Enterprise Performance, Privatization and the Role of Ownership in Poland

In both economically developed and developing countries, privatisation, budget austerity measures and market liberalisations have become key aspects of structural reform programs in the last three decades. These three recommended policies were parts of strong revival of classical and new-classical school of thought since the middle of 70s. Such programs aim to achieve higher microeconomic efficiency and foster economic growth, whilst also aspiring to reduce public sector borrowing requirements through the elimination of unnecessary subsidies. For firms to achieve superior performance a change in ownership from public (state ownership) to private has been recommended as a vital condition. To assess the ownership role, the economic performances of private, public and mixed enterprises in Poland is compared through the use of factor analysis method. The extracted factors, using data of two years, 1998 and 2000, do not pick ownership as a key performance factor.


Introduction
Both developed and developing countries have progressively engaged in ambitious privatisation programs for several decades. Over the years, the number of privatisation transactions has grown. From 2000 to 2007, the sale of state-owned assets reached $497.7 billion in OECD countries. To illustrate the relevance of this policy, table 1 shows how the change in European state-owned enterprises shares in GDP for the year 2006, and is grouped with income level in accordance with the OECD's classification.
Insert Table 1 about here The change does not only respond to privatisation strategies, but is also strongly linked to them. It reflects the declining role of the public sector as owner of productive assets in the economy.
Microeconomic theory suggests that incentive and contracting problems create inefficiencies as a result of public ownership; provided that managers of state-owned enterprises pursue objectives that differ from those of private firms (political view) and are less monitored (management view). Objectives are distorted, as well as faced with softened budget constraints because bankruptcy is not a plausible threat to public managers and gives rise to soft-budget constraint. As a preventative measure of financial distress, it is thus in the central government's own interest to bail public managers out in times of financial distress. The set objectives for privatisation programs in different countries to achieve are far broader, and fundamentally involve the improvement of microeconomic efficiency. Generally, there are four explicit objectives in such programs. i) to attain higher efficiency in terms of allocation and productivity; ii) to create a stronger role for the private sector within the economy; iii) to advance the financial health of the public sector; and iv) to liberate resources for allocation in other essential areas of activity within the government (normally associated with social policy).
Privatisation programs should, consequently, be considered by looking at the level at which the stated aims have been reached, on one hand, and what role the ownership has played to reach all the above goals, on the other hand. Theoretical arguments behind the view that privatisation can attain these aims as well as surveys of the empirical literature are reviewed.
The purpose of this article is to investigate whether ownership has been a significant characteristic of enterprise performance in Poland. This attempt is part of a broader investigation series, which is being conducted to discover the characteristics of ownership with regards to enterprise performance. In this article, the performance of three differently owned companies, state, private and mixed, will be considered and factor analysis methodology will be deployed. This will permit the use of quantitative and qualitative data alongside each other to extract common factors of these types of activities.
The paper has four further sections. The second section is dedicated to reviewing literature; including theoretical arguments, which support the view that private ownership is favoured over public ownership. Specific testable inferences are proposed as guidelines to the empirical survey. The third section presents a viable methodological option to assess the characteristic of ownership in the context of enterprise performances in Poland. The fourth section is devoted to analysing results. And the final section is consists of concluding remarks.

The Managerial Perspective.
Low-powered incentives, according to the 'managerial' perspective, are behind imperfect monitoring in public-owned enterprises. The managers of state-owned enterprises are poorly monitored because the firms are not traded in the market as they are with private firms. This means that the threat of take-over when the firm performs poorly is abolished. According to Yarrow, (1986) and Vickers and Yarrow, (1989), shareholders are unable to observe and affect the performance of the enterprises.
Another argument, which is put forward by this perspective, is that of SOE (state owned enterprises) debt actually perceived as being public debt and traded under different conditions. Debt markets cannot play the role of disciplining the managers of public-owned enterprises. It has been argued that this problem can be solved by privatisation, without having to pursue complete divestiture.
Furthermore, managers of SOEs can increase the scale of production, since bankruptcy is a non-credible threat under public ownership. In contrast, for a private manager, this would be a real threat of failure, which could reduce productive efficiency.

