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Mälardalen University Dissertations
No. 6







DYNAMICS OF THE INTERNET


A Transformation Analysis of Banking and Finance






Anita L Du Rietz

2003

















Department of Innovation, Design and Product Development
Mälardalen University

Page 2

Copyright © Anita L. Du Rietz, 2003.
ISBN number: 91-88834-07-7
Printed by Arkitektkopia, Västerås, Sweden
Distribution: Mälardalen University Press

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62

Table 6. Parameter estimates. Two time periods.
Dependent variable: Value added. Linear fixed effect model.

Five banks Four banks

Variable 1997:4-2000:1

Regression 5

2000:2-2002:2

Regression 6

1997:4-2000:1

Regression 7

2000:2-2002:2

Regression 8

Intercept


5.097
(1.039)

16.183***
(5.469)

15.991*
(2.051)

16.140**
(2.428)

α
(k)

0.035
(0.281)

0.184**
(2.248)

0.166
(1.598)

0.242**
(2.700)

β
(i)

0.266*
(1.876)

0.369*
(1.705)

0.042
(0.317)

0.435*
(1.913)

γ
(w)

0.469
(1.482)

-0.140
(-0.673)

-0.283
(-0.499)

0.050
(0.219)

= ( + +
+ -1)


0.308
(0.502)


-1.241***

(-2.836)


-0.589
(-0.827)


-1.146
(-1.629)

R2 0.758 0.931 0.856 0.934

N 45 45 35 36
Figures in parentheses below the parameter estimates are t-statistics.
*, **, *** = The correlation is significant at the 0.10 level, 0.05 level and 0.01 level respectively (2-tailed)



With the division of the data set into two periods the results suggest that the estimated

coefficient for i has increased from 0.27 during 1997:4-2000:1 to 0.37 during 2000:2-2002:2,

all banks included (see regression 5 and 6). The estimates are significantly different from

zero at the10 percent level. The positive impact on labor productivity of k has increased and

has become significant. These results suggest that an increase in Internet payments per

employed with one percent has improved labor productivity with 0.27 percent during the first

years (1997:4-2000:1) and this impact has increased during the following years (2000:2-

2002:2) to 0.37 percent. For the four main big banks in Sweden the estimated increase of

labor productivity was 0.44 percent from a one percent increase of Internet payments per

employee during the last period (2000:2-2002:2).



Borttaget: the

Borttaget: 0

Borttaget: 5

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63

RTS test. In table 6, only regression 6 results in a significant estimate of . The negative

estimate suggests decreasing returns to scale for all estimated equations except the first one

(5). The estimates of returns to scale suggest a stronger effect of decreasing returns to scale

for the second period compared to the whole period (estimate = -0.63 in regression 4) for all

five banks. One interpretation of these results is that a further diffusion of the Internet has

entailed a production structure characterized by stronger negative returns to scale.



One conclusion from these estimated production functions is that one cannot always assume a

common relation between production inputs. The results suggest that the four main banks

have shared a similar production structure, where increased inputs of labor and capital have

been the main forces in increasing productivity. A new technology as the Internet has

changed this structure. As the diffusion of Internet services increases the banks may improve

productivity by decreasing labor inputs. The behavior is not the same for all banks. A small

savings bank in our sample confirms the divergence from the general behavior. This bank

invested in a new technology separate from the other cooperating savings banks. Their

premises were sold and leased back.



3.7 Summary and Conclusions

In this paper we have tried to assess the productivity impact of Internet penetration in

banking. For this purpose new data has been collected on Swedish banks for the period

1999:4 - 2002:2. Banking productivity correlates positively with innovation output, even

when controlling for the skill composition of labor as well as for physical capital intensity.



The innovation output is defined as the number of Internet payments by banking customers.

The impact of the Internet on output is particularly difficult to measure in a service sector,

which has to be taken into account when regarding our results (for a discussion of output

measures see for example Stiroh [1998]). Given the weaknesses of existing data, it may be

difficult to distinguish the contribution of IT and in our case the Internet from random shocks

that affect productivity, independent of the choice of analytical methods.

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