Elisabeth Mueller
email: mueller@zew.de

Ron Anderson

Christian Laux

G32 -Financing Policy|Capital and Ownership Structure < G3 -Corporate

Keywords: benefits of control, capital structure, company growth, small and medium-sized enterprises JEL classification: G32, M13, L20 benefits of control, capital structure, company growth, small and medium-sized enterprises

This paper studies the influence of the benefits of control on the capital structure and the growth of private companies for a sample of 8,964 UK companies with limited liability observed for up to 5 years. It is hypothesised that companies in which existing owners would lose more control if they expanded, have smaller equity increases, are more highly levered and grow more slowly. Potential loss of control is measured as the difference in the probability of winning a vote for the largest owner before and after a hypothetical equity increase. Evidence is found that is consistent with the hypotheses.

Introduction

In many industrialised countries, governments are concerned that small companies find it difficult to obtain enough finance to realise their growth potential (e.g., [START_REF] Cressy | Introduction: Funding Gaps[END_REF]; Wren and Storey (2002)). However, owners would sometimes not accept additional finance, even if it were available, because they want to remain in control of their companies. They want to remain in control because they obtain private benefits over and above the financial return on their investment. In order to stay in control they need to forego some growth opportunities, if the opportunities are too extensive to be realised with debt finance alone. This means that companies do not reach their growth potential and employ fewer people than would otherwise be the case.

Surveys document the importance that owners give to keeping control of their companies (e.g., [START_REF] Cressy | The Financial Conditions for Swedish SMEs: Survey and Research Agenda[END_REF]; [START_REF] Poutziouris | The Financial Affairs of Private Companies[END_REF]). However, so far, it is unclear what effect this may have on company characteristics such as the use of different financing instruments and the growth of companies. This paper tries to answer this question.

A possible method of investigating the consequences of the private benefits of control is first to calculate how much control the largest owner would lose in a hypothetical equity increase. This potential loss of control can then be related to company characteristics of interest. The extent of control is approximated by the probability that the largest owner will win a vote. Potential loss of control is calculated as the difference in the probability of winning a vote before and after a hypothetical equity increase. This paper tests three hypotheses. The first two hypotheses are related in that they are concerned with the use of financing instruments; the last hypothesis is related to company growth. First, we expect a negative relationship between potential loss of control and the size of equity increases (H1a). Owners who lose more control for a given equity increase face a cost component in addition to the required return on the new equity -the cost of losing influence. This makes equity increases less attractive. Second, it is hypothesised that potential loss of control is positively related to the leverage of companies (H1b). Companies where existing owners would lose more control in an expansion will rely more extensively on bank financing and will therefore have higher leverage than otherwise comparable companies.

Owners are prepared to pay higher interest rates for additional loans in order to stay in control. Third, the relationship between potential loss of control and company growth is anticipated to be negative (H2). This is a consequence of the first two hypotheses. Some growth opportunities become unprofitable, if they are financed with debt in a situation when debt levels are already high. Even if the returns of the growth opportunity exceed the cost of equity capital, equity finance may not be used, because a control premium is demanded.

Fewer growth opportunities will be realised as a result, and company growth will be smaller.

To study these effects, information on private UK companies with limited liability is used. The dataset comprises 8,964 companies and covers the years 1997 to 2001. Private companies are used because their ownership is typically more concentrated than that of public companies. Owners of companies with dispersed ownership do not suffer a loss of control in an equity expansion, because they have negligible influence to start with. There are also public companies with concentrated ownership but because private and public companies are different in many respects, it would be difficult to combine them in one analysis.

For the identification of the effect of potential loss of control on company characteristics, it is important to deal with two related econometric problems. First, for the econometrician it is not possible to observe whether companies have growth opportunities and yet do not use them, as the owners want to stay in control. It is only possible to work with proxies for growth opportunities, but the proxies themselves are typically influenced by decisions taken by the owners or managers. For example, expenditure on R&D is often used as a measure of growth opportunities. However, an owner who wants to stay in control would not undertake R&D in the first place, even if possibilities to expand existed. Second, it is not possible to directly investigate the relationship between loss of control due to actual ownership changes and the dependent variable of interest, because actual ownership changes are inherently endogenous. For example, an owner would only give up control if he had profitable growth opportunities. It would therefore not be surprising to find that companies in which the largest owner lost control are growing faster.

One way of dealing with the above-mentioned problems is to work with hypothetical equity increases that are the same for all companies. This mitigates the endogeneity problem and does not require a good measure of growth opportunities. The loss of control resulting from the hypothetical equity increase needs to be quantified and can then be used in the regression analysis. Section 3 of this paper gives a detailed description of how potential loss of control is calculated.

The empirical results confirm the expected effects of potential loss of control. Companies in which the largest owner would lose more influence in the face of an equity increase have smaller equity increases, are more highly levered and exhibit slower growth than comparable companies.

The remainder of the paper is organised as follows. Section 2 gives an overview of the related literature. Section 3 covers the measurement of potential loss of control. Section 4 highlights important institutional details of private companies in the UK. Section 5 explains the dataset. Section 6 presents the empirical analysis. Finally, section 7 concludes. What are private benefits of control? Although the theoretical literature has analysed the effects of the private benefits of control, there is rarely a specific analysis of the sources of the benefits. In general, benefits can be divided into two categories: pecuniary and nonpecuniary. The pecuniary benefits can be a managerial salary that is higher than the market rate or perks that can be taken, for example, an expensive company car. The non-pecuniary benefits can be even more important than the pecuniary ones. They include the prestige and social status that comes with ownership as well as the power to decide on the business strategy of the company and independence from superiors. Non-pecuniary benefits can be especially strong if the owner is the founder of the company or if the company has been controlled by the owner's family for a long time, because then the relationship between owner and company is closer.

There are two main ways in which private benefits are modelled. One strand of the theoretical literature assumes that private benefits reduce company resources and are costly to extract, i.e. for each pound of private benefits enjoyed, the company's value decreases by more than one pound. This fits well for private benefits that are mainly pecuniary (e.g., [START_REF] Burkart | Why Higher Takeover Premia Protect Minority Shareholders[END_REF]; [START_REF] Bennedsen | The Balance of Power in Closely Held Corporations[END_REF]). A second strand models private benefits as existent in addition to monetary benefits. Their enjoyment is not using up company resources (e.g., [START_REF] Aghion | An Incomplete Contracts Approach to Financial Contracting[END_REF]Zwiebel (1995)). This approach captures more the non-pecuniary elements of private benefits since no costly diversion strategy is required. For private companies the non-pecuniary benefits seem to be more important, since all owners typically have a significant stake and are therefore informed about the situation of How do the private benefits of control affect company characteristics? In the following, some recent contributions incorporating private benefits will be discussed. The model by [START_REF] Anderson | Financing and Corporate Growth Under Repeated Moral Hazard[END_REF] is probably most closely related to the empirical analysis in this paper. It sheds light on how the choice of financing and growth of a company are related.

The companies are characterised by a contracting inefficiency that allows insiders to divert cash flow for their private benefit, i.e. the model assumes pecuniary benefits of control. In the first stage, an entrepreneur can do R&D and start a company. In the second stage, the company can either be financed with outside equity or with debt. If debt financing is chosen, the entrepreneur may remain in the position even if an outside manager could do the job better. If equity is chosen, the entrepreneur risks being replaced by a more able outside manager. It can be seen that equity promotes higher second stage growth than debt, but this advantage must be traded off against the disadvantage of reduced incentives to do R&D in the first stage. [START_REF] Cressy | Business Borrowing and Control: A Theory of Entrepreneurial Types[END_REF] focuses on loss of control due to bank loans, leaving loss of control due to equity aside. It is assumed that the larger the bank loan is, the larger is the loss of control.

Then, since the utility of entrepreneurs depends negatively on the size of the bank loans, companies will borrow less than is optimal. For some of the entrepreneurs the loss aversion diminishes over time; they start to borrow more and their companies grow.

A strand of the literature that is less directly related to this paper concerns models that analyse ownership structures after an original owner sells shares to obtain finance. [START_REF] Bebchuk | A Rent-Protection Theory of Corporate Ownership and Control[END_REF] looks at the choice between a concentrated and a dispersed ownership structure in the context of an IPO, when private benefits of control exist but are not necessarily costly to extract. He finds that larger private benefits tend to favour concentrated ownership, because by either keeping a controlling stake or by selling a controlling stake to one new owner, the original owner can enjoy the private benefits. [START_REF] Bennedsen | The Balance of Power in Closely Held Corporations[END_REF] derive efficient ownership structures for the case where private benefits are costly to extract. The founder of the company can choose an ownership structure with several large shareholders. They find that for every possible constellation there is a one-share one-vote ownership structure that maximises efficiency. This is due to the alignment effect, from which it follows that the company value increases in the cash flow stake of the controlling coalition. They also derive a coalition formation effect which says that the coalition with the smallest cash flow stake wins, because it has the largest group of shareholders from whom to expropriate.

In sum, there are theoretical models that derive the implications of private benefits of control on company characteristics, but there is no model that brings out the hypotheses concerning the effect of private benefits on capital structure, equity issuance and company growth that are tested empirically in this paper.

