Table 6 Multinomial logistic regression model—estimation results (models with regional fixed-effect dummy variables)

Basilicata

1.365*** (0.144)

1.031 (0.112)

0.988 (0.107)

2.226*** (0.346)

2.033*** (0.320)

2.024*** (0.319)

Bolzano

1.823*** (0.188)

0.936 (0.171)

0.904 (0.168)

0.401*** (0.107)

0.848 (0.365)

0.820 (0.353)

Calabria

4.686*** (0.356)

3.793*** (0.297)

3.664*** (0.288)

2.152*** (0.275)

2.082*** (0.271)

2.085*** (0.272)

Campania

0.838* (0.0847)

0.892 (0.0916)

0.907 (0.0933)

1.213 (0.187)

1.152 (0.179)

1.150 (0.179)

Emilia Romagna

0.866 (0.0758)

0.843* (0.0749)

0.860* (0.0765)

0.673*** (0.101)

0.706** (0.106)

0.698** (0.105)

Friuli Venezia Giulia

1.019 (0.148)

1.096 (0.160)

1.160 (0.170)

1.064 (0.249)

1.067 (0.251)

1.063 (0.251)

Lazio

1.131 (0.0986)

1.072 (0.0951)

1.055 (0.0937)

1.007 (0.146)

0.983 (0.144)

0.981 (0.144)

Liguria

0.799 (0.136)

0.697** (0.120)

0.738* (0.128)

0.990 (0.256)

0.903 (0.235)

0.899 (0.234)

Lombardia

0.906 (0.0892)

1.083 (0.109)

1.200* (0.122)

0.840 (0.138)

0.892 (0.149)

0.880 (0.147)

Marche

2.463*** (0.213)

1.775*** (0.157)

1.755*** (0.155)

0.824 (0.140)

0.886 (0.152)

0.887 (0.152)

Molise

1.288 (0.238)

0.983 (0.185)

1.015 (0.191)

1.459 (0.417)

1.291 (0.370)

1.286 (0.369)

Piemonte

0.574*** (0.0610)

0.631*** (0.0681)

0.659*** (0.0712)

0.760* (0.125)

0.804 (0.133)

0.812 (0.135)

Puglia

1.518*** (0.121)

1.483*** (0.122)

1.486*** (0.122)

2.312*** (0.295)

2.056*** (0.266)

2.074*** (0.269)

Sardegna

1.830*** (0.161)

1.500*** (0.136)

1.416*** (0.129)

2.354*** (0.326)

2.235*** (0.316)

2.211*** (0.313)

Sicilia

0.667*** (0.0530)

0.625*** (0.0513)

0.600*** (0.0494)

0.888 (0.114)

0.928 (0.122)

0.921 (0.121)

Toscana

1.317*** (0.109)

1.255*** (0.106)

1.295*** (0.109)

1.030 (0.143)

0.958 (0.134)

0.994 (0.140)

Trentino

1.157 (0.143)

1.226 (0.155)

1.227 (0.155)

0.699 (0.168)

0.593** (0.143)

0.592** (0.143)

Umbria

1.334*** (0.139)

1.297** (0.137)

1.305** (0.138)

1.014 (0.184)

0.897 (0.164)

0.908 (0.166)

Val d’Aosta

0.507* (0.180)

0.457** (0.163)

0.463** (0.165)

0.502 (0.300)

0.578 (0.346)

0.592 (0.355)

Veneto

1.160 (0.110)

1.052 (0.102)

1.139 (0.111)

0.694** (0.121)

0.687** (0.121)

0.680** (0.120)

2

0.323*** (0.0259)

0.239*** (0.0396)

0.226*** (0.0381)

3.186*** (0.820)

2.163* (0.951)

2.150* (0.946)

3

0.762*** (0.0624)

0.656** (0.108)

0.621*** (0.105)

2.370*** (0.636)

1.796 (0.801)

1.791 (0.799)

4

0.472*** (0.0363)

0.381*** (0.0622)

0.372*** (0.0619)

1.735** (0.452)

1.479 (0.653)

1.475 (0.652)

5

0.890 (0.0858)

0.554*** (0.0959)

0.526*** (0.0926)

3.264*** (0.926)

2.007 (0.908)

2.013 (0.912)

6

0.462*** (0.0393)

0.339*** (0.0572)

0.314*** (0.0540)

2.531*** (0.671)

1.729 (0.769)

1.703 (0.758)

7

0.542*** (0.0402)

0.410*** (0.0661)

0.399*** (0.0656)

4.620*** (1.176)

3.330*** (1.456)

3.321*** (1.453)

8

0.645*** (0.0687)

0.604*** (0.108)

0.607*** (0.111)

3.404*** (0.975)

2.950** (1.347)

2.942** (1.345)

9

0.294*** (0.0304)

0.216*** (0.0384)

0.202*** (0.0366)

5.210*** (1.371)

3.000** (1.330)

3.001** (1.331)

10

1.706*** (0.123)

1.057 (0.171)

1.012 (0.167)

4.848*** (1.236)

3.052** (1.339)

3.061** (1.343)

11

1.102 (0.145)

0.751 (0.148)

0.694* (0.138)

2.938*** (1.042)

2.258 (1.132)

2.247 (1.128)

Processors

0.516*** (0.0203)

0.671*** (0.0282)

0.887** (0.0509)

1.095 (0.0666)

Producers/Processors

0.689*** (0.0246)

0.665*** (0.0254)

0.720*** (0.0458)

0.731*** (0.0475)

Mixed (A,B,C)

0.641*** (0.0816)

0.931 (0.123)

0.624** (0.147)

0.916 (0.218)

Constant

0.161*** (0.0117)

0.319*** (0.0223)

0.266*** (0.00318)

0.333*** (0.0586)

0.380*** (0.0682)

0.0543*** (0.00648)

0.0188*** (0.00476)

0.0712*** (0.00151)

0.0220*** (0.00997)

0.0226*** (0.0102)

Observations

59,249

59,249

59,249

59,249

59,249

59,249

59,249

59,249

59,249

59,249

BIC

− 3930.179

− 3369.610

− 6084.229

− 6231.841

BIC Diff

147.612