Rights < D2 -Production and Organizations < D -Microeconomics, L14 -Transactional Relationships|Contracts

Keywords: D23 -Organizational Behavior|Transaction Costs|Property trade, professional, associations, services

Introduction

Business associations are an important interface for information exchange and lobbying of government. However, the literature on business associations has long recognised a tension between, on the one hand, the collective lobbying function of associations to represent business interests on behalf of their members (the logic of influence), and on the other hand, on the individual benefits that members can receive from their associations (the logic of services) (see e.g. Streeck and Schmitter, 1985;Grant, 1987[START_REF] Grant | Pressure groups[END_REF][START_REF] Van Waarden | Two logics of collective action? Business associations as distinct from trade unions: the problems of associations or organisations[END_REF][START_REF] Van Schendelen | National, public and private EC lobbying[END_REF]Greenwood, 1995;May et al. 1998;[START_REF] Bennett | Business associations and economic development: re-exploring an interface between state and market[END_REF].

The tension between these two logics can have the effect of reducing the resources and concern that associations exercise for political and collective activities in favour of providing individual services. [START_REF] Olson | The logic of Collective Action: Public Goods and the Theory of Groups, Second Edition[END_REF] recognised the dangers of collective actions becoming sidelined as a by-product of the service activities of associations rather than being their main role, whilst Hirschmann (1970,1982) interpreted the dynamics of associations in terms of the need for association managers to reduce membership exit through providing sufficient mechanisms to allow adequate voice, to inspire loyalty or to stimulate involvement between members and their association in order to retain a focus on the lobbying and representation function.

The literature that has analysed these tensions has recognised that beneath the political activities of associations there must lie an economic rationale that trades-off the costs and benefits. This gap has arisen despite the growing recognition of the importance of associations providing effective service delivery by government. For example, in the UK a "model trade association" was launched in 1996 by the Department of Trade and Industry (DTI, 1996;[START_REF] Berry | Government's objectives and the 'model' trade association[END_REF]. In Europe, the efficiency and effectiveness of trade association management has been the subject of a number of studies by contributors to Greenwood (1995Greenwood ( , 2002Greenwood ( , 2003)). A UK Trade Association Forum has sought to stimulate improvements in trade association management, and has published a best practice guide following the earlier DTI model (see www.taforum.org), drawing on collaborative joint work and consultancies with the DTI and supported by CBI (see e.g. MacDonald, 2001). Similarly, the work of [START_REF] Boléat | Trade Association Strategy and Management[END_REF][START_REF] Boléat | Trade associations: facing up to new challenges[END_REF], which has been influential on the Trade Association Forum, has stimulated a stronger focus on association management capacity.

Despite this considerable recent output, there has been little attention devoted to how members actually evaluate their associations, and how systematic differences between associations influence user evaluations. This paper first reassesses how political and economic factors influence the size and service structure of associations. It then uses the results of a survey of small and medium sized firms to assess how members respond to different associations in terms of their level of use and the impact they receive from association services. These user evaluations are then employed to infer how association size and staff resources influence the benefits that members receive.

The associations examined throughout are sector bodies. Since the empirical information on which the paper is based is derived from firms themselves, the associations concerned span a wide spectrum of both trade and professional associations.

The politics and economics of the association services market

Sector associations in Britain, like those in the US and many other countries, are voluntary organisations that business owners or their manager choose to join or not.

Businesses choose to join in order to receive either individual or collective benefits, or both. Associations are one of a range of sources of business service information, advice and representation that can be used by a firm (other main sources are consultants, lawyers and accountants, trade suppliers, customers, government agencies, etc.). The market for associations differs from other business service providers because they provide not just priced services, but also social activities, collective services such as group insurance and purchasing, and the critically important role of lobbying and representing the interests of their members. Hence associations have to satisfy a range of different drivers of their service market. Sector associations usually combine individual advisory support services to members with collective services such as representation of collective interests. Their collective action activities are their core service and, in effect, their 'unique selling point'. Their chief market and hence cost advantage should result from using this collective service mission, either to gain presence as a 'brand name capital' to develop their marketing, or to gain economies of scope (chiefly of information) through developing the interconnectedness of their staff skills of collective service provision with their individually tailored advice services (see [START_REF] Schneiberg | Can transaction cost economics explain trade associations?[END_REF][START_REF] Taylor | The communal resource: Transaction costs and the solution of collective action problems[END_REF][START_REF] Bennett | Can transaction cost economics explain voluntary Chambers of Commerce[END_REF][START_REF] Jordan | Olson Triumphant? Recruitment strategies and the growth of a small business organisation[END_REF][START_REF] Jones | All for one and one for all': Transaction cost and collective action[END_REF]. Previous analysis has demonstrated that their services generally fall into the field that [START_REF] Wilson | Business services 1982-1992: growth, industry characteristics, financial performance[END_REF] term Group II services, with low revenue per employee. They are largely 'task-interactive' [START_REF] Mills | Towards a pure typology of service organisations[END_REF]) that fill specific and often one-off business needs. [START_REF] Jackson | An empirical investigation of the differences in goods and services as perceived by organised buyers[END_REF] argue that such services have specific characteristics that relate to the specific buyer: the owner-manager or senior manager of the business. Often the defining characteristics of association services appear to be high human asset specificity, 'brand name capital' offering market access and instant recognition, a level of trust of the service, combined with high interconnectedness of transactions based on knowledge of the sector market and information deriving from the representation function [START_REF] Schneiberg | Can transaction cost economics explain trade associations?[END_REF][START_REF] Bennett | Exploring the market potential and bundling of business association services[END_REF][START_REF] Jones | All for one and one for all': Transaction cost and collective action[END_REF].