Previous Empirical Evidence

Survey evidence shows the reluctance of many owners to make use of private equity or venture capital, because they do not want to lose control over their company. In a survey of private companies in the UK, [START_REF] Poutziouris | The Financial Affairs of Private Companies[END_REF] find that 50% of the owners would not consider issuing external equity. They indicate that 'maintaining control / keeping the company in the family' is one of their main business goals. This attitude was also found in a survey of Swedish small and medium-sized companies (SMEs) in which many owners indicated that they would rather sell the whole company than take on additional owners F o r P e e r R e v i e w [START_REF] Cressy | The Financial Conditions for Swedish SMEs: Survey and Research Agenda[END_REF]).

Indirect evidence for the existence of non-pecuniary benefits in private companies has been provided by [START_REF] Moskowitz | The Returns to Entrepreneurial Investment: A Private Equity Premium Puzzle?[END_REF]. They find for the USA that equity holdings in private companies yield about the same return as equity holdings in public companies. However, the volatility of an index on private companies is, if anything, higher than the volatility of an index on public companies. Given that households are typically not well diversified in private equity, it is difficult to explain why households hold private equity at all if only the financial return is being considered. The authors suggest that non-pecuniary benefits of control might be one explanation.

So far, there is no direct evidence on the size of the private benefits of control for private companies. For public companies the importance of private benefits can be inferred from the price differential between voting shares and non-voting shares (e.g., [START_REF] Lease | The Market Value of Control in Publicly-Traded Corporations[END_REF], Zingales (1994), [START_REF] Nenova | The Value of Corporate Voting Rights and Control: A Cross-Country Analysis[END_REF] and [START_REF] Doidge | U.S. Cross-Listings and the Private Benefits of Control: Evidence from Dual-Class Firms[END_REF]). Another possibility is to gauge private benefits from block trades of shares by looking at the difference between the price of a share in the block and the price of a share on the stock exchange. [START_REF] Barclay | Private Benefits from Control of Public Corporations[END_REF], for example, find that equity blocks of at least 5% of common stock trade at an average premium of 20% for a sample of 63 US block trades between 1978 and 1982. This suggests that there are private benefits that only accrue to holders of large blocks. It is crucial for the analysis to find a measure that describes how much influence the existing owners would lose in an equity expansion. Influence is measured by means of a probabilistic voting model; more specifically, the measure of influence is the probability of winning a vote for the largest owner in a yes-no decision that is taken with a simple majority. The measure for a decrease in influence is therefore the difference between the probability that the largest owner will win a vote given the current ownership structure and the probability that the largest owner will win a vote in a new ownership situation after a hypothetical equity increase. The difference between the two probabilities is termed 'potential loss of control'.

For the calculation of the measure it is assumed that all owners vote independently of each other with equal probability for or against the largest owner. It is now possible to calculate the probability that the largest owner will win by considering all possible voting patterns.

Cubbin and Leech (1983) used this probabilistic voting model to identify the influence of shareholders in UK public companies. 2 .

For the equity increase it is assumed that all but the largest owner increase their stake.

Here the idea is that the largest owner is more likely than the others to be wealth constrained, 2 Cubbin and Leech (1983) developed a formula to calculate approximate probabilities in the case of many owners with this voting model. For this analysis, it is possible to calculate the exact probabilities, because the number of owners is smaller in private companies. Felsenthal and Machover (1998, p. 36;p. 171f.) discuss the interpretation of measures of voting power. The Banzhaf measure reflects voting power as the degree to which an owner's vote is able to influence the outcome of a decision; and the Shapley-Shubik measure reflects voting power as the expected payoff that an owner gets from a fixed prize that is allocated to the winning coalition. [START_REF] Leech | An Empirical Comparison of the Performance of Classical Power Indices[END_REF] finds that the Banzhaf measure reflects variations in the power of shareholders of British listed companies better than the Shapley-Shubik measure. The measure used here is a linear function of the Banzhaf measure. It was, among others, also used by [START_REF] Nickell | What Makes Firms Perform Well?[END_REF] because the largest owner has already a considerable investment in the company. First, an equity increase of 10% is considered, because this volume is the most prevalent in the sample.

Second, an average loss of control measure is calculated that considers different volumes of equity increases with the relative frequency in which they occur in the sample. 3 The two measures, calculated under the assumption that each owner votes individually, are denoted by Ind 10 for the 10% increase and by Ind avg for the average of different sizes of equity increases, respectively. 4

A short example should help clarify the measure. Consider a company with three owners of which the largest owner holds 40% of the equity, the second largest 35%, and the smallest 25%. In a vote, the largest owner will always vote 'yes' and the other two owners vote independently of each other. There are four cases to consider. First, all three owners vote 'yes' and the largest owner wins the vote. Second and third, only one of the smaller owners votes 'yes' but the largest owner nevertheless wins the vote. Fourth, both smaller owners vote 'no' and the largest owner loses the vote. It follows that the largest owner has a 75% probability of winning a vote. The probability of winning can be calculated in the same fashion after a hypothetical change in ownership.

Because family ownership is very common for private companies, it is worth trying to incorporate family structures into the voting behaviour as a robustness check. It is possible that family members have the same opinion on company matters and therefore vote most of the time together. However, it could also be that family quarrels lead to family members 3 The relative weights are 0.71 for a 10% increase, 0.1 for a 30% increase, 0.07 for a 50% increase, 0.03 for a 70% increase and 0.09 for a 90% increase. 4 As a robustness check the potential loss of control was calculated for the case that equity is increased by an additional owner who enters the company. The results were very similar. expressing opposing views. In order to model family voting behaviour, it is assumed that all owners with identical last names belong to one family and that members of one family vote as one bloc, i.e. all members of a family vote either 'yes' or 'no'. This is one of several possibilities to model family voting behaviour, but it has a specific advantage. It is possible that some owners transfer part of their ownership to their spouse for tax reasons. The decision on how to vote the shares remains, however, with the original owner, i.e. own and transferred shares are voted identically. The potential loss of control measure incorporating family structures takes this possibility into account. Again, a 10% equity increase (Fam 10) and a weighted average of different sizes of equity increases (Fam avg) are considered. 5

Figures 1 and 2 allow a better understanding of the potential loss of control measures by plotting them with respect to the share of the largest owner. It can be seen that potential loss of control is especially high for medium-sized ownership shares. If the largest owner holds a share that is sufficiently above 50%, then potential loss of control is equal to zero, because the owner has full control before and after the hypothetical equity increase. In many instances of the hypothetical ownership change the largest owner does not lose any control.

If all but the largest owner increase their stake by 10% (Ind 10), then the largest owner will not lose any control in 63% of cases. The respective value for the measure considering an average of different volumes of equity increases (Ind avg) is 37%. Descriptive statistics of the potential loss of control measures is given in Table 1.

The measure of potential loss of control has specific strengths and weaknesses. An advantage of this measure is that it takes the whole distribution of ownership into account, i.e.

it has different values depending on whether the remaining shares are dispersed or concen-5 Restricted by the information available in the dataset, a company is defined to be in family ownership, if two or more owners have the same last name. This is the case for 44% of companies in the sample. This measure can best be viewed as a reflection of a priori voting power that abstracts from particular personalities and ignores affinities or disaffinities between voters. It is important to recognise that independent voting is part of the definition of the extent of control, not an assumption about how the world works.

A potential limitation of this measure is its focus on the effect of an equity expansion on the largest owner. This implicitly assumes that the largest owner is influential in decisions regarding the capital structure, equity issuance and growth. Although this need not be the case in every company, it can be regarded as a good first approximation.

Potentially, there are other measures that could be used as an approximation for loss of influence in a hypothetical equity increase. The ownership share of the largest owner is a possibility. However, for decisions taken by simple majority it does not matter whether the largest owner is marginally or significantly above 50%, a point that is not reflected by this measure. The use of the part of the largest owner's share that exceeds 50% is also problem- atic. This could be seen as a measure for a buffer zone, reflecting how much ownership the largest owner could give up before losing influence. However, once the ownership falls below 50%, the distribution of shares among all other owners becomes of crucial importance for the amount of influence the largest owner would lose.

A measure of potential loss of control derived from hypothetical ownership changes is used in this analysis in order to mitigate the problem of endogeneity. This problem is encountered when looking at actual ownership changes. One could try to measure the loss of control afflicting the largest owner after an actual increase of equity. This could be related to company growth after the change. To the extent that companies with ownership changes grow faster than other companies with the same characteristics in the same industry, one would have a measure of the reward required for relinquishing control. However, since only companies with good growth opportunities would consider issuing new equity, a problem of reverse causality would be encountered. Of course, one could try to find instruments for the actual loss of control. It would, however, be extremely difficult to find a variable that is related to the decision of owners to give up control, but not to resulting company characteristics such as the capital structure or the company's growth. Furthermore, the use of actual ownership changes would not allow us to investigate how private benefits of control affect the leverage of a company, because, by an accounting identity, all increases in equity will lead to a reduction in leverage.

It can be argued that the potential loss of control measure does not solve the endogeneity problem completely. The measure is a function of the ownership structure, and if the ownership structure is endogenous, so will be potential loss of control. However, by analysing hypothetical and not actual ownership changes this problem is reduced. It would still be desirable to instrument potential loss of control, but no suitable instruments are available. In order to determine the probability that the largest owner will win a vote, it is necessary to know the percentage of votes held by each owner. This task is complicated by the fact that private limited companies can have several classes of shares and voting rights can vary according to share class. However, many companies have only one class and, in general, the smaller a company is, the fewer share classes it has.

