited geographical dimension or, at least, that they decrease with (physical) distance (Audretsch and Feldman 1996; Audretsch 1998; Keller 2002; Madariaga and Poncet 2007), as channels of technological diffusion are reinforced at the regional level (Girma and Wakelin 2001; Girma 2005; Ayyagari and Kosová 2010). We do not deal with the issue of distance, but some empirical evidence for Italy can be found in Mariotti et al. (2011).
- 9.
- 10.
AIDA data set reports the balance sheets of Italian corporations with a value of production greater than roughly 800,000 euro, http://www.bvdep.com.
- 11.
REPRINT is the census of the foreign affiliates with a turnover higher than 2.5 million euros per year and provides information on the starting date of the operations for all manufacturing and business services affiliates, see Mariotti and Mutinelli (2010). We consider as business services FDI: Logistics, information technology and software design, and professionals services; GDP data come from ISTAT.
- 12.
- 13.
We exclude observations for which value added, employment, and capital are missing, negative or null. Furthermore we “clean” our sample from outliers, dropping the extreme 1 % values for the distribution of the following variables: capital intensity, yearly capital intensity growth rate, yearly capital growth rate and yearly employment growth rate.
- 14.
A comparison of the distribution of firms from our database for different years, sectors, and Provinces (NUTS3) with the distribution of firms registered by Chambers of Commerce shows a strong correlation. The Unioncamere (Chambers of Commerce) dataset covers all the active firm in a given year and province, by 2 digit Ateco 2002 (national version of NACE rev 1.1), but does not contain any further information about the firms. The correlation with our dataset, calculated with Pearson and Spearman Indices, spans from 0.82 for sector/year/province (Person) to 0.97 for year/province (Spearman). Complete results are available on request. Hence, firm level data used in constructing our productivity measure seems to be a good approximation of the true population of firms across provinces and sectors.
- 15.
Nominal series have been deflated using sector-specific price indexes from the National statistical institute, ISTAT.
- 16.
This index allows a comparison of firms performance within a specific sector without imposing a common technology to the firms belonging to the same sector. The relative weights of the production factors are individually measured and reflect the different production technology at firm level and the flexure of the single production functions adopted by the firms. In the case of semi-parametric estimation à la Olley and Pakes (1996) or Levinsohn and Petrin (2003), these relative weights are assumed to be identical and are estimated as equal for every firm in a same industry with a precision loss.
- 17.
The share is computed as the number of business services FDI in province j at time t over the total number of business services FDI in Italy at time t.
- 18.
For a review of the impact on productivity of internal and external factors, see Syverson (2011).
- 19.
In what follows we make use of a slightly modified version of Martin et al. (2011) specification.
- 20.
See Syverson (2011).
- 21.
On the issue see Briant et al. (2010). Note that the mean area of Italian provinces is 2,816 km2 with a coefficient of variation of 0.17; while French departments and Spanish provinces have a mean area of 5,666 km2 and 10,118 km2, respectively, with a much higher coefficient of variation, 0.33 (France) and 0.47 (Spain).
- 22.
In our empirical analysis we consider 20 sectors of Manufacture, Section D (NACE rev 1.1), in detail: 15 Food and beverages, 17 Textiles, 18 Wearing Apparel, 19 Leather (luggage etc.), 20 Wood (except furniture), 21 paper Products, 22 Publishing and printing, 24 Chemicals, 25 Rubber and plastics, 26 Non metallic minerals, 27 Basic metals, 28 Fabricated metal products (except machinery), 29 Machinery, 30 Office machinery and computers, 31 Electrical machinery, 32 Communication equipment, 33 Optical instruments, 34 Vehicles, 35 Other transports, 36 Furniture and others manufactures.
- 23.
The overall turnover of the sector s, province j and time t is computed using firm level data from the AIDA dataset.
- 24.
Most provinces have an average manufacturing industries share relatively small, less than 1 %, even if there are some remarkable exceptions, such as Prato, where the economic structure is skewed towards Textiles. It is worth noting that in Prato textiles represented over 18 % of the economic activity in 2001 (and has had a declining trend, to 12 % in 2006), and more than 56 % of total manufactures. In Lecco, manufacture of fabricated metal products represents around 7.5 % of the whole economic activity of the province and nearly 35 % of manufacturing.
- 25.
Highly agglomerated provinces are likely to be on average more productive and so foreign firms may decide to locate in such provinces in order to exploit agglomeration externalities. This might bias the estimated coefficient. For this reason, we decide to include also this variable. The data necessary to build this indicator come from ISTAT.
- 26.
In fact, the higher productivity of the manufacturing firms could be caused from the presence of an overall services sector more developed and efficient in a given province; data coming from ISTAT.
- 27.
With the only exception of HH jst .
- 28.
The variables used as control are: log of age and age squared, plus a categorical variable of firm size, all contemporaneous to the TFP measure. Size is built on the distribution of sales by sector and year, this variable consist in 5 classes each of them encompasses 20 % of sales distribution. All these data coming from AIDA.
- 29.
Since the variable is expressed in log, the maximum gain in productivity related to the firm age is at X ∗ ≈ 1. 46.
- 30.
In specifications with the interaction terms, the interacted variables are always centered (zero mean).
- 31.
Since the variables of interest of foreign presence vary at aggregate level (province by year) while the dependent variable is at firm (year) level, we are aware of the possible distortion in the Standard errors, see Moulton (1986). There are number of ways to correct this, the most widely used is to apply a arbitrary variance-covariance matrix at an higher cluster level (cluster command in Stata). Given the structure of our data, with an high variability in the number of firms by cluster (province-year) the asymptotic properties of the variance estimator needed are not verified. Angrist and Pischke (2008) and Wooldridge (2008) suggest using a two-step estimator. We followed this procedure and our results do not change, result available upon request.
- 32.
Rev. 1.1. For the sector list see footnote 21.
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Acknowledgements
We wish to thank M. Belloc, L. Benfratello, G. de Arcangelis, F. Luchetti, R. Marimon, M. Sanfilippo, and participants of International Conference “The role of business services for innovation, internationalization and growth”, Rome, 2010. G. Giovannetti thanks firb for financial support. This research is carried out in collaboration with ICE-Italian Institute of Foreign Trade. The chapter’s findings, interpretations, and conclusions are entirely ours as any remaining mistake.
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Appendices
Appendix
1 Foreign-Owned Business Service Firms in Italy: Some Graphs
Share of Foreign-controlled firms (%, 2007). Source: ISTAT
Number of employees in Foreign-owned firms. Source: ISTAT
Value added per person employed (1,000 euro, 2007). Source: ISTAT
Profitability (%, 2007). Source: ISTAT
Share of Foreign-controlled firms’ R&D expenditure. Source: ISTAT
2 Robustness of Different Clustering Levels
We test the robustness of our results to different clustering levels, aiming to check if cross-sectional dependence in the error term may invalidate our findings. Results are reported in Table 9.4, the first column reports standard errors (SE) clustered at firm level, as in our baseline regression, just for comparison purposes. In column (2)–(4), SE are clustered by year and different geographical level, estimated coefficients of ln(FBS jt−2) are statistically significant, at conventional levels, throughout different cluster specification, suggesting that cross-sectional dependence seems not to invalidate our results.
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Armenise, M., Giovannetti, G., Santoni, G. (2015). Do FDI in Business Services Affect Firms’ TFP? Evidence from Italian Provinces. In: Mussida, C., Pastore, F. (eds) Geographical Labor Market Imbalances. AIEL Series in Labour Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55203-8_9
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