Ictel 2015 (daebum, jung) GRDS International Conferences

Effectiveness of Higher Education to Labor Productivity

Jung, Dae Bum (Jinju Health College)

Ⅰ. Introduction

Key words of current world trends

Globalization

Knowledge-based economy

Communicopia

post-information

Ubiquitous-computing (mobile based network)

Harbison & Myers’s study

Education - Economic development

Human resource as the core factor for

the economic development

Limitation

Use the cross-section data (1950s – 1960s)

Research methods : Correlation, Coefficient

Direction of this study Long-term higher education rate Labor productivity Research method

Variable control Regression formulation

Compared and contrasted various regression Model

Explain labor productivity

Explain the contribution of higher education toLabor productivity

Find Unobservable individual effect to labor productivity

Ⅱ. Theoretical Background (Precedent Research)

Researcher Analysis Year Analysis Target Index for Educational Development Index for Economic

DevelopmentAnalysis Method

Harbison &

Myers1959 75 countries

% of enrollment in Schools

GNP / capitaEstimation of cross-sectional correlation

coefficient% of public education fee

Bannett 1955-1956 69 countries

% of academic educationin secondary school

GNP / capitaEstimation of cross-sectional correlation

coefficient% of vocational educationin secondary school

Curle 1958 57 countries

% of enrollment in secondary school

Rate of savings

Estimation of cross-sectional correlation

coefficient

GNP / capita

% of investment in education

Rate of economic growth

% of enrollment in elementary school

Infant mortality rate

Bowman 1950-1955 83 countries % of literacy GNP / capitaEstimation of cross-sectional correlation

coefficient

Ⅲ. Research Methods

Analysis Data Data from Korea Information Service-Fi-

nancial Accounting Systems(KIS-FAS)

Financial Information of Companies (1990-2009) Listed on Korea Stock Ex-change

Population of Higher Education

Economically Ac-tive population

Labor Productiv-ity

Item Variables Details of Variables

Dependent

Variables

The sales / person

(lnSPP)

the value after taking a natural logarithm to the total sales/the number

of employees (1 million won)

Explanator

y Variables

Quality of human

capital

(LnEDU)

the value after taking a natural logarithm to population with college

degree available for economical activities (1 thousand people)

Capital intensity /

person

(lnFCP)

the value after taking a natural logarithm to {(intangible fixed asset-con-

struction temporary account/the number of employees)} (1 million won)

Training cost / person

(lnEEP)

the value after taking a natural logarithm to (the total cost for training

the number of employees) (1 thousand won)

Employees

(lnNOE)the value after taking a natural logarithm to the number of employees

Proportion of incentives

(INC)Proportion of the incentives per quarter (%)

Research Variables

Year Average Standard Deviation Minimum Maximum

1990 4.496 0.674 2.706 7.719

1991 4.634 0.704 2.710 7.886

1992 4.750 0.701 2.831 8.057

1993 4.849 0.706 2.592 8.084

1994 5.010 0.679 2.975 8.352

1995 5.146 0.688 3.138 8.619

1996 5.278 0.691 3.406 8.972

1997 5.437 0.714 3.508 9.523

1998 5.578 0.773 3.494 9.794

1999 5.690 0.746 3.666 9.747

2000 5.776 0.743 3.891 8.827

2001 5.833 0.733 4.038 8.872

2002 5.930 0.760 4.163 8.765

2003 5.957 0.744 4.199 8.587

2004 6.068 0.761 4.401 8.809

2005 6.121 0.752 4.394 8.784

2006 6.189 0.778 4.416 8.732

2007 6.254 0.778 4.166 8.761

The average of the sales per person (N=216)

Variables

Average (Standard Deviation)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Ln

EDU8.074 8.183 8.310 8.408 8.456 8.522 8.584 8.616 8.763 8.806 8.858 8.913 8.969 9.096 9.145 9.195 9.243 9.293

Ln

EEP

3.787

(1.357)

4.024

(1.376)

4.135

(1.401)

4.1770

(1.564)

4.374

(1.524)

4.584

(1.476)

4.677

(1.427)

4.626

(1.396)

3.900

(1.736)

4.092

(1.575)

4.357

(1.537)

4.367

(1.760)

4.692

(1.504)

4.724

(1.638)

4.880

(1.449)

5.018

(1.483)

5.166

(1.513)

5.190

(1.542)

Ln

FCP

11.510

(0.754)

11.694

(0.753)

11.830

(0.740)

11.939

(0.755)

12.095

(0.708)

12.232

(0.698)

12.373

(0.710)

12.588

(0.735)

12.809

(0.772)

12.902

(0.758)

12.908

(0.760)

12.929

(0.732)

12.958

(0.750)

12.999

(0.742)

13.066

(0.746)

13.145

(0.712)

13.230

(0.744)

13.310

(0.741)

Ln

NOE

6.866

(1.156)

6.877

(1.145)

6.852

(1.144)

6.827

(1.150)

6.806

(1.145)

6.820

(1.152)

6.811

(1.158)

6.759

(1.156)

6.595

(1.188)

6.564

(1.185)

6.578

(1.193)

6.551

(1.175)

6.505

(1.154)

