The impact of EU and national cohesion policies on regional GDP per capita

As in Coppola et al. (2018) we estimate the average partial effects of policy funds using a control-function approach, assuming that funds are randomly allocated, conditional on a set of observable covariates W.

The panel specification for GDP per capita equation is:

D. yrt = a1yrt−1 + a2iflprivrt+ a3D. poprt + a4jSFjrt + a5jNatjrt + a6PERIOD2∗ SOUTH +

a₇PERIOD₃∗ SOUTH + a_8𝑗W_rt−1+ a_i + a_t+ ε_rt [6]

Where r = 1,…, 20 refers to regions; t = 1,…, n refers to years; and j = 1,…, m refers to the type of fund being considered. Variables a_r and a_t are region and year fixed effects respectively; and ε_rt is the customary independent and identically distributed (i.i.d.) error term. The dependent variable D. yrt is the (log) variation of GDP per capita; the lagged dependent variable yrt−1 allows for the dynamic structure inherent in the data.

Variables gfiprivrt and D. poprt, are the (log) ratio of gross fixed private investment⁴⁴ over GDP and the (log) variation of population respectively. SFjrt refers to the European SFs (whose types are indexed by j: in the exercises below we will take all the EU funds, inclusive of national cofinancing, aggregated together, as well as the ERDF taken in isolation⁴⁵); and Natjrt stands for an array of national funds related to regional policies (also indexed by j). Both SFjrt and Natjrt are taken as log ratios over GDP, in accordance with common practice in the literature. The author control for territorial differences in total factor productivity (as well for other sources of time-varying unobserved heterogeneity, such as the rate of human capital accumulation) through the interaction terms PERIOD_n*SOUTH, where SOUTH is a binary variable equal to 1 for the Mezzogiorno regions; PERIOD_2 and PERIOD_3 are binary variables equal to 1 respectively in the second (2000–06) and third (2007– 13) SFs’ programming period. Finally, as In Coppola et al. (2018), we include a vector of Wrt−1 variables presiding over the regional allocation of funds⁴⁶, with a view to control for the role of fund assignment. This control function approach (Wooldridge, 2004) is particularly convenient in our

44 Following Marrocu and Paci (2008, p.4) “the private component has been obtained as the difference between total and public investments”. The elaboration of total investment series is described in Section 3.1 and the elaboration of public investment series is described in Section 2.3.2.

45 Estimation of a similar equation in Coppola et al. (2021) reveals that the European Regional Development Fund (ERDF) is by far the most effective of all EU funds.

46As in Coppola et al. (2018), W^it–1 includes lags of policy funds, GDP per capita and gross fixed investment; measures of regional female rates of unemployment and sectoral shares of employment and value added; and politically based indicators (political orientation of each regional government and alignment of the political orientation of each regional government with the national government). We assume that funds can react only with delay to changes in the economic or political environment.

64

application for the following reasons. First, although there have been in time some explicit rules presiding to the allocation of funds between regions (especially as far as the Convergence objective was concerned), these rules have never fully presided to the allocation of funds, even in the case of EU funds. An important consequence of this situation is that in our sample there are no regions which do not receive any kind of funding. This is true for SFs, and even more for nationally financed funds. Hence a counterfactual strategy based on the creation of a control group (for instance, receiving no funding) cannot be enacted in our case.

Besides, this approach is very convenient in our case because it is readily adapted to the modelling of multiple continuous treatments (the various policy funds, some of which we may want to jointly include in a regression).

All variables are briefly described in Table 20. We estimate equation [6] through a dynamic panel (fixed-effects) method. Similarly, to what has been done in Coppola et al. (2018), the endogeneity of all current regressors was tested and not found to be significant (these diagnostics are available upon request). On the other hand, the adoption of a GMM Arellano-Bond set-up was not deemed as appropriate given the relatively low number of our cross-section units and the relatively high number of time periods in our sample (see Attanasio et al., 2000).

Tab. 20. Summary statistics of variables used for the update and extension of Coppola et al (2018) and legend of the equations

Variable Description Source Mean Std.

Dev. Min Max

y (log) GDP per capita

Own elaboration on

Prometeia

3.21 0.27 2.66 3.62

iflpriv (log) ratio of private gross fixed investment over GDP

Own

elaboration -1.66 0.19 -2.15 -1.14

pop (log) population Prometeia 7.56 1.06 4.76 9.21

fdr (log) ratio of EU funding plus national co-financing over GDP

Spesa statale

regionalizzata -5.19 0.92 -7.66 -2.50

erdf (log) ratio of ERDF funds over GDP Spesa statale

regionalizzata -6.79 1.23 -9.21 -3.52

trasf_c_y (log) ratio of Current account subsidies over GDP

Spesa statale regionalizzata trasf_ci_y (log) ratio of Current account subsidies

to firms over GDP

Spesa statale

regionalizzata -5.60 0.83 -9.88 -3.62

trasf_cf_y (log) ratio of Current account subsidies to households over GDP

Spesa statale

regionalizzata -5.37 0.78 -7.07 -3.39

ncf_y (log) ratio of National cohesion funds over GDP

Spesa statale

regionalizzata -6.32 1.60 -15.14 -2.72

trasf_k_y (log) ratio of Capital subsidies over GDP Spesa statale

regionalizzata -3.75 0.68 -5.77 -1.96

trasf_ki_y (log) ratio of Capital subsidies to companies over GDP

Spesa statale

regionalizzata -4.14 0.82 -6.92 -2.02

iflpubb_y (log) ratio of public gross fixed investment over GDP

Own elaboration (Section 2.3)

