Christian Von Hirschhausen

Astrid Cullmann

Andreas Kappeler

Nicole Adler

Gert Brunekreeft

Jean-Michel Glachant

Manfred Horn

Karsten Neuhoff

Konstantin Staschus

Beatriz Tovar

Efficiency Analysis of German Electricity Distribution Utilities -Non-Parametric and Parametric Tests

Keywords: C14 -Semiparametric and Nonparametric efficiency analysis, econometric methods, electricity distribution, benchmarking, Germany Efficiency analysis, econometric methods, electricity distribution, benchmarking, Germany JEL Classification: L51, L43, L94, C14

INTRODUCTION

Efficiency analysis has played a crucial role in defining regulatory policies, mainly in industries characterized by natural monopolies and/or by public ownership.

Examples are telecommunication (e.g. Uri, 2001), transport [START_REF] Coelli | A Guide to Frontier Version 4.1: A Computer Programm for Stochastic Frontier Production and Cost Function Estimation[END_REF]Perelman, 1998, 2000), energy (e.g. Jamasb andPollitt, 2001, 2003), schooling (e.g. [START_REF] Mizala | The Technical Efficiency of Schools in Chile[END_REF], hospitals (e.g. Steinmann and Zweifel, 2003), and even museums (e.g. [START_REF] Bishop | The Efficiency of Museums: a Stochastic Frontier Production Function Approach[END_REF] using a stochastic analysis of museums in South Western England). Efficiency analysis is also increasingly applied for other sector-specific analysis, such as farming (e.g. [START_REF] Latruffe | Determinants of Technical Efficiency of Crop and Livestock Farms in Poland[END_REF], on crop and livestock farms), banking (e.g. [START_REF] Färe | The Effect of Risk-Based Capital Requirements on Profit Efficiency in Banking[END_REF]Hauner, 2005), or the cement industry (Tsekouras and Skuras, 2005). In the electricity sector, efficiency analysis has played a particularly important role in the liberalization process towards a competitive industry structure and marketorientated regulation, both in electricity transmission and electricity distribution.

The objective of this paper is to assess the relative efficiency of decision-making units (here: electricity distribution companies), in order to generate information for an incentive-oriented regulation, with benchmarking and yardstick competition.

Data envelopment analysis (DEA) and stochastic frontier analysis (SFA) are the most commonly used methods in the literature on benchmarking and efficiency analysis in the electricity sector. They have been particularly useful in the regulatory process in Great Britain, Switzerland, the Nordic States, the Netherlands, and Austria. Many authors concentrate on scale effects, and the optimal size and relative efficiency of utilities. [START_REF] Jamasb | Benchmarking and Regulation : International Electricity Experience[END_REF] give an F o r P e e r R e v i e w 3 extensive comparison of international efficiency studies for the electricity sector, stressing the importance of the proper variable choice. In a subsequent paper, [START_REF] Jamasb | International Benchmarking and Yardstick Regulation: An Application to European Electricity Distribution Utilities[END_REF] perform an international benchmarking study of 63 utilities from six European countries comparing several SFA and DEA specifications. Although they determine a high correlation among the models, the results for single utilities differ noticeably. Using panel data of 59 Swiss distribution companies over eight years, Farsi and Filippini (2004) argue that different methodologies may lead to different results, but that the reasonable outof-sample "prediction errors suggest that panel data models can be used as a prediction instrument in order to narrow the information gap between the regulator and regulated companies" (p.2). In a similar panel data analysis for six Latin America countries, [START_REF] Estache | The Case for International Coordination of Electricity Regulation: Evidence from the Measurement of Efficiency in South America[END_REF] also show that national regulators can reduce information asymmetry through cross-country efficiency analysis.

Other examples of panel-data approaches are [START_REF] Burns | Benchmarking von Netzkosten -Data Envelopment Analyse am Beispiel der Stromverteiler in Großbritannien[END_REF] with a dynamic benchmarking analysis for 12 regional electricity distribution utilities in Great Britain, [START_REF] Hjalmarsson | Productivity in Swedish Electricity Retail Distribution[END_REF] analysis of Swedish electricity retail distributors, and the productivity study of Norwegian electricity utilities conducted by [START_REF] Forsund | Productivity Development of Norwegian Electricity Distribution Utilities[END_REF]. Quality is recently explicitly taken into account and related to benchmarking in a study by Giannakis, et al. (2004) on the UK electricity distribution utilities.

The present paper is the first productivity analysis of German electricity distributors to date. We address both traditional issues of electricity sector benchmarking, such as the role of scale effects and optimal utility size, as well as 4 new evidence specific to the situation in Germany. Regarding the latter, we consider the potential effects of three structural variables: consumer density, grid composition (cable versus aerial lines), and differences between East and West German distribution companies. Our empirical section thus follows the structural criteria set out by the German association agreements ("Verbändevereinbarung Strom VV II+"), a predecessor of the new energy law of 2005. The data cover 307 (out of 553) German electricity distribution utilities. 2 Our study is motivated by two factors: first, efficiency analysis in electricity distribution currently faces serious issues in determining whether there are significant returns to scale. The question arises whether or not smaller utilities do have systematically lower efficiency scores than larger ones, implying increasing returns ("big is beautiful"). [START_REF] Filippini | Are Municipal Electricity Distribution Utilities Natural Monopolies[END_REF], [START_REF] Filippini | Regulation of the Slovenian Electricity Distribution Companies[END_REF], and a number of other studies suggest significant economies of scale in "small" electricity distribution systems, e.g. Switzerland and Slovenia. Smaller utilities could reduce costs by merging and thereby extending their sub-optimal service territory size. This would suggest that the current, atomized structure of the German electricity utilities may be subject to structural change as well. Second, in the wake of liberalization, the German electricity industry is currently undergoing structural change from local monopolies to regulated competition. Observers suggest that liberalization will lead to a structural change of the sector, which has up to now comprised a large number of companies: four in high-voltage transmission, 56 in regional distribution, and 553 in local electricity distribution.

These numbers contrast sharply with the U.K. system, for instance, which features only 13 regional electricity companies altogether. Surprisingly, the literature on that issue in Germany is sparse: Riechmann (2000)

investigates the efficiency of the 53 regional distributors in Germany with DEA and finds significant cost reduction potentials. [START_REF] Haupt | Anwendung der Vergleichsmarktanalyse auf die Netzentgelte in der Stromwirtschaft[END_REF] compare network access prices of German electricity distributors and identify reasons for differences beyond the decision framework of the companies.

They consider structural variables in order to take explicit account of regional specificities, for example settlement density and consumer structure; but they do 7 not include a comparative efficiency analysis. [START_REF] Growitsch | Network Access Charges, Vertical Integration, and Property Rights Structure -Experiences from the German Electricity Market[END_REF] try to explain the high network charges in German electricity distribution, but efficiency considerations are not included in the analysis. In a study for the German energy consuming industry, Frontier Economics and Consentec (2003) assess a sample of 27 regional and local electricity distributors, using turnover as input, and peak load, units sold, and structural parameters as output. Interestingly, a regional distributor in East Germany was found to be on the efficient frontier, indicating that the traditional post-reunification bias towards higher costs in East German distribution may have decreased by now.

