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Journal of Economic Structures

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

Table 6 System-GMM estimation, with income level dummy—dependent variable: H_TECH_EXP

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

H_TECH_EXPORT(-1)

0.236789***

0.741529***

0.854569***

0.703670***

0.780160***

0.587154***

0.765262***

0.809830***

0.671679***

0.720504***

 

(0.005227)

(0.019844)

(0.058652)

(0.011090)

(0.028682)

(0.010479)

(0.035567)

(0.069993)

(0.020100)

(0.005511)

LN Y

57.62584***

48.04960**

223.0494**

53.12590***

81.63015***

49.74834***

28.45970*

90.20161**

132.3779***

90.58492***

 

(7.304258)

(19.92995)

(90.79123)

(18.93544)

(14.67859)

(5.874587)

(15.94707)

(43.72893)

(11.15474)

(4.651450)

(LN Y)2

− 4.591437***

− 2.893456***

− 11.70970**

− 3.777497***

− 4.509308***

− 3.326673***

− 1.898104**

− 4.767502**

− 7.825082***

− 5.389862***

 

(0.396822)

(1.057637)

(4.576053)

(0.944178)

(0.832030)

(0.319518)

(0.827464)

− 2,273,958

(0.556027)

(0.272625)

FIXCAP

1.304169***

0.231345***

0.816835***

− − 0.009115

0.421406***

0.247221***

0.203645**

0.339544*

0.320666***

0.312091***

 

(0.036813)

(0.079434)

(0.217823)

(0.093477)

(0.084836)

(0.042997)

(0.085012)

(0.202160)

(0.032643)

(0.028752)

TRADEBAL

0.283799***

0.089768**

0.464309**

0.134262**

0.145593***

0.132924***

0.132186*

0.102118

0.082905**

0.145065***

 

(0.041314)

(0.043828)

(0.214121)

(0.057602)

(0.043122)

(0.040632)

(0.068508)

(0.114978)

(0.039377)

(0.018102)

RIR

-0.068959***

− 0.252808***

0.084250

− 0.337585***

− 0.031565

− − 0.280788***

− 0.085206**

− 0.381140**

− 0.272525***

− 0.036240***

 

(0.020840)

(0.066148)

(0.109295)

(0.029050)

(0.065948)

(0.016423)

(0.034347)

(0.167445)

(0.019368)

(0.011189)

RER

0.232459***

0.068454***

0.003740

0.275325***

0.059429**

0.084079***

0.093170***

0.081635**

0.185173***

0.087675***

 

(0.009322)

(0.013290)

(0.029717)

(0.031180)

(0.024724)

(0.007473)

(0.021427)

(0.038013)

(0.015196)

(0.004771)

INNOV*L_INCOME

 

11.44694**

0.034257

0.025689

0.002514

0.000900***

0.000334

1.191435***

9.559737***

0.354823***

  

(4.904051)

(0.045220)

(0.325650)

(0.007118)

(0.000266)

(0.000349)

(0.397017)

(0.532337)

(0.021854)

INNOV*M_INCOME

 

2.480264*

0.005784***

0.007554***

2.41E−05

3.50E−05***

5.14E−06

0.903410***

2.568074***

0.203470***

  

(1.266479)

(0.001513)

(0.001195)

(4.98E−05)

(1.30E−05)

(5.33E−06)

(0.301082)

(0.303696)

(0.011192)

INNOV*H_INCOME

 

2.211333***

0.002617***

0.001134***

3.37E−05***

− 0.000115***

7.91E−05**

1.017846***

1.296513***

0.079976***

  

(0.706753)

(0.000928)

(0.000377)

(1.18E−05)

(3.25E−05)

(3.96E−05)

(0.333024)

(0.414988)

(0.010572)

Obs

822

314

341

279

361

575

708

391

533

584

Countries

73

43

41

39

51

52

64

50

58

66

Number instruments/number cross-section ratio

0.781

0.767

0.61

0.974

0.725

0.846

0.516

0.52

0.897

0.879

Prob J

0.438822

0.531840

0.273664

0.456632

0.553086

0.212586

0.210725

0.852185

0.468131

0.374773

AR(1)

− 0.378769

− 0.503872

− 0.378553

− 0.398227

− 0.440035

− 0.375286

− 0.530192

− 0.489457

− 0.346786

− 0.359988

P-value

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

AR(2)

− 0.039303

0.050483

− 0.101763

− 0.006809

0.073359

− 0.072131

0.006581

0.100403

− 0.002028

0.035577

P-value

0.2283

0.4902

0.1050

0.9271

0.2485

0.1375

0.8316

0.1391

0.9672

0.4148

  1. Information in brackets is the standard error associated with the coefficient
  2. Level of statistical significance: (***) denotes 1%, (**) denotes 5% and (*) denotes 10%
  3. 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. 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)
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