Previous studies of the services supplied by associations have also argued that, as membership bodies, they offer specific benefits as intermediaries between motives of pure market/self interest and collective or political commitment (see e.g. Hollingsworth and Boyer, 1997;May et al. 1998). Associations also contrast with private sector service suppliers since they usually do not seek to create a profit, but trade in some form of equilibrium of income and expenditure which tends to emphasise cost recovery 'for the good of their sector'. This has encouraged the view that an association can, in some cases, deliver more than market systems of client-consultant links because it can draw on elements of social linkage, trust and wider collective relationships (see e.g. [START_REF] Boswell | Social co-operation in economic systems: a business history approach[END_REF][START_REF] Bruyn | A future for the American Economy: the social market[END_REF][START_REF] Bennett | The logic of membership of sectoral business associations[END_REF][START_REF] Cassey | Social capital and regional economies in Britain[END_REF]. This may offer special advantages to associations because they can bundle collective with individual services, thus reducing the transaction costs of providing advice, and perhaps increasing its applicability and improving its content by employing highly skilled staff who know the sector intimately. Hence, the political and social capital within the member-to-member cohesion locked up in a sector may provide a resource that improves the quality not only of the collective but also of the individual services that an association can offer.

This potential for an association to provide combined package suggests that there may be some specific advantages of service 'bundling' that are particularly relevant to associations. First, bundling can combine specific and collective services, thus helping to mitigate any free rider problems [START_REF] Olson | The logic of Collective Action: Public Goods and the Theory of Groups, Second Edition[END_REF], whereby firms can benefit from services (such as representation) irrespective of whether they are members or not.

Second, bundling can offer service economies of scale and scope, thus seeking to overcome the pressures for fragmentation in the association market. Third, bundling may also raise entry barriers to other associations, or improve the competitiveness of the bundled associations, which could also reduce pressures for fragmentation. Providing two or more bundled services raises market entry costs to other and reduces member search and thus opportunities for others to compete. Fourth, bundling may offer the members an effective 'insurance' option: businesses join an association to provide ready access 'in case' they may need a service and other supports. This motivation has been found relevant in other association studies [START_REF] Bennett | Can transaction cost economics explain voluntary Chambers of Commerce[END_REF][START_REF] Bennett | The logic of membership of sectoral business associations[END_REF][START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF].

Fifth, bundles should lower client search costs as a result of one-stop shopping, allowing a firm to use the same association to supply a range of needs. Sixth, if customer needs are similar, as they often are within a tightly defined sector, then a bundle may be able to satisfy a wide range of businesses, thus gaining cost advantages.

Seventh, there is likely to be an 'environmental' factor, that since most competitor associations have similar dynamics (of having to balance individual and collective service needs), that it is difficult to compete except through bundled products.

The supply of services is also influenced by fixed and variable costs. For most professional and advisory services the main costs are the variable costs of core staff [START_REF] Mills | Towards a pure typology of service organisations[END_REF][START_REF] Perry | Flexible production, externalisation and the interpretation of business service growth[END_REF][START_REF] Kotler | Business representation in the UK since 1979: the case of trade associations[END_REF][START_REF] Wilson | Business services 1982-1992: growth, industry characteristics, financial performance[END_REF], including the costs of highly specific technical staff. Fixed costs arise from the IT, website and communication systems required for an association to exist at all, costs of any library and documentary sources, rents and any specialist equipment. Variable costs, however, including staff as well as transport and delivery costs, are likely to be the main cost element of the sort of services most associations are likely to provide. For each of these costs there is usually a critical threshold of volume that is required to make the support service viable and allow market entry. This depends on the interaction of fixed costs with variable costs across the market of potential demand (of the association's sector), which in turn will be influenced by the level of market penetration by the association: what proportion of the sector businesses are members (see e.g. [START_REF] Phillips | The optimum size of a producer service firm facing uncertain demand[END_REF]. This has been referred to in a number of contexts as the need for a 'critical mass' of both supplier services or resources, and sufficient scale of potential demand and depth of market penetration in order to justify the investment required for service development (see e.g. [START_REF] Chandler | Scale and scope: the dynamics of industrial capitalism[END_REF][START_REF] Harrison | Lean and Mean: the changing landscape of corporate power in the age of flexibility[END_REF][START_REF] Bennett | Britain's Chambers of Commerce: A national network development study[END_REF]. The critical threshold is likely to vary for different mixes of services supplied, and hence the form of service bundling that occurs.

A key purpose of this paper is to investigate whether trade associations services differ from those of other suppliers in how these costs operate. For example, in a study comparing chambers of commerce, which are locally based business associations, and Training and Enterprise Councils, which between 1990 and 2001 were governmentfinanced suppliers of individual and collective business services, [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF] found: first, that there was an initial critical size threshold for both bodies at a level of between about one and two staff per thousand businesses in the local market catchment.

Second, this size threshold is a relative measure; relative to the size of the business sector. The existence of this threshold has important implications for how effective sector-based trade associations can be in a highly fragmented market. Most trade associations have small membership and few staff. [START_REF] Bennett | Business associations and economic development: re-exploring an interface between state and market[END_REF], for example, found that 74% of trade associations have less than 200 members, and 34.5% have less than 30 members, whilst 74% of professional associations have more than 200 members.

If no threshold exists for sector bodies this may explain some of the difficulties the DTI and Trade Association Forum have experienced in trying to encourage association development. Thus, the existence or not, of a minimum size threshold for the effectiveness of sector associations is an important empirical question which it is sought to answer below.

In addition to a size threshold, [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF] also found that beyond the threshold there was clear evidence of internal economies of scale with increasing chamber of commerce size. There was an increasing probability of using chambers as staff size increased, with a significant nonlinear relationship, indicating internal economies of scale. This can be interpreted as chiefly arising from synergies between services ('bundling'). For managers focused on market penetration and use of chamber services, therefore, bigger is better: market penetration disproportionately increases for every additional staff member (provided that these resources are effectively applied). But for the government-financial Training and Enterprise Councils, [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF] found that once the size threshold is reached returns to scale are linear with staff size: each government support service thus appears as discrete, with little or no benefits of bundling and internal economies of scale or scope. [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF] found that the impact of association services differs from that of their use. Whilst the Training and Enterprise Council continued to show no influence on client impact from increasing staff sizes, chambers of commerce do show a significant influence of staff size on impact, but with a tendency for there to be diseconomies at larger staff sizes. This implies that service impact (as a measure of service quality), can be increased only to a point in size of association. This again would have important implications for the DTI (1996) 'model trade association' and the Trade Association Forum.