For the calculation of voting rights, the most important distinction is between ordinary and preference shares. Ordinary shares confer voting rights and the right to obtain a dividend should one be declared. Preference shares, on the other hand, usually have no voting rights attached, and the owners are not directly involved in decisions concerning the company's affairs. To make up for this disadvantage, preference shares have the right to a fixed dividend.

Owners of ordinary shares only obtain a dividend if the amount distributed is sufficient to satisfy the claims of the owners of preference shares. Preference shares are typically cumulative, which means that skipped dividends of previous years must be made up as soon as dividend payments are resumed. For example, an owner of a 7% preference share would have a right to a payment of 14% of the nominal value of the share should dividends have been skipped once. Preference shares often become voting shares should no dividend be paid over a specified number of years.

The dataset allows us to differentiate between ordinary and preference shares. For this analysis, ordinary shares are treated as voting shares and preference shares are treated as non-voting shares. In rare cases, the voting rights could be arranged differently.

One might think that the issuance of non-voting equity is a possible way to raise funding Although this mode of financing avoids costs related to loss of control, there are other disadvantages to be considered. It is difficult to find investors who are willing to invest money without having any influence on company strategy. If owners are willing to do it, then they will need to be compensated by higher expected dividend payments, which makes this form of financing expensive. 6 The empirical analysis will separately control for the potential loss of control and the use of preference capital.

Decision Making

Owners of private companies come together in annual general or extraordinary meetings to decide on company matters. Decisions are normally passed with a simple majority. However, the Companies Act lists a few decisions that require a 75% majority or indeed unanimity. In the Articles of Association, the owners can also agree to have stricter majority requirements for some decisions than are prescribed by the Companies Act.

Most decisions are taken by ordinary resolutions, which require a 50% majority. For example, they are used to increase share capital or to give authority to the directors to allot shares. Ordinary resolutions are used for all matters unless the Articles of Association or the Companies Act require another type of resolution. Extraordinary resolutions require a majority of 75%. These are necessary to modify the rights of classes of shareholders or for winding-up a company. Special resolutions, also requiring a 75% majority, are used for important matters such as alterations to the Memorandum or to the Articles of Association as well as for reductions of capital. There are also elective resolutions that must be passed Since ordinary resolutions are the most common type, this analysis will concentrate on them and will use a 50% majority requirement to determine the probability of winning for the largest owner. Furthermore, for a 75% majority requirement a different measure of control would be necessary. It is not sufficient to take into account that 75% of the votes are required to pass a decision. One also needs to consider that to preserve the status quo, only one vote above 25% is required. 7

Data

The database FAME (Financial Analysis Made Easy), distributed by Bureau van Dijk, is used as the basis for the analysis. It includes all companies in the UK that satisfy at least one of the three following criteria: a turnover higher than £750,000, a pre-tax profit higher than £45,000, or shareholder funds greater than £750,000.

The detail of the information available in FAME depends on the publication requirements, since only publicly available information can be included. In the UK all companies with limited liability are required to deposit their accounts with Companies House, so that their trading partners and the general public can inquire. The specific publication requirements depend on the size of the company.

The database provides information on financial data from balance sheets and from profit and loss accounts for the years 1989 to 2001. However, information on owners and directors 7 The information on institutional details in this section are taken from the Companies Act 1985[START_REF] Barclay | Private Benefits from Control of Public Corporations[END_REF](Dey (1994)) and from the internet site of Companies House (www.companieshouse.gov.uk). The dataset used for this analysis includes companies that went out of business between 1997 and 2001. 8

Only independent companies are included into the dataset, because a company that is 100% owned by another company has no direct owner who would care about control. Also, independent companies whose largest owner is a company or neither a company nor an individual (e.g. a trust or a fund) are excluded. These types of owners will not have an interest in control comparable to that of individuals. Companies from the financial sector, i.e. in financial intermediation and insurance, are also excluded since their capital structure will differ systematically from the capital structure of the rest of the companies. Furthermore, the 1 st and the 100 th percentile of financial ratios and growth rates have been deleted, because they contained implausibly large or small values. All reported results in this paper relate to the trimmed sample.

Next, the definitions of the variables used in the analysis are given. It is important to note that the term 'equity', as employed here, refers to the issued capital, i.e. to the nominal value of the shares. It does not include accumulated profits. Therefore, all observed equity increases are due to an issuance of additional shares. Leverage is the ratio of the sum of current and long-term liabilities to total assets. 'Preference ratio' is the ratio of preference ROA is the ratio of profit before taxes to total assets. All regressions contain industry dummies at the one-digit level of the UK SIC code.

Table 2 presents full descriptive statistics of the companies that are included in the sample.

One can see that the companies are relatively small; the median number of employees being 53, whereas the mean is 130. In contrast to public companies, private companies are generally characterised by a limited number of owners. The average company in the sample has 2.7 owners and companies with more than ten owners are exceptional. The distribution of the share of the largest owner has two mass points. There is only one owner with 100% ownership in about 27% of the companies, and in about 18% of the companies the largest owner has exactly 50% of all shares.

6 Empirical Analysis

Effect on Growth in Equity

This subsection presents the test of hypothesis H1a by relating the potential loss of control to the size of the equity increase. A negative relationship is expected, because owners who lose more control for a given equity increase find equity increases more costly. In the sample, the equity growth rate is zero for the vast majority of observations. Only 3.8% of observations have a positive growth rate, and 4.4% of observations have a negative growth rate. From a theoretical point of view there is no prediction on the relationship between potential loss of control and equity decreases. Therefore only companies with a zero or positive growth rate in equity have been chosen for this analysis. In order to get unbiased results, a tobit model with robust standard errors is used.

The dataset covers the time period 1997 to 2001, but on average a company is observed for less than three years. Some companies went into or out of business during the sample period and, over time, more companies have been covered in the ownership section of the database. Because the information on the time-series dimension is very limited, the tobit regression is done as a pooled time-series cross-section analysis. The standard errors are corrected to allow for heteroscedasticity and auto-correlation of the observations pertaining to one company. Since there is persistence in the characteristics of companies, it is likely that the error terms from observations relating to one company are correlated.

As can be seen from Table 3, there is a clear indication that potential loss of control has a negative effect on the size of equity increases, which is consistent with hypothesis H1a. This table shows full regression results for the two potential loss of control measures Ind 10 and Ind avg. The results for the measures that take the family structure into account (Fam 10 and Fam avg) are very similar and therefore only presented in abbreviated form. Potential loss of control has a negative coefficient that is significant to at least the 5% level for the assumption of individualistic voting. For the assumption of family voting the coefficient is negative but not significant. The effect is also of high magnitude. As calculated for regression (a), the marginal effect for the unconditional expected value of equity growth (zero or positive growth rate) is -0.64. Using this marginal effect, an increase of potential loss of control of one standard deviation leads ceteris paribus to a decrease in equity growth of 0.1 percentage points. This effect is larger if only companies with equity increases are considered. The marginal effect conditional on being uncensored (positive growth rate) is -2.52. This translates into a decrease of the equity growth rate of 0.4 percentage points for a one standard deviation increase of potential loss of control ceteris paribus. 9

To identify the influence of the private benefits of control, it is necessary to control for other characteristics of the ownership structure that could have an influence on the dependent variable. As further control, the ownership share of the largest owner is included. Since the appropriate functional form was unknown, this variable was first included as a polynomial of order four. The highest power was then dropped, if it wasn't significant to at least the 10% level. Ultimately, only the linear term was kept.

9 Conditional on positive equity growth, the mean growth rate is 20.2% and the median is 4.8%. Potential loss of control -10.17 In around three quarters of the companies, the largest owner is also a manager. In this case, the ownership share of the largest owner proxies for the incentive to exert effort in managing the company. If the largest owner is not a manager, then the ownership share proxies for the intensity of monitoring. Larger owners have a better incentive to monitor, because they reap a higher proportion of the gains from monitoring. Their decisions are less affected by the possibility of free riding.10 From the negative coefficient of share of the largest owner, it can be concluded that better incentives to exert effort or better incentives to monitor lead to smaller equity increases. It could be that empire building, i.e. growing the company above a level that maximises value, can be prevented.

A dummy that equals one if the largest owner is at the same time manager is also included.

The significant negative coefficient shows that equity increases are smaller, if the largest owner is at the same time manager. This could also be an indication that empire building is prevented.

Two controls for the company's capital structure are included: leverage and the preference ratio (ratio of preference capital to total equity capital). The regression shows that companies with higher leverage rely more on equity financing. The positive effect of leverage is consistent with the pecking order theory [START_REF] Myers | Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have[END_REF]). Companies will use debt financing until their debt capacity is reached, only then will they issue new shares. The proceeds of an equity increase can be either used to finance future growth or to reduce leverage. [START_REF] Pagano | Why Do Companies Go Public? An Empirical Analysis[END_REF], for example, find that Italian companies used the proceeds of an initial public offering mainly to reduce leverage. The preference ratio shows the extent of the use of preference capital. The use of preference capital gives an indication of the control aversion of owners since it allows the raising of equity without losing control. It is also an expensive form of financing since the shareholders need to be compensated for their lack of influence on company strategy through higher dividends.

The positive influence of this variable on equity increases is difficult to interpret, because it is not possible to observe whether the equity increase relates to ordinary or preference stock.

