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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

Table 2 Parameter estimates of cost function

From: Efficiency, and economies of scale and scope in Japanese agricultural cooperatives

Parameter

Area-specific frontier models

Meta-frontier model

Urban area

Rural area

Coefficient

Std. Err.

Coefficient

Std. Err.

Coefficient

Std. Err.

α0

11.3000***

0.0376

11.9585***

0.0852

11.2067***

0.0439

α1

0.4014***

0.0334

0.2218***

0.0757

0.2195***

0.0265

α2

− 0.0914***

0.0125

0.1171***

0.0152

0.0031

0.0077

α3

0.4092***

0.0314

0.6586***

0.0339

0.4970***

0.0155

β1

0.9590***

0.0831

0.8559***

0.1059

0.9191***

0.0425

β2

0.3088***

0.1004

0.1302*

0.0671

0.2476***

0.0400

β3

− 0.3659***

0.0322

− 0.1373***

0.0470

− 0.2332***

0.0159

α11

0.1148**

0.0541

− 0.1304

0.1170

0.1601***

0.0327

α12

0.0217*

0.0128

0.0776***

0.0173

0.0385***

0.0048

α13

− 0.0091

0.0233

0.2692***

0.0363

− 0.0135

0.0132

α22

− 0.0158**

0.0074

− 0.0144*

0.0074

− 0.0189***

0.0027

α23

0.0218***

0.0079

− 0.0265***

0.0091

0.0022

0.0030

α33

0.0069

0.0176

− 0.0530

0.0544

0.1165***

0.0121

β11

1.2248***

0.2648

0.2543

0.3232

0.3116***

0.1046

β12

− 1.4967***

0.2402

0.1532

0.2099

− 0.1751**

0.0819

β13

0.0892

0.0590

− 0.0311

0.1048

0.0462*

0.0266

β22

1.4452***

0.2651

− 0.3401*

0.1934

0.0755

0.0856

β23

0.0935

0.0572

0.0188

0.0526

− 0.0187

0.0175

β33

− 0.0898**

0.0421

− 0.0003

0.0636

− 0.0268

0.0174

δ11

0.0913

0.0685

− 0.2788**

0.1197

− 0.0485

0.0377

δ12

0.0202

0.0648

0.1828*

0.0952

0.0943***

0.0360

δ13

− 0.0227

0.0186

− 0.0505

0.0369

− 0.0327***

0.0081

δ21

− 0.1752***

0.0264

− 0.0051

0.0358

− 0.0451***

0.0109

δ22

0.1496***

0.0304

0.0082

0.0250

0.0508***

0.0094

δ23

− 0.0017

0.0083

0.0168

0.0114

− 0.0003

0.0034

δ31

− 0.1137**

0.0498

0.3269***

0.0684

0.1057***

0.0243

δ32

0.0468

0.0434

− 0.3128***

0.0645

− 0.1012***

0.0228

δ33

− 0.0108

0.0145

− 0.0101

0.0214

− 0.0246***

0.0055

DMy05

− 0.1438***

0.0275

− 0.1365***

0.0364

− 0.1041***

0.0123

DMy06

0.1009***

0.0325

− 0.0937**

0.0422

0.0460***

0.0154

DMy07

0.3900***

0.0570

− 0.0389

0.0736

0.2286***

0.0280

DMy08

0.3231***

0.0563

− 0.1150

0.0754

0.1835***

0.0304

DMy09

0.2344***

0.0483

− 0.1578**

0.0630

0.1179***

0.0264

DMy10

0.1735***

0.0353

− 0.1403***

0.0498

0.0880***

0.0203

DMy11

− 0.0172

0.0330

− 0.2986***

0.0418

− 0.0674***

0.0198

DMy12

− 0.0341

0.0398

− 0.3128***

0.0448

− 0.0944***

0.0204

DMy13

− 0.1285***

0.0344

− 0.3430***

0.0413

− 0.1278***

0.0205

DMy14

− 0.1380***

0.0355

− 0.3729***

0.0417

− 0.1473***

0.0208

DMy15

− 0.0593*

0.0354

− 0.3304***

0.0443

− 0.1038***

0.0199

DMy16

0.0063

0.0370

− 0.2336***

0.0443

− 0.0495***

0.0187

DMy17

− 0.1124***

0.0349

− 0.3052***

0.0425

− 0.1312***

0.0180

DMy18

− 0.4348***

0.0450

− 0.0500

0.0474

− 0.0916***

0.0250

DMy19

− 0.5686***

0.0477

− 0.1344**

0.0562

− 0.1727***

0.0274

Inefficiency effects

 AGPS (ρ1)

0.8017***

0.0816

− 0.0114

0.0228

  

 RMBR (ρ2)

0.0001

0.0016

− 0.0111***

0.0029

  

 UEMR (ρ3)

− 0.1687***

0.0317

0.0035

0.0109

  

 LAST (ρ4)

0.0120***

0.0058

0.0473***

0.0063

  

 ln σ2

− 3.7037***

0.1680

− 4.0047***

0.1363

− 3.6143***

0.2857

 iligt γ

2.3079***

0.4369

− 1.1892

1.4844

2.1719***

0.3232

 μ

    

0.2312***

0.0458

 η

    

0.0221***

0.0028

Log likelihood

282.27

 

279.14

 

1032.23

 

Observations

320

 

432

 

752

 
  1. *, **, *** denote a significant estimator at the 10%, 5%, and 1% level, respectively
  2. ln σ2 = ln (σv2 + σu2) and ilgt γ are the inverse logit of σu2/(σv2 + σu2), respectively