Information Technology, Efficiency and Productivity: Evidence From

Korean Local GovernmentsNakil Sung

University of Seoul, nisung@uos.ac.kr

International Telecommunications Society

15th Biennial Conference 2004Berlin, Germany

Contents

1. Research Motivation2. Research Methods3. Result 1: Efficiency and TFP

Growth Estimation4. Result 2: Regression Results5. Conclusion

1. Research Motivation2. Research Methods3. Result 1: Efficiency and TFP Growth

Estimation4. Result 2: Regression Results5. Conclusion

Yes, Too Many Studies on IT Productivity Effects

The first generation of studies often provided mixed empirical results on the Solow’s productivity paradox until the late 1990’s The productivity paradox was partly resolved

by observing faster productivity growth in developed countries.

The second generation of studies focuses on the performance of IT-using sectors. Many studies agree that rapid productivity

growth in IT-producing sectors led to better performance of national economy.

Research Motivation

The Second Generation of ‘IT Productivity’

Literature Recent studies are fairly successful in

confirming positive effects of IT. For example, Jorgenson (2001), Brynjolfsson

and Hitt (1996, 2000), Stiroh (2001), Mun and Nadiri (2002).

These studies mainly use micro data such as industry or firm data. The use of micro data is a good way of

identifying ‘IT productivity effects’ because it provides researchers with a chance of distinguishing IT-heavy users from IT-light users.

Research Motivation

But, More Studies Are Still Needed In Some

Areas As usual, the current literature does not

distinguish (technical) efficiency from productivity. Only Milana and Zeli (2002) examine the

relationship between IT investments and technical efficiency.

Is there any better measure of IT-using activities? Many studies use the purchase costs of IT-

related equipment as a proxy for the state of IT.

On the other hand, the performance of IT users must be affected by effective use and applications of IT.

Research Motivation

Korean Case Provides a Good Research Opportunity

Because… The Korean government has reported an

index of IT-using activities (called Informatization Index) for all local governments. This index measures a wide range of IT-related

activities.

Also, like other countries, good and reliable data on local public services are publically available in Korea.

Research Motivation

Informatization IndexComponents Measures

Support • Number of IT related meetings and plans per year

Investment and

Equipments

• Ratio of IT related to total budget• Number of servers and PC’s• Purchase costs of software• Diffusion rate of e-mail ID’s• Computer and information security activities• Efficiency of network management etc

Human and Organizational

Factors

• Ratio of IT related to all staffs• IT related education activities• Number of IT related license holders etc

Usage and Applications

• Usage degree and pattern of bulletin board and homepage

• Application of IT to administrative process, Development degree of e-government (including electronic handling of public services)

• Degree of electronic approvals etc

Research Motivation

Then, the Study Has Two Objectives

Measuring (technical) efficiency and productivity growth for all Korean local governments By applying conventional methods

Examining the effects of IT on (technical) efficiency and productivity growth By using the Information Indexes

Research Motivation

1. Research Motivation2. Research Methods3. Result 1: Efficiency and TFP

Growth Estimation4. Result 2: Regression Results5. Conclusion

Research Strategy: Two Stage Approach

First Stage: Measurement of (technical) efficiency and TFP growth by using distance functions. Both efficiency and productivity growth are defined

and measured by using distance function The distance function is estimated by applying data

envelopment analysis (DEA).

Second Stage: Efficiency and productivity regressions Efficiency scores and productivity growth rates are

regressed on some regional characteristic variables and the Informatization Index.

Research Methods

Technical Efficiency: Output-Oriented Measure

Research Methods

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Production Possibility Curve

Period-s (output-oriented) Malmquist productivity index

Malmquist productivity index between period-s and period-t

Research Methods

Malmquist Productivity Index

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Research Methods

Decomposition of Malmquist Productivity Index

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Efficiency Change

Technical Change

Charnes-Cooper-Rhodes (CCR) Model: constant returns-to-scale (CRS) assumption

Bankers-Charnes-Cooper (BCC) Model: variable returns-to-scale (VRS) assumption convexity condition:

Research Methods

Data Envelopment Analysis

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1. Research Motivation2. Research Methods3. Result 1: Efficiency and

TFP Growth Estimation4. Result 2: Regression Results5. Conclusion

Two Levels of Local Governments in Korea

Efficiency and TFP Growth

KOREA Metropolitan Cities: 7

Provinces: 9

Districts (Gu): 69

Cities (Shi): 70

Counties (Gun): 83Samples

Input and Output VariablesVariables Definition

InputVariables

NLSP Number of local servants per 100 personsCEXP Annual constant expenditures per capita

OutputVariables

PRWS Penetration rate of water supplyAUPP Area of urban parks per personRRLA Ratio of road length to areaNMVP Number of registered motor vehicles per

personPRWR Penetration rate of sewage and refuse

disposalCSWP A seating capacity of social welfare

institutions per 100 personsNSRP Number of Basic Livelihood Security

recipients per 100 personsNCPP Number of building construction permits

per 100 householdsNCAP Number of civil affairs and petition cases

per person

Efficiency and TFP Growth

Application of DEA Models Both CCR (CRS) model and BCC (VRS) model are

applied to input and output data over the period 1999-2001. Then the estimates are averaged.

