European regional convergence revisited The role of intangible assets

Transcription

1 European regional convergence revisited The role of intangible assets Jesús Peiró-Palomino Department of Economics, University Jaume I (peiroj@uji.es) 9th IRIMA workshop on intangible investment for competitiveness Micro-macro evidences EUROPEAN COMMISSION, Brussels Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

2 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

3 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

4 Introduction Regional socioeconomic cohesion is of paramount importance for the EU Objective of convergence: reduction of regional disparities in terms of GDP per capita by helping those regions below 90% of the EU average Regional convergence has been an issue of intense debate in the economic literature Different convergence approaches and a wide variety of statistical techniques have been proposed Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

5 Introduction Distribution dynamics approach, introduced in the 90 s by Quah (1993a,b, 1996a,b,c, 1997) It evaluates the evolution of the cross-section distribution of GDP per capita over time, analyzing changes in its external shape, intra-distribution dynamics and long-run tendencies Some relevant examples: Quah (1996b), López-Bazo et al. (1999), Cuadrado-Roura et al. (2002), Le Gallo and Ertur (2003), Le Gallo (2004) and Fischer and Stumpner (2008) General conclusion: Bimodal distribution GDP per capita. Two differentiated groups of regions, or convergence clubs Results aligned to the pioneering findings by Quah (1996b): existence of twin peaks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

6 Introduction Contribution to the literature Previous analyses focused on periods before the latest EU enlargements New members are economies in transition with levels of GDP per capita far below the EU average Interest in the most recent years, given that the recession has had an uneven impact across regions Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

7 Introduction Contribution to the literature Analyse the distribution dynamics of GDP per capita for a sample of 260 EU regions (NUTS 2) in the period Evaluate the influence of a set of intangible assets (IA) on the convergence process Four intangibles: Technological capital Human capital Life-long learning Social capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

8 Intangible assets and GDP per capita Literature review Intangible assets important in the creation of knowledge Positive effects in efficiency and productivity, which translate into economic growth and regional welfare Remarkable disparities in the IA stock are found at the regional level Some evidence: Dettori et al. (2012), Melachroinos and Spence (2014), Rodíguez-Pose and Crescenzi (2008), Rodríguez-Pose and Comptour (2012), Bronzini and Piselli (2009), Peiró-Palomino and Tortosa-Ausina (2015) Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

9 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

10 Distribution dynamics Some basic notions Nonparametric technique Data-driven methods Data are smoothed using Kernel smoothing ˆf t(y) = 1 nh y n K i=1 ( 1 h y y Y i y ) The Kernel selected in a Gaussian kernel: ( 2π ) 1 K (x) = exp ( 1 ) 2 x 2 (1) (2) Bandwidth (h) selection: Sheather and Jone s (1991) solve-the-equation plug-in method Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

11 Distribution dynamics Some basic notions Y i and M i are the income of region i in period t and t + s; s 1. f t(y) and f t+s(m) are the associate cross-section distributions for all the regions Following Rosenblatt (1969): f t+s(m) = v s(m y)f t(y)dy (3) 0 where and 1 ˆf t,t+s(y, m) = nh yh m ˆv s(m y) = ˆf t,t+s(y, m) ˆf t(y) n K i=1 ˆf t(y) = 1 nh y ( ) ( ) 1 1 y Y i y K m M i m h y h m n K i=1 ( 1 h y y Y i y ) (4) (5) (6) Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

12 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

13 Sample and variables Sample: 260 regions from EU-27 (NUTS 2) Variables: Regional income per capita (euros of 2005) Capital (K). Gross fixed capital formation (millions of euros of 2005) Labor (L). Employed people (in thousands) Technological capital (TC): patents per million inhabitants Human capital (HC): Population aged with tertiary education (%) Long-life learning (LR): Population aged in the labor force participating in programs of education and training (%) Social capital (SC). Social trust measure. % of respondents who declared trusting each other Sources: Eurostat and European Values Survey (1999 and 2008) Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

14 Descriptive statistics Technological capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

15 Descriptive statistics Human capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

16 Descriptive statistics Life-long learning Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

17 Descriptive statistics Social capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

18 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

19 Results Unconditioned analysis: Per capita GDP distribution, selected years Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

20 Results Unconditioned analysis: Per capita GDP distribution, selected periods Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