METHODOLOGY AND DATA

Methodology

To measure the relative efficiency of the German distribution utilities we use traditional data envelopment analysis (DEA) as well as stochastic frontier analysis (SFA), including the multi-output multi-input version of the distance function, mainly as a verification method. Our analysis is confined to the measurement of technical efficiency. DEA is a non-parametric approach determining a piecewise linear efficiency frontier along the most efficient utilities to derive relative efficiency measures of all other utilities. The efficiency scores can be obtained either within a constant returns to scale (CRS) approach or a less restrictive variable returns to scale (VRS) approach. The VRS approach compares companies only within similar sample sizes; this approach is appropriate if the utilities are not free to choose or adapt their size. Therefore we argue that the CRS approach is more relevant for our analysis. It assumes that companies are flexible to adjust -y i + YM O 0,

Lx i -XM O 0, M O 0.
L is the efficiency score, and M a *1 N vector of constants. Assuming that the firms use E inputs and M outputs, X and Y represent * E N input and * M N output matrices, respectively. The input and output column vectors for the i-th firm are represented by i

x and i y . The constraints ensure that the i-th firm is compared to a linear combination of firms similar in size. To determine efficiency measures under the VRS assumption a further convexity constraint RM=1 has to be considered. The system is solved once for each firm (for details, see [START_REF] Jamasb | International Benchmarking and Yardstick Regulation: An Application to European Electricity Distribution Utilities[END_REF], 1612, and Coelli, et al., 1998, Chapter 6).

Calculations can be done using an input-orientation or an output-orientation. The input-orientation considers the output to be fixed so that the input has to be adjusted in order to maximize efficiency. In the output-orientation, inputs are considered fix and the objective is the maximization of output. Traditionally, with the legal duty to serve all customers in a predefined service territory. In order to avoid potential errors of misspecification, we calculated both input-and output efficiency measures. The two measures provide the same value under CRS and estimate exactly the same frontier, therefore identify the same set of utilities being on the efficient frontier; (see, [START_REF] Coelli | A Guide to Frontier Version 4.1: A Computer Programm for Stochastic Frontier Production and Cost Function Estimation[END_REF]. Our VRS results obtained under the output-orientated approach do not differ significantly in comparison to the inputorientation.

DEA is a relatively uncomplicated approach. The determination of an explicit production function is not required. However, since DEA is a non-parametric approach the impact of the respective input factors on the efficiency cannot be determined. Furthermore, DEA does not account for possible noise in the data and outliers can have a large effect on the result. We therefore introduce a second methodology, the stochastic frontier analysis (SFA). SFA delivers a parametric approach to efficiency benchmarking. The theory of stochastic frontier production functions was originally proposed by Aigner, Lovell and Schmidt (1977) as well as Meeusen and van den Broeck (1977). This approach requires the definition of an explicit production or cost function. Based on the usual OLS regression a parallel shift of the original production function yields the efficiency frontier. This is caused by an underlying assumption splitting the error term into a stochastic residuum (noise) and an inefficiency-term. Usually, stochastic errors are assumed to be distributed half-normally. Originally the model was specified for crosssectional data. Hence, the mathematical expression of the production process is: SFA is more complex than DEA in terms of data requirements and handling, but has the advantage of allowing to deal with multiple-outputs multiple-inputs 4 in the form of a distance function, originally proposed by Shephard (1970). The basic idea is that in the case of a given production possibility frontier, for every producer the distance from the production frontier is a function of the vector of inputs used, X , and the level of outputs produced, Y. For the output-oriented model the distance function is defined as: in Y, and decreasing in X. is the scalar distance by which the output vector can be deflated (Coelli 2002) and can be interpreted as the level of efficiency. The output distance function aims at identifying the largest proportional increase in the observed output vector Y provided that the expanded vector ( / ) Y is still an element of the original output set [START_REF] Grosskopf | Estimating Output Substitutability of Hospital Services: A Distance Function Approach[END_REF]. The input orientated approach is defined on the input set ( ) L Y and considers, by holding the output vector fixed how much the input vector may be proportionally contracted. The input distance function is expressed by: inputs is specified as (Coelli, 2002)

( ) i i i i Y x v u = + , 1,..., i N = (1)
If
{ } ( , ) max :( / ) ( ) i D X Y X L Y = (3) ( , ) i D X Y is non-decreasing,
( ) 0 1 1 1 1 1 1 1 1 1 ln ln ln ln ln 2 1 ln ln ln ln 2 M M M K m m m n m n k k i m m n k K K K M km k l km k m k l k m D y y y x x x x y = = = = = = = = = + + + + + (4)
In order to maintain the homogeneity and symmetry a number of restrictions need to be imposed. For homogeneity the following restrictions have to be considered:

1 1 1
1, 0 and 0

K K K k kl km k l k = = = = = = (5)
For symmetry, two other restrictions have to be observed:

and mn nm kl lk = = (6)
Imposing homogeneity and symmetry restrictions and normalizing equation ( 4) by dividing it by one of the inputs delivers the estimating form of the input distance function:

( ) ( )

1 0 1 1 1 1 1 1 1 1 1 1 1 1 ln ln ln ln ln 2 1 ln ln ln ln ln 2 M M M K k K m m m n m n k K m m n k K K K M k l k kl km m I K K K k l k m x x y y y x x x x y D x x x = = = = = = = = = + + + + + (
1 0 1 1 1 1 1 1 1 1 1 1 1 1 ln ln ln ln ln 2 1 ln ln ln ln 2 M M M K ki Ki m mi mn mi ni k Ki m m n k K K K M ki li ki kl km mi i i Ki Ki Ki k l k m x x y y y x x x x y v u x x x = = = = = = = = = + + + + + + (8)
As discussed above, a certain distribution for i u has to be assumed in order to . Further, the inefficiency can also be assumed to be constant over time resulting in [START_REF] Battese | Frontier Production Function, technical efficiency and panel data: with application to Paddy Farmers in India[END_REF].

( ) { } ( ) exp it i u u T t = (Battese

Choice of Variables and Data

In the literature, a variety of specifications is employed depending on what exactly is being investigated and which variables are being used as inputs and as outputs. 5

The choice of variables for input and output needs to take into account the international experience with electricity distribution benchmarking, and is We estimate a base model, using the traditional input and output variables, and then add variations to this model. The base models include labour, grid size, and peak load (as the proxy for transformation capacity) as inputs, and units sold, the number of customers, and the inverse density index as outputs. In the extended models, we disaggregate both the consumers (number of industrial and number of residential customers) and the type of cable (aerial and underground), and we introduce an additional input variable: the electricity losses by each company. Last but not least, we compare the performance of utilities in East Germany and in West Germany to check whether this historical distinction still causes a structural difference between the two regions. The data specification is as follows:

-Labour input is estimated by the number of workers. 6 Some of the utilities have their own generating plants and we only dispose of employment data covering all workers in the electricity utility. To get an approximation of workers employed in electricity distribution, we subtract one employee for each 20 GWh electricity produced (following Auer, 2002, p. 128);

-capital input is approximated by the length of the existing electricity cables.