For sector associations, there is considerable scope for doubt as to whether bundling or other economic factors encourage greater development of benefits with association size.

The evidence quoted earlier demonstrates that the vast majority of sector associations is small, and has been for many years. It appears that the underpinning unique selling point of sector associations is their ability to form and sustain a consensus among members on the sectoral issues that are being represented and the services that they are providing. However, generally, the larger the size of the sector in terms of number of firms, the greater will be the diversity of their members' interests, the greater will be the potential for conflicting issues to emerge, and hence the greater will be the difficulty they have of finding a consensus. This has generally tended to encourage fragmentation into small associations and represents a natural limit to the numerical size of membership, and hence to the scale of the organisation and its management structure (Grant, 1987[START_REF] Grant | Business and politics in Britain[END_REF][START_REF] Grant | Pressure groups[END_REF][START_REF] Olson | The logic of Collective Action: Public Goods and the Theory of Groups, Second Edition[END_REF][START_REF] Bennett | Can transaction cost economics explain voluntary Chambers of Commerce[END_REF]May et al., 1998) and the economic benefits of bundling and economics of scale and scope that can be achieved. This discussion leads to examination of two main hypotheses in the rest of this paper: First, is there a size threshold which influences the market penetration (level of use) and effectiveness of sector associations? This would suggest that associations have to exceed this threshold in order to be sustainable in competition for their market of members. Second, does the market penetration and effectiveness of associations differ once a size threshold (if any) has been reached: are there non-linear size benefits (economies of scale and scope) as found with chambers of commerce, or does effectiveness take another form?

The associations examined cover the whole spectrum of sector bodies that firms use.

They include both trade and professional associations. The fundamental distinction between these bodies is that trade associations tend to have members that are the firm, whilst professional associations have individual firm owners, managers or senior staff as their members. As a result the membership of professional associations is normally much larger than trade associations and their service packages differ, with a stronger role for personal training, certification and insurance services in professional bodies. A distinction between these associations is maintained in the analysis below. However, in terms of staff size and costs, professional and trade associations overlap, so that differences should not be exaggerated. Indeed the empirical analysis demonstrates they are subject to the same forces.

Empirical analysis

Methodology and survey

This paper focuses on the extent of use and the impact of business sector associations.

To assess use and impact levels, the paper utilises the results of a large-scale survey of businesses in Britain undertaken in 1997 by the University of Cambridge ESRC Centre for Business Research (CBR). In the CBR survey the use of advice is defined as that which seeks to meet the strategic objectives of the business over the previous three years. Respondents also rate the impact of that advice in meeting their business objectives on a five-point scale. The sampling frame is drawn from Dunn and Bradstreet through contact by mail with telephone chasing. It covers small and medium sized businesses ranging from 1 to 500 employees, 58% in manufacturing and 42% in business service sectors; it thus covers the main service market of firms of most relevance to sector associations. From an initial random sampling frame of 9909 firms, there are 2474 respondents, but for the purposes of this paper, respondents who were users of sector associations and give sufficient information on all the required questions number 1909. The response rates and non-responses have been systematically compared by age, employment, turnover, pre-tax profit and legal status, for the total sample and for the questions used in this analysis. There is no significant response or non-response bias which affects our analysis (full details are given in [START_REF] Bullock | Survey design, response bias and sample characteristics in the 1997 CBR SME survey[END_REF].

This allows the conclusions to be treated as a national representative picture. The CBR data on firms are combined with the character of each membership association derived from a survey of associations [START_REF] Bennett | Business associations and economic development: re-exploring an interface between state and market[END_REF]. This survey covered all trade and professional associations in Britain, although some non-responses removed that sector from the rest of the analysis.

Many factors influence the use by different businesses of advice from business service organisations. Earlier studies have isolated a range of variables related to the type of firm, particularly its size, as well as the type of supplier of advice (see e.g. [START_REF] Storey | Understanding the small business sector[END_REF][START_REF] Robson | The use and impact of business advice by SMEs in Britain: An empirical assessment using logit and ordered logit models[END_REF]. In the analysis below we include a range of the variables that control for the type of firm that have been previously found to influence the use and impact of advice. These are the size of the firm (number of employees), its rate of profit, rate of employment growth, its sector, whether it is an exporter, its internal staff skill level, and its innovation record. In addition, a test is made of whether service impact is increased by the association staff having delivered the service by a site visit. The formal definition of these variables is given in the Appendix. This paper is mainly concerned with the characteristics of the business associations used. The chief variable cost of a professional services organisation is its staff, and the other chief aspects of the economics of the business are mainly driven by staff numbers; as argued above, the size of premises, telephone and communication costs, stationery, IT equipment etc. are all strongly related to staff size in business service industries.

Hence, staff size is a major factor included in this study. However, to control the relative level of resources available to a given firm in the context of the size of their sector the measure used is staff per 1000 businesses in the sector. In addition, a number of other variables that may potentially describe the character of different associations are also included: the age of the association, the total size of its membership, and whether the association is a trade or professional body.

In the analysis below the results are first presented for use of trade and professional associations, and then for their impact. This is followed by an assessment of the probability of use and impact related to size. Use levels of associations are assessed using a logit model, whilst impact is assessed using an ordered logit model. In estimating these models the sample sizes differ slightly because small numbers of respondents do not respond to all questions. These differences are generally too small to have any influence on the generality of the results.