However, most equity increases will concern ordinary stock, since only 4.4% of the companies in the sample use preference capital. 11 The positive influence can therefore indicate that companies with a high preference ratio have exhausted their ability to issue non-voting stock and are forced to issue ordinary stock.

Past profitability has no effect on equity increases. This could be because past profitability has two conflicting effects on equity growth. First, companies that were successful in the past will have accumulated retained earnings and don't need to rely on equity finance. Second, if success if persistent, then profitable companies have more growth opportunities and require more capital to expand. Neither of the effects is dominant in the regression.

The regression also includes two controls for company size. Size measured as the natural logarithm of the number of employees has a significant positive effect on equity increases.

The absolute amount of equity is also related to size. It is included to control for any level effects. This variable is not significant in the current specification, but it has a significant positive effect once the natural logarithm of the number of employees is excluded. Company age is also controlled for. One reason for older companies having smaller equity increases could be that retained earnings are sufficient for their investment needs. As a robustness check, the effect of potential loss of control on the probability of an equity increase was tested with a probit regression (not reported). The dependent variable is equal to one for equity increases and equal to zero for no changes in equity. Potential loss of control has a significant negative influence on the probability that an equity increase is observed.

Also, the coefficients of the other regressors keep their sign and significance level.

To summarise, the results of this subsection show strong evidence for the negative influence of potential loss of control on the size of equity increases. Overall, the results are consistent with hypothesis H1a. The share of the largest owner has a positive influence on leverage. 13 The size of the The regression also includes a control for the use of preference capital. The results show that a higher preference ratio is related to higher leverage. This is consistent with the view that more excessive use of debt finance is a means for the original owners to keep control.

Effect on Leverage

In regressions explaining leverage, it is common to control for growth opportunities. It is, however, difficult to find a good measure of growth opportunities for private companies.

For public companies, the market to book value is commonly used. This measure is not available for private companies, since there is no observable market value. R&D expenditure is a further frequent choice, but it is not available in the dataset. For this analysis the capital expenditure ratio (ratio of capital expenditure to total assets) is chosen. Companies with good growth opportunities will in general invest more in machinery and equipment than other companies. The coefficient of this variable shows that companies with more growth opportunities, i.e. a higher ratio, have lower leverage. This result is consistent with the theory on underinvestment as described by [START_REF] Myers | Determinants of Corporate Borrowing[END_REF] which concludes that companies with many growth opportunities should use less leverage.

comparable across the three subsections. The results of the other regressors were not affected. The control for past profitability shows that profits are partly used to reduce leverage.

This observation is consistent with an aversion to losing control to banks. 14

The positive influence of size on leverage can be attributed to a more stable cash flow stream and therefore a higher debt capacity for larger companies. Age has a negative effect on leverage because retained profits become more important over time, increasing shareholders funds and reducing leverage.

Effect on Company Growth

The third specification is concerned with the explanation of company growth. In general, company growth is volatile, fluctuating from one year to the next depending on the market conditions the company is confronted with. This makes it more difficult to identify the influences on company growth. In order to reduce the noise in the dependent variable, a growth rate that is averaged over a time period of four years (1998-2001) is chosen. The regressors refer to the beginning of the period (1998). 15 Consequently, ordinary least squares with standard errors that are robust to heteroscedasticity is an appropriate method to apply to the cross-section of observations.

Table 5 presents results on the testing of hypothesis H2. 16 It can be seen that companies 14 There can also be an effect of reverse causality. If companies with high leverage need to pay higher interest rates, then profitability can be reduced. The results for the other regressors remain the same, when the first lag of return on assets is excluded. 15 The results are qualitatively identical when the time period 1997-2000 is considered.

16 The company growth regression covers fewer companies than the previous ones because not all companies are observed over this period. An estimation with a Heckman correction for attrition bias has therefore been employed. But since the error terms of the selection and the main equation have not been correlated, the results using the standard OLS technique are shown. whose owners would lose more control in an expansion indeed grow more slowly. This is consistent with the hypothesis that owners who want to stay in control are willing to give up growth opportunities. The effect is negative and significant to at least the 5% level for all potential loss of control measures. The size of the effect, calculated for regression (a), is substantial. A one standard deviation increase in potential loss of control leads ceteris paribus to a 0.44 percentage points decrease in annual growth.

The share of the largest owner has a negative effect on company growth. As in the previous specifications, this variable was first included as a polynomial of order four. However, only the linear term showed ultimately a significant effect. The negative effect of the share of the largest owner is consistent with the argument that better incentives to exert effort or better incentives to monitor can prevent empire building. The dummy variable indicating whether the largest owner is a manager has no significant influence on company growth.

Leverage has a significant negative effect on growth, which is consistent with the pecking order theory [START_REF] Myers | Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have[END_REF]). A company with financial slack will take all profitable growth opportunities, but if only risky debt can be used, some growth opportunities will be passed up. 17 A negative relationship between leverage and growth is also found by [START_REF] Lang | Leverage, Investment, and Firm Growth[END_REF] for public companies. When splitting the sample into companies with high and low growth opportunities, however, they find the negative relationship only confirmed for companies with low growth opportunities.

17 It can be argued that leverage is endogenous in this regression. Companies in difficulties will have small growth rates and low profitability. Leverage can build up, if interest and capital cannot be serviced any more.

This leads to an effect from small growth rates to high leverage. In this situation it would be appropriate to instrument leverage, but no good instruments are available. Therefore a specification without leverage was tested. The signs and significance levels of the other regressors remain the same. This regression also includes a control for the use of preference capital. More extensive use of preference capital is related to slower growth. A high preference ratio is a sign that it is getting more difficult to issue additional preference capital. The alternative of issuing common (voting) stock is not attractive for owners who want to stay in control.

Controlling for past profitability shows that companies that were profitable in the past are likely to grow more in the future; a persistence of company success can be seen.

In addition, the size and age of companies have been controlled for. The regressions show no relationship between the size of a company and its subsequent growth. The age of a company, however, has a negative effect.

Conclusion

This paper studies the influence of private benefits of control on the issuance of new equity, leverage and company growth. It is hypothesised that companies in which existing owners would lose more influence in an expansion have smaller equity increases, use more debt and grow more slowly. Evidence supporting the predicted effects on the size of equity increases, leverage and company growth is found. The effect of loss of control on company growth is especially important in economic terms.

Overall, these findings are consistent with the view that there are demand side constraints of finance. These results also agree with previous survey evidence that highlighted the importance that owners attach to control [START_REF] Cressy | The Financial Conditions for Swedish SMEs: Survey and Research Agenda[END_REF]; [START_REF] Poutziouris | The Financial Affairs of Private Companies[END_REF]). This paper takes the analysis a step further by showing that the owner's fear of losing control can affect company characteristics such as capital structure and growth.

These results have important implications for economic policy. The main implication is that it is important to differentiate between the demand and supply side constraints of finance. Governments are concerned with supply side constraints. For example, the UK government has started the 'Enterprise Investment Scheme' which gives various tax advantages for individuals investing in a company with which they were previously unconnected (Bank of England, 2001). However, in the UK it seems that few companies are affected by supply side constraints of finance. Only 1.7% of the companies mentioned 'access to finance'

as their main problem in the NatWest/SBRT Quarterly Survey of Small Business in Britain (NatWest/ SBRT, 2002). The caution that emanates from this paper is that when observing companies that have growth opportunities but do not grow, one needs to be careful to consider both demand and supply side constraints. One should not immediately conclude that there is a market failure in the provision of equity capital. It is especially important to consider demand side constraints of finance when designing government programmes to support small and medium-sized companies.

The negative effect of loss of control on company growth has possible implications for the aggregate growth of economies. The fact that owners value control implies that their companies do not exploit all growth opportunities and therefore do not create as much employment and value added as they could. This can have an effect on aggregate growth, if some growth opportunities are idiosyncratic to a certain company, for example, if specific knowledge is required or if synergies with existing activities are necessary. Under such circumstances, it may not be possible for other companies to exploit the growth opportunities.

  No owner would allow other owners to divert substantial resources for private benefit.

  1

Figure 1 :Figure 2 :

 12 Figure 1: Potential Loss of Control (Ind 10) According to Share Largest Owner

  , because voting rights remain concentrated with the original owners.

  . These resolutions are used to amend the duration of the authority of directors to allot securities or to dispense with the holding of annual general meetings.

  from the year 1997 onwards. This restricts the time period covered by the analysis to the years 1997 to 2001. Since the database contains only the current ownership structure and the current directors, it was necessary to extract information from older versions of the database to construct time-series for the variables relating to this information.

  **,**,*=significant on the 1, 5 and 10 percent level. Tobit estimation considering non-negative growth rates of equity. 980 observations are uncensored (larger than zero), 24,380 observations are censored (equal to zero). The regressions differ according to the potential loss of control measure used. The regressions shown in full refer to individualistic voting; regression (a) employs Ind 10 and regression (b) Ind avg. The abbreviated results refer to family voting; (c) employs Fam 10 and (d) Fam avg. The regressors are expressed as ratios. Robust standard errors allowing for heteroscedasticity and autocorrelation are in parentheses. The regressions contain industry and year dummies as well as their interaction terms.

  be used to signal company quality to banks. The owner is only willing to invest a large amount of private wealth, when convinced of the quality of the company.Banks may give more loans to companies that have at least one large owner. This reasoning relates to the work ofLeland and Pyle (1977) who made a similar argument about the ownership share of managers. The positive effect of the largest owner being at the same time manager can have a similar interpretation. The additional commitment of the largest owner increases the debt capacity of the company.