We differentiate between voltage levels (high, medium and low voltage) by introducing a cost factor for each type of line. 7 In addition, in one of the extended models, we distinguish between the cable grid and the aerial grid (following Auer, 2002, and others); cable is supposed to be more expensive -in addition, we distinguish between utilities situated in West Germany and those in East Germany (the former GDR). The latter are expected to display particularities due to their socialist heritage which may have a significant effect on their efficiency scores. An example is increasing costs because of the rough surface a utility has to cope with; another one is the density of a territory that a utility has to serve. Also, electricity consumption has plummeted significantly since the end of socialism, and the existing networks may have been too extensive. The electricity distribution data used in this study include information on 307

German distribution companies in the year 2001 (or, for some variables, previous years). The data was collected from publicly available sources. 12 We have verified that the sample is representative: it covers 56% of the total number of utilities, and 60.3% of the electricity sold. For the models 3-7, we have 197 observations. Summary statistics of the data are presented in Table 1.

Insert Table 1 4. EMPIRICAL RESULTS

Overview of the Model Runs

The presentation of results is divided into two broad sections: in the first section, the base model is developed, consisting of two inputs (labour, network size) and three outputs (units sold, no. of customers, inverse density index). Subsequently, we run variations of the base model, including the use of turnover as a monetary variable (instead of units sold), a disaggregation of the consumers (industrial and residential), as well as a disaggregation of the network (into aerial lines and underground cable). Further, we consider peak load as a proxy for transformer capacity (for which no data is available). To extend the classical efficiency measurement approach quality is included: grid quality is approximated by the sum of losses as a further input variable. Results for East and West Germany will also be discussed separately. Models 1 and 2 are separately estimated with DEA (CRS and VRS) and SFA 13 , and the correlations and rank-correlations are checked. For models 3-7, we limit ourselves to DEA. 14 The average efficiency for model 1 is 63.2%. Twelve utilities are on the efficient frontier. We observe a positive correlation between the size of the utility and its efficiency score: the average efficiency for the 154 largest utilities is 69%, whereas for the smaller half of the sample it is only 57%. In particular, there seems to be a problem with very small utilities: the 25 smallest distribution companies average an efficiency score of only 41.4 %.

If we use the VRS specification of model 1 instead, the efficiency scores rise significantly: 32 of the 307 utilities are on the efficient frontier. This can be explained by the fact that now utilities of similar size are compared with each other and not with the best ones in the sample. With VRS, the average efficiency increases to 68.3%, 5.1% higher than the results under the CRS assumption. For individual utilities, this improvement is significantly higher, in particular for the smaller ones. However, also the largest companies are considered more efficient under the VRS assumption because they are operating in an area of decreasing returns to scale. This may imply that they are too big to be efficient. Further analyses and specifications will show that some of the inefficiencies detected in the base model can be explained by further firm-specific characteristics or structural variables.

Insert Figure 1 F In the following analysis we address the first two structural variables that are used in the association agreement's assessment of potential cost drivers: density, and East-West structure. It is reasonable to assume that regional particularities can have a strong impact on the efficiency of distribution utilities, although they are outside their decision framework. As mentioned before an example is increasing costs because of the rough surface a utility has to cope with; another one is the density of a territory that a utility has to serve.

In model 2 we measure the first structural variable, the inverse density index, defined as service territory in kilometres divided by the number of inhabitants of the region. 15 This structural variable increases the efficiency of utilities in sparsely settled regions. 16 The average productivity for model 2 increases moderately compared to model 1, from 63.2% to 66.7%. Fifteen utilities are on the efficient frontier, three more than under the CRS assumption for model 1. Figure 2 Table 3 presents the correlation between the DEA and the SFA approaches for our first two models. All correlations are above 75% and thus seem to be significant.

We can conclude that the obtained DEA measures can generally be assumed to be robust and coherent. For the following analyses, we apply the (stricter) constant returns to scale approach to the sample. This implies that the size of companies is flexible. Given the recent liberalization of the sector, and the fact that mergers do occur regularly, this seems to be the more realistic assumption.

Insert Table 3 4.

Analysis of Differences between East and West Germany

The model also permits an analysis of structural differences in efficiency between

West and East German distribution utilities. 19 In fact, the association agreement includes a structural variable "East-West", implying that East German utilities have on average higher costs than their West German counterparts. This is supposedly due to the structural legacy inherited from socialist times, as well as to the drop in electricity consumption in East Germany after reunification, whereas This result may suggest that investment efforts of the last decade have led to an accelerated modernization process in East Germany, and thus a more efficient use of resources. Electricity production and distribution can now revert to a modernized power station park distribution system. The results tend in the same direction as those of Frontier Economics and Consentec (2003, p. 19), which find that some East German utilities have some of the highest efficiency scores.

Results from the Extended Model

We now estimate the efficiency taking into account a broader set of variables. In order to keep the results comparable, we estimate our base model with the reduced data sample of 195 utilities ("model 2b").

Peak Load, Turnover and Quality

In addition to the traditional inputs, grid line and labour, we consider peak load (as a proxy for transformer capacity, for which no data is available) to be a separate cost factor. Model 3 thus contains three input and three output variables. The efficiency estimates are slightly higher than before, averaging 73%. We find no structural correlation between the size of a utility and its peak load as a structural variable affecting efficiency. In the case of lacking cost data, it may therefore make sense to work with two different variables accounting for capital costs.

In model 4, we introduce a monetary variable, turnover, as output measure. The results are not significantly different from those obtained with model 2: the average efficiency decreases by about 1% point. This is due only to the drop of the average efficiency of the 50% largest distribution utilities. The average of the Model 5 attempts to identify the relation between quality and efficiency. We define the quality of distribution by the electricity losses that occur during this process. As could be expected, the average efficiency of all utilities increases when we introduce this additional input. We do not detect any difference in the relative change of efficiency scores between the large and small utilities. This result contradicts the common view that large utilities are in possession of better grids.

Grid Composition and Customer Structure

A third structural variable that may have an impact on efficiency scores is the composition of the grid, i.e. the relation of aerial lines to cable lines. The idea behind this reasoning is that cable lines are on average more expensive than aerial lines. However, regional utilities are often not free to choose the most appropriate grid type. This is particularly true in densely settled areas where national law prohibits aerial lines. We approach the issue in the following way: to show the real cost structure of the capital input we substitute in model 6 the simple grid size variable of the basic model by a weighted sum of cable and aerial grid. Since cable lines are on average more expensive we define an upwards factor of 1.25 for each km of cable line, and thereby indirectly consider higher prices for cables. This The first noticeable result is that average efficiency remains almost unchanged; it increases slightly to 67.0% compared to the 66% in the original specification (model 2b). The modest change in efficiency is not surprising: some utilities use a grid with a higher proportion of cable lines, others with more aerial lines. These two tendencies compensate each other while the average productivity remains almost the same. The efficiency of single utilities, in contrast, changes more significantly: utilities with a higher share of cables benefit from this transformation. All in all, the grid composition does not add much to the interpretation of results, a finding also suggested by Frontier Economics and Consentec (2003, p. vii) who doubt that grid composition is a significant cost driver.

