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A spatial analysis on the determinants of tourism performance in Japanese Prefectures

  • Perspectives on Spatial Dynamics: Cities, Culture and Environment
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Abstract

Assuming tourism as a place-oriented activity where tourist flows often cross regional borders, local and global indicators of spatial autocorrelation can be useful tools in order to identify and to explain different patterns of regional tourism dynamics and their determinants. These techniques recently became widely used in applied economic studies, as a result of their useful insights to understand spatial phenomena and benefiting from the existence of geo-referenced data and adequate software tools. This tendency is also observed in the tourism sector in the last few years. In this work, an exploratory spatial analysis and a spatial econometric model are applied to the case of Japanese Prefectures, leading to the identification of the specific spatial aspects prevailing in Japanese regional tourism dynamics. Spatial heterogeneity and agglomeration processes are identified, with a view on policy and managerial recommendations, offering a contribution to explore potential synergies arising from inter-regional cooperation in crucial aspects of tourism development. The results reveal the existence of such spatial effects, reflecting the importance for tourism of central areas of Japan, while revealing that competition effects among Japanese Prefectures prevail over positive regional spinoffs identified in other countries. It was also possible to observe that regions where tourism plays a more prominent role in terms of its importance within regional employment do not present a relatively high performance in terms of economic impact and benefits. The results suggest that a more balanced regional economic structure and higher levels of education of the work force contribute for improvements in tourism value added. Finally, the important role of foreign tourism boosting regional tourism performance is revealed.

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Author information

Authors and Affiliations

  1. Centre for Advanced Studies in Management and Economics (CEFAGE-UALG), University of Algarve, Faro, Portugal

    João Romão

  2. Graduate School of Economics and Business Administration, Hokkaido University, Sapporo, Japan

    Hisamitsu Saito

Authors
  1. João Romão
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  2. Hisamitsu Saito
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Corresponding author

Correspondence to João Romão.

Appendices

Appendix 1: Map of Japan and identification of the prefectures

figure a

Appendix 2

Prefecture

Pop

GDP

GDPT

EMP

EMPT

N

NF

LS

GRAD

Hokkaido

5.506

18,359

1.138

2.509

0.255

23,284

2.055

1.257

0.513

Aomori

1.373

4436

0.270

0.640

0.058

3540

0.059

1.196

0.102

Akita

1.086

3470

0.197

0.503

0.047

3132

0.064

1.293

0.082

Iwate

1.330

4138

0.241

0.631

0.057

4262

0.083

1.177

0.105

Yamagata

1.169

3653

0.204

0.566

0.050

4258

0.053

1.223

0.100

Miyagi

2.348

7871

0.455

1.059

0.101

7239

0.159

1.212

0.267

Niigata

2.374

8683

0.507

1.156

0.108

7342

0.099

1.230

0.209

Fukushima

2.029

6977

0.368

0.934

0.086

7821

0.087

1.231

0.165

Ishikawa

1.170

4363

0.276

0.582

0.058

5945

0.188

1.177

0.141

Tochigi

2.008

7923

0.510

0.977

0.096

8254

0.128

1.158

0.218

Gumma

2.008

7513

0.386

0.965

0.093

6656

0.073

1.194

0.217

Nagano

2.152

7724

0.540

1.091

0.107

11,925

0.289

1.251

0.242

Toyama

1.093

4361

0.220

0.546

0.047

2738

0.085

1.182

0.136

Ibaraki

2.970

11,316

0.649

1.420

0.124

3582

0.092

1.294

0.345

Gifu

2.081

7095

0.523

1.023

0.099

4367

0.223

1.183

0.245

Fukui

0.806

3297

0.185

0.402

0.036

2257

0.020

1.175

0.094

Saitama

7.195

20,156

1.368

3.482

0.308

3282

0.075

1.288

1.113

Chiba

6.216

19,639

1.482

2.899

0.283

18,358

2.254

1.266

1.036

Yamanashi

0.863

3187

0.224

0.415

0.046

5061

0.597

1.168

0.107

Tokyo

13.159

91,375

5.652

6.013

0.575

41,912

8.720

1.447

2.659

Shiga

1.411

5950

0.301

0.674

0.058

3179

0.119

1.286

0.196

Kanagawa

9.048

30,356

2.112

4.147

0.394

13,979

0.825

1.263

1.788

Shizuoka

3.765

15,519

0.781

1.897

0.188

15,631

0.601

1.221

0.465

Aichi

7.411

31,710

1.725

3.676

0.330

11,444

1.070

1.263

1.090

Mie

1.855

7390

0.411

0.895

0.081

5638

0.096

1.197

0.213

Osaka

8.865

36,744

2.404

3.815

0.361

19,620

3.093

1.311

1.250

Nara

1.401

3558

0.330

0.597

0.052

1954

0.046

1.210

0.247

Wakayama

1.002

3523

0.198

0.451

0.042

3630

0.093

1.141

0.106

Kyoto

2.636

9789

0.727

1.219

0.126

11,986

1.435

1.414

0.412

Hyogo

5.588

18,542

1.342

2.490

0.231

9829

0.394

1.245

0.899

Tottori

0.589

1779

0.099

0.287

0.025

2235

0.023

1.176

0.062

Shimane

0.717

2354

0.118

0.348

0.030

2224

0.011

1.183

0.069

Okayama

1.945

7067

0.375

0.900

0.075

3698

0.067

1.220

0.249

Hiroshima

2.861

10,753

0.580

1.343

0.118

6913

0.239

1.251

0.410

Yamaguchi

1.451

5732

0.284

0.665

0.061

3249

0.037

1.167

0.160

Kagawa

0.996

3627

0.210

0.462

0.042

2522

0.042

1.194

0.132

Tokushima

0.785

2861

0.156

0.347

0.029

1409

0.022

1.203

0.091

Ehime

1.431

4862

0.276

0.652

0.059

2810

0.045

1.173

0.172

Fukuoka

5.072

17,913

1.169

2.263

0.217

11,727

0.617

1.241

0.653

Kochi

0.764

2179

0.176

0.336

0.033

2394

0.016

1.162

0.069

Oita

1.197

4209

0.223

0.550

0.054

5044

0.363

1.160

0.123

Saga

0.850

2766

0.175

0.409

0.038

1973

0.038

1.176

0.083

Nagasaki

1.427

4362

0.306

0.651

0.064

5040

0.361

1.192

0.126

Kumamoto

1.817

5535

0.387

0.834

0.082

5225

0.331

1.160

0.180

Kagoshima

1.706

5463

0.331

0.777

0.075

5036

0.126

1.233

0.146

Miyazaki

1.135

3503

0.248

0.531

0.049

2481

0.064

1.227

0.095

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Romão, J., Saito, H. A spatial analysis on the determinants of tourism performance in Japanese Prefectures. Asia-Pac J Reg Sci 1, 243–264 (2017). https://doi.org/10.1007/s41685-017-0038-0

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