Skip to main content
Journal of Economic Structures

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

Table 3 S-GMM estimation—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***

0.637376***

0.866711***

0.815331***

0.534881***

0.681528***

0.501507***

0.652418***

0.686687***

0.728189***

 

(0.005227)

(0.036398)

(0.013993)

(0.008791)

(0.005597)

(0.016524)

(0.025004)

(0.008388)

(0.011115)

(0.005813)

LN Y

57.62584***

123.0758***

73.28574***

71.18630***

141.4240***

71.47007***

42.04625***

32.56416***

63.07160***

59.06819***

 

(7.304258)

(36.28240)

(13.99600)

(13.22959)

(7.172046)

(6.352812)

(10.55410)

(10.98463)

(5.737070)

(4.350452)

(LN Y)2

− 4.591437***

− 7.068219***

− 3.969270***

− 4.093837***

− 8.058674***

− 4.585833***

− 3.296316***

− 1.953200***

− 3.959310***

− 3.682506***

 

(0.396822)

(1.914156)

(0.713901)

(0.678123)

(0.392043)

(0.352939)

(0.538337)

(0.546943)

(0.299825)

(0.248012)

FIXCAP

1.304169***

0.401613***

0.113691*

0.124897*

0.307468***

0.203506***

0.958559***

0.706857***

0.261594***

0.284430***

 

(0.036813)

(0.151802)

(0.059633)

(0.072001)

(0.016720)

(0.065059)

(0.056140)

(0.059532)

(0.025747)

(0.033372)

TRADEBAL

0.283799***

0.162523*

− 0.056649

− 0.043252

0.106606***

0.093091**

0.731358***

0.095067**

0.068627**

0.061456***

 

(0.041314)

(0.096389)

(0.051108)

(0.046071)

(0.021848)

(0.045886)

(0.053885)

(0.040727)

(0.028192)

(0.023221)

RIR

− 0.068959***

− 0.031818

− 0.044415**

− 0.230746***

− 0.062411***

− 0.209459***

− 0.089647**

− 0.127239**

− 0.047110**

− 0.021734**

 

(0.020840)

(0.068555)

(0.020431)

(0.050033)

(0.014828)

(0.027904)

(0.037412)

(0.049800)

(0.018295)

(0.009533)

RER

0.232459***

0.107816***

0.010930**

0.057487***

0.056323***

0.079752***

0.157960***

0.059802***

0.092677***

0.065064***

 

(0.009322)

(0.031299)

(0.005392)

(0.007718)

(0.007124)

(0.021110)

(0.016781)

(0.009649)

(0.004882)

(0.002787)

INNOVATION

 

4.590632**

0.000531***

0.001694**

2.81E−05***

3.53E−05**

3.07E−06***

0.505997***

0.778590***

0.085216***

  

(2.116659)

(0.000122)

(0.000753)

(7.28E−06)

(1.49E−05)

(7.12E−07)

(0.036089)

(0.151254)

(0.006325)

Obs

822

267

298

249

412

679

687

390

489

584

Countries

73

44

40

38

61

65

63

49

55

66

Number instruments/number cross-section ratio

0.781

0.659

0.875

0.842

0.754

0.631

0.73

0.857

0.818

0.894

Prob J

0.438822

0.356827

0.398423

0.346422

0.438620

0.278122

0.331336

0.423937

0.301871

0.453112

AR(1)

− 0.378769

− 0.326715

− 0.610460

− 0.483374

− 0.294903

− 0.401180

− 0.455426

− 0.490436

− 0.438758

− 0.388040

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

− 0.009095

− 0.066793

− 0.073292

− 0.082484

− 0.075214

− 0.004522

0.111041

0.055437

0.056261

P-value

0.2283

0.8834

0.2991

0.3178

0.1055

0.1071

0.8892

0.1057

0.2982

0.1931

  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)
Back to article page