-4.19 0.47 -5.61 -2.68

esf (Log) ratio ofEU Social Fund (ESF) over GDP

Spesa statale

regionalizzata -7.21 0.81 -9.21 -4.33

al (log) ratio of EU Agricultural Fund over GDP

Spesa statale

regionalizzata -7.29 1.42 -9.21 -2.93

cofin (log) ratio of National co-financing over GDP

Spesa statale

regionalizzata 0.64 0.31 -0.06 1.59

tdis_f Rate of female unemployment Spesa statale

regionalizzata 12.67 6.75 2.59 34.21

oquota_iss Industry(employment) share

Own

elaboration on Prometeia data

0.18 0.07 0.06 0.32

65

oquota_ser Services (employment) share

Own

elaboration on Prometeia data

0.68 0.06 0.54 0.85

vquota_agr Agriculture (value added) share

Own

elaboration on Prometeia data

0.03 0.01 0.01 0.05

vquota_iss Industry (value added) share

Own

elaboration on Prometeia data

0.19 0.06 0.07 0.33

vquota_cos Construction (value added) share

Own

elaboration on Prometeia data

0.06 0.01 0.04 0.13

vquota_ser Services (value added) share

Own

elaboration on Prometeia data

0.72 0.06 0.58 0.86

vquota_pub Public sector (value added) share

Own

elaboration on Prometeia data

0.20 0.05 0.11 0.33

alignement Political alignment

Own

elaboration on Prometeia data

0.60 0.49 0.00 1.00

lpul_ser (Log) Services productivity (value added/employment)

Own

elaboration on Prometeia data

4.09 0.10 3.84 4.27

eqi The Quality of Government index

Own elaboration (Section 2.4.3)

-0.55 0.79 -2.10 0.88

index_picci Golden and Picci corruption index

Own elaboration (Section 2.4.3)

1.00 0.49 0.18 1.77

total New public efficiency index (all assets)

Own elaboration (Section 2.4.3)

-0.31 0.03 -0.37 -0.18

roads New public efficiency index (roads)

Own elaboration (Section 2.4.3)

-0.26 0.05 -0.47 0.03

rails New public efficiency index (rails)

Own elaboration (Section 2.4.3)

0.02 0.06 -0.23 0.30

buildings New public efficiency index (buildings)

Own elaboration (Section 2.4.3)

-0.20 0.05 -0.40 -0.03

health New public efficiency index (health)

Own elaboration (Section 2.4.3)

-0.46 0.07 -0.74 -0.31

water New public efficiency index (water)

Own elaboration (Section 2.4.3)

0.61 0.09 0.18 0.85

others New public efficiency index (others)

Own elaboration (Section 2.4.3)

0.04 0.05 -0.13 0.28

LEGEND OF THE EQUATIONS: Region and year fixed effects, or region-idiosyncratic trends, are always included in the estimates, and not shown in the interest of parsimony. The D. symbol stands for a first difference. R² is the coefficient of determination adjusted for degrees of freedom not inclusive of the effect

66

of region and year fixed effects, Rss is the residual sum of squares. A-B is the Arellano-Bond test for serial correlation present at lags one and two C-W is the Cook-Weisberg test for heteroskedasticity, R is the Reset test for functional form and omitted variables (carried out including quadratic terms of fitted values).

Table 21 shows the results for the auxiliary regressions selecting the relevant Wrt−1variables. Specifications in Table 21 only include regressors that have a t-ratio above one, or that are instrumental in getting good diagnostics, as required by the application of this control function approach (Wooldridge, 2004). According to this approach, one can assume that funds are randomly allocated, conditional on observable covariates (Coppola et al., p.4). Indeed, we find satisfactory diagnostics for most auxiliary regressions (the Reset test for national cohesion policies being the main exception to this rule, as was already the case in Coppola et al., 2018). We do not find much of a relationship between EU and national funds, while there are some complementarities among the latter. The ERDF is strongly countercyclical, while aggregate EU funds only react countercyclically to private investment. Among the other potential determinants of fund allocation, we find a role for sectoral shares. Political variables have a significant effect only for aggregate EU funds.

Tab. 21. European Union (EU) and national cohesion funds, nationally funded current account subsidies, nationally funded capital account expenditures and auxiliary regressions for the fund-allocation mechanism.