The Political Perspective
It is argued by the 'political' perspective that distortions in the aim, the function (Shapiro and Willig (1990)) and the constraints private managers face, through the so-called soft budget constraint problem (Kornai (1980(Kornai ( , 1986), result in lower efficiency under public ownership.
Public managers, who have a tendency to report to politicians and pursue political careers themselves, incorporate objective function aspects relating to the maximisation of employment in their actions. Their desire to maximise their employment is at the expense of efficiency and political prestige (the empire building hypothesis).
Managers do not face the risk of bankruptcy because of soft budget constraint. Wherever firms have engaged in unwise investments, it is in the central government's interest to bail them out using the public budget. The rationale behind this is that the bankruptcy of a firm would be very costly from a political stand-point, and such burden would be distributed within well-defined political groups, such as unions.
The cost of a bail out can instead be shared by the taxpayers, a less organised and larger group in society with assorted interests and preferences. This is because under public ownership, the threat of bankruptcy is non-credible. Thus, we can, by way of a rather simple assumption, obtain the soft budget constraint result as the equilibrium in the race between the public manager and the central government (or "ministry of finance"). This supposition is such that the political loss associated with closing a publicly-owned company is greater than political costs of using taxpayer money to bail it out (or public debt, i.e. future tax collection).

Evidence
Empirical studies to evaluate the privatisation performance can be categorised into two groups: Microeconomic and macroeconomic evidence. More tangible conclusions can be drawn from the microeconomic perspective rather than from the macroeconomic one. The following case studies span prior to and following privatisation. They exhibit countryspecific, cross-sector evidence that looks into performance changes of firms in different sectors within the same country, as well as cross-country evidence that uses data from publicly traded firms in different countries to evaluate changes in their financial status.

Microeconomic Evidence
Some empirical evidences strongly support the view that privatisation has positive effects on profitability and efficiency at the microeconomic level. However, alongside these results, there are, at the same time, some studies, which point to opposite results.
The first piece of evidence consists of case studies, among which Galal, et. al. (1994) shows comprehensive evidence. This study looks at the performance of twelve privatised firms in four different countries. The methodology of their case study is counterfactual and makes projections of the firms' performance fall under the privatisation scenario and a hypothetical "public ownership scenario". Changes in welfare are measured by way of a comparison between these two scenarios. In four cases, consumer welfare has increased; in five of them it has decreased, and it has remained unaltered in the rest. In nine cases, the government has a net gain, and the firm's buyers gained in all of them. Through the partial equilibrium nature of this analysis, a distinctly positive effect of privatisation on total welfare is shown by these firm studies.
The second type of study focuses on one specific country and analyses evidence across industries. LaPorta and López-De-Silanes (1998) analyse the performance of 218 enterprises in Mexico in 26 different sectors between 1983 and 1991. An essential aspect of this work is the authors' decomposition of the changes in profitability into price increases, labour reduction and productivity gains. Two common criticisms of privatisation are addressed by their analysis. The first is that at the expense of society, through charging higher prices, the profitability of firms has increased. The second is that firms have made profits at the expense of workers, whose labour contracts are less generous and involve significant layoffs. Results indicate that profitability, measured through the ratio of operating income to sales, rose by 24 percentage points. However, such gains are decomposed into the following components: i) an increase in price constitutes 10% of the results; ii) laid-off workers constitute 33%; iii) productivity gains constitute 57%. A regression analysis is also carried out to identify the role of market power and deregulation in determining privatisation outcomes. Smith et al (1996) study privatisation in Slovenia. They use a country-wide database with privatised firms from 1989 to 1992. Their objective is to analyse the effect of various types of ownership on performance. The results indicate a visibly positive effect of privatisation on ownership performances. Foreign ownership, for example, has shown an outstandingly positive effect on the performance when it comes to distinguishing the effects of different types of ownership. However, it appears that employee-owned firms have performed relatively better than those owned through foreign investment. Gupta et.al (2008) examine the consequences of privatisation program in the Czech Republic.
They used data of the year 1992 at the firm-level for firms with 25 or more workers. The results they found show that privatised firms are among more profitable firms. However, for the government of the Czech Republic the main objective was to maximizing government revenues through selling public assets. Mestiri (2010) investigates the impact of privatization on the Tunisian government owned airline, Tunisair, over the period of 1976-2007. 20 % of the capital of the Tunisair was privatized by the government using the initial public offering method in July 1995. The author used data envelopment analysis to evaluate the efficiency of Tunisair privatization.
After privatization Tunisair has experienced a better economic efficiency, as its technical efficiency scores have increased from 0.743 to scores close to 1.
Cross country evidence starts with a very important study by Megginson et al (1994). They analyse pre and post privatisation performance of 61 companies from 18 countries and 32 industries, which were privatised between 1961 and 1990 through public offerings. D'Souza and  carry out the same type of study by using 78 companies from 25 countries, including 10 LCDs that faced privatisation during 1990 to 1994 through public offering. Their sample includes 14 banks, 21 utility and 10 telecommunication companies.
Boubakri and Cosset (1998) use data of 79 companies from 21 developing countries. These firms were privatised between 1980 and 1992 through public offerings. Claessens and Djankov (1998) use the largest data set, consisting of 6.300 manufacturing firms in seven Central and Eastern European countries, namely Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic and Slovenia. The performance indicators are analysed by presenting mean and median levels of profitability, sales, operating efficiency, leverage, capital expenditures and employment. There are, in most cases, controls for whether the markets are competitive; regulated or unregulated, as well as controls for partial versus full privatisation. The evidence is robustly in favour of the better performance of firms after privatisation. Profitability has largely increased with varying specifications, periods of time and groups of countries.