Use and Impact

The estimates of the probability of using the business support bodies derive from a logit model where the response variable compares whether or not a firm has used the association for advice in its sector over the last three years. These results are presented in Table 1. Initially this is an exploratory analysis, so we do not expect all variables initially to be significant; we move to a final best estimator later in the discussion.

In Table 1 three models are estimated. Model 1 includes all variables listed in the Appendix. Only three are statistically significant: the age of the firm, its size in terms of number of employees and the type of association (whether it is a trade or a professional body). Although most variables are not significant, their signs (positive or negative) are generally in line with expectations. The probability of using an association as a source of advice and information increases with the age and size of a business, with its profitability, and when it is a growing firm or an innovator. Use of associations is higher for manufacturing than service sectors. Use of associations is less likely the higher the internal skill level available, which is to be expected since less external sources are required. It is, however, surprising that exporters are lower users of associations. Concerning association characteristics, use of an association declines with association size, perhaps reflecting the lesser intensity or exclusivity offered. It also declines with association age, perhaps reflecting that older associations are less likely to be seen as a progressive and formal-backing source of advice to firms. The use of associations increases with the relative size of staff as a proportion of the number of firms in the sector, and is higher for professional than trade associations. The dummy variable (LESS70) suggests that use of an association is higher for the very small associations with less than 70 members, which is in line with the results for the variable measuring association membership size.

Various further test estimates on the variables in Model 1 are performed by including each, one at a time, with the three significant variables. One of these estimates is reported in Model 2, which includes the profitability of the firm and the staff size of the association. All these tests show, as in the case of model 2, that no individual variable became significant when the others are removed (except firm age, size and types of association). However, inspecting the correlation co-efficient between the variables shows a high degree of multicolinearity between the age of the respondent firm and its employment size. This is indeed a normal pattern in characteristics of small and medium sized firms. It means that both should not be included in the same estimation equation. Dropping firm age and taking each of other variables results in the final estimator, model 3. This is a highly significant equation explaining association use, based on the employee size of the respondent firm, the staff size of the association per 1000 businesses in the sector, and the type of association (trade or professional). We take this as the final model the way in which the firms in this survey use associations.

Model 3 confirms our key hypothesis that the use of associations will depend chiefly on the resources that it can deploy (in terms of staff). That this influence is higher for professional than trade associations is an important finding, suggesting the view that they deliver more direct market benefits. This confirms the importance of understanding these bodies, whereas most of the previous literature has tended to focus solely on trade associations. The significant variable relating to the characteristics of the firm is its size in terms of number of employees. The effect of employee size is commonly found to be a strong positive correlate of many aspects of firm behaviour, especially in analysis of small and medium sized firms. In this model, use of associations increases with firm size, suggesting that the firm's internal management structure, the extent of its staff skills, the degree of its focus or marketing and growth, and the extent of its expenditure on R & D and innovation, are all features of size which tend to increase association use. This assessment can be taken further by including estimates by the user firms of the impact of the advice they receive from the association they used. Impact is assessed by respondents in the survey on a 5-point scale from 1 (no impact) to 5 (crucial impact).

Because of this ordering relationship an ordered logit model is used. This assesses the relation of impact to the variables found significant in model 3. But also an additional variable is added to test whether advice was received as a result of a site visit by association staff or not.

In previous attempts to find explanatory variables for the level of impact deriving from the use of external suppliers of business advice it has been found that there are few significant influences related to the type of firm (see e.g. [START_REF] Robson | The use and impact of business advice by SMEs in Britain: An empirical assessment using logit and ordered logit models[END_REF]. This has led to the view that impact of advice is dependent on the nature of specific business problems as they arise that are unique to each firm's trading environment and management structure, rather than being generic to particular types of firms. For our sample, this result is generally confirmed in the estimates in Table 2, with the size of the respondent firm having no significant relationship to impact levels. However, the staff size, types of association, and site visits all significantly influence impact. As with use levels, impact is higher for larger staff size, for professional compared to trade associations, and is higher where site visits have occurred.

Shape of association cost functions

A major concern of this paper is whether the use and impact of sector associations changes as a linear or non-linear function with the size and resources that an association can deploy, since this will indicate whether bundling and service economies of scale and scope exist. The logit and ordered logit models, estimated above, provide estimates that are general measures of the relation of categorical variables (use, impact) to a range of continuous and categorical explanatory variables. The relationships do not have to be linear in either a logit or ordered logit model. In order to assess the possible influence of different cost functions, the shape of the probability function of using an association can be re-estimated for each value of any of the significant explanatory variables [START_REF] Hosmer | Applied Logistic Regression[END_REF][START_REF] Bryman | Qualitative Data Analysis for Social Scientists[END_REF]. Our attention focuses on the role of staff size since this is the main association variable of significance that we are interested in. Table 3 shows the probability of using an association as a function of relative staff sizes (per thousand of the sector's business population). The shape of the resulting marginal rate of change in the probability functions per member of staff is shown in Figure 1.

These estimates of the probability function have some profound implications for interpreting the management of associations. There is initially a rising probability of use with number of staff per 1000 local businesses, but the marginal rate of increase begins to decline after about 0.5 staff per thousand member businesses. Table 3 and Figure 1 thus give only slight evidence of there being a minimum critical size threshold for an association to achieve a relatively stable position in terms of market penetration. It appears that associations will be used almost irrespective of their staff size and hence the staff technical expertise on offer. This indeed confirms why so many very small associations continue to survive despite calls by government for merger (DTI, 1996;[START_REF] Berry | Government's objectives and the 'model' trade association[END_REF]. Indeed, there may be no minimum staff size at all since many associations survive on purely voluntary inputs and have no staff. There is no evidence that larger staff sizes offer economics of scale and scope effects. The marginal probability declines to an almost constant low level. This means that once a specific staff size is reached (at about three member of staff per thousand businesses) the increased marginal impact of further staff numbers steadily declines in marginal importance.