  Note: ***,**,*=significant on the 1, 5 and 10 per cent level. OLS estimation. The regressions differ according to the potential loss of control measure used. The regressions shown in full refer to individualistic voting; regression (a) employs Ind 10 and regression (b) Ind avg. The abbreviated results refer to family voting; (c) employs Fam 10 and (d) Fam avg. The regressors are expressed as ratios. Robust standard errors allowing for heteroscedasticity and autocorrelation are in parentheses. The regressions contain industry and year dummies as well as their interaction terms.

  Note: ***,**,*=significant on the 1, 5 and 10 per cent level. OLS estimation. The regressions differ according to the potential loss of control measure used. The regressions shown in full refer to individualistic voting; regression (a) employs Ind 10 and regression (b) Ind avg. The abbreviated results refer to family voting; (c) employs Fam 10 and (d) Fam avg. The regressors are expressed as ratios and refer to the year 1998. Robust standard errors allowing for heteroscedasticity are in parentheses. The regressions contain industry dummies.

  . and Storey, D. J. (2002), 'Evaluating the Effect of Soft Business Support Upon Small Firm Performance', Oxford Economic Papers 54, 334-365. Zingales, L. (1994), 'The Value of the Voting Right: A Study of the Milan Stock Exchange Experience', Review of Financial Studies 7, 125-148. Zwiebel, J. (1995), 'Block Investment and Partial Benefits of Corporate Control',

Table 1 :

 1 Descriptive Statistics -Potential Loss of Control

	Variable	Mean	Median	St. Dev.	Min	Max
	Ind 10	0.087	0	0.14	0	0.5
	Ind avg	0.11	0.054	0.13	0	0.5
	Fam 10	0.046	0	0.11	0	0.5
	Fam avg	0.066	0	0.11	0	0.5
	Note: Number of observations is 28,366.

trated. For example, if the largest owner is the only one with a substantial stake and the other owners hold only a negligible investment, then the largest owner will have much more influence than when faced with only a few other owners, who also have a large number of shares.

8

  It should be noted that merger and acquisition (M&A) activity is not important for the results. With the use of the Zephyr database, distributed by Bureau van Dijk, the companies engaging in M&A have been identified. The results are not affected when those companies are excluded from the analysis. capital to total equity capital. Preference capital is equity capital that is not vested with voting rights. The 'capital expenditure ratio' is the ratio of capital expenditure and other investments to total assets. It is used as a proxy for growth opportunities in the analysis.

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

Table 2 :

 2 Descriptive Statistics -Company Information

	Variable	Mean	Median St. Dev.	Min	Max
	Number of employees	130	53	573	1	34,763
	Age (in years)	27	21	20	1	145
	Total assets (in m. £)	8.5	3.2	47.9	0.015	4,368
	Equity (Issued capital) (in m. £)	0.246	0.025	1.52	0.000001	77.8
	Growth equity (in %)	0.30	0	7.89	-35	150
	Avg. annual growth of
	total assets 1998-2001 (in %)	5.89	4.54	13.4	-44.8	87.6
	Leverage (in %)	56.6	58.5	23.5	0	119.0
	Preference ratio (in %)	1.78	0	10.3	0	90.0
	Capital expenditure ratio (in %)	2.54	0.70	5.10	-18.7	28.6
	ROA (in %)	6.31	5.08	9.07	-25.2	49.6
	Number of owners	2.74	2	2.02	1	26
	Share largest owner (in %)	64.8	54.0	26.2	8.3	100
	Dummy largest owner manager	0.75	1	0.43	0	1
	Note: Number of observations is 27,800-28,366.

Table 3 :

 3 Effect on Growth in Equity

	Dep. Variable: Non-negative Growth Rate Equity (in %)
		(a -Ind 10)	(b -Ind avg)
	Potential loss of control	-20.48***	-19.93**
		(8.01)	(8.36)
	Share largest owner	-9.17**	-9.40**
		(4.04)	(4.09)
	Dummy largest owner manager	-11.14***	-11.32***
		(2.14)	(2.14)
	Leverage	27.28***	27.14***
		(4.67)	(4.67)
	Preference ratio	16.89**	16.99**
		(8.32)	(8.32)
	First lag ROA	-15.07	-15.21
		(12.06)	(12.06)
	Ln employees	3.99***	4.02***
		(0.764)	(0.764)
	Ln age	-8.28***	-8.23***
		(1.53)	(1.53)
	Equity	1.27	1.27
		(0.781)	(0.784)
	Number of observations (companies)	25,360 (8,859)	25,360 (8,859)
	Wald test, χ 2 (58)	493.0***	490.8***
	Abbreviated further regression results:	(c -Fam 10)	(d -Fam avg)

  Table4presents the results for the leverage equation. Consistent with hypothesis H1b, potential loss of control has a positive influence on leverage. Owners who want to stay in control prefer to use debt more extensively than owners who are not confronted with a potential loss of control. In order to stay in control, they may accept paying a higher interest rate to obtain further loans. The results are significant to the 1% level for all potential loss of control measures. The economic significance of this effect is also not negligible. For regression (a) it has been calculated that a one standard deviation increase of potential loss of control increases leverage ceteris paribus by 0.96 percentage points.12

Table 4 :

 4 Effect on Leverage

	Dep. Variable: Leverage (in %)
		(a -Ind 10)	(b -Ind avg)
	Potential loss of control	6.81***	6.63***
		(1.48)	(1.57)
	Share largest owner	4.32***	4.39***
		(0.885)	(0.904)
	Dummy largest owner manager	3.66***	3.72***
		(0.455)	(0.455)
	Preference ratio	5.95***	5.90***
		(1.71)	(1.71)
	Capital expenditure ratio	-6.19**	-6.21**
		(2.92)	(2.92)
	First lag ROA	-64.7***	-64.6***
		(2.29)	(2.29)
	Ln employees	2.80***	2.79***
		(0.177)	(0.177)
	Ln age	-11.04***	-11.07***
		(0.307)	(0.306)
	Number of observations (companies)	26,522 (8,964)	26,522 (8,964)
	R squared	0.23	0.23
	Abbreviated further regression results:	(c -Fam 10)	(d -Fam avg)
	Potential loss of control	11.12***	9.66***
		(1.73)	(1.78)

Table 5 :

 5 Effect on Company Growth

	Dep. Variable: Average Annual Growth of Total Assets, 1998-2001 (in %)
		(a -Ind 10)	(b -Ind avg)
	Potential loss of control	-3.14**	-3.44**
		(1.34)	(1.45)
	Share largest owner	-2.13***	-2.30***
		(0.793)	(0.826)
	Dummy largest owner manager	-0.472	-0.493
		(0.491)	(0.490)
	Leverage	-2.65***	-2.65***
		(0.926)	(0.926)
	Preference ratio	-3.51**	-3.53**
		(1.75)	(1.75)
	First lag ROA	21.94***	21.92***
		(2.56)	(2.56)
	Ln employees	0.024	0.026
		(0.150)	(0.150)
	Ln age	-0.862***	-0.851***
		(0.268)	(0.268)
	Number of observations (companies)	4,377 (4,377)	4,377 (4,377)
	R squared	0.05	0.05
	Abbreviated further regression results:	(c -Fam 10)	(d -Fam avg)
	Potential loss of control	-6.12***	-5.69***
		(1.76)	(1.80)

For more recent analyses of the private benefits of control see, for example,[START_REF] Nicodano | Private Benefits, Block Transaction Premiums and Ownership Structure[END_REF] and[START_REF] Dyck | Private Benefits of Control: An International Comparison[END_REF].

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

The optimality of one-share one-vote was studied by[START_REF] Grossman | One Share-One Vote and the Market for Corporate Control[END_REF] and[START_REF] Harris | Corporate Governance: Voting Rights and Majority Rules[END_REF] in the context of public companies faced by take-over threats.

In an exploratory specification, the ownership share was interacted with the dummy for the largest owner being a manager. Since the difference of the two effects was not significant, results are shown without the interaction term.

The preference ratio, i.e. the ratio of preference capital to total equity capital, is quite high for companies that use preference capital. Its mean is 40.7% whereas its median is 35.7%.

This specification contains more observations than the previous one explaining equity growth. Here all companies are included, whereas the equity growth specification excluded companies for the years in which they had negative equity growth.

This specification was also first estimated with the ownership share of the largest owner included up to its fourth power. The third power was significant, but since the cubic form showed a positive relationship over the relevant range, the specification with only the linear term is shown. This makes the results better

Finance and Governance < G -Financial Economics, M13 -


Fluctuations, and Cycles < E -Macroeconomics and Monetary Economics, C52 -Model Evaluation and Testing < C5 -Econometric

Keywords: Phillips curve, expectation, Europe Phillips curve, expectations, Europe JEL classification numbers: E31, C52

This paper examines inflation dynamics in Europe. Econometric specification tests with pooled European data are used to compare the empirical performance of the New Classical, New Keynesian and Hybrid specifications of the Phillips curve. Instead of imposing any specific form of expectations formation, direct measures, ie Consensus Economics survey data are used to proxy economic agents' inflation expectations. According to the results, the New Classical Phillips curve has satisfactory statistical properties. Moreover, the purely forward-looking New Keynesian Phillips curve is clearly outperformed by the New Classical and Hybrid Phillips curves. We interpret our results as indicating that the European inflation process is not purely forward-looking, and inflation cannot instantaneously adjust to changes in expectations. Consequently, even allowing for possible non-rationality in expectations, a lagged inflation term enters the New Keynesian Phillips curve for inflation dynamics in Europe.