Interestingly enough, in both Boubakri and Cosset (1998) and D'Souza and Megginson
(1998), profitability increased more than operating efficiency in regulated (or noncompetitive) industries. Thus, higher profitability does not necessary imply higher efficiency, and the market structure links both concepts. The idea that a certain degree of market power is being exploited by firms is also supported by the evidence. In all cases, capital expenditure (investment) systematically increased, reflecting both growth and the post-sale restructuring which took place. Employment increased in all cases, including those of developing countries.
It seems that this evidence on employment is inconsistent with that in, for example, LaPorta and López-De-Silanes (1998) work. There are two justifications for such inconsistency.
Firstly, a non-negligible selection bias is generated. The cross-country studies analysed by the authors use only data from firms that were sold via public offerings. Such firms are the ones expected to have higher potential for profitability. Secondly, the author's country-specific study incorporates data from three years prior to the privatisation of all firms. This potentially eliminates labour redundancy prior to sales. Fully privatised firms perform better than partially privatised ones in all of the cases. Frydman et al (1997) reported improvement in corporate performance that was consistent with the results shown above, in the case of transition economies. Robustly positive performance alterations in a large sample of firms in Central and Eastern Europe were reported by Frydman et al (1998) and Claessens and Djankov (1998). They were interested in testing the political view, i.e. whether the withdrawal of political intervention provides an explanation for the positive results. The former paper found outstanding improvements in total factor productivity and a decline in excess employment in firms without state intervention. It controlled for institutional differences and the endogenity of privatisation choices. The latter paper found evidence that entrepreneurial behaviour drives the efficiency gains on removal of state intervention. The authors conclude that the performance results of privatised companies are the features of a greater willingness to comprehend risks and a liberty to make decisions without state intervention. Brawn, et. al. (2005) analyse the effects of privatization on state owned manufacturing firms in Hungary, Russia, Romania, and Ukraine. They use time series data of annual observations to compare both before and after privatization performance. They used longitudinal econometric methods to obtain comparable estimates across countries. The result shows a substantially positive effect of privatization on productivity in Romania and Hungry.
Moreover, the estimated effects for Romania are significantly bigger than for Hungary. The estimated effects in Ukraine are positive, but lower than Romania and Hungary. Contrary to these countries, the estimated effects are negative for the last county, Russia.

Macroeconomic Evidence
There is no certain evidence of the effects of privatisation at the macroeconomic level.
However, it is possible to provide an overview of the patterns observed in key aggregate variables and structural reform measures were also put in place to some extent in most countries. These policy measures include, amongst others, trade liberalisation, fiscal adjustment, tax reform and weakening of controls to capital inflows. Whilst it is impossible to attribute observed trends to one isolated policy, we can argue, on the basis of theoretical arguments, that macroeconomic trends are connected.
Evidence supporting the claim that privatisation reduces the burden on public financing is shown in the aforementioned studies. Following reform, both low and middle income countries have, on average, succeeded in eliminating net subsidies to public enterprises.
SOEs display a surplus in their operation as far as middle income countries are concerned.
This can result from reforms in management and the introduction of competition, as well as the concept of "best" firms being those which have remained in the hands of the government.
For example, oil companies and natural monopolies like electric utilities.
Reforms are being considered in countries where the trend in fiscal deficit is still largely negative. There, the most favourable trend is that of the deficit in upper middle income economies -where the most aggressive reformers can be found, such as Argentina, Chile,