Similar conclusions can be drawn from the estimates of the probability of receiving impacts of different magnitude with different staff sizes shown in Tables 4 and5, with the change in marginal probability shown in Figure 2. The marginal rate of change in probability declines steadily where no site visits are involved, indicating no benefits of bundling and economics of scale and scope. However, Table 5 and Figure 2, for cases where a site visit is involved, show as in figure 1, a very small increase in the marginal rate of change of probabilities of impact up to 0.5 staff per thousand businesses. However, the decline in impact after that point is as rapid as those for no site visit.

Hence site visits, as a service support, offer a greater probability of impact, and are subject to a minimum size for viability. But in general, our results indicate the general ease of market entry for associations and the small or zero minimum threshold to establish and/or maintain an association. There are thus few major benefits of economies of scale for larger sized associations in terms of the service impact they achieve with their members.

Conclusion and Implications

This paper has presented one of the first analyses of how use and impact of business associations is influenced by association characteristics and firm type. The paper has particularly focused on the evidence for thresholds of association size, and the role of internal and external economies of scale, as indicators that association managers can use to set realistic targets for market penetration and scale of staff organisation required, as well as what government might use as an indicator of association effectiveness.

The empirical results demonstrate that of many possible influences on associations, most characteristics of the user businesses are usually of little significance. Of course, these results must be set in the context that the analysis is of sector associations, so that the firm's sector is already included in the analysis: firms have self-selected to join those bodies that are most relevant to their sector markets. But once the sector is taken into account, for most purposes, the differences in size of business is the only other characteristic of the firm that significantly influences the take-up of association services.

Moreover, no firm characteristics (other than sector) significantly influence the impact received. These results are in line with other studies, which suggest that the impact of most advisory services is problem-specific, not firm-specific. This should be a salutary warning to association managers (and to government policy) that targeting the marketing and development of association services by firm type is unlikely to yield significant benefits.

A major focus of the paper is to assess how the probability of use and impact of associations is influenced by the character of the association itself. The statistical estimates show that it is association staff resources that are the most important explanatory factor and not the age of the association or its absolute number of members.

Moreover, these staff resources are best measured as relative factors: staff per thousand businesses in the sector. This result is not surprising, and is in line with earlier findings for chambers of commerce and government-financed Training and Enterprise Councils [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF].

This has two important implications for debates about association management. The size of resources certainly has some influence on the use and impact of associations on the firms that use them. But the changing marginal probability of the scale of impact and use declines to a small level after a very low threshold of staff size. A size of about 0.5 full time members of staff per thousand businesses is sufficient to achieve significant use and impact levels. Hence, an important implication is that associations should at least seek to achieve that size of resources. This supports the argument of DTI (1996) and the efforts of the Trade Association Forum that a minimum best practice level should be achieved by all associations. But a second implication is that, beyond this size there is a declining rate of improvement in association use and impact. Hence, the argument for amalgamating associations into larger units is not sustained; instead it appears that the market favours a large number of small associations focused on specific sectors.

These results suggest that the internal cost structure of associations is not strongly nonlinear; i.e. after fixed costs have been covered to achieve a viable size, there are few economies of scale and scope which influence variable costs. Indeed the marginal benefit of the association staff declines as association staff size further increases. These findings throw new light on the tension between the logic of representation and the logic of services. They indicate that there is an ability to combine (or 'bundle') these two types of service at small staff scales, which gives associations their market niche, but that the marginal benefits decrease as the associations organisation increases in size.

Firms seem to get greatest benefit from small and intimate bodies where a small staff know their sector well and can deal directly with the firm's specific problems. This tends to confirm [START_REF] Hirschman | Exit, voice and loyalty[END_REF][START_REF] Hirschman | Shifting involvements: private interest and public action[END_REF] interpretation of the concepts of exit, voice, loyalty and involvement. Of course, this is a statistical generalisation: large associations can and do develop for sectors where there is strong internal coherence. But the implication is that, in general, these are the exceptions rather than the common pattern, and that endemic fragmentation of sector associations will continue. There are significant differences between trade and professional associations, as indicated by the highly statistically significant dummy variable for association type. This indicates a much higher level of use and level of impact achieved for professional compared to trade associations, all other things being equal. Although the focus of questions used in the survey data in this paper cannot give an explanation of this difference, it is likely that it results from their different membership structures: professional bodies generally have individuals as members, trade bodies have firms. As a result, professional bodies tend to focus more heavily on individual service benefits, whereas trade bodies make an offer to the firm as a whole. The marketing of the latter is more diffuse and difficult to satisfy, as our results suggest. This may indicate that trade bodies need to develop a stronger focus on individual benefits to the owner/manager of their member firms, or their personnel. Our results certainly suggest that in the tension between the logic of services and logic of representation, professional bodies are better able to make an individual service impact.

These results can be compared with similar estimates by [START_REF] Bratton | Critical mass and economics of scale in the supply of services to business support organisations[END_REF]. The estimates here for trade and professional associations show a similar emphasis on the importance of staff resource relative to the scale of their largest membership. But the marginal probability of impact increases only up to 0.5 staff per thousand businesses for trade and professional associations, and mostly for intensive services involving a site visit.

Business associations contrast with the government-backed services (such as Training and Enterprise Councils). For these, once they are established at a minimum size level, it appears that managers will not be able to expand market penetration or impact as a result of increasing staff resources. The crucial decision for managers of government support bodies such as these is, therefore, that once they have achieved minimum scale, after this size, all additional staff costs have to be met by additional government finance if there is a public policy requirement to deliver a larger volume of services; the additional costs will not be defrayed by the market demand for services and the impact they offer. This paper is a first attempt to link the politics and economics of sector associations using information from their users. Clearly there is considerable further research

  Despite this phenomenon being recognised for many years, it has not been systematically investigated.