Operation environment of local governments should be taken into account.

Method 1: First, evaluate local governments under handicaps and second, use this information to evaluate local governments in better environments.

Method 2: Evaluate local governments only within the group.

Efficiency and TFP Growth

Average Technical Efficiency Scores (1999-

2001)Method 1 Method 2

CRS Model

VRS Model

CRS Model

VRS Model

District (Gu)

Mean 0.850 0.999 0.851 0.999STD 0.145 0.005 0.144 0.005

City(Shi)

Mean 0.772 0.984 0.820 0.991STD 0.156 0.027 0.142 0.019

County(Gun)

Mean 0.657 0.976 0.657 0.976STD 0.182 0.051 0.182 0.051

TotalMean 0.753 0.986 0.769 0.988STD 0.181 0.036 0.180 0.035

Note: STD implies standard deviation

Efficiency and TFP Growth

Average TFP Growth Rates (1999-2001)

Note: STD implies standard deviation

Method 1 Method 2Efficiency Change

TFPChange

Efficiency Change

TFPChange

District (Gu)

Mean 2.7% 4.2% 2.7% 4.8%STD 9.2% 10.8% 9.1% 11.2%

City(Shi)

Mean 3.5% 4.5% 0.6% 4.0%STD 10.2% 15.4% 9.0% 14.3%

County(Gun)

Mean 5.2% -8.6% 5.2% -8.6%STD 10.8% 11.0% 10.8% 11.0%

Total Mean 3.9% -0.5% 3.0% -0.5%STD 10.1% 13.9% 9.9% 13.7%

Efficiency and TFP Growth

1. Research Motivation2. Research Methods3. Result 1: Efficiency and TFP Growth

Estimation4. Result 2: Regression

Results5. Conclusion

Definition of variables TIE: technical inefficiency score,

dTFP: TFP growth rate (Malmquist productivity index) RC: regional characteristic variables ZSCORE: Informatization Index

Estimation technique: censored Tobit method The TIE takes a value between 0 and infinity.

Regression Results

Efficiency and Productivity Regressions

ZSCORERCdTFPorTIEzjj0

11

TETIE

Regional Characteristic Variables

Variables Definition

Regional Characterist

icVariables

SIZE1 Dummy for regions with population of more than 100,000 and less than 300,000

SIZE2 Dummy for regions with population of more than 300,000

DISTRICT Dummy for districtsCITY Dummy for citiesPOPD Population densityAPLS Area per 100 local servantsNETP Number of establishments, including

individuals and corporation, per personNSEP Number of service related

establishments, including hotels and restaurants, per person

RWTR Number of workers per personCLTP Amount of collected local tax per

person

Regression Results

Technical Efficiency Regressions

Equation 1 Equation 2 Equation 3 Equation 4SIZE1 -0.237*** -0.271***

SIZE2 -0.374*** -0.457***

DISTRICT -0.194**

CITY -0.090POPD -0.015***

APLS 0.001*** 0.002*** 0.002***

NETP 1.347 2.453 2.618NSEP 9.891* 13.721** 21.925***

RWTR -0.257 -0.453 -0.822***

CLTP 0.104*

ZSCORE -0.128* -0.162** -0.189** -0.179**

Note: *,**,** implies statistical significance at 10%, 5%, and 1% level, respectively

Regression Results

TFP Growth Rate Regressions

Note: *,**,** implies statistical significance at 10%, 5%, and 1% level, respectively

Equation 1 Equation 2 Equation 3 Equation 4SIZE1 0.060** 0.063***

SIZE2 0.061** 0.057**

DISTRICT 0.111***

CITY 0.108POPD 0.002APLS -0.001*** -0.000 -0.000*

NETP -1.051 -1.105 -1.029NSEP 4.061* 3.263 0.022RWTR 0.126 0.143 0.264**

CLTP 0.073***

ZSCORE 0.063** 0.055** 0.077* 0.039Adjusted R2 0.174 0.223 0.086 0.203

Regression Results

1. Research Motivation2. Research Methods3. Result 1: Efficiency and TFP

Growth Estimation4. Result 2: Regression Results5. Conclusion

Summary Local governments in more populous regions tend to be more

technical efficient and to experience higher TFP growth. Local governments in more business- or industry-centered

regions may operate closer to production frontier and enjoy higher TFP growth.

There exists a negative (positive) relationship between gross regional product and technical efficiency (TFP growth).

Local governments with higher level of informatization operate closer to production frontier and experience higher TFP growth rate.

Conclusions

Contribution The study successfully confirms a positive

role of IT in improving technical efficiency and accelerating productivity growth.

The study provides strong cases on the development of e-Government projects in many countries.

Conclusions

Thank You For Your Attention!