Information Technology, Efficiency and Productivity Evidence From by nml23533

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									Information Technology, Efficiency
 and Productivity: Evidence From
    Korean Local Governments

                Nakil Sung
     University of Seoul, nisung@uos.ac.kr

 International Telecommunications Society
       15th Biennial Conference 2004
              Berlin, Germany
         Contents

1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and TFP
   Growth Estimation
4. Result 2: Regression Results
5. Conclusion
1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and TFP Growth
   Estimation
4. Result 2: Regression Results
5. Conclusion
                                          Research Motivation

     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 Index
 Components                             Measures
   Support       • Number of IT related meetings and plans per year
                 •   Ratio of IT related to total budget
                 •   Number of servers and PC’s
Investment and   •   Purchase costs of software
  Equipments     •   Diffusion rate of e-mail ID’s
                 •   Computer and information security activities
                 •   Efficiency of network management etc
 Human and       • Ratio of IT related to all staffs
Organizational   • IT related education activities
   Factors       • Number of IT related license holders etc
                 • Usage degree and pattern of bulletin board and
                   homepage
  Usage and      • Application of IT to administrative process,
 Applications      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
1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and TFP Growth
   Estimation
4. Result 2: Regression Results
5. Conclusion
                                            Research Methods


    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
    Y1

                                                             OA
                                                        TE 
                B                                            OB
                              Production Possibility Curve



           A




    O                                          Y2



                                                             y
Distance Function :   d o ( x, y )  min {  :                    P( x) }
                                                             
                                                                    Research Methods


      Malmquist Productivity Index
   Period-s (output-oriented) Malmquist productivity
    index
                                              s
                                           d o ( xt , y t )
              mo ( x s , xt , y s , y t )  s
               s

                                           d o ( xs , y s )

   Malmquist productivity index between period-s
    and period-t
     mo ( x s , xt , y s , y t )  [m ( x s , xt , y s , y t )  m ( x s , xt , y s , y t )]
                                      s
                                      o
                                                                   t
                                                                   o
                                                                                           1/ 2

                                       s                   t
                                  d ( xt , y t ) d ( xt , y t ) 1 / 2
                               [     o
                                      s
                                                         o
                                                          t
                                                                ]
                                  d ( xs , y s ) d ( xs , y s )
                                      o                   o
                                                             Research Methods



        Decomposition of Malmquist
            Productivity Index

                                 t
                              d o ( xt , yt ) d os ( xt , yt ) d os ( x s , y s ) 1 / 2
mo ( x s , xt , y s , yt )  [ s               ][ t              t                ]
                              d o ( x s , y s ) d o ( xt , y t ) d o ( x s , y s )



         Efficiency                           Technical
          Change                               Change
                                               Research Methods

        Data Envelopment Analysis
   Charnes-Cooper-Rhodes (CCR) Model: constant
    returns-to-scale (CRS) assumption

    Min ,                               The optimal solution
                                           to this LP problem is
      s.t.     x k  X  0               the output distance
                                           function.
                y k  Y  0
                 0

   Bankers-Charnes-Cooper (BCC) Model: variable
    returns-to-scale (VRS) assumption
     convexity condition:      j   1
1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and
   TFP Growth Estimation
4. Result 2: Regression Results
5. Conclusion
                        Efficiency and TFP Growth

Two Levels of Local Governments
            in Korea

 KOREA   Metropolitan      Districts (Gu):
         Cities: 7         69



         Provinces: 9      Cities (Shi): 70


                           Counties (Gun):
         Samples           83
                                     Efficiency and TFP Growth

    Input and Output Variables
     Variables                         Definition
 Input      NLSP   Number of local servants per 100 persons
Variables   CEXP   Annual constant expenditures per capita
            PRWS   Penetration rate of water supply
            AUPP   Area of urban parks per person
            RRLA   Ratio of road length to area
            NMVP   Number of registered motor vehicles per person
            PRWR   Penetration rate of sewage and refuse disposal
 Output     CSWP   A seating capacity of social welfare institutions
Variables          per 100 persons
            NSRP   Number of Basic Livelihood Security recipients
                   per 100 persons
            NCPP   Number of building construction permits per 100
                   households
            NCAP   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   Mean       0.850        0.999       0.851     0.999
 (Gu)      STD        0.145        0.005       0.144     0.005
  City     Mean       0.772        0.984       0.820     0.991
 (Shi)     STD        0.156        0.027       0.142     0.019
County     Mean       0.657        0.976       0.657     0.976
(Gun)      STD        0.182        0.051       0.182     0.051
           Mean       0.753        0.986       0.769     0.988
 Total
           STD        0.181        0.036       0.180     0.035
  Note: STD implies standard deviation
                                            Efficiency and TFP Growth

     Average TFP Growth Rates
           (1999-2001)
                            Method 1                   Method 2
                     Efficiency      TFP        Efficiency    TFP
                      Change        Change       Change      Change
District   Mean        2.7%             4.2%      2.7%        4.8%
 (Gu)       STD        9.2%             10.8%     9.1%       11.2%
  City     Mean        3.5%             4.5%      0.6%        4.0%
 (Shi)      STD        10.2%            15.4%     9.0%       14.3%
County     Mean        5.2%             -8.6%     5.2%       -8.6%
(Gun)       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%
 Note: STD implies standard deviation
1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and TFP Growth
   Estimation
4. Result 2: Regression
   Results
5. Conclusion
                                            Regression Results

      Efficiency and Productivity
              Regressions
        TIE or dTFP  0    j RCj   z ZSCORE  

   Definition of variables                       1
     TIE: technical inefficiency score,   TIE     1
                                                 TE
     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

Regional Characteristic Variables
        Variables                              Definition
                 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 districts
                 CITY       Dummy for cities
  Regional       POPD       Population density
Characteristic
 Variables       APLS       Area per 100 local servants
                 NETP       Number of establishments, including
                            individuals and corporation, per person
                 NSEP       Number of service related establishments,
                            including hotels and restaurants, per person
                 RWTR       Number of workers per person
                 CLTP       Amount of collected local tax per person
                                                     Regression Results

Technical Efficiency Regressions
                 Equation 1     Equation 2      Equation 3      Equation 4
   SIZE1          -0.237***                                     -0.271***
   SIZE2          -0.374***                                     -0.457***
 DISTRICT                         -0.194**
    CITY                           -0.090
   POPD                                          -0.015***
    APLS          0.001***        0.002***                       0.002***
   NETP             1.347          2.453           2.618
   NSEP            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
                Equation 1      Equation 2     Equation 3      Equation 4
  SIZE1           0.060**                                       0.063***
  SIZE2           0.061**                                        0.057**
DISTRICT                         0.111***
   CITY                            0.108
   POPD                                           0.002
   APLS          -0.001***        -0.000                         -0.000*
   NETP           -1.051          -1.105          -1.029
   NSEP           4.061*           3.263          0.022
  RWTR             0.126           0.143         0.264**
   CLTP                                                         0.073***
 ZSCORE           0.063**         0.055**         0.077*          0.039
Adjusted R2        0.174           0.223          0.086           0.203
Note: *,**,** implies statistical significance at 10%, 5%, and 1% level,
respectively
1. Research Motivation
2. Research Methods
3. Result 1: Efficiency and TFP Growth
   Estimation
4. Result 2: Regression Results
5. Conclusion
                                               Conclusions


                     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.
Thank You For Your
    Attention!

								
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