Figure 1 :

 1 Figure 1: Marginal increase in predicted probability of using a business association for each additional staff member per 1000 businesses in their sectors. (The probability levels are estimated from the logit model 3 inTable 1) 86x89mm (440 x 440 DPI)

Table 1 :

 1 possible on the detailed internal unit costs of association organisation and on how members respond to different service offers. It is hoped that this paper will be a useful stimulus to this further work. DTI (1996) A best practice guide for the Model Trade Association, Department of Trade and Industry, London (updated 1999: ww.dti.gov.uk/EAM/MTA/MTAinfo.html) Grant, W. (ed.) (1987) Business interests, organisational development and private interest government; Walter de Gruyter, Berlin. Expectations of seeking advice from a trade or professional association;

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

Table 2 :

 2 The impact on clients of the trade and professional association services they receive. Multivariate estimates of an ordered logit model standard errors in parentheses (*** p <0.01; ** p < 0.05; * p < 0.1).

	No. of staff per	Probability of using a trade or	Change in probability per
	1,000 businesses	professional association	member of staff
	0.1	29.29	-
	0.2	30.42	0.113
	0.5	31.95	0.510
	1.0	33.13	0.236
	1.5	33.84	0.142
	2	34.34	0.100
	3	35.06	0.072
	4	35.57	0.051
	5	35.97	0.040
	6	36.30	0.033
	7	36.57	0.027

Table 3 :

 3 Probability of using a trade and professional association with different staff sizes. Notes: These calculations assume average firm size, and the PA Dummy is 0.

	No. of	No	Slight	Moderate	Important	Crucial	Change in
	staff per	Impact	Impact	Impact	Impact	Impact	probability of an
	1,000						important or
	businesses						crucial impact
	0.1	18.35	42.12	29.10	8.96	1.48	-
	0.2	17.85	41.81	29.58	9.23	1.53	0.320
	0.5	17.21	41.38	30.22	9.59	1.60	0.143
	1.0	16.74	41.04	30.70	9.87	1.65	0.066
	1.5	16.47	40.83	30.98	10.04	1.68	0.040
	2	16.28	40.68	31.17	10.17	1.71	0.032
	3	16.01	40.46	31.45	10.34	1.74	0.020
	4	15.83	40.30	31.64	10.46	1.76	0.014
	5 6 7	15.68 F r 40.18 15.57 40.08 o 15.47 39.99	31.79 31.91 32.02	10.56 10.64 10.71	1.78 1.80 1.81	0.012 0.010 0.008
			P
			e
			e r
				R e v
					i e
					w

Table 4 :

 4 Probability of impacts of external advice (without a site visit) from trade and professional associations at various staff sizes. Notes: The calculations assume average firm size, the PA Dummy is 0, and no site visit (i.e. visit dummy is 0).

	No. of	No	Slight	Moderate	Important	Crucial	Change in
	staff per	Impact	Impact	Impact	Impact	Impact	probability of an
	1,000						important or
	businesses						crucial impact
	0.1	10.95	34.60	36.89	14.89	2.67	-
	0.2	10.62	34.09	37.23	15.30	2.76	0.411
	0.5	10.21	33.41	37.66	15.84	2.88	0.418
	1.0	9.91	32.89	37.96	16.26	2.98	0.104
	1.5	9.73	32.59	38.14	16.51	3.04	0.062
	2	9.61	32.37	38.26	16.69	3.08	0.040
	3	9.44	32.06	38.42	16.94	3.14	0.030
	4	9.32	31.84	38.53	17.12	3.18	0.018
	5	9.23	31.67	38.62	17.27	3.21	0.017
	6	9.16	31.53	38.69	17.38	3.24	0.014
	7	9.10	31.42	38.75	17.48	3.26	0.012

Table 5 :

 5 Probability of impacts of external advice combined with a site visit from trade and professional associations at various staff sizes. Notes: The calculations assume average firm size, the PA Dummy is 0, and a site visit (i.e. visit dummy is 1).

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


M Ali Choudhary
email: a.choudhary@surrey.ac.uk

Vasco J Gabriel
email: v.gabriel@surrey.ac.uk

M Ay

Is There Really a Gap Between Aggregate Productivity and Technology?

Keywords: E23 -Production &lt, E2 -Consumption, Saving, Production, Employment, and Investment &lt, E -Macroeconomics and Monetary Economics, E32 -Business Fluctuations|Cycles &lt, E3 -Prices, Business Fluctuations, and Cycles &lt, Productivity, Technology, Welfare, Hours, Dynamic Mark-ups Productivity, Technology, Welfare, Hours, Dynamic-Markups JEL classifi cation: O47, O51, E32, E23

Is There Really a Gap Between Aggregate Productivity

and Technology?

Introduction

In an influential paper, Basu and Fernald (2002, BF hereafter) show in their study of 34 U.S. private economy industries that during 1959-1989 there is a significant gap between aggregate productivity and technology growth. Indeed, productivity growth may fractionally be higher or lower than actual technological growth due to product markets imperfect competition or resource allocation. A rise in productivity growth above that of technological growth is treated as welfare improving, in that more output is distributed and consumed without changes in technology. Other papers provide evidence on the existence of a wedge between the rate of technical change and factors productivity growth, see [START_REF] Bloch | Technical change and total factor productivity growth: a study of Singapore's manufacturing industries[END_REF] and [START_REF] Kumbhakar | Factor productivity and technical change[END_REF], for example. This paper revisits the empirical findings of the aggregate productivity and technology gap (the gap hereafter) by: a) accounting for labour utilization and cyclical variations in the product markets and b) using a more efficient estimation method. While the former points feature in BF's discussions as possible ways of explaining their findings 1 , studies such as [START_REF] Basu | Productivity Growth in the 1990s: Technology, Utilization, Or Adjustment?[END_REF], Basu, Fernald and Kimball (2006, BFK henceforth) -which introduce multilevel factor utilization, but not changes in dynamic product-markets -do not return to assess the effect on the gap using the same time scale and methodology. The latter point is contentious in the light of [START_REF] Burnside | What Do Production Function Regressions Tell Us About Increasing Returns to Scale and Externalities?[END_REF] study, which questions the validity of 2SLS to estimate sectoral Solow residuals, suggesting instead the use of a system estimator that accounts for cross-sectoral inter-relations. This paper deals with these shortcomings.