Introduction

Expectations have a central role in the inflation process and monetary policy design, since the effects of monetary policy on employment and production depend on credibility and thus expectations formation. It is very generally accepted that changes in monetary policy regimes affect the formation of inflation expectations and inflation dynamics. However, many recent empirical studies find evidence that the inflation process is persistent, ie that inflation is strongly correlated with its own lagged values (see eg [START_REF] Gordon | The Time-Varying NAIRU and Its Implications for Economic Policy[END_REF]. Persistence may be due to deep parameters or institutional constraints (such as indexation) in the economy or due to more transient factors such as expectations or policy regimes.

According to [START_REF] Erceg | Imperfect Credibility and Inflation Persistence[END_REF] inflation responds sluggishly to shocks because of private sector's gradual learning about monetary policy inflation target.

On the other hand, inflation persistence is due to imperfect information in [START_REF] Ehrmann | Uncertain Potential Output: Implications for Monetary Policy[END_REF], which argue that inflation expectations change slowly, as agents do not know whether a shock hitting the economy is temporary or permanent. Gaspar, Smets and Vestin (2005) suggest that the monetary policy regime and inflation persistence are related.

In empirical studies of inflation dynamics, it is often assumed that expectations are rational. After the emergence of the rational expectations theory in the 1970's, there has been until recently very little interest in modelling expectations, although it has been argued that rationality of expectations may be an overly restrictive assumption. If we choose to avoid imposing rational expectations, we can use models which model expectations formation through limited information channels [START_REF] Woodford | Imperfect Common Knowledge and the Effects of Monetary Policy[END_REF][START_REF] Adam | Optimal Monetary Policy with Imperfect Common Knowledge[END_REF]), sticky information (Mankiw andReis 2001, 2002), or bounded rationality and learning (Sargent [START_REF] Sargent | The Conquest of American Inflation[END_REF][START_REF] Sims | Implications of Rational Inattention[END_REF][START_REF] Evans | Learning and Expectations in Macroeconomics[END_REF]. Alternatively, we can use directly measured expectations, which are based on surveys or financial markets information.

Since the late 1950s research on inflation dynamics has been largely based on the Phillips curve. Originally [START_REF] Phillips | The relation between unemployment and the rate of change of money wage rates in the United Kingdom, 1862-1957[END_REF] and [START_REF] Samuelson | Analytical aspects of anti-inflation policy[END_REF] hypothesised a stable negative relationship between unemployment and inflation. [START_REF] Phelps | Phillips curves, expectations of inflation, and optimal unemployment over time[END_REF] and [START_REF] Friedman | The role of monetary policy[END_REF] augmented expectations in the Phillips curve via wage bargaining and price setting. In the 1970s, [START_REF] Lucas | Econometric policy evaluations: A critique[END_REF] presented the rational expectations hypothesis, which holds that inflation expectations cannot systematically differ from actual inflation. In empirical work applying this type of Phillips relation, which is nowadays often called the New Classical Phillips curve, real economic activity is often measured by the output gap.

The New Keynesian Phillips curve in its original form is purely forwardlooking and based explicitly on microfoundations. In this specification timecontingent price setting can be derived using Taylor's overlapping contracts model [START_REF] Taylor | Aggregate dynamics and staggered contracts[END_REF]), Rotemberg's model of quadratic costs of price adjustment [START_REF] Rotemberg | Monopolistic Price Adjustment and Aggregate Output[END_REF] or the [START_REF] Calvo | Staggered prices in a utility-maximizing framework[END_REF] model with random price adjustment. All these models relate current inflation to currently expected future inflation and the current driving variable. In the New Keynesian theory, excess demand enters through real marginal costs, which is empirically measured by the output gap or real unit labour cost (labour income share). The hybrid specification of the New Keynesian Phillips curve [START_REF] Galí | Inflation dynamics: A structural econometric analysis[END_REF] includes elements of both forward-looking and backward-looking price setting, since it has the lagged inflation term as an additional explanatory variable. In the Hybrid model only The results of this study suggests that when using directly measured inflation expectations both the New Classical and New Keynesian Phillips curves are able to capture European inflation dynamics. However, a specification test by [START_REF] Davidson | Estimation and inference in econometrics[END_REF] indicate that the New Keynesian Phillips curve is outperformed by New Classical Phillips curve. More specifically, when comparing the relative weights of the expectations terms of alternative models, we get evidence in favour of the New Classical specification with lagged expectations. Thus, there seems to be sluggishness (or delay) in the effect of expectations on inflation contrary to the New Keynesian specification with current expectations. The empirical performance of the Hybrid Phillips curve is also reasonable. The Wald test of coefficient restrictions suggests that the empirical fit of this specification is clearly better than that of the purely forward-looking New

2

Three Phillips curve relationships and specification tests

Standard specifications

The New Classical, the New Keynesian and the Hybrid Phillips curves involve very different assumptions about the role of expectations in the inflation process.

In the New Classical Phillips curve [START_REF] Phelps | Phillips curves, expectations of inflation, and optimal unemployment over time[END_REF][START_REF] Friedman | The role of monetary policy[END_REF][START_REF] Woodford | Interest and Prices[END_REF] [START_REF] Galí | Inflation dynamics: A structural econometric analysis[END_REF] relates current inflation to both currently expected future inflation and the lagged inflation rate.

Only some firms are assumed to be forward-looking and to set their prices optimally. The rest are assumed to be backward-looking in their pricing decisions.

Thus, in the Hybrid modification of the New Keynesian Phillips curve, the lagged inflation term is needed as an additional explanatory variable

t 1 t 2 1 t t 1 t y Ê + + = + (2.4)
According to equation (2.4), the current inflation rate depends not only on the expected path of the driving variable (ie the output gap in this study), but also on the lagged inflation rate, t-1 . Therefore, the Hybrid Phillips curve implies persistence in inflation. The inflation process is the more persistent, the larger the indexation parameter 2 .

Alternative Phillips curve relationships are based on time-dependent optimal price setting with nominal rigidities. Since optimal pricing decisions are based on the present value of expected profits, expectations play a crucial role in pricing decisions in all specifications. The three specifications have clearly different policy implications. The New Classical Phillips curve implies that monetary policy will have only temporary effects on real economic activity. By contrast, longer-lasting real effects of monetary policy can be modelled using the New Keynesian Phillips curve. The Hybrid model is able to explain inflation persistence due to delayed effects of monetary policy on inflation.

Estimating formulas

The standard specifications for the New Classical, New Keynesian and Hybrid Phillips curves ie equations (2.1), (2.2) and (2.4) need to be modified slightly, when rational expectations are not imposed and inflation expectations are measured directly. In estimations we used the following modified formulas

t * t t y ˆ + = (2.5) t * 1 t t t y ˆ + = + (2.6) and t 1 t * 1 t t y ) 1 ( + + = + (2.7)
where the terms

{ } t 1 t * t E = and { } 1 t t * 1 t E + + =
refer to period t-1 and period t representative market expectations, which are not necessarily rational. As Adam and Padula ( 2003) have shown, we can derive the New Keynesian Phillips curve with directly measured expectations. In applying equations (2.5)-(2.7) to the data, one need not assume any specific form of non-rationality in expectations. Since the task here is to compare the different models on their own terms, the theoretical restrictions are imposed in the estimated specifications of the New Keynesian models. Thus, in the New Keynesian Phillips curve, the imposed value of ß is 0.97 and, as seen in equation (2.7), the sum of forward-and backward-looking components is restricted to unity for the Hybrid Phillips curve.

When estimating alternative specifications separately, clear statistical

preference cannot yet to be claimed for any of the Phillips curve relationships. In order to facilitate the comparison of models, we applied two statistical tests to the data. The New Classical and New Keynesian Phillips curves were compared using -9 -a specification test proposed by [START_REF] Davidson | Estimation and inference in econometrics[END_REF]. This test analyses, whether current or lagged expectations dominate the inflation process

( ) ( ) t * 1 t 2 * t 1 t y 97 . 0 + + = + (2.8)
Equation (2.8) includes both expectations variables and then encompasses both models under consideration as special cases. The sum of the estimated coefficients 1 and 2 was restricted to one in order to analyse the relative weights of alternative components in the inflation process, as the test typically puts strong weight on either of the variables compared. With the same driving variable in both specifications, we were able to focus clearly on the timing of the expectations term in the Phillips curve relationship.

The Wald test is based on parameter restrictions and it is weaker than the specification test. It was used to compare the three alternative Phillips curves against the following very general model, which incorporates all the specifications as special cases , where * t refers to period t inflation forecast, made in period t-1. As shown in table 1, with both cases we could not reject the joint hypothesis that the constant a is equal to zero and the coefficient of the expectations, b, is equal to one. Thus, we found evidence that since the beginning of the 1990s survey-based forecasts were unbiased, which means that inflation expectations seem to have not been far from rationality 3 .

3 Qualitatively similar results with pooled euro area data can be found in Paloviita (2005a) -13 -assume that both inflation expectations and the output gap are measured correctly and they are not correlated with each other or with the error term. However, since least squares is not necessarily an appropriate estimation method for alternative Phillips relations, empirical results are mostly reported using only the generalised method of moments (GMM). In order to avoid too many instruments, ie possible small sample problems due to 'overinstrumenting', only two or three instruments were used in Phillips curve estimations.