In addition, we now consider the structure of customers. We distinguish between industrial and households customers taking into account the difference in the price structure of the end user prices. Figure 4 shows that the higher is the share of industrial customers to total customers, the lower is the efficiency increase from model 2b to 7. In model 4 we already noticed the same result: that an increase of the share of industrial customers represents a disadvantage for the utilities in terms of efficiency.

Insert Figure 4 4.3.3 Characteristics of most inefficient utilities In order to detect some regularities of inefficiency we now give a short summary of technical characteristics for the most inefficient firms. For this purpose we consider the extended models 2b to 7 and sorted out first the 34 most inefficient firms across all models. In a second step we compared these technical indicators (inputs and outputs) of the less efficient utilities with those of the more efficient ones (see Table 4). Within the 34 most inefficient utilities, only one is located in East Germany, which tends to confirm the result that West German utilities tend to be less efficient when compared to their East German counterparts. Notice that all inefficient utilities have proportionally a higher share of network length with respect to their number of customers. 20 In a second step we check if there are any regularities between inefficiency and the amount of electricity sold for each customer. Relative to the firm size, there seems to no such correlation. Further we note that inefficiency is related to the electricity sold per employee. Most of the inefficient firms provide a relative low share of electricity sold and employees.

Across all firms we find low revenue per km network length and a share of network length per MWh electricity sold.

Insert Table 4 4.3.4 Correlation analysis of DEA models

In this section we again check the robustness of our results by conducting a correlation analysis for the respective model specifications. Table 5 shows the correlation analysis for models 2b to 7. Overall, the correlation among the models is high. All are above 79%. Thus we can conclude that the obtained DEA efficiency measures can generally be assumed to be robust.

CONCLUSIONS

This paper has extended the literature on efficiency analysis to the electricity distribution sector in Germany. In addition to conventional DEA analysis, we have performed SFA distance function estimations, and find relatively high correlations between the two methods. We have addressed the general issue of optimal utility size and specific issues related to the "Balkanization", i.e. the atomisation of the German electricity distribution industry. The results suggest that returns to scale play only a minor role: only very small utilities have a significant cost disadvantage. Low customer density is found to affect the efficiency score significantly in the lower third of the sample. The grid composition does not produce systematic effects. Surprisingly, East German utilities show a higher average efficiency than their West German counterparts. Peak load as a structural input variable does not seem to be an important determinant of efficiency, when compared to the base model without peak load. Utilities with a high share of industrial customers seem to suffer a disadvantage by the efficiency analysis.

Further research should focus on using cost data for the inputs to make comparison of allocative efficiency possible. Additional analysis of quality and its relation with efficiency seems to be the most urgent policy relevant question, but it would require a substantial effort in data collection and treatment. and capital costs (CAPEX). For curiosity reasons, we have also run a model specification using turnover as the only input. If one assumes identical profitability among the distribution companies, then turnover can be used as a proxy for total costs (TOTEX).

F

10 Alternatively, other studies use the inverse of the losses as an output.

11 Density is also one of the structural variables defined in the German association agreements.

12 The sources of the data are the following:

Verlags Some data were also discovered by internet research on the utilities' homepages.

13 DEA was conducted using the Computer program DEAP Version 2.1; for SFA, we used the software package FRONTIER Version 4.1. Both were written by T.

Coelli.

14 In Figure 1 as well as in all subsequent figures, the utilities are ordered by units sold and, thereby, by size. Thus, utility no. 1 is the largest in size, and utility no.

307 the smallest. 15 The inverse density index may also pick up some of the topographical particularities, since regions with a rough topography tend to have a lower density.

16 DEA considers this effect under the present specification as an increase in output that will increase the estimated efficiency of utilities in sparsely settled areas. Companies with a higher inverse density index and thereby a territory with few customers per square kilometer will increase their efficiency. are operating in an area of decreasing returns to scale, whereas the smaller utilities of our sample are operating in an area of increasing returns to scale. However, to make a definitive statement about the optimal utility size for the German electricity distribution sector, a more detailed analysis would be required. 19 In our analysis we cannot incorporate a "modernisation" measure. This is due to the fact that because of limited data availability we apply a static model. Only with panel data we would be able to provide measures of technical change by means of dynamic DEA and SFA models. 20 The ratio of network length and number of customers varies around 0.6. The more efficient firms show a significantly lower ratio. Only some very small more efficient utilities have a higher quotient. But this is due to the fact that they can compensate the high utilization of the input factor capital with a very low utilization of labour.

  briefly describes the institutional context of electricity sector reform in Germany. Section 3 presents our methodology and data. Results from the basic model, and from several extended models, estimated by using DEA and SFA are provided in Section 4; we also carry out a correlation analysis of different estimations. Section 5 concludes.2. ELECTRICITY SECTOR REFORMS IN GERMANYGermany is the largest electricity producer and consumer in the European Union, and thus plays an important role in the liberalization of the sector launched by the European Commission in the mid 1990s. However, until recently, Germany has held the red lantern of the countries resisting European electricity deregulation, together with France and a few other countries. Subsequently the European Union has speeded up its attempts towards liberalization and vertical unbundling of the electricity sector. The so-called "acceleration directive" (2003/54/EC) requires legal unbundling of electricity distribution companies with more than 100,000 connected customers, i.e. creating legally independent commercial units for generation, transmission, and distribution. This goes well beyond the former EU electricity directive 96/92. Given the slow progress of liberalization in most member states, the acceleration directive also calls for an intensification of regulatory oversight and the introduction of an explicit regulatory body in each country, responsible for regulating electricity distribution. A review of the implementation of the acceleration directive is scheduled for 2007.Consequently, in Germany the electricity industry will now be subordinated to exante regulation for the first time in its history. Under the former directive 96/92, Germany had implemented a model of negotiated access and had -to that end --regulation. The electricity industry and the large electricity consumers were given freedom to negotiate network access prices and conditions in so-called association agreements ("Verbändevereinbarungen").Given the systemic information advantage of the electricity industry over the customers, and the hesitation of the German government to establish a sufficient countervailing power in a regulatory agency, self-regulation did not succeed in bringing prices down, or in establishing a significant level of competition. In its annual benchmarking reports, the European Commission has regularly criticized the German approach of self-regulation of network access charges (e.g. EuropeanCommission, 2003).3 The new German energy law, in force since July 2005, therefore set up a regulatory agency and required ex-ante regulation of network access. As observed in other countries implementing UK-style reforms, e.g. the Netherlands and Austria, the process of unbundling and the introduction of exante regulation are likely to lead to conflicts between the incumbent operators, potential market entrants, and the regulatory authorities. These conflicts revolve around the absolute level of access tariffs, the relative level, and non-tariff discrimination.