We find that although both factor utilization and dynamic changes in product markets largely account for the gap observed in BF, the dynamic changes in product markets are less important.

Furthermore, a large fraction of the gap disappears in BF when we use a 3SLS estimator.

The following section lays out the structure of the empirical model and Section 3 presents the results. Section 4 concludes.

Empirical Model

Technology Estimation

Hall's (1990) cost-minimization procedure implies the firm level growth in output consists of weighted input growth, corrected for imperfect competition, and technological improvements 2 . Hence, for an industry i with j-th input x, the growth in output y is given by

4y i t = µ i n P j=1 s i,j t 4 x i,j t + 4τ i t , (1)
where, dropping time, µ i , s i,j and 4τ i denote industry i 0 s markup, the share of input j (1, ..., n) in industry i 0 s gross output and Solow residuals, respectively. Inputs comprise of labour, capital and material. The industry-level gross output technology shocks, 4τ i t , are aggregated using a weighted sum scheme where each industry carries the weight of its nominal share, w i , in total (nominal) value added of N industries,

4τ = N P i=1 w i t 4τ i t 1 -μi s im t .
(2) where μi , s im t denote the estimated average industry markup and share of materials in industry i and time t. The fraction 1/1 -μi t s im t converts the estimated gross output technology shocks into value added shocks.

1 Indeed, BF write in the abstract (page 963) that '[e]mpirically,....gaps are important, even though we abstract from variations in factor utilization and estimate only small average sectoral markups.'

2 Implicitly, our specification has the obvious advantage of not assuming constant returns to scale and perfect competition in product markets when estimating technical change, unlike [START_REF] Mullen | Long-run Technical Change and Multifactor Productivity Growth in US Manufacturing[END_REF], for example, and studies therein. The estimation of aggregate technology raises two issues: a) time variation of markups are ignored and b) a lack of control for factor utilization can also overstate the importance of estimated technology growth in (1); a point originally taken up in [START_REF] Solow | Technological Change and the Aggregate Production Function[END_REF], but also followed by many studies including Burnside, Eichenbaum and Rebelo (1996), BF and BFK.

For the latter issue, we follow [START_REF] Basu | Productivity Growth in the 1990s: Technology, Utilization, Or Adjustment?[END_REF] and BFK and correct for work effort and capacity utilization in (1) by introducing growth in hours-per-worker in the model,

4y i t = µ i n P j=1 s i,j t [4x i,j t ] + β i 4 h i + 4τ i t . (3)
The new terms in (3) are β i and 4h i . The former is a composite term with parameters that do not need identification for this exercise and are derived assuming that workers are paid a shift premium when utilization is raised. The latter term is growth in hours-per-worker and enters twice in (3). It first appears (implicitly) in the square brackets in (3), as labour has to be adjusted for hours, since firms use both the number of employees as well as hours-per-worker in production. It then appears as the second term on the right-hand-side of (3) to control for capital utilization and labour effort.

The reason why growth in hours-per-worker is a reasonable proxy for labour and capital utilization is through its link with data on over-time pay. A caveat in these estimation is the assumption that there are no adjustment costs to varying factor utilization per se.

To control for cyclical variations in the markup, we assume in (4) that it is composed of μi , a steady-state value of the markup for each industry, and a cyclical component, the strength of which is captured by the coefficient ϕ:

µ i t = μi + ϕ i z t , (4)
z captures the cyclical movements in the markup. The coefficient ϕ may be constrained to be the same across industries if cyclical variations in product markets are correlated. Similar specifications are used in [START_REF] Domowitz | Market Structure and Cyclical Fluctuations in US Manufacturing[END_REF], [START_REF] Haskel | Price, Marginal Cost and the Business Cycle[END_REF], [START_REF] Ryan | The Behaviour of Productivity and Growth Rates and Price-Cost Margins During Contractions and Expansions[END_REF][START_REF] Ryan | Cyclical behaviour of productivity growth and price-cost margins: asymmetry and market power effects[END_REF] and [START_REF] Choudhary | Costly Customer Relations and Pricing[END_REF] and has not been applied to aggregate technology estimations. We estimate (3) by substituting for (4). Finally, the aggregation procedure in (2) is used to compute our aggregate technology shocks.

Productivity Estimation

Aggregate productivity growth is estimated as the difference between aggregate value-added of the economy, 4v, in our case value-added in the manufacturing sector, and the sum of share-weighted growth in primary inputs, dx V , so that

F o r P e e r R e v i e w 4p = 4v -dx V , (5)
dx V = s l 4 l + s k 4 k.
Here, s k and s l denote the shares of capital and labour in output defined as the cost of capital or labour in total value added. These shares need not sum to one. The terms 4l and 4k are growth in aggregate (i.e., total manufacturing) capital and labour stocks. BF show that by expressing industrylevel growth in value-added output in terms capital and labor (excluding material ), and combining

the result with industry level version of ( 5), there results extra terms explaining the gap. Some of this gap can be due to technology and the rest due to different types of factor reallocation. Aggregate productivity growth is shown to be given by 4p = (µ -1)

N P i=1 n P j=1 w i s i,j t 1 -s i,m t [4x i,j t ] + µ(R K + R L ) + R m + R µ + 4τ , (6a) µ =
N P i=1 w j s i,m t 1 -s i,m t µ i t (6b)
where µ represents the aggregate steady-state markup weighted by the nominal shares of each industry in N industries. The industry markup µ i t is estimated using (3) and using (4). The terms R are resource allocation arising from capital, labour, intermediate goods, and imperfect competition, respectively in (6a). Of these, the former two can only be extracted as residuals and the latter two can directly be estimated. It is clear in (6a) that with perfect competition, and hence no resource reallocation terms, growth in productivity and technology are equal.