Mexico and Malaysia.
A central effect observed in all income groups is that of financial sector development (see Demirguc and Levine (1994) and McLindon (1996)). For both low and middle income economies, reforms have had an impact on that indicator of capital market development; whereas, in high income countries, capitalisation of the stock market has remained stable. All such economies show a positive trend. Upper middle income countries have reached levels of capitalisation similar to those in high income economies (approximately 55% of GDP). The low-income group is approximately 16% and lower middle income economies are roughly 25%.
This mobilisation of resources and consistency of reforms has subsequently attracted more direct investment by foreigners. Middle income countries show a positive trend in foreign direct investment; whereas, low-income countries, in which reforms and privatisation have been more aggressive, show a significant increase of such investment in later years. Lastly, in terms of GDP growth, the pattern is rather stable across income groups with no clear trend.
However, in low and lower middle income economies, variability is larger.
Conversely, unemployment shows a rather irregular pattern across countries. Aggressive, late and less aggressive reformers illustrate an increase in the unemployment rate. Argentina and Poland are examples of aggressive reformers, where the unemployment rate rose by 9 and 8 percentage points, respectively, between 1990 and 1996. France and Hungary are amongst the late and less aggressive reformers, where unemployment grew 3.5 and 3%, respectively, throughout the same period. In terms of privatisation, it is not possible to draw any conclusions on the overall unemployment rate. In recent years, unemployment has shown a rising trend in most countries around the world (see Demirguc and Levine (1994) and McLindon (1996)).
As theoretical stand points support the policy adjustment of selling the government owned enterprises to private buyers and argue that the implementation of policy would lead to higher economic efficiencies of privatised firms, better allocations of resources and consumers benefits, the empirical studies show mixed results. Some studies indicate very higher economic and financial achievements from the policy reform namely privatisation and some show negative results.

Data and Methodology
To assess enterprise performance and the role of ownership in Poland there are several methodologies. These include: total factor productivity, factor analysis, cost benefit analysis and ratio analysis. Among these methods, factor analysis may be more useful than the others as our aim is to incorporate quantitative and qualitative variables alongside each other. This technique can be used to measure comparative enterprise performance and the subsequent role of ownership in output results from the Statistical Package for Social Sciences (SPSS).
Factor analysis is a mathematical tool which can be used to examine a wide range of data sets. It has been used in disciplines as diverse as economics, chemistry, sociology and psychology because of its ability to analyse the performance of a variety of different aspects.
The main functions of factor analytic techniques can be summarised as follows: (1) to reduce the number of variables and (2) to detect structure in the relationships between variables, that is to classify variables. Therefore, factor analysis is applied as a data reduction or structure detection method.
The term factor analysis was first introduced by Thurstone in 1931. Many statistical methods can be used to study the relation between independent and dependent variables. However, the factor analysis approach is unique in that it studies patterns to discover the relationship among many dependent variables. Its goal is to discover something about the nature of the independent variables that affect dependent variables; without measuring those independent variables. Consequently, when independent variables are observed directly, answers obtained by factor analysis are hypothetical and tentative. The conditional independent variables are called factors.
A typical factor analysis advocates answers to four major questions: 1. How many different factors are needed to explain the pattern of relationships among these variables? 2. What is the nature of those factors?
3. How well do the hypothesized factors explain the observed data?
4. How much purely random or unique variance does each observed variable include?
Factor analysis needs a set of data points in matrix form. The terms 'row designee' and 'column designee' are referred to the row and column identifiers of the matrix. This terminology is used because of the very wide range of data matrix types that may be analyzed by factor analysis. To carry out this method the data must be bi-linear. Therefore, the row entities and the column entities must be independent of each other. Factor analysis comprises of both component analysis and common factor analysis. The purpose is to discover simple patterns in the pattern of relationships among the variables. Above all, it seeks to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called factors.

