Productivity measurement in gambling: plant-level evidence from the United Kingdom.

• Author: Paton, David
• Position: Symposium

1. Introduction

   As predicted in a seminal article by Baumol (1967), the service sector has continued to grow at a more rapid pace than the goods sector in advanced industrial economies. Given that service industries now constitute a large proportion of economic activity, assessment of productivity in such sectors has become an even more important aspect of the public policy agenda. However, as noted in Griliches (1994) and Nordhaus (2002), it is notoriously difficult to measure productivity in service industries (mainly due to problems with output deflators) and in some cases, even in defining the relevant output.

   Gambling is one of the fastest-growing service industries. While there has been considerable attention paid to the rise in gambling revenue, there have been virtually no studies of total factor productivity (TFP) in this sector. The purpose of this article is to fill this gap, based on an analysis of U.K. establishment-level data. These data are derived from the Annual Respondents Database (ARD) file, constructed by the U.K. Office for National Statistics (ONS), consisting of individual establishment records from the Annual Census of Production. The ARD file contains detailed data on output, capital, materials, employment, and numerous plant and firm characteristics and is quite similar to the U.S.-based Longitudinal Research Database (LRD). This information can be used to construct measures of TFP.

   The use of plant-level data offers two key advantages. One advantage is that deflation is not likely to be a serious problem, since plants in the same industry are likely to face similar factor prices. The ARD also contains data on relatively homogeneous plants. Thus, measurement errors relating to difference in output mixes are not likely to be as severe. A second advantage is that the use of plant-level data allows us to assess and explain (with additional plant and firm characteristic) relative productivity. We are especially interested in assessing the relationship between proxies for investment in information technology and TFP. There is limited evidence on the impact of information technology on economic performance in services.

   The remainder of the article is organized as follows. In section 2 we discuss some general productivity measurement issues. Section 3 presents some background information on the U.K. gambling industry, and section 4 describes the rich, longitudinal data set of gambling establishments. Section 5 presents the econometric method used to assess and explain the relative productivity of these facilities. Section 6 contains our empirical results, and section 7 presents caveats and preliminary conclusions.

2. Productivity Measurement in Services

   General Issues

   To compute real output, data are required on turnover or receipts, as well as a price index to deflate nominal output. (1) Unfortunately, producer or wholesale price indexes are not available for the outputs of many service industries, due to the great difficulty in defining measurable units of output and adjusting for quality changes. We consider the latter issue first. Changes in quality result from heterogeneous inputs and outputs and shifting weights in the use of such inputs and outputs. They also arise from the introduction of new products and services and the disappearance of old ones. An increase in the rate of technological change (for example, the rise in the rate of investment in computers) can potentially exacerbate difficulties in adjusting prices for changes in quality.

   Although it is usually relatively easy to identify the resources used to produce services (that is, capital, labor, and materials), there is still the problem of deflation of inputs. Academics have been especially frustrated at the difficulty in constructing accurate measures of capital input, which would be used in constructing estimates of a capital productivity index as well as a TFP index. Therefore, many researchers have resigned themselves to the analysis of labor productivity, typically measured as real output divided by the number of employees or hours worked. The benefit of labor productivity is that it is likely to be measured with greater precision than TFP. However, labor productivity measures do not take account of the possibility that companies may substitute capital for labor, as is likely in an industry experiencing rapid technological change. Still, McGuckin and Nguyen (1995), Disney, Haskel, and Heden (2000), and Foster, Haltiwanger, and Krizan (2001) have made inferences regarding overall economic efficiency based on labor productivity indexes.

   There is a disadvantage associated with using the simpler productivity measure. As noted by Perloff and Wachter (1980, p. 116), the use of Q/L, or the average product of labor, as a measure of productivity has "numerous serious, if not quite fatal conceptual flaws". Christiansen and Haveman (1980, p. 3) assert that "although [these] productivity measures ... have serious weaknesses, the picture of productivity change which they yield is not greatly different from that of more complete measures."

   Three flaws can be enumerated. First, to ensure reliability, output and input measures must be consistent--that is, they must refer to the same production activity. Because there are many production activities implicitly underlying any aggregate measure of output, a meaningful composite measure must be formulated by denominating the value of each output measure by an appropriate price index. However, when labor is denominated in hours, conceptual problems arise because a labor-hours measure corrects for only one of the many heterogeneous aspects of workers, namely and obviously the number of hours each works. Additional adjustments are needed. For example, the age/sex/skill composition of the labor force varies over time as well as from sector to sector. Since average labor productivity indexes are primarily used for inter-temporal comparisons, changes in the composition of the workforce will affect measured Q, but will not be reflected accurately in a Q/L index unless the changes are perfectly correlated with the way L is measured. This conceptual problem can be overcome by adjusting L for the heterogeneity of the labor force, and thereby, creating an index with efficiency labor units in the denominator.

   Chinloy (1980) describes one method for constructing such an index based on methods used by the U.S. Bureau of Labor Statistics (BLS). This index is calculated on the basis of changes in both number of hours worked and hourly wages earned by different types of workers, classified by age and education level. Similar indexes of labor productivity or quality have been used by Jorgenson, Gollop, and Fraumeni (1987) and Dean, Kunze, and Rosenblum (1988) in studies of aggregate economic growth. It is important to note that these indices are also based on the assumption that labor markets are perfectly competitive, as noted in Chinloy (1980).

   Chinloy (1980) defines labor quality, LQ, changes as follows:

   [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

   where [h.sub.it] is hours worked by the ith type of labor in year t; [v.sub.it] is the share of total compensation paid to the ith type of labor; and {[b.sub.it] = ([h.sub.it]/[m.sub.t])} is the share of total hours worked devoted to the ith labor type. The discrete approximation for Equation 1 is as follows:

   [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

   where [QUALIND.sub.t] is a quality index that approximates the left-hand side of Equation 1. In constructing these indices, the key data requirements are a set of employment attributes to identify each of the i different types of labor.

   Several ways are used to aggregate over heterogeneous outputs in either partial factor productivity or TFP indexes. The base-year approach adjusts output values by the price of each product in the base year. The deflated price approach adjusts the value of each product by a current average price index. The choice between the two approaches is important. According to Baumol and Wolff (1984), the base-year measure is a defensible index for productivity growth comparisons. However, the authors point out that it is not a useful indicator of inter-industry or inter-sectoral differences in absolute levels of productivity. Similarly, the deflated price index is meaningful for intra-industry comparisons of absolute levels of productivity over time, but it, too, fails to provide meaningful cross-sectional comparisons. The search for a valid cross-sectional index of absolute production still continues.

   A second problem with labor productivity measures is that the average product of labor could be related to the business cycle. Thus, such measures may be capturing effects that are unrelated to technical progress. In this regard, Gordon (1979) contends that firms retain more workers in the last stage of a business cycle than is justified ex post by the future level of output. As a result of such biased ex ante expectations, Q/L will decline absolutely until firms adjust their hiring patterns to their corrected expectations about future demand.

   A third and perhaps the most serious concern regarding labor productivity measures is that neither labor nor capital is the sole source of productivity improvements. Labor-saving improvements resulting from other factors of production are improperly attributed as an improvement in labor productivity when these other factors are not held constant. A major problem with the use of labor productivity as a metric for economic performance is that it measures the efficiency of only one input and does not control for the possibility that the plant, firm, or industry can substitute capital, materials, or services for labor. Many shun partial factor productivity indices precisely for this reason. A useful and meaningful productivity framework must therefore identity the source of the productivity improvement and their...