Data and Empirical Evidence

We estimate (3) and (6a) using an updated version (annual data from 1959 to 1996) of BF's dataset3 . We confine our investigation to 21 manufacturing industries, where technology shocks are most likely to occur and for which we were able to match the manufacturing sector with the NBER industrial dataset for hours. An Appendix containing all the estimations results is available upon request. We first estimate industry level technology shocks using ( 3) and (4) (ignoring hours) and then proceed to aggregation using (2). Subsequently, we set 1959 to unity and cumulate the estimated aggregate shocks. The estimation of productivity is based on the procedure in Section (2.2). In Fig. 1 (and in all our plots below), the bold line is aggregate productivity. The thin continuous line replicates BF's aggregate technology using only average sectoral markups 4 . We use 2SLS procedure to each sector's regression, with Hall-Ramey instruments (oil price changes, government defence spending and political party in power) as well as a measure of monetary shocks, as in [START_REF] Burnside | What Do Production Function Regressions Tell Us About Increasing Returns to Scale and Externalities?[END_REF] and BFK. The half-broken lines plot aggregate technology using only the steady-sate markups, μi , in (4) -removing the cyclical-markup -and the corresponding technology residuals. Our cyclical variable z is detrended industrial output, using the Hoddrick-Prescott filter, widely used to capture the business cycle and we constrain ϕ to be same across sectors. The broken line is least restrictive in that markups are time-varying at industry level.

Clearly, the gap changes with dynamic product markets. For example, with average sectoral markups method, as in BF, the gap is bigger during 1965-1985, but smaller thereafter. The 1990 productivity slowdown is sharper using dynamic markups or the actual steady-state markup. Thus, dynamic markups seem to matter, although, as we will shortly find, factor utilization is even more important.

Estimations with Capacity Utilization

We now introduce hours as in (3) to control for work effort and capacity utilization using the method of estimation in Section (3.1). In Fig. 2, we plot BF's aggregate technology with and without hours.

When labour utilization is not controlled for (the thin continuous line), there is a persistent welfare gap post-1965 and it can attributed to internal economic reorganization, as previously discussed. In the period pre-1965 the opposite is true. When hours are introduced (broken line), the technology level changes on two counts. First, the productivity and technology are closely matched. Second, post-1970 economy displays a productivity growth which has elements of beneficial economic reorganization and also periods where productivity lags behind the technology shocks. Consider the five year period of [1985][1986][1987][1988][1989][1990]. BF say that this period benefitted from significant resource reallocation. However, 4 Our replication looks different from the one in BF Fig. 2, Page 988. In their paper, they mistakenly used the standard Solow residuals instead of the revenue weighted residuals. The corrected plot looks similar to our Fig. 1 above even for their 34 industries. correcting for capacity utilization one concludes that productivity growth was, in fact, technology driven. Therefore, we have two different stories for productivity growth for US manufacturing in this period. This is non-trivial, as it has different policy consequences.

Estimations with 3SLS

Thus far we have used 2SLS. We now reestimate using system estimation, namely 3SLS, as recommended by [START_REF] Burnside | What Do Production Function Regressions Tell Us About Increasing Returns to Scale and Externalities?[END_REF] 5 . Given that shocks are likely to be common and also correlated across sectors due to externalities (as in [START_REF] Caballero | External Effects in U.S. Procyclical Productivity[END_REF]), one needs to account for this feature and thus deliver more efficient estimates6 . We use average sectoral markups and estimate the aggregate technology using the corresponding residuals from the Hall's style regression.

In Fig. 3, we plot the results with and without hours. When we simply replicate BF using 3SLS (the thin continuous line), the gap is far narrower than what is reported in BF. In the case where hours are included, the same is obtained. However, with 3SLS, the story of productivity is rather different to the one obtained using 2SLS, especially post-1970. Indeed, the method of 2SLS shows continuous resource reallocation after 1970. Using 3SLS, the gap disappears until 1980, after which we find that productivity growth is lower than technology growth. This understatement of productivity growth may be due to high adjustment costs; indeed this era is one of computerization. Moreover, using hours and 3SLS method the gap is fully reduced during 1959-1985.

Conclusion

In this paper we examined the aggregate productivity and technology wedge as proposed in [START_REF] Basu | Aggregate Productivity and Aggregate Technology[END_REF]. We find that, once we properly control for the dynamic nature of the product market, capacity utilization and sectoral externalities, the aggregate productivity and technology gap practically disappears. Thus, the picture that emerges is one where factor reallocations seem to play a minor role in the dynamics of productivity growth. This result suggests, therefore, that imperfections and frictions in output and factor markets appear to be less important than previously thought. 8 196019651970197519801985[START_REF] Hall | Invariance Properties of the Solow's Productivity Residual[END_REF]1995PROD BF (2002) TECH with Dynamic Mup TECH with S-state Mup 8 196019651970197519801985[START_REF] Hall | Invariance Properties of the Solow's Productivity Residual[END_REF]1995PROD BF (2002) TEC HOURS 8 1960 1965 1970 1975 1980 1985 1990 1995 PROD No Hours Hours

  Is There Really a Gap Between Aggregate Productivity and Technology? 2

Fig

  Fig 1. Technology and Productivity with Imperfect Competition

Fig

  Fig 2. Productivity and Technology with Hours

Fig

  Fig 3. Productivity and Technology Levels Using 3SLS.

Based on Jorgenson, Gollop and Fraumeni (1987), downloaded from Dale Jorgenson's webpage and combined with the data used in BF.

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

Another alternative is GMM system estimation. However, heteroskedasticity and autocorrelation tests at the sectoral level do not reveal significant problems, thus confirming the appropriateness of 3SLS.

Two specification tests (LR and Breusch-Pagan) reject at the 1% significance level the null of cross-sectoral uncorrelated disturbances, thus favouring the system specification against the equation-by-equation estimation.

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