  the one optimal firm size at least in the German context. However, we also calculate the VRS model in order to report scale efficiency information, which is delivered by the difference between the CRS and VRS scores.The determination of the efficiency score of the i-th firm in a sample of N firms in the CRS model is equivalent to the following optimization: min L,M L s.t.

  the electricity sector assume the output fixed in a market

  (or the logarithm of output) of the i-th firm, i x is a *1 k vector of input quantities of the i-th firm, is a vector of parameters to be estimated, -negative random variables usually assumed to be half normal distributed (iid. |N(0, U 2 )|), thereby accounting for individual technical inefficiency. This original specification has been extended by different assumptions on the distribution of i u , such as truncated normal or two-parameter gamma distributions. The further extension of the model to panel data, systems of equations, and time-varying technical efficiencies follows more recent developments in econometrics and productivity analysis.

  is non-decreasing, positively linearly homogeneous and convex

  Y is located on the outer boundary of the production possibility set then ( within the efficiency frontier.

  separate stochastic and inefficiency effects. Most common are the normal distribution

  availability. In this respect, Germany has to be ranked among the least developing countries. The absence of regulation has thus far prevented the systematic collection of the relevant data. We therefore have to cope with a data set providing a representative sample for one year only(2001).

  . Thus, we substitute the simple grid size variable of the basic model by a weighted sum of cable and aerial grid; 8 -in one model we also take into account the maximum peak load as a further cost factor to approximate transformer capacity; -the amount of electricity distributed to end users (units sold) and the total number of customers are used as output variables; in an extended model we use turnover (national revenue) instead of units sold as a further output variable; 9 -losses are included as a proxy for the technical quality of the grid or the service quality of the grid. We consider losses as an input; 10 -the use of the inverse density index (settled area in kilometres per customers supplied) in the base model specification is motivated by the argument that utilities with a dense customer structure have a natural cost advantage over those with a weak customer density. When taken as an output, the inverse density index improves the performance of sparsely inhabited distribution areas;11

  the same. An explanation of this uneven effect of the turnover variable could be that larger utilities generally have more industrial customers in relation to household customers. Since industrial customers obtain lower prices and thus lead to a lower average per unit revenue, this has an adverse effect on the efficiency score.

  the disadvantage of the utilities that are forced to maintain a high proportion of cable lines.

Figure

  Figure 4: Correlation between Customer Structure and Efficiency

  can be interpreted as error term which reflects the difference between the observed data realizations and the predicted points of the estimated function.

	ln i D Replacing ln i D by a composed error term (the stochastic error i v and the
	technical inefficiency i u ) yields the common SFA form. It can be estimated by a
	stochastic frontier production function defined as	( ) y f x v u = +	. For
	( 1,..., ) I i I =	firms, this econometric specification with	0 ln i D	=	i	i u	, in its
	normalized form is expressed by:
		F o
		r
		P e
		e
		r
		R e
		v i
		e w
								7)
		13

  If we use the VRS specification of model 2 instead, the efficiency scores rise significantly. 35 of the 307 utilities are on the efficient frontier, which can again be explained by the fact that now utilities of similar size are compared with each other, and not with the best ones in the sample. With VRS, the average efficiency increases to 69.9%, 3.2% higher than the results under the CRS assumption. For individual utilities, this improvement is significantly higher, in particular for the smallest but also the largest ones. Figure3shows the difference in efficiency scores between the VRS and the CRS model. It seems that the optimal utility size, i.e. the one where the VRS and the CRS efficiency scores converge, is around utility number 100 in our sample. This corresponds to about 200 GWh sold.

	Submitted Manuscript
	relevant for efficiency. Therefore, it is more appropriate to compare our SFA
	results with DEA VRS.
	F o F o
	r r
	P e P e
	e e
	r r
	efficient by becoming smaller. 18	R e R e	compares the CRS result for
	model 2, including the inverse density index, with the CRS result from model 1 v i Insert Figure 3 v i
	(without structural variable). It turns out that for the 190 largest utilities, the structural effect is less significant (average efficiency increase of 0.4%), whereas e w 4.2.3. Correlation analysis and verification with SFA e w
	for the smaller ones, density is an important cost driver (average increase of Before we continue the DEA estimations of the extended models we check the
	6.9%); the effect is particularly strong for the 50 smallest utilities, 17 because robustness of our results with a stochastic frontier analysis (SFA), the input
	considered relatively: density, as a cost driver, has a higher impact for smaller distance approach. The correlation analysis of the results serves as verification of
	utilities the obtained DEA efficiency measures. For verification with SFA we use a
	translog input distance function specification. In fact, this is a curved productivity
	Insert Figure 2 function, similar to DEA-VRS where economies of scales are not considered to be
		20 21

Page 21 of 45 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Figure 3 also makes one issue clear: on the one hand smaller utilities could significantly gain in efficiency by merging; in this area, considerable economies of scale can be realized. On the other hand also larger utilities could become more

  network sizes have remained constant. In order to test the East-West hypothesis, we split up the sample into 259 West German utilities and 48 East German ones.

	utilities feature an average efficiency of 77%, against a West German average of
	65%.
	F o
	r
	P e
	e
	r
	R e
	v i
	e w
	The results of the East-West comparison are somewhat surprising: East German
	22

  -und Wirtschaftsgesellschaft der Elektrizitätswerke m.b.H. -VWEW:

	-"Jahresdaten der Stromversorger 2001"; VWEW Energieverlag GmbH, Frankfurt
	am Main, Heidelberg. (2002) for number of customers, units sold, number of
	employees and grid data.

-VDEW-Statistik 1996/1997 Leistung und Arbeit; VWEW-Verlag, Frankfurt am Main; (1997/98) for inverse density index and peak load.