21 Results Unconditioned analysis: Distribution dynamics Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

22 Results Unconditioned analysis: Distribution dynamics Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

23 Results Unconditioned analysis: Distribution dynamics Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

24 Results Unconditioned analysis: Long-run analysis, ergodic distribution Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

25 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

26 Conditioned analysis Counterfactual income series and stochastic kernels Fiaschi et al. (2012) method Estimation of the marginal impact of the IA on per capita GDP Y = β 0 + M β j Z j + j=1 N β k X k + ε (7) j=1 Construction of counterfactual series of income To isolate the effect of one particular IA (ρ), the counterfactual income is calculated by assuming no regional differences in the stock of ρ, impossing the sample mean for all regions For the rest of IA we use the actual regional value Counterfactual stochastic kernels are estimated using the new counterfactual series Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

27 Determinants of per capita GDP Variable Coefficient SE t-statistic p-value Intercept Capital (K) Labor (L) Technological capital (TC) Human capital (HC) Life-long learning (LR) Social capital (SC) Capital city N 260 Country effects Yes R F-statistic LM lag LM error Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

28 Determinants of per capita GDP Variable Coefficient SE t-statistic p-value Intercept Capital (K) Labor (L) Technological capital (TC) Human capital (HC) Life-long learning (LR) Social capital (SC) Capital city N 260 Country effects Yes R F-statistic LM lag LM error Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

29 Results Conditioned analysis, counterfactual distribution for technological capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

30 Results Conditioned analysis, distribution dynamics for technological capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

31 Results Conditioned analysis, ergodic distribution for technological capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

32 Results Conditioned analysis, counterfactual distribution for human capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

33 Results Conditioned analysis, distribution dynamics for human capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

34 Results Conditioned analysis, ergodic distribution for human capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

35 Results Conditioned analysis, counterfactual distribution for long-life learning Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

36 Results Conditioned analysis, distribution dynamics for life-long learning Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

37 Results Conditioned analysis, ergodic distribution for life-long learning Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

38 Results Conditioned analysis, counterfactual distribution for social capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

39 Results Conditioned analysis, distribution dynamics for social capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

40 Results Conditioned analysis, ergodic distribution for social capital Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

41 Results Testing the equality of the distributions (Li, 1996) Null hypothesis (H 0 ) T-test statistic p-value f 00 = f f 07 = f f 00 = f f = f f = f f = f f 00 = f f 00 = f f = f 00.11TC f = f 00.11HC f = f 00.11LR f = f 00.11SC Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

42 Results Testing the equality of the distributions (Li, 1996) Null hypothesis (H 0 ) T-test statistic p-value f 00 = f f 07 = f f 00 = f f = f f = f f = f f 00 = f f 00 = f f = f 00.11TC f = f 00.11HC f = f 00.11LR f = f 00.11SC Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

43 Results Testing the equality of the distributions (Li, 1996) Null hypothesis (H 0 ) T-test statistic p-value f 00 = f f 07 = f f 00 = f f = f f = f f = f f 00 = f f 00 = f f = f 00.11TC f = f 00.11HC f = f 00.11LR f = f 00.11SC Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

44 Results Testing the equality of the distributions (Li, 1996) Null hypothesis (H 0 ) T-test statistic p-value f 00 = f f 07 = f f 00 = f f = f f = f f = f f 00 = f f 00 = f f = f 00.11TC f = f 00.11HC f = f 00.11LR f = f 00.11SC Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

45 Results Testing the equality of the distributions (Li, 1996) Null hypothesis (H 0 ) T-test statistic p-value f 00 = f f 07 = f f 00 = f f = f f = f f = f f 00 = f f 00 = f f = f 00.11TC f = f 00.11HC f = f 00.11LR f = f 00.11SC Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

46 Index 1 Introduction 2 Distribution dynamics 3 Sample and variables 4 Results: unconditioned analysis 5 Results: conditioned analysis 6 Concluding remarks Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42

47 Concluding remarks The unconditioned analysis showed that the cross-sectional distribution of income is bimodal and has a long right tail The conditioned analysis revealed that the IA considered might explain to some extent the shape of the European cross-regional distribution of income The effect of the intangibles is, however, relatively limited and highly heterogeneous The clearest effects of the IA on convergence are found for regions with middle and middle-high relative income levels TC has the most remarkable effects Limitations: Other elements apart from the ones considered in this paper might have affected regional convergence The new economic scenario after the crisis may modify future convergence tendencies Peiró-Palomino, J. (UJI) ARS (2016), 57 (1), Brussels, 25th October / 42