First, the New Classical and New Keynesian Phillips curves ie equations (2.5) and (2.6) were estimated separately with least squares and the GMM. For both specifications two alternative expectations terms (June or December forecasts)

and two alternative driving variables (the HP-filtered or OECD output gaps) were used. When using the GMM, the instrument sets included always two predetermined variables: the lagged output gap and the lagged inflation rate. Jstatistic was used to test overidentifying restrictions of both specifications.

GMM results for the New Classical and New Keynesian Phillips relations are reported in Appendix 1. For both specifications we always obtained a positive coefficient for the output gap and only in one case for the New Keynesian specification, the estimated parameter was not statistically significant. When comparing alternative driving variable results of both models, we notice that the estimated coefficient was always higher with the HP filtered output gap. On the other hand, when using June forecasts instead of December forecasts, we got higher estimates. All in all, the New Classical Phillips curve results were quite reasonable, since the overidentifying results were never rejected at 5 per cent level. However, the empirical fit of the New Keynesian Phillips curve was poor, 4 For all countries in the sample, the correlation is 0.967. We also estimated the Hybrid Phillips curve using GMM, since IV methods might be needed because of errors-in-variables and/or simultaneity problems.

Also for the Hybrid model, predetermined variables were chosen for instruments:

two lags of the output gap and second lag of inflation. As shown in table A2.2,

The Hybrid Phillips curve results were slightly mixed: expectations were more forward-looking, when HP filtered output gap was used and by contrast, more backward-looking, when OECD output gap estimates were used. However, the relative weight of backward-looking expectations varied only between 0.46 and 0.53. All estimated coefficients for the driving variable were plausible and significant, and overidentifying restrictions were never rejected. All in all, the results in Appendix 2 indicates that the empirical fit of the New Keynesian Phillips curve can be improved by adding the lagged inflation term, ie by using the Hybrid model. When comparing the New Classical Phillips curve to the general model, the test results were slightly mixed: in three cases the New Classical specification is accepted, but only at the 1 per cent level. Qualitatively, the Wald test results

INSERT TABLE 3 HERE INSERT TABLE 4 HERE

seemed to be robust with respect to exact choice of current or lagged expectations (ie choice of the month, June or December) and choice of the output gap measure.

To conclude, the test results suggested that both the New Classical and Hybrid

Phillips curves provide a better description of the European inflation process than does the New Keynesian Phillips curve. Changing the timing of the expectations term in the New Keynesian Phillips curve, ie replacing current expectations by lagged expectations, we obtain the New Classical Phillips curve, which gives a better approximation of the inflation process than does the New Keynesian specification. In this case, we do not assume any backward-looking price setting.

On the other hand, when the backward-looking expectations term is added to the purely forward-looking New Keynesian Phillips curve, the empirical fit is much improved.

Empirical evidence against the purely forward-looking New Keynesian Phillips curve can be found also in other studies with directly measured expectations. Paloviita (2005b) examines euro area inflation dynamics since the late 1970s and uses OECD inflation forecasts to proxy inflation expectations. Also The New Keynesian Phillips curve has been widely studied also under the assumption of rational expectations, but the empirical evidence has been mixed. [START_REF] Galí | Inflation dynamics: A structural econometric analysis[END_REF] If a possible departure from rational expectations is allowed, directly measured expectations in principle provide a channel through which inflation persistence could be introduced to the New Keynesian Phillips curve with microfoundations for optimal price setting. However, although this channel seems to be important, this study suggests that it is not powerful enough to properly explain all of the persistence in the European inflation process. This is reasonable, since inflation expectations seem not to be very far from rationality. To conclude, since expectations have important autonomous effects on the monetary policy environment, expectations should be taken explicitly and independently into account in conducting monetary policy. Moreover, there seems to be evidence of inflation persistence which cannot be reduced to the persistence of expectations.

This is important because it implies the presence of some sort of structural basis for the short-run correlation between inflation and output. 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

  behave optimally when adjusting prices while the rest use rules of thumb or indexation, which is based on recent history of aggregate prices.In this study we analyse the inflation process in eleven out of twelve countries presently constituting the euro area 1 . Inflation dynamics in these countries is examined by comparing the empirical performance of the New Classical, New Keynesian and Hybrid Phillips curves. In comparison, econometric specification tests are applied to pooled European data. Consensus Economics survey data are used to proxy inflation expectations and in estimations least squares and the generalised method of moments (GMM) are used. Special attention is paid to the role of expectations in inflation dynamics.

  not purely forward-looking, and the inflation rate does not adjust instantaneously to changes in expectations.The rest of this study is structured as follows. In chapter 2 the three different Phillips curves and econometric specification tests are presented. Empirical evidence on European inflation dynamics is reported in chapter 3. Chapter 4 concludes.

  expected current inflation, currently expected future inflation, lagged inflation rate, and current output gap are the explanatory variables in the general model. If the parameters b and c are equal to zero, the model reduces to the New Classical Phillips curve. Alternatively, if the coefficients a and c are equal to zero, we obtain the New Keynesian Phillips curve. Moreover, the general model reduces to the Hybrid model if the parameter a is equal to zero. Using the Wald restrictions, we were able to determine whether restricted specifications are accepted by the data.

  three different Phillips curves were compared using the Wald test of coefficient restrictions (see tables 3 and 4). When the parameter restrictions of the New Keynesian Phillips curve were tested against the general model, the Wald test clearly rejected parameter restrictions implied by the New Keynesian Phillips curve specification. When the same test was used to evaluate the Hybrid model against the general model, the Hybrid model was clearly accepted in all cases.

  which uses the same econometric specification tests in comparison of different Phillips relations, suggests that the New Keynesian Phillips curve is outperformed by the New Classical and Hybrid Phillips curves. The basic features of the New Keynesian Phillips curve are also rejected in Paloviita and Mayes (2005), who use real time information and directly measured inflation expectations (ie OECD forecasts) in different specifications of the Phillips curve.Gorter(2005) argues that with survey-based expectations, for France the New Keynesian Phillips curve is appropriate, but for Germany and Italy the Hybrid specification is needed.[START_REF] Adam | Inflation dynamics and subjective expectations in the United States[END_REF] find evidence that with surveybased expectations the Hybrid Phillips curve must be used to describe the US inflation process properly.

  Figure 1.Inflation history, December inflation forecasts for the next year and the output gaps

  Figure 2. Consensus Economics and OECD inflation forecasts for the next year

  constructed series for Germany, France, Italy and Spain, which dominate the euro area, with a combined weight of over 80 per cent. Inflation histories have diverged across countries since the beginning of the 1990s. Only Greece and Portugal experienced two-digit inflation rates in the first years of the sample. In the whole sample, the average inflation rate was above 3 per cent in Greece, Ireland, Italy, Portugal and Spain, and below 3 per cent in rest of the economies. There has been remarkable heterogeneity in output gap developments across the countries. Finland, Ireland, and Portugal belong to the country group with more a divergent output gap history, while in other countries the output gap has been less volatile.

			Submitted Manuscript
	INSERT FIGURE 1 HERE
	In order to construct the pooled European data until the year 2004, annual inflation rates and two alternative driving variables (HP filtered output gap and OECD output gap estimates) were constructed using the OECD Economic F o r P e e r Figure 1 gives INSERT TABLE 1 HERE R e v The unbiasedness of Consensus Economics inflation forecasts was tested by
	estimating the equation	t	=	a +	b	* t	i e
							w
	For Greece, both survey forecasts are available since 1993. For the other ten
	countries, June forecasts are available since 1990 and December forecasts since
	1989. Thus, in estimations the sample is from 1989-1991 till 2004.

Outlook data set and OECD National Accounts. Inflation was measured by annual changes in consumer prices and corresponding inflation forecasts for each country were obtained from Consensus Economics monthly survey. Since we cannot pin down the exact timing of expectations term with annual data, we used both June and December estimates for the next calendar year. The HP filtered output gap 2 is defined as the difference between log real GDP and Hodrick-Prescott filtered log real GDP with smoothing parameter of 100. OECD output gap estimates are based on production function method. Empirical analysis is based on eleven out of twelve countries presently constituting the euro area. Since OECD output gap estimates and Consensus Economics inflation forecasts are not available for Luxembourg, it is not included in the data. Availability of data varies somewhat. 2 When constructing HP filtered output gaps, we used long real GDP series since 1973. Moreover, we used OECD real GDP forecasts for the years 2005 and 2006 in order to reduce the impact of the end-point problem. Page 10 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Further analysis of June and December forecasts (not reported here) showed that forecast errors are positively correlated and forecast errors seem not to be orthogonal to lagged information, as assumed under rational expectations. With both cases, regressing forecast error on lagged inflation rate and lagged output gap led to rejection of the null hypothesis that estimated coefficients are jointly equal to zero. These results indicate that deviations from full rationality may be important in empirical analysis of the Phillips curve relationship.