Factor Analysis Method
This method can be used to identify whether a number of variables of interest Y1, Y2, ..., Yl, are linearly related to a smaller number of unobservable factors F1, F2, ..., Fk. Factors are observed in factor analysis; whereas, in other methods such as regression analysis they are not. The hypothesized factor model under certain conditions has certain implications. These implications in turn can be tested against the observations. To explain this method three variables, Y 1 , Y 2, and Y 3, and three factors have been extracted. It is assumed that each Y variable is linearly related to the two factors, as follows: The error terms e 1 , e 2 , and e 3 , demonstrate that the hypothesized relationships are not exact.
The parameters are referred to as loadings. For example, is called the loading of variable Y1 on factor F2. It is expected that the loadings have roughly the following structure if, for example, Y 1 is assumed to be a quantitative variable and Y 2 and Y 3 are two qualitative variables: Loading on: The zeros in the preceding table are not expected to be exactly equal to zero.
By `0' we mean approximately equal to zero and by `+' a positive number substantially different from zero.
From the above equations it may be observed that the loadings can be estimated and the expectations tested by regressing each Y against the two factors. However, this is not feasible as the factors cannot be observed. An entirely new strategy is required.
The simplest model of factor analysis is based on two assumptions.
A1: The error terms e i are independent of one another, and such that E(e i ) = 0 and Var (e i ) = .
A2: The unobservable factors F i are independent of one another and of the error terms, and are such that E(F j ) = 0 and Var(F j ) =1.
In more advanced models, the condition that the factors are independent can be relaxed. As for the factor means and variances, the assumption is that the factors are standardized. It is an assumption made for mathematical convenience; since the factors are not observable, we might as well think of them as measured in standardized form. To examine the implications of these assumptions let each observable variable be a linear function of independent factors and error terms, and be written as The variance of Yi can be calculated as follows: The variance of Yi consists of two parts: The first, the communality of the variable, is the part that is explained by the common factors F1 and F2. The second, the specific variance, is the part of the variance of Yi that is not accounted for by the common factors. If the two factors were perfect predictors of grades, then e 1 = e 2 = e 3 = 0 always, and To calculate the covariance of any two observable variables, Yi and Yj, we can write Var ( ) + ) + (0) (1) Var ( + All the variances and co-variances can be shown on the following table: The variances of the Y variables are in the diagonal cells of the Variables Y1, Y2, and Y3 are given, the observed variances and co-variances of those variables can be calculated and arranged in an observed variance co-variance matrix as follows: Thus, is the observed variance of Y1, S 12 the observed co-variance of Y1 and Y2, and so on. As the S 12 = S21, S 13 = S 31 , and so on; the matrix, in other words, is symmetric. advantage of the factor model. In particular, it is expected that some loadings will be close to zero, while others will be positive or negative and substantially different from zero. For this reason, factor analysis usually proceeds in two stages.
The First Stage: One set of loadings is calculated. This will yield theoretical variances and co-variances according to a certain criterion that fit the observed loadings as closely as possible. These loadings, however, may not agree with the prior expectations, or may not lend themselves to a reasonable interpretation. Thus, the second stage is needed. The Second Stage: The first loadings need to be "rotated". This should be done in order to arrive at another set of loadings. This will fit the observed variances and co-variances. This stage is more consistent with prior expectations and it can be easily interpreted.
In practise, the most widely used method for determining a first set of loadings is the principal component method. This is not, however, the only method for factor analysis. It is also possible to use the principal factor (also called principal axis) and maximum likelihood methods. The principal component method looks for values of the loadings that bring the estimate of the total communality as close as possible to the total of the observed variances, while co-variances are ignored. The table below shows the elements of the factor model on which the principal component method concentrates.