Dependent variables D.(EU

funding + national cofinancing)

D. (ERDF) D.(Current- Account Subsidies to Firms)

D.(Nationall y funded Cohesion Funds)

D.(Capital- Account Subsidies)

D.(Public Investment Expenditure)

Regressors

fdr (t-1)

0.1680 (0.01) fdr

(t-2)

0.1440 (0.01) erdf

(t-1)

0.0732 (0.33)

-0.0636 (0.04)

-0.0081 (0.76) esf

(t-1)

-0.0294 (0.65)

-0.0206 (0.52) al

(t-1)

0.0738 (0.10)

0.0594 (0.17)

0.0485 (0.03)

0.0472 (0.04) cofin

(t-1)

-0.1530 (0.18)

-0.1010 (0.33) trasf_c_y

(t-1)

0.1976 (0.00) trasf_cf_y

(t-1)

-0.2758

(0.00) trasf_ci_y

(t-1)

0.3188 (0.00) trasf_ci_y

(t-2)

0.2700 (0.00)

-0.0448 (0.37) ncf_y

(t-1)

0.0550 (0.09)

0.2593 (0.000)

0.0437 (0.19) trasf_ki_y

(t-1)

0.2551 (0.00) trasf_ki_y

(t-2)

0.2005 (0.00) iflpubb_y

(t-1)

0.0400 (0.47)

-0.0408 (0.65)

-0.0853 (0.24)

0.0441 (0.46)

-0.1685 (0.00) iflpriv_y

(t-1)

-0.6172 (0.03)

-1.3668 (0.00)

0.172 (0.52)

0.6862 (0.03) y

(t-1)

-4.1019 (0.00)

-4.7924 (0.01) y

(t-2)

-4.2367 (0.01) tdis_f

(t-2)

-0.0134 (0.18)

-0.0443 (0.00)

67

oquota_iss (t-1)

-11.7380 (0.02)

-4.3948 (0.12) oquota_ser

(t-1)

-15.5100 (0.00)

-2.8209 (0.24) vquota_agr

(t-1)

-21.1091 (0.02) vquota_iss

(t-1)

6.4859 (0.01) vquota_cos

(t-1)

17.7938 (0.00) vquota_ser

(t-1)

-4.0691 (0.28) vquota_pubb

(t-1)

5.6121 (0.06) alignment

(t-1)

-0.0739 (0.08)

-0.0419 (0.30)

0.0535 (0.35) lpul_ser

(t-1)

1.2056 (0.18)

2.5684 (0.06)

-0.3526 (0.59)

Number of observations 420 440 420 440 420 460

RSS 63.4781 184.4562 68.2195 154.504 50.4333 57.6016

R² 0.49 0.48 0.36 0.34 0.38 0.37

A-B 0.75 0.21 0.63 0.48 0.35 0.49

C-W 0.83 0.98 0.15 0.65 0.37 0.37

R 0.43 0.17 0.16 0.02 0.11 0.51

Note: D. () are log variations. P value of t-ratios are shown in parentheses.

Tables 22 presents the main results obtained for equation (6) with the Wrt−1 vector. As in Coppola et al.

(2018), we find a positive impact of EU funds, while national funds, including the subsidies to firms financed by national funds (that were significant in Coppola et al., 2018), are not significant at all or, like the national cohesion funds, have a negative sign. Several arguments are proposed to explain the different impact such as i) the expenditure composition – in fact, national funds may finance more non-core items compared to European Structural Funds mostly focused on core assets; and, more convincingly, ii) the policy governance, revealing the effectiveness of the multilevel and multiannual programming governance of EU funds. We are aware that it has been often argued that EU funds are mismanaged in Italy (see De Angelis et al., 2021, for a recent analysis). Our results indicate that national policies could be much more decisively mismanaged than EU funds.

68 Tab. 22. Equation (6) with the W_rt−1 vector⁴⁷

Regressors (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

yrt−1 -0.1626 -0.2336 -0.1782 -0.1705 -0.1620 -0.1431 -0.207 -0.1751 -0.1712 -0.1449 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) iflpriv_rt 0.0234 0.0315 0.0227 0.323 0.0223 0.0217 0.0279 0.0210 0.0325 0.0196 (0.01) (0.04) (0.01) (0.05) (0.01) (0.01) (0.03) (0.02) (0.03) (0.01) D. poprt -1.3342 -1.2779 -1.2643 -1.22 -1.3412 -1.3359 -1.323 -1.2669 -1.2033 -1.3634 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

fdr 0.0038 0.0033 0.0041 0.0033 0.0037

(0.01) (0.02) (0.01) (0.04) (0.01)

erdf 0.0032 0.0029 0.0033 0.0028 0.0032

(0.01) (0.01) (0.00) (0.01) (0.01)

trasf_ci_y 0.0008 0.0006

(0.43) (0.54)

ncf_y -0.0023 -0.0022

(0.05) (0.06)

trasf_ci_y -0.0011 -0.0007

(0.66) (0.77)

Iflpubb_y 0.0016 0.0001

(0.45) (0.94)

Observations 420 400 420 400 420 420 400 420 400 420

RSS 0.0634 0.0583 0.0625 0.0585 0.0631 0.0667 0.059 0.0623 0.0582 0.0658

Note: Dependent variable: D.y.; P value of t-ratios are shown in parentheses.

Nel documento A Quantitative Evaluation of EU and National Cohesion Policies (pagine 68-73)