Table 1 :

 1 Sample Summary Statistics

	Page 37 of 45		Submitted Manuscript
	Variable	Obs.	Mean	Std. Dev.	Min	Max
	Units sold (MWh)	307	346306.3	1182148	5675	1420000000
	No. of customers	307	38494.9	103225,8	7	1060000
	Grid size (km)	307	1190.7	3328,4	20,60	38899
	Labour	307	100.6	291,1	2	3077
	Inv. Density Index	307	0.0027	0,0034	0,00022	0,028
	Peak load (kW)	195	78265.6	2191046	1363	2041000
	Turnover (mn.EUR)	195	49595.1	1806072	597	2182300
	Losses (MWh)	195	21690.9	1065133	53	1340262
	Aerial Line (km)	195	213.5	5807	1	5529
	Cable Line (km)	195	1189.9	26047	12,4	192234
	Non res. Customers	195	45197.8	1118686	60	909000
	Res. Customers	195	4639.5	137061	2	151000

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 :

 2 Model Specification for the Analysis

				Submitted Manuscript			Page 38 of 45
		Labour	Grid	Losses	Peak	Units	Turnover	No. of	Inverse
			Size		Load	Sold		Customers	Density I.
	Model 1	I	I			O		O
	Model 2	I	I			O		O	O
	Model 2, VRS	I	I			O		O	O
	Model 2, West/East	I	I			O		O	O
	Model 3	I	I		I	O		O	O
	Model 2b	I	I					O	O
	Model 4	I	I				O	O	O
	Model5	I	I	I		O		O	O
	Model 6, Grid disaggregated	I	I			O		O	O
	Model 7, Cust. disaggregated	I	I			O		O	O

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 3 :

 3 Correlation Analysis for Models 1 to 2, Sample Size 307

	Page 39 of 45		Submitted Manuscript
	Samle Utilities	DEA -VRS	DEA -VRS	SFA -DF	SFA -DF
		Model 1	Model 2	Model 1	Model 2
	DEA -VRS Model 1	1.00	0.97	0.81	0.74
	DEA -VRS Model 2		1.00	0.77	0.76
	SFA -DF Model 1			1.00	0.89
	SFA -DF Model 2				1.00

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 4 :

 4 Efficiency Differences between Large and Small Utilities

		Submitted Manuscript		Page 40 of 45
	Model DEA	Note	Average	First 50% of	Last 50% of
				Utilities	Utilities
	DEA 1	307	0.63	0.69	0.57
	DEA 2	307, Inverse	0.67	0.70	0.64
		Density Index
	DEA 2, VRS	307, Inverse	0.70	0.73	0.67
		Density Index
	DEA 2b	195, Inverse	0.66	0.67	0.64
		Density Index
	DEA 3	195, Peak load	0.73	0.74	0.72
	DEA 4	195, Turnover	0.65	0.66	0.64
	DEA 5	195, Losses	0.71	0.71	0.71
	DEA 6	195, Grid	0.67	0.68	0.65
		Composition
	DEA 7	195, Customer	0.69	0.71	0.68
		structure

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 5 :

 5 Correlation Analysis between DEA Models from Section 2 (Reduced Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Differences for each Utility between Measures of Model 2 and Model 1

	Page 41 of 45			Submitted Manuscript
	Data Sample)
		DEA -	DEA -	DEA -	DEA -	DEA -	DEA -
		Model 2b	Model 3	Model 4	Model 5	Model 6	Model 7
		CRS	CRS	CRS	CRS	CRS	CRS
	DEA	-
	Model 2b	1.00	0.93	0.90	0.90	1.00	0.98
	CRS
	DEA	-
	Model	3		1.00	0.82	0.86	0.93	0.93
	CRS
	DEA	-
	Model	4			1.00	0.79	0.90	0.89
	CRS
	DEA	-
	Model	5				1.00	0.90	0.88
	CRS
	DEA	-
	Model	6					1.00	0.97
	CRS
	DEA	-
	Model	7						1.00
	CRS

Input: labour, grid size.

Output: customers, units sold. Page 42 of 45 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Page 43 of 45 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Input: labour, grid size. Output: customers, units sold, inverse density index. Page 44 of 45 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UKSubmitted Manuscript

< L -Industrial Organization, L51 -Economics of Regulation < L5 -Regulation and Industrial Policy < L -Industrial Organization

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Submitted Manuscript


Revisited * G Reza Arabsheibani

James Callaghan Building

Swansea SA2 8PP Singleton Park

Alan Marin

IF NOT COMPUTERS THEN WHAT? RETURNS TO COMPUTER USE IN THE UK

Keywords: Computers, Wages, Productivity

  L'archive ouverte pluridisciplinaire

Introduction

Since the early 1970's, despite the fact that supply of skilled labour relative to unskilled labour has risen, thereby making unskilled labour scarcer, wage inequality has dramatically risen in the US. For example, the premium for the male college educated workers relative to high school educated workers rose from 30% in 1979 to about 70% in 1995 [START_REF] Blanchflower | The Causes and Consequences of the Changing Income Inequality: W(h)ither the Debate?[END_REF]. The UK has also experienced a rapid rise in inequality [START_REF] Schmitt | The Changing Structure of Male Earnings in Britain: 1974-88[END_REF][START_REF] Gosling | What Happened to Men's Wages Since the Mid 1960's?[END_REF], while some other OECD countries have experienced a similar rise albeit at a much slower rate (for a comparison see Blanchflower and Slaughter, 1998, Table 1).

While the facts speak for themselves, the reason(s) for such a pronounced rise in inequality is far from settled. There are, however, a number of explanations that have been proposed. Although other suggestions, such as de-unionization and deindustrialization have been put forward, the two main competing theories are the impact of international trade, via the Stolper-Samuelson effect, and the impact of skill-biased technology. It is the latter view that has attracted much of attention from labour economists. The argument, in this case, is that this type of technology tends to increase the productivity of skilled rather than unskilled workers and hence increases the wage of the more skilled relative to the less skilled, thereby increasing wage inequality [START_REF] Bound | Changes in the Structure of Wages in the 1980's: An Evaluation of Alternative Explanations[END_REF][START_REF] Katz | Changes in the Relative Wages, 1963-1987: Supply and Demand Factors[END_REF][START_REF] Berman | Implication of Skill-Biased Technological Change: International Evidence[END_REF]Machin and Van Reenen, 1998;[START_REF] Haskel | Does the Sector Bias of the Skill Biased Technical Change Explain Changing Skill Premia?[END_REF]. In attempting to test for the impact of new (skill-biased) technology on wages [START_REF] Krueger | How Computers are Changing the Wage Structure: Evidence from the Microdata 1984-89[END_REF], using US data, demonstrated that those who use computers at work have 24.6% wage advantage in 1994 and 37.4% wage advantage in 1989, compared to workers who do not use computers.

Since the important analysis of Krueger a number of studies, mostly for countries other than the US (but see [START_REF] Autor | Computing Inequality: Have Computers Changed the Labour Market?[END_REF], for the US), have emerged confirming the significant and positive impact of computer use on wages. [START_REF] Reilly | Human Capital and Information: Employer Size Wage Effects[END_REF] found a 13.5% advantage for Canada; [START_REF] Arabsheibani | The Impact of Computer Use on Earnings in the UK[END_REF] found a 23% advantage in 1985 and a 20% advantage in 1990 for the UK; DiNardo and Pischke (1997) found a 17% advantage for Germany in 1991; [START_REF] Miller | Computer Skills and Wages[END_REF] results

show an advantage of 10-15%, depending on the specification of the earnings function, for Australia in 1993; Oosterbeek estimated an 11% advantage for the Netherlands in 1993; [START_REF] Morrissette | Computers, Fax Machines and Wages in Canada: What Really Matters?[END_REF] found a 14% advantage for Canada in 1994 and [START_REF] Entorf | Does Unmeasured Ability Explain the Higher Wages of the New Technology Workers?[END_REF] found an advantage of around 10% for a pooled sample of three separate years in the 1980's for France. Although all the above studies confirm the Krueger results a number of authors argue against Krueger's reasoning that computers increase productivity and that is why they have a positive impact on pay. The main argument is that the computer use variable is picking up the effect of unobserved heterogeneity. Reilly, for example, shows that when firm size variables are included in the wage regression the computer use variable becomes insignificant. This follows [START_REF] Hammermesh | Commentary[END_REF] unobserved differences in human capital between establishments. Arabsheibani et al (2003), however, show that, at least in the case of the UK, the computer use variable is robust to the inclusion of a wide set of variables, including employer size.

