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

Table 1 S-GMM estimation—dependent variable: LN OUTSHARE

From: Does innovative capacity affect the deindustrialization process? A panel data analysis

Regressors

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

Model 10

BASELINE

R&D

RESEARCHERS

TECHNICIANS

ARTICLE

PATENTS

TRADEMARK

INCENG

INDOUT

INES

LN OUTSHARE(-1)

0.855405***

0.624268***

0.770473***

0.626377***

0.469256***

0.565939***

0.701665***

0.604291***

0.585251***

0.610188***

 

(0.008559)

(0.017541)

(0.026241)

(0.038554)

(0.078385)

(0.025998)

(0.019422)

(0.039854)

(0.044090)

(0.012790)

LN Y

0.290860***

1.150675***

1.450406**

0.681888**

3.126427*

0.728305***

0.297857*

1.787952***

0.974405***

0.752495***

 

(0.042533)

(0.238470)

(0.670192)

(0.325015)

− 1,619,443

(0.138941)

(0.164530)

(0.582881)

(0.326356)

(0.189320)

(LN Y)2

− 0.019428***

− 0.069696***

− 0.092534**

− 0.045808***

− 0.175123*

− 0.051235***

− 0.024000***

− 0.096139***

− 0.063117***

− 0.044139***

 

(0.002538)

(0.013202)

(0.035809)

(0.016596)

(0.089952)

(0.007930)

(0.009081)

(0.031806)

(0.018059)

(0.010725)

LN RELPRICE

0.151979***

0.141222***

0.077309

0.191591***

1.031461***

0.112148***

0.117706***

0.292029***

0.211497***

0.507475***

 

(0.011588)

(0.015451)

(0.054712)

(0.067241)

(0.287530)

(0.022550)

(0.036492)

(0.111148)

(0.065909)

(0.016915)

FIXCAP

0.001440***

0.003483***

0.005214***

0.006046***

0.009748**

0.004217**

0.001884***

0.007024***

0.004898***

0.005720***

 

(0.000399)

(0.001025)

(0.001870)

(0.001984)

(0.004776)

(0.001750)

(0.000608)

(0.002314)

(0.001796)

(0.000940)

TRADEBAL

0.002210***

0.001184*

0.003955***

0.010270***

0.006753**

0.004840***

0.001537***

0.007305***

0.005780***

0.003643***

 

(0.000320)

(0.000620)

(0.000951)

(0.001580)

(0.003324)

(0.000961)

(0.000526)

(0.001484)

(0.000960)

(0.000412)

RIR

− 0.001655***

− 0.004519***

− 0.010197***

− 0.003794***

− 0.011288***

− 0.004269***

− 0.001881***

− 0.002621***

− 0.007023***

− 0.002751***

 

(0.000244)

(0.000666)

(0.001234)

(0.001142)

(0.002708)

(0.000551)

(0.000352)

(0.000859)

(0.000976)

(0.000185)

RER

0.000756***

0.000701***

0.002102***

0.001135**

0.001493**

0.001016***

0.000615***

0.001434***

0.001148**

0.000465**

 

(0.000101)

(0.000130)

(0.000369)

(0.000528)

(0.000646)

(0.000194)

(0.000158)

(0.000318)

(0.000558)

(0.000182)

INNOVATION

 

0.055828***

2.92E−05***

9.61E−05***

7.51E−07**

9.16E−08***

2.87E−08***

0.008698***

0.024898***

0.001277***

  

(0.007367)

(1.11E−05)

(2.28E−05)

(3.38E−07)

(1.08E−08)

(7.67E−09)

(0.001639)

(0.008555)

(0.000230)

Obs

990

495

452

294

575

696

732

397

459

644

Countries

78

53

47

42

67

63

66

52

50

67

Number instruments/number cross-section ratio

0.859

0.849

0.83

0.881

0.448

0.778

0.742

0.827

0.84

0.851

Prob J

0.264783

0.166808

0.408991

0.226811

0.641532

0.296433

0.185479

0.354973

0.321484

0.387103

AR(1)

− 0.508060

− 0.457165

− 0.455419

− 0.481332

− 0.357658

− 0.446859

− 0.445705

− 0.439178

− 0.459184

− 0.423271

P-value

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

AR(2)

0.056039

− 0.038469

− 0.013386

0.009455

0.025117

0.012329

0.027153

0.027043

− 0.031441

− 0.015865

P-value

0.1315

0.3561

0.7944

0.9029

0.6008

0.7538

0.4486

0.6068

0.5550

0.7023

  1. i) Information in brackets is the standard error associated with the coefficient
  2. (ii) Level of statistical significance: (***) denotes 1%, (**) denotes 5% and (*) denotes 10%
  3. (iii) S-GMM: based on Arellano and Bover (1995), two stages and no time dummy. AR (1) and AR (2) tests to verify the presence of first-order and second-order serial correlation in the waste in difference
  4. iv) The number of instruments to the number of cross-section ratio needs to be higher than 1. Although the S-GMM estimates are adherent for samples with short periods and a high number of individuals, the diversity of instruments may generate the overlapping of instruments on the variables used, generating bias in the result (Roodman 2009)