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Local manufacturing productivity markers: an empirical study of the Italian counties

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We analyse productivity differentials across about 63,000 manufacturing firms located in 103 Italian counties, in order to shed light on the relation between the business environment and firm performance. We find that a limited set of local variables related to institutional quality, local credit development, market access and innovation environment significantly contribute to explaining manufacturing productivity differentials in Italy. Our empirical findings confirm that firm competitiveness reacts to the local business environment on a multidimensional scale. This suggests that better targeted regional policies at the national and EU level, including measures for fostering convergence or decentralizing wage negotiations, should take into account the interdependence between productivity and the economic environment.

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  1. See for instance Melitz and Ottaviano (2008).

  2. Tabellini (2010) suggests, for example, that the judicial system performs differently in Southern and Northern Italy, with judges taking much longer to complete investigations and to rule on civil cases in the South than in the North, even though the formal framework is similar.

  3. Please note that we estimate Eq. (1) separately for each 2-digit (NACE rev3) sector considered in the paper.

  4. TL reduces to a CD once you drop squared and the interaction terms.

  5. As an example, the labour elasticity implied by the TL production function is equal to \({\beta }_{{ it}}^{l} =\beta _{l} +2\beta _{ll} {l}_{{ it}} +\beta _{lk} {k}_{{it}}\).

  6. Moreover, the GMM approach is also robust to Ackerberg et al. (2006) critique that labour coefficient will not be identified in the two step estimation if its choice is a function of unobserved productivity shock, see Wooldridge (2009).

  7. Quintile dummies are computed by sector and year and guarantee a larger deal of flexibility, we also use the log of sales as control for production size and results are robust and available upon request from the authors.

  8. NUTS stand for the Nomenclature of Territorial Units for Statistics, a hierarchical system used by Eurostat to produce regional statistics. The last revision of the NUTS classification (2013) divides European Union territory in 98 major socio-economic regions (NUTS1), 276 basic regions for the application of regional policies (NUTS2) and 1342 small regions for specific diagnoses (NUTS3). In the rest of the paper, we focus on the 103 (NUTS3) counties defining the Italian territory.

  9. See Syverson (2011) for a detailed discussion on both internal and external markers of firm productivity. Note that county’s covariates are lagged to limit simultaneity bias; reverse causality is ruled out by the assumption that an individual firm is not able to fully determine local business environment.

  10. For employment size, we identify 4 classes: less than 10 workers, from 10 to 49, from 50 to 249, above 250.

  11. We choose to let errors to be correlated at the regional level (NUTS2) a higher geographical scale than counties (NUTS 3) to account for the fact that one of the covariates—R&D expenditure—is only available at the NUTS2 detail.

  12. Unfortunately R&D expenditure is only available at the regional level (NUTS2) not at the county one (NUTS3).

  13. The census is carried out by the Italian National Institute of Statistics (ISTAT).

  14. In counties with municipalities submitted to mandatory administration public spending contract of about 0.5% of county’s value added.

  15. The number of municipalities under mandatory administration is normalized by the overall number of municipalities under the county administration. This information is publicly available from the Italian Ministry of Internal Affairs. Since this event is unlikely to be correlated with firm level innovations, we include the contemporaneous value in order to maximize sample size.

  16. Unfortunately information on outstanding credit at the county level is available only from 1998.

  17. Source: Bank of Italy and the Italian National Institute of Statistics (ISTAT).

  18. The European Observation Network for Territorial Development and Cohesion adopted by the European Commission on 7 November 2007 ( The accessibility index is equal to 1 for the average European NUTS 3 territory and increases for better connected counties.

  19. As a baseline, we define an LLMA as UA if the population is above the 90th percentile of the distribution in year 2001. In Table 9 in “Appendix”, however, we report the estimation results for different thresholds: above 75th, 95th, 99th percentile as well as the threshold of 500 thousand residents used in Giacinto et al. (2014); our main findings are fully robust to the UA cut-off.

  20. A possible concern using those data is that it tends to under-represent small and medium enterprises. In terms of number of firms, indeed, AIDA reports 78.5% of firms below with less than 50 workers for year 2010, while those represent around 95% of the sample in the business register.