In a study damaging to the proponents of the view that there is a productivity effect of computers, [START_REF] Dinardo | The Returns to Computer Use Revisited: Have Pencils Changes the Wage Structure Too?[END_REF] argue that Krueger's study is an attempt to uncover the "treatment" effect of computer usage. The treatment effect would be the effect on wages if we choose a worker, at random, who does not use a computer at work and place him/her in an identical job with a computer. In other words it is the change in a worker's wage if we assigned at random a number of computers to non-users. Given the shortcomings of cross-section studies, and particularly in the absence of instruments for computer use (or computer knowledge), DiNardo and Pischke resort to using indirect evidence. They find that using pencils or pens at work also has a positive and significant effect on wages and consequently they argue that since pencils are used by around 65% of the sample, therefore their use cannot signify a scarce skill. In general, we do not expect pencils to raise productivity. DiNardo and Pischke propose that pencils have a positive effect on wages because they are used by workers who would be highly paid anyway. If this argument is true for pencils then the same could be said about computers. Given that 65% of their sample use pencils, this claim is not as strong as it seems at first sight since it would be stating that 65% of the workers are likely to be highly paid.

Using a very similar approach for Canada, Morissette and Drolet (1998) show that using fax machines positively and significantly affects earnings and that it yields a higher return F o r P e e r R e v i e w 5 compared to computers. They, therefore, agree with DiNardo and Pischke, in that computers and fax machines are simply picking up the unobserved differences between workers and jobs. [START_REF] Entorf | Does Unmeasured Ability Explain the Higher Wages of the New Technology Workers?[END_REF], using French data, show that more able workers use computers but those workers also become more productive. This implies that the effect of computers on wages is a combination of the unobserved ability of workers and the productivity enhancing effect of the technology itself. One problem with this study may be driving the results. Their measure of income is discrete and given at monthly level. If hours worked are different between workers, especially for those who use new technologies compared to those who do not, then these will be captured by the variable representing computer use.

Similarly to most other researchers, we neither have access to panel data that includes information on computer use, nor are we able to experiment directly with the treatment effect. In this paper, therefore, we also rely on indirect information to shed more light on the productivity augmenting effect of computers. Our results indicate that, after controlling for ability, for using tools and for improvements in computer use, computer use still positively and significantly determines pay. We interpret these results as favouring the productivity augmenting argument for computer use.

2.The Data

The data used in this paper comes from Sweep V of the National Child Development Study (NCDS). The NCDS comprises all the children born in the UK during the first week of March 1958 (March 3 rd to March 9 th ). This is an advantage because we do not need to worry about cohort effects. Subsequently, information on these individuals was obtained when they were aged 7, 11, 16, 23 and finally at the age of 33 in the last available sweep. In the earlier sweeps parents, teachers and others were also surveyed.

The survey for Sweep V was carried out in 1991. This last sweep is the only one containing information on using computers at work.

The data set contains, among other things, the standard cross-section information on education, region, race, sex, marital status, and job related characteristics such as tenure, occupation, industry, firm size, sector of employment, union membership and working full time. It also possible to link to the data in the earlier sweeps for the same individuals. This allows us to include two very important variables for our purposes. These are the results of a reading test at the age of seven, R7, and a mathematics test at the age of 7, M7. These two variables are used to control directly for ability differences between individuals. Although we also have access to test results at a later age, we use the tests at age seven in order to minimize the effects of the education system on performance in aptitude tests. The sample used included all employees. In addition the survey provides information on using tools at work. We regard tools as what could be called an "anti-pencil" measure (the term "anti" as in "anti-matter"), in the sense that tools are usually used more by blue collar (manual) workers. Hence, in a DiNardo and Pischke world, we would expect a negative impact of tools on wages since they represent lower paid workers. Our dependent variable is the natural logarithm of hourly gross wage. Table 1 presents the sample means and standard deviations for the whole sample and for the computer user and non-computer user groups separately.

(Table 1 Here) Overall 59% of the sample use computers at work, significantly higher than in the sample used for our previous findings, where the percentage was 41% in 1990. (However, that sample, The British Social Attitudes survey, covered all ages in the labour force, and the average age of the computer users was below that of those who did not use computers. As stated earlier, all of the sample in the current study were aged 33, and it thus omits the older workers who were less likely to use computers.) For a number of variables the user and non-user groups have similar means. However, there are interesting differences between the two groups. On average, the computer users earn £2.56 more per hour and have over 1.3 years more tenure. Even more interestingly, they have higher ability as measured by both of our ability measures. Their average mathematics score at the age of 7 is 59.44 as compared to 48.67 for the non-users (the mathematics score is out of 80).

Their average reading score at the age of 7 is 85.68 compared to 75.33 for the non-users (the reading score is out of 100). With respect to self-assessed ability to write well (Writegood) and to speak well (Speakgood) they also score about 20-30% higher. These differences indicate that the regression results presented in studies quoted above may suffer from (ability) bias and that it is extremely important to include measures of ability in the relevant regressions.

Other important differences can be seen in education, firm size and full-time employment. On average, computer users are more highly qualified. For example, 22% of the users have a Degree compared to 5% of the non-users, whist only 1% have no qualification compared to 11% of the non-users. They tend to be working for larger firms: 26% of users work for firms of above 500 worker size compared to 17% of non-users, whilst 10% work for firms with 10 or less workers compared to 20% of the non-users. 87% of the users work full-time compared to 69% of the non-users. Of those who use computers, 76% indicated that they have got better at using them during the past ten years.

Empirical Results

Table 2 presents the results of our wage equations. Computer is the dummy representing our computer-use variable. It takes the value of 1 if an individual uses computers at work and zero otherwise. A wide set of other variables including education and ability variables (R7, M7), are also included to control for other differences between individuals.

Column 1, therefore, presents the results of running the following equation: Where X i is a vector of characteristics, is the corresponding vector of coefficients, is the wage premium associated with computer use and u i is the error term.

(Table 2 Here)

The results indicate that the coefficient of Computer is highly significant and positive. To calculate the return to computer use, since our computer variable is a zero-one dummy, we follow a method suggested by [START_REF] Halvorsen | The Interpretation of Dummy Variables in Semilogarithmic Equations[END_REF]. The estimated coefficient implies a return to computer use of 100(e 0.175 -1) or 19.1 %. This is similar to our previous estimates for the UK, which were just over 20% for 1985 and 1990 (even though the previous estimates were based on a different sample and a different set of variables).