	Submitted Manuscript Submitted Manuscript
	F o r INSERT FIGURE 2 HERE Figure 2 compares Consensus Economics December inflation forecasts with F o r
	P P
	e e
	e r e r
	inflation expectations.	R e v R e v
		i e i e
	w 3.2 Empirical comparison of alternative Phillips curves w
	The three different Phillips curves were applied separately to pooled European
	data. Moreover, statistical preference of different specifications was examined
	using econometric specifications test presented in 2.2. When measuring inflation
	expectations directly, we can in principle use least squares in estimations, if we
	-12 -

. When OECD inflation forecasts are used to proxy inflation expectations, we get evidence that in 1977-1990, when inflation was high and volatile in many European countries, inflation expectations Page 11 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

corresponding OECD estimates, also made in December. For Germany, France, Italy and Spain the correlation between these variables 4 varies between 0.942 and 0.985. Overall, both proxies seem to follow a similar pattern. We would conclude that in comparing alternative measures of inflation expectations, we obtained support for the reliability of Consensus Economics forecasts as a proxy for were biased. By contrast, the hypothesis of unbiasedness cannot be rejected in the euro area for the period 1991-2003.

Page 12 of 30

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

Page 13 of 30 Editorial

	Submitted Manuscript
	since the overidentifying restrictions were always rejected at 5 per cent level simultaneity problems were not taken into account, which means that least squares
	according to the Hansen test 5 . estimation method was assumed to be sufficient. As shown in table A2.1, Hybrid
	Phillips curve results were quite reasonable: relative weights of backward-looking
	INSERT TABLE 2 HERE expectations were 0.38-0.41 and all estimated coefficients were statictically
	significant. Thus, we got evidence that forward-looking expectations dominate the
	In order to assess the statistical preference for the New Classical or New inflation process in Europe.
	Keynesian specification, the Davidson and MacKinnon (1993) specification test
	was next applied to the data using GMM with lagged inflation rate and two lags of
	F o r the output gap as instruments. The test results are reported in table 2. They show that in explaining inflation dynamics, in all cases lagged inflation expectations F o r
	P P
	e e
	e r e r
	R e v R e v
	i e i e
	w w
	Next, the Hybrid Phillips curve ie equation (2.7) was estimated using least
	squares and the GMM (see Appendix 2). First, possible measurement errors or
	-14 --15 -

Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK had clearly bigger relative weights than current expectations. In three cases the relative weight of the lagged expectations term was about 1 and in one case very large, 1.4. The result of the dominant role of the lagged expectations term was robust to choice of the output gap measure. Moreover, the test results were qualitatively similar with June and December forecasts. Thus, the choice of exact timing of current and lagged expectations (ie in the middle or in the end of the period) seemed not to change the test results. Overall, when comparing the New Classical and New Keynesian Phillips curves with the specification test, we got evidence in favour of the New Classical Phillips curve.

18 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  examines the US and[START_REF] Galí | European inflation dynamics[END_REF] the euro area inflation process. Both of these studies indicate that the New Hybrid model is favoured for France and Italy, but the results are mixed for Spain. By contrast,[START_REF] Rudd | Can Rational Expectations Sticky-Price Models Explain Inflation Dynamics? Finance and Economics Discussion Series[END_REF] find little evidence for the rational forward-looking behaviour implied by the New Keynesian theory.The results obtained suggest that with survey-based inflation expectations one could capture European inflation dynamics with the New Classical Phillips curve.Also the New Keynesian Phillips curve fit the data, but poorly. According to econometric specification tests the New Classical and the Hybrid Phillips curves clearly outperformed the New Keynesian Phillips curve. Thus we found evidence against the basic features of the New Keynesian model. The evidence of this paper suggests that the inflation process is not purely forward-looking and that the inflation rate cannot adjust instantaneously to new information. The backwardlooking or sluggish features are different in the New Classical and Hybrid models, but the results indicate that they are important in European data.

	Page Submitted Manuscript
	(2003) the 4 Conclusions
	Expectations are crucial in the inflation process and for the effects of monetary
	policy. In empirical studies of inflation dynamics, different Phillips curve
	F o r specifications have been used. These have different policy implications. If inflation is purely a forward-looking phenomenon, as the New Keynesian Phillips curve suggests, a fully credible disinflation is possible without output losses. On F o r
	P the other hand, if inflation process is persistent, there is a fundamental short-run P
	e tradeoff between inflation and output. In the latter case, we need alternative e
	e r models with sluggish or backward-looking features to describe inflation dynamics e r
	Keynesian Phillips curve provides a reasonably good approximation of inflation dynamics. Sbordone (2002), who uses a two-steps estimation procedure, also R e v accurately. The empirical application poses a challenge: when applying Phillips curve to the data, rational expectations have typically been assumed, but it may be an excessively restrictive assumption for economic behaviour. R e v
	argues that US inflation dynamics can be captured fairly well with a purely i e In this study three different Phillips curves were applied to the pooled i e
	forward-looking model. By contrast, McAdam and Willman (2003) find evidence that the New Keynesian Phillips curve fits euro area data poorly. Moreover, w European data since the beginning of the 1990s. As we did not use a priori assumption of expectations formation, inflation expectations were proxied by w
	Jondeau and Le Bihan (2001) argue that both with US and euro area data the Consensus Economics survey-based inflation forecasts. Moreover, two different
	Hybrid specification fits better than the New Keynesian Phillips curve. measures for the output gap were used. Alternative specifications were compared
	Benigno and López-Salido (2001) find that for Germany the New Keynesian using the specification test by Davidson and MacKinnon and the Wald test of
	Phillips curve is appropriate, but for France, Italy and the Netherlands the Hybrid coefficient restrictions.
	models are needed (the results are mixed for Spain). According to Sondergaard
	-18 --19 -

27 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  West HAC Standard errors, p-values in parenthesis.

	Table 1. Pooled European data Notes: Newey-Page Submitted Manuscript Unbiasedness of Consensus Economics inflation forecasts * t t b a + = Joint Hypothesis June forecast December forecast (a,b) = (0,1) F=0.582 (0.560) F=0.273 (0.762)
	F o r F o r
	P P
	e e		2002 2003
	e r e r	CE	OECD
	R e v R e v
		i e i e
		w w
	-27 --28 -

Table 2 .

 2 Comparison

of New Classical and New Keynesian Phillips curves

  GMM using Barlett kernel with fixed bandwidth. Instruments: lagged inflation rate, t-1, and two lags of output gap, t-1 and t-2. Numbers in parentheses are standard errors, * indicates significance at 5 per cent level. J-statistic corresponds to Hansen test of overidentifying restrictions (below in brackets the associated p-values are reported).

	t	=	( ) t *	+	1 (	)	( 0	.	t * + 1	) t t y 97 +
											Driving variable
										HP filtered output gap	OECD output gap
										S.E. of the	S.E. of the	S.E. of the	S.E. of the
										NCPC	NKPC	NCPC	NKPC
	June forecast							0.782	0.870	0.840	0.890
										Encompassing test	Encompassing test
											J-statistic	J-statistic
	June forecast						1.041	0.237	0.037	1.411	0.249	0.023
									(0.179)* (0.036)*	[0.060]	(0.290)* (0.049)*	[0.175]
	Driving variable HP filtered output gap S.E. of the NCPC S.E. of the NKPC S.E. of the OECD output gap NCPC S.E. of the NKPC 0.716 0.905 0.739 0.872 Encompassing test Encompassing test J-statistic J-statistic 1.038 0.113 0.017 1.171 0.099 0.014 (0.141)* (0.031)* [0.246] (0.183)* (0.029)* [0.333] r o P e Notes: F December forecast December forecast e r
											R e v
											i e
											w
											-29 -

Table 3 .

 3 Wald

test results with HP filtered output gap

																		Submitted Manuscript
	t	=	a	t *	+	b	t * +	1	+	c	t		+	d	t y 1
																		Joint	June forecast	December forecast
																	Hypothesis
	NKPC vs general							(a,b,c) = (0,0.97,0) F=28.122	(0.000)	F=49.514	(0.000)
	model
	HPC vs general model			(a,b+c) = (0,1)	F=0.616	(0.541)	F=0.381	(0.684)
	NCPC vs general								(a,b,c) = (1,0,0)	F=4.904	(0.003)	F=3.154	(0.027)
	model
	Notes: GMM using Bartlett kernel with fixed bandwidth.
	Instruments:	ˆ y	t	1	, y	t	2	, y	t	3	,	t	2	,	t	3	.
								F o r
																	P
																		e
																		e r
																		R e v
																		i e
																		w
																		-30 -

Page 29 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 4 .

 4 Wald

test results with OECD output gap

	t	=	a	t *	+	b	t * +	1	+	c	t	+	d	t y 1
														Joint	June forecast	December forecast
												Hypothesis
	NKPC vs general
	model								(a,b,c) = (0,0.97,0) F=22.762	(0.000)	F=41.138	(0.000)
	HPC vs general model		(a,b+c) = (0,1)	F=0.261	(0.771)	F=0.538	(0.585)
	NCPC vs general
	model									(a,b,c) = (1,0,0)	F=3.613	(0.015)	F=3.418	(0.019)
	Notes: See table 3.

Page 30 of 30 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

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

Quite similar results were obtained, when using least squares (not reported here). For both specifications, when comparing alternative driving variables, higher estimates were always got with HP filtered output gaps. Moreover, June forecasts yielded in all cases higher coefficients than December forecasts. However, for the New Keynesian specification, least squares results were poor, since in one case we got a negative coefficient and only one of the estimated parameters was statistically significant. The residual were strongly correlated in all cases.

Appendix 1

New Classical and New Keynesian Phillips curve results