  21. More specifically, we use 2-digit production prices to deflate value added, total fixed assets prices for capital, production prices of intermediate inputs for materials, setting 2000 as the base year. Note that since we only observe the book value of capital, we build real capital series using a perpetual inventory method. For a general firm i, the real value of capital stock at time t is derived as the real stock at time \(t-1\) minus depreciation plus the real investment between \(t-1\) and t. Depreciation rates are at the 2-digit industry level and are measured as the ratio between aggregate amortizations and net capital from the national institute of statistics (ISTAT).

  22. Extreme values may be due to miss reporting but may also be induced by management strategies (e.g. by moving all the real estate assets to a separate legal entity) that may affect production function estimates. Results are reported in Table 10 and fully confirm our main findings.

  23. Following Giacinto et al. (2014) in the OLS specification, we also include three-digit industry fixed effects to allow for more heterogeneity at the industry level.

  24. Note that the reported coefficients for the TL specification are industry averages.

  25. Note that the 0.90 return to scale parameter is slightly below constant return to scale but it is significantly higher than the one in Gopinath et al. (2015), where they find 0.71 for Italy, and 0.80 for the overall sample (France, Germany, Italy, Norway, Portugal, and Spain), using the Amadeus dataset (covering all Europe) by Bureau van Dijk, the same data producer of Aida (covering only Italy).

  26. The TFP (GMM-TL) has been centred to a zero mean by sector in order to control for differences in industrial composition between the North and the South of the country. The difference in mean is statistically significant at 1% confidence level.

  27. Recall that TFP is measured in natural logs.

  28. As a further robustness check in Table 8 in Annex, we report a number of nested model specifications where the individual regressors are added one at a time in order to check the stability of regression coefficients as the model specification is progressively enlarged. Results are largely confirmed both for the sign and for the significance of the individual variables.

  29. Where \(X^{*}\) is derived as \(X^{*}=\frac{\beta _{\mathrm{age}} }{-2*\beta _{\mathrm{age}^{2}}}\). Since the underlying variable is expressed in logarithm, the corresponding levels are given by: \(e^{3.05}=21.11\)

  30. They show that both geographical distance and time travel to next urban agglomeration are negatively associated with regional per capita GDP growth, but travel time seems to be a stronger predictor of regional growth.

  31. In particular, Cingano and Schivardi (2005) find that firm productivity growth is positively affected by both localization economies and overall city size.


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We would like to acknowledge comments from Tullio Buccellato, Maurizio Fiaschetti, Fabiano Schivardi, participants in conferences held at LUISS, Rome and the Sant’Anna School in Pisa, as well as two anonymous referees.

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Correspondence to Stefano Manzocchi.



In Table 8, we report the estimate for nested model specifications where the individual regressors are added one at a time in order to check the stability of regression coefficients as the model specification is progressively enlarged. Estimated parameters are stable, both in magnitude and significance, across all the specifications. Note that column 6 is equivalent to column 1 in Table 5 in the main text.

Table 8 Nested models for local markers of firm productivity

Table 9 reports the main results with different threshold for the identification of a LLMA as UA. As in the main text, we first rely on the distribution of population between LLMAs in year 2001 but apply different cut-off values, above the \(75{\mathrm{th}}\), the \(95{\mathrm{th}}\) or the \(99{\mathrm{th}}\) percentile; moreover, we also apply the same arbitrary cut-off as in Giacinto et al. (2014), above 500 thousand residents.

Table 9 Local markers of firm TFP, changing UA definition

Table 10 controls for unobservable determinant of firm performance at the regional level by including a full set of dummy; results are largely confirmed, especially regarding the primary role of credit access as well as innovation and agglomeration economies.

Table 10 Local markers of firm TFP, controlling for regional unobservable

Finally, in Table 11 we test our results to the exclusion of firms with a capital intensity or value added per worker above/below the 99th/1st percentile of the industry-year distribution. Extreme values may be due to miss reporting but may also be induced by management strategies (e.g. by moving all the real estate assets to a separate legal entity) that may affect the reliability of production function estimates. Results largely confirm our main findings.

Table 11 Local markers of firm TFP, excluding outliers

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Manzocchi, S., Quintieri, B. & Santoni, G. Local manufacturing productivity markers: an empirical study of the Italian counties. Ann Reg Sci 59, 255–279 (2017).

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