The new frontier: Firms technical efficiency and GVCs

Transcription

1 WORKSHOP ITALY IN THE GVCS. COUNTRY, REGIONS AND FIRMS ANALYSES Rome, April 28, 2016 The new frontier: Firms technical efficiency and GVCs Mariarosaria Agostino (University of Calabria) Emanuele Brancati (Luiss University) Anna Giunta (Roma Tre University and Rossi-Doria Centre) Domenico Scalera (University of Sannio) Francesco Trivieri (University of Calabria)

2 PRESENTATION OUTLINE Motivations Research questions Literature background Data Measuring efficiency: methods and results The determinants of efficiency: methods and results Conclusions This is a work in progress!! 2

3 MOTIVATIONS In the last two decades, the development of Global Value Chains (GVCs) has brought to a worldwide interconnection of industries and spurred an impressive growth in world trade. GVCs require firms to operate in a context where productive activities are unbundled in multiple stages and tasks carried out by several different firms geographically scattered but connected by multiple relationships on one side, GVCs require firms higher capabilities and efficiency standards on the other, GVCs offer valuable opportunity to upgrade technical knowledge, innovation channels, managerial skills, access to new markets. 3

4 RESEARCH QUESTIONS Does participation in GVCs actually exert a positive impact on a firm s efficiency? Does participation in GVCs translate into higher resilience during times of crisis? What are the effects of participation in GVCs for small firms? Is the effect of GVCs magnified by relational rather than hierarchical or captive governance mode? 4

5 LITERATURE BACKGROUND (1) Recent firm-level investigations document the (positive) effects of belonging to GVCs on firms productivity, efficiency and growth. Very little on governance and performance (Agostino et al., 2015 and 2016; Brancati et al., 2015; Baldwin and Yan, 2014; Barba Navaretti et al., 2011; Békés et al., 2011; Del Prete et al., 2015; Giovannetti et al., 2015; Pietrobelli and Saliola, 2008; Saliola and Zanfei, 2009; Veugelers, 2013). 5

6 LITERATURE BACKGROUND (2) Concerning the relationship between participation in GVCs and resilience, Baldwin (2009), Bems et al. (2011) and Alessandria et al. (2011) point at vertical specialization and inventory adjustments as potential channels of transmission of international downturns. Bekes et al. (2011) and Accetturo and Giunta (2016) explore performance heterogeneity across the firms' position in a value chain. 6

7 DATA We employ micro-data coming from MET survey on the Italian industry: Three-wave survey: 2009, 2011, and Roughly 25,000 observations per wave, representative at size (all classes), region, and industry levels. Rich set of information including: purchasing/selling matrix, type of good sold & purchased, inter-firm networks, participation in the design of the final product, R&D, innovation, export We merge MET data with firm-level balance-sheet data from BvD AIDA. 7

8 MEASURING EFFICIENCY (1) Our efficiency measure is obtained through an application of DEA (Data Envelope Analysis), a linear-programming-based methodology providing non-parametric measures (scores) of efficiency based on distance functions from a benchmark production frontier In our case efficiency scores are obtained by using the input-output variables: 8

9 MEASURING EFFICIENCY (2) The basic idea underlying distance functions is the existence of a production frontier, i.e. a locus of technically efficient inputoutput combinations, given the existing technology. The distance between this frontier and any combination not on the frontier is a measure of technical inefficiency. We consider as benchmark for each observation in a given year all other observations in the same year (contemporaneous frontiers). We assume variable returns to scale (i.e. not all firms are operating at their optimal scale, which is plausible when focusing on SMEs). We do not impose any a priori hypothesis on the production function form, and we allow for different technologies in different industries and different years. 9

10 METHODOLOGY (1) We compare the observed distributions of DEA scores for different samples. DEA scores are plotted on x-axis and cumulative share of firms on y-axis, ordered by level of efficiency. Different samples: GVC=1 versus GVC= versus 2010 versus 2012 Small (employees<50) versus larger firms A shift to the right in the distribution of efficiency scores indicates increasing efficiency. 10

11 METHODOLOGY (2) GVC=1 i.e. a firm is considered to belong to a GVC if (at least) one of the following conditions occurs: it exports intermediate goods; it is both an exporter and importer; it is either an exporter or importer and declares to be involved in significant and longlasting relationships with foreign partners. Otherwise, GVC=0 11

12 METHODOLOGY (3) By comparing the observed distributions of DEA scores for different samples and using the nonparametric Kruskal-Wallis test (Manello et al., 2015), we test whether firms technical efficiency varies significantly: according to firms participation in GVCs across years 2008, 2010 and 2012 depending on firms size When H 0 : location parameters of the distribution are the same across samples is rejected, efficiency is significantly different across groups of firms/years. 12

13 RESULTS (1) The two lines show the cumulative distribution of efficiency relative to each subsample. For any score y 0, the share of firms with y y 0 is greater for No GVC than GVC firms. If this difference is statistically significant, participation in GVC implies higher efficiency 13

14 RESULTS (2) Whole sample 2008 *** *** *** *** 14

15 RESULTS (3) GVC=0 GVC=1 *** 15

16 RESULTS (4) Small firms Large firms *** *** 16

17 RESULTS (5) Small firms; GVC=0 Small firms; GVC=1 *** Large firms; GVC=0 Large firms; GVC=1 * 17

18 THE DETERMINANTS OF EFFICIENCY Our estimating model is the following: TEFF it = β 0 + β 1 GVC it + β k CTRL kit + ε it where indexes i and t respectively represent firm and time (2008, 2010, 2012). 18

19 THE DETERMINANTS OF EFFICIENCY

20 THE DETERMINANTS OF EFFICIENCY 20

21 THE DETERMINANTS OF EFFICIENCY 21

22 THE DETERMINANTS OF EFFICIENCY 22

23 THE DETERMINANTS OF EFFICIENCY Endogeneity. We also run IV regressions, instrumenting firms participation in GVC by the average values of dummies Import and Export computed over the other N-1 firms and the fitted probabilities from a first stage Probit model. DWH test does not reject the hypothesis of no endogeneity 23

24 CONCLUSIONS Participation in GVCs has significant effects on firms efficiency. Its impact is generally positive; may buffer the crisis consequences; is more important for small firms (less than 50 employees); for firms belonging to relational GVCs. 24

25 WORK IN PROGRESSS THANK YOU!! COMMENTS ARE REALLY WELCOME!!! 25