6.477

(1.160)

6.450

(1.187)

6.421

(1.217)

6.396

(1.231)

6.378

(1.258)

INC9.383

(4.664)

8.560

(4.812)

7.602

(4.976)

7.218

(5.228)

7.780

(5.557)

7.903

(5.848)

7.664

(5.991)

5.652

(6.922)

5.958

(8.035)

7.948

(9.202)

8.076

(9.23)

8.135

(9.197)

9.882

(10.500)

10.851

(13.464)

12.124

(16.765)

13.285

(23.274)

12.573

(17.189)

14.277

(25.122)

N 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216

Explanatory variables technical statistics

Research Model Basic statistics model

* = firm , * = year*L= the number of labor, *Q=gross sales *EDU= the level of education

* = firm , * = year

Research Model

*= the value after taking a natural logarithm to the sales per person

* = the value after taking a natural logarithm to the percentage of people who completed middle school and high school out of all the people involved in economical activity

*= the value after taking a natural logarithm to capital intensity per person

*= the value after taking a natural logarithm to capital intensity per person

*

Analysis of correlation [Labor productivity – HER(Higher Education

Ratio)]

Ⅳ. Study Results

the sales per person (1990-2007)

people with higher education among economically active population

(1990-2007)

Spearman's correlation coefficient 0.96**

N 373

Notes. * : p < 0.05, ** : p< 0.01

Change of labor productivity to HER

YEAReconomically active population

with more than college education(EDD)

the sales per person(SPP)

the average sales of company(SPPP)

1990 3,211 242,687 484,309

1991 3,583 263,915 531,404

1992 4,068 279,507 559,920

1993 4,487 290,365 586,850

1994 4,704 317,486 644,109

1995 5,025 352,854 736,868

1996 5,350 394,950 815,067

1997 5,520 474,145 880,504

1998 6,399 539,473 821,897

1999 6,679 572,381 909,222

2000 7,031 552,099 1,016,294

2001 7,431 556,407 1,032,935

2002 7,863 606,382 1,075,459

2003 8,927 582,117 1,050,310

2004 9,371 642,522 1,153,542

2005 9,848 657,241 1,178,319

2006 10,337 698,761 1,204,078

2007 10,867 730,776 1,267,300

unit: EDU=1,000 people, SPP=10,000 won, SPPP=1,000,000 won

Figure 1. The change of the rate of people with higher education among economically active population and the average sales of companies by

year

Notes: SPPP= the average sales EDU= college graduates rate among economically active population

Figure 2. The change of the rate of people with higher education among economically active population and the sales per person by

year

Notes:SPP= the sales per personEDU= college graduates rate among economically active population

Analysis result of the panel data

  FE POLS RE

LnEDU0.710

(33.56)**

0.286

(12.51)**

0.672

(20.86)**

LnEEP0.051

(11.83)**

0.043

(9.37)**

0.055

(12.44)**

LnFCP0.486

(36.00)**

0.781

(85.30)**

0.515

(38.79)**

LnNOE-0.008

(-0.53)

-0.036

(-5.91)

-0.042

(-3.79)**

INC0.001

(3.90)**

0.002

(5.13)**

0.001

(4.01)**

Adj-R2 0.756 0.796 0.587

N 3888 (216 companies) 3888 3888 (216 companies)

Notes : 1. * : p < 0.05, ** : p< 0.012. The value in parenthesis is t.3. variables:

LnEDU = the value taken natural logarithm into the number of economically active population with more than college educationLnEEP = the value taken natural logarithm into the education fee per personLnFCP = the value taken natural logarithm into capital intensity per personLnNOE = the value taken natural logarithm into the number of workersINC = incentive index

Analysis result of the panel data (per size of companies)

  FE POLS RE

LnEDU 0.429(18.44)**

0.665(23.93)**

0.440(19.26)**

LnEEP 0.044(9.76)**

0.030(5.08)**

0.043(9.63)**

LnFCP 0.297(33.11)**

0.538(53.88)**

0.309(34.86)**

INC 0.002(5.08)**

0.002(4.10)**

0.002(5.03)**

SSIZE -0.433(-12.40)**

-1.616(-43.71)**

-0.514(-14.95)**

MSIZE -0.301(-11.02)**

-1.166(-37.85)**

-0.360(-13.34)**

MLSIZE -0.184(-8.79)**

-0.804(-29.37)**

-0.221(-10.57)**

Adj-R2 0.573 0.665 0.586

N 3888 (216 companies) 3888 3888 (216 companies)

Notes : 1. * : p < 0.05, ** : p< 0.012. The value in parenthesis is t.3. variables:

SSIZE= number of workers<350MSIZE= 350<number of workers<690MLSIZE= 690<number of workers<1320LSIZE= number of workers>1320divided by quartile of the number of workers

Ⅴ. Conclusions & Suggestions

Conclusions

People with long-term higher education has a positive effect on labor productivity

The importance of human capital development shows in case what Korea faced on 1997

The investment in human capital by company will be increase

Suggestions

Need more support to HR Development

at the level of nation and company

Further research need private approach with va-

riety method for more practical information to gov-

ernment, company and individuals

Thank you