Elements of Principal Component Methods
Variable Observed Variance, Communality, The communality is the part of the variance of the variable that is explained by the factors.
The larger this part, the more successful the postulated factor model can be said to be in explaining the variable. The principal component method determines the values of the , which make the total communality (Tt in the Table)

Data and Variables
Data on turnovers, profits, total assets and total number of employees for the years 1998 and 2000 have been collected from four different sources: Major Companies of Europe, Amadeus, and DataStream. All data has been converted to a same-base currency, the US dollar.
As Figure 1 illustrates, it was not until 1993 that most EU countries undertook ambitious programmes, principally through public share offerings of public enterprises. The EU privatization during the 1990s, has a pattern of almost continuous growth, from US$13 billion in 1990 to US$66 billion in 1999, followed by a decline to US$13 billion in 2002 ( Figure 1). The pattern has reached its peak point during the 1998 to 2001. We decided to pick up the year 1998 and 2000 as the privatisation revenue in EU has reached its highest level.
Insert Figure 1 about here Productivity and performance are respectively represented by variables called PROD and PROF. The former variable is created through the turnover divided by the number of employees (essentially a crude measure of gross labour productivity). The latter variable is created through profit divided by the number of employees. Since PROD and PROF can measure some aspects of performance, we will refer to them together as reflecting "productivity & performance" even though this is slightly misleading. In this analysis, performance will be represented by PERF. We have not yet used the rate of profit as a variable; although we could have since it is given by PROD/PROF, which means that its constitutive elements are included in the empirical analysis.
Ownership is treated as a categorical or nominal variable. Nominal data relates to qualitative variables or attributes, such as gender or ownership, and is a record of category membership.
Nominal data is defined by labels: it may take the form of numbers, but such numbers are merely arbitrary code numbers.

Result Analysis
The output from this package, however, is comprised of different elements ranging from descriptive statistics to the rotated component matrix -the main focus being on the principal component matrix. In general, the further refinement of factor analysis through for example rotation has not significantly enhanced or modified the results. Consequently, the principal components of factor analysis are solely reported here.
The main purpose of this exercise is to first ascertain which variables are highly loaded (i.e., highly correlated to a factor) or, in other words, which extracted factors pick up which variables; and, second, to determine common characteristics. It is assumed that performance is a function of turnover, profit, total assets, productivity, performance, ownership, concentration, and total number of employees: Performance = f (turnover, profit, total assets [or tassets], total number of employees, productivity, performance, ownership and concentration).
In these exercises (which compare the performance of state, mixed, and private companies in Poland to find the role of ownership) state companies are assigned a value of 0, private companies a value of 1, and mixed companies a value between 0 and 1 depending on the percentage of shares owned by the state. Two years, 1998 and 2000, have been chosen for analysis, and annual data for these three types of companies has been collected. In the preceding illustration, the number of factors and their nature were hypothesized in advance. It was reasonable to assume that size and performance were two factors influencing enterprise performances. In the metropolitan area where the data was selected, the ownership of enterprises is presumably unrelated to the size and performance of the enterprises in Poland.

Conclusion
For the last three decades, the characteristic of ownership has been at the centre of economic debates and polices all over the World. From a theoretical perspective, trouble related to inducement and contracting leads to inefficiencies as a result of public ownership. This is due to managers of state-owned enterprises pursuing aims which differ from those of private firms (political view) and due to such managers facing less observation (management view).
The budget constraints faced by the managers are softened, and their objectives are subsequently distorted. Soft-budget constraints result from bankruptcy not being a probable threat to public managers, as it is in the interest of the central government to bail them out in case of financial distress.
However, this paper investigates the evolution of selected measures, and relays that evolution with privatisation -summoning established theoretical principles, particularly those concerned with establishing a connection between ownership and performance. As previously mentioned, the evaluation of privatisation programs includes efficiency as well as equity issues. This paper argues that the distributive effects of privatisation policies require further research efforts and focus, particularly at the empirical level.
Factor analysis is used to assess the role of ownership with respect to enterprise performances. It is a method for investigating whether a number of variables of interest are linearly related to a smaller number of non-observable factors. The parameters of these linear functions are referred to as loadings. Under certain conditions, the theoretical variance of each variable and the co-variance of each pair of variables are expressed in terms of the loadings and the variance of the error terms. The communality of a variable is the part of its variance that is explained by common factors, whereas, it's specific variance is the part of the variance of the variable that is not accounted for by common factors. The whole approach usually develops in two stages. In the first stage, one set of loadings is calculated and yields theoretical variances and co-variances that fit the observed ones as closely as possible according to a certain criterion. These loadings, however, may not agree with prior expectations, or may not lend themselves to reasonable interpretation. Thus, in the second stage, the first loadings are "rotated" in an effort to arrive at another set of loadings that fit equally well to the observed variances and co-variances, but are more consistent with prior expectations or more easily interpreted.