Column 2 of Table 2 presents the results of expanding equation 1 by introducing into the regression the variable which measures using tools at work (Tools), and running a regression of the following form: ln Hourly Wage i = X i + Computer i + Tools i + v i The coefficient of Tools, , as expected, is negative and significant indicating a wage disadvantage of 3.6% for tool users at work while the computer advantage falls very Column 3 presents the results of the first of our pieces of indirect evidence on the productivity effects of computers. In this case we use the answer to the following question: "In the last 10 years have you got better in using a computer to solve problems or give information?"

We construct a dummy variable, Compbtr, to indicate if the individual has got better in using computers (Compbtr=1 if the individual got better and zero otherwise). If computers do not, in themselves, increase productivity, then getting better at using them should not matter. It is the mere fact of using them which is important as a proxy for unobserved inherent ability. As shown in column 3, the coefficient of Compbtr is highly significant and positive, indicating that getting better in computer use, representing an improvement in computer skills and productivity, is rewarded in the labour market.

Our second piece of evidence is presented in column 4. Here we include job specific variables and run the regression in the following form: ln Hourly Wage i = X i + Computer i + Tools i + and Tools to be affected in similar ways, particularly when occupations are entered into the regression. Our results, however, are different to both of the above studies. Using computers and getting better at using them are still rewarded. The coefficients of both variables do fall indicating that previous regressions were suffering from omitted variable bias, but they remain positive and significant. It seems that in the absence of controlling for industry, occupation and firm size, Computer picks up some of their effects on wages.

After such controls are accounted for, Computer still shows a direct and significantly positive effect, a result that points strongly towards the productivity explanation. Tools, on the other hand, is no longer significant. It is difficult at this stage to put forward a convincing case that Tools no longer represents unobserved heterogeneity but Computer still does.

Our last piece of evidence is presented in column 5. In this case we use answers to the question:

"On the days that you were using computers, how many hours do you typically spend in front of it with the screen switched on?"

We construct a variable, Intensity, which is the answer to the above question divided by the respondent's typical working hours per day. This variable, which measures the Again we propose that if computers do not matter in themselves, then the intensity of their use should not matter. The coefficient for Intensity is positive and significant, again lending support to the productivity argument. This result, however, should be interpreted with more caution than the other results presented. Firstly, it is possible that Intensity may be picking up finer distinctions between occupations. More importantly, the hours of having a screen switched on does not necessarily imply hours of use. If this is true, then it is likely that the Intensity measure overestimates actual computer use and hence its coefficient may be biased. However, the result here is opposite to Oosterbeek's, who finds, using a similar idea but a different measure of intensity, that the more intensive use of computers does not matter in the Netherlands.

We have also re-estimated the regressions for full-time workers only. The results are shown in Table 3. Although there are, inevitably, some small differences in estimated coefficients, all the conclusions are robust with respect to the exclusion of the part-timers.

(Table 3 Here)

Conclusions

In this paper we consider the question of whether computers raise the productivity of those who use them. Although using cross-section data to answer such a question is not free of problems, we present evidence that points towards the productivity enhancing role of computers. It is not merely using computers that matter, in terms of obtaining F o r P e e r R e v i e w 13 additional earnings, but getting better at using them also matters even after a whole set of other controls are entered in the regression. On the other hand there is no remaining effect for using tools on the job once the other controls are introduced.

We cannot, and would not, claim that we have accounted for all of the conceivably possible unobserved heterogeneity which could affect the higher wages associated with computer use. A different type of data or experiment is required for that. However, we do propose that, given our results, there is strong evidence in support of the productivity augmenting role of new technology.

F

  variable is constructed from the following question:"In your work do you ever use a computer or word processor with a TV type screen (usually known as VDU)?"

  i = X i + Computer i + u i

  .7%. At this stage our results are consistent with DiNardo and Pischke's results.

where

  Fsize is a set of dummies representing the size of the establishment, Occupation is a set of dummy variables representing the occupation of the individual and Industry is a set of dummies representing the industry in which the individual works. If the Reilly results applied then we would expect Computer to become insignificant when firm size is entered into the regression. In a DiNardo and Pischke world we would expect Computer

  hours spent on computers, is designed to indicate the intensity of computer use. Those who do not use computers at work have a zero value for Intensity.

  claim that employer size represents

	F o r
	P e e
	r
	R e v
	i e
	w
	4

Table 1 .

 1 Means (and Standard Deviations) of Selected Characteristics of the Sample. The standard deviations of the 0-1 dummy variables are calculated from the expression for the standard deviation of proportions. The above sample size applies to all variables except Intensity which has 3535 cases.

	o r
	P e e
	r
	R e v
	i e
	w

Table 2 .

 2 Regression Results, Dependent Variable ln (Hourly Wage)

		Submitted Manuscript
	Variable	I	II	III	IV	V
	Computer	0.178	0.174	0.138	0.070	-
		(13.72)	(13.42)	(7.60)	(4.01)
	Tools	-	-0.031	-0.031	-0.003	-0.006
			(2.61)	(2.89)	(0.23)	(0.54)
	Compbtr	-	-	0.050	0.031	-
				(2.89)	(1.91)
	Intensity	-	-	-	-	0.058
						(2.89)
	Other Controls	Education As I	As I	As I	As IV
		Tenure			+
		Tenure 2			Industry
		Married			Firm Size
		R7, M7			Occupation
		Union
		Fulltime
		Writegood
		Speakgood
		Sex, Race
		Private
		Region
	R-Squared	0.516	0.517	0.518	0.593	0.589
	N	3572	3572	3572	3572	3535

Page 19 of 20

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 3 .

 3 Regression Results, Full Time Workers Only, Dependent Variable ln (Hourly Wage)

						Page 20 of 20
	Variable	I	II	III	IV	V
	Computer	0.171	0.165	0.123	0.070	-
		(11.73)	(11.26)	(6.17)	(3.63)
	Tools	-	-0.049	-0.049	-0.014	-0.012
			(3.68)	(3.70)	(1.06)	(0.94)
	Compbtr	-	-	0.057	0.034	-
				(3.05)	(1.97)
	Intensity	-	-	-	-	0.045
						(1.99)
	Other Controls	Education As I	As I	As I	As IV
		Tenure			+
		Tenure 2			Industry
		Married			Firm Size
		R7, M7			Occupation
		Union
		Writegood
		Speakgood
		Sex, Race
		Private
		Region
	R-Squared	0.398	0.401	0.403	0.495	0.489
	N	2834	2834	2834	2834	2801

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Submitted Manuscript

We would like to thank Leon Feinstein for help and advice with the data and Jonathan Wadsworth for his comments on the paper. We are also grateful to the ESRC Archive, Peter Shepherd and Centre for Longitudinal Studies for the use of the National Child Development Study.