A European-Type Wage Equation from an American-Style Labor Market: Evidence from a Panel of Norwegian Manufacturing Industries in the 1930s

Bardsen, Gunnar; Doornik, Jurgen; Klovland, Jan Tore Working Paper A European-Type Wage Equation from an American- Style Labor Market: Evidence from a Panel of Norwegian Manufacturing Industries in the 1930s Working Paper, No. 2004/4 Provided in Cooperation with: Norges Bank, Oslo Suggested Citation: Bardsen, Gunnar; Doornik, Jurgen; Klovland, Jan Tore (2004) : A European- Type Wage Equation from an American-Style Labor Market: Evidence from a Panel of Norwegian Manufacturing Industries in the 1930s, Working Paper, No. 2004/4, ISBN 82-7553-230-2, Norges Bank, Oslo, https://hdl.handle.net/11250/2498549 This Version is available at: https://hdl.handle.net/10419/209829 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/deed.no ANO 2004/4 Oslo March 20, 2004 Working Paper Research Department A European-type wage equation from an American-style labor market: Evidence from a panel of Norwegian manufacturing industries in the 1930s by Gunnar Bårdsen, Jurgen Doornik and Jan Tore Klovland ISSN 0801-2504 (printed), 1502-8143 (online) ISBN 82-7553-229-9 (printed), 82-7553-230-2 (online) Working papers from Norges Bank can be ordered by e-mail: [email protected] or from Norges Bank, Subscription service, P.O.Box. 1179 Sentrum N-0107Oslo, Norway. Tel. +47 22 31 63 83, Fax. +47 22 41 31 05 Working papers from 1999 onwards are available as pdf-files on the bank’s web site: www.norges-bank.no, under “Publications”. 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A European-type wage equation from an American-style labor market: Evidence from a panel of Norwegian manufacturing industries in the 1930s Gunnar Bårdsen∗Jurgen Doornik†Jan Tore Klovland‡ 20 March 2004 Abstract Using a newly constructed panel of manufacturing industry data for interwar Norway, we estimate a long-run wage curve for the 1930s that has all the modern features of being homogeneous in prices, proportional to productivity, and having an unemployment elasticity of −0.1. This result is more typical of contemporary European than U.S. wage equations, even if the labour market in interwar Norway possessed distinctively more ‘American’ features than those associated with present-day European welfare states. We also present some new Monte Carlo evidence on the properties of the estimators used. JEL Classification: E24,N24 Keywords: wages, depression, panel data, dynamics ∗Norges Bank and Norwegian University of Science and Technology, Trondheim †Nuffield College, Oxford ‡Norwegian School of Economics and Business Administration, Bergen 1 1Introduction There are two main empirical approaches to the explanation of wage behavior. First, the dynamic Phillips curve, giving a negative relationship between wage growth and the unemployment rate, has a prominent role in the empirical literature. The second approach is the dynamic wage curve, which gives a negative long-run relationship between the wage level and the unemployment rate. The empirical evidence favours the Phillips curve specification for the US, while wage curve specifications dominate the European literature. Blanchflower and Oswald (1994) in particular have made a strong case for the wage curve as a general phenomenon. While they also report wage-curve specifications on US data, their results are refuted by Blanchard and Katz (1997). Blanchard and Katz (1997, 1999) provide an elegant attempt at reconciling the conflicting evidence by utilizing the fact that the Phillips curve is nested within the wage-curve specification. This makes it easy to discriminate empirically between the two models. To illustrate their point, consider the following stylized wage-curve specified as an error correction model (ECM): ∆wt=c+∆pct+α∆qt−δut−α[w−pc −q]t−1+εt,(1) where the variables are nominal hourly earnings W, labor productivity Q, the unemployment rate U, and retail prices PC. Lowercase letters denote natural logarithms of the corresponding variables denoted in capitals, so xt≡ln Xtand growth rates are given as ∆xt≡xt−xt−1. Blanchard and Katz (1999) argue that α=(1−µλ),0·{µ, λ}·1, where (1 −λ)is the direct effect of productivity on the expected real wage, and (1 −µ)is the direct effect of productivity on the reservation wage. Thus, if there are no effects from productivity, so that µ=λ=1,theECM-term[w−pc −q]t−1drops out and the Phillips-curve specification remains. According to Blanchard and Katz (1999), underlying labor market conditions and institutional settings are the crucial determinants of wage behavior with systematic structural differences between Europe and the United States. Productivity effects on wages are assumed to be higher in Europe than in the US, which implies a small magnitude of µand λ. This explains the presence of an ECM-term in European equations. The small magnitude of µis related to the greater role of unions and more stringent hiring and firing regulations in the European labor markets. The smaller European λcould be caused by a bigger informal sector, although the evidence here is less well documented. A more general statement is perhaps that λwill be higher the weaker the rights of workers, and that µwill 2 be higher the more diverse the total labor market is. During the depression years of the interwar period, European manufacturing workers were often in danger of losing their jobs due to business cycle fluctuations. Employment protection and worker rights in Europe were much weaker than in postwar years, and the social security system was not nearly as well developed. Alternative employment opportunities in informal labor markets were largely nonexistent, although some employment could be found in agriculture and fishing, paying subsistence wages. In many respects, interwar European labor markets possess features that are closer to typical American labor settings than to present-day European markets. Empirical analysis of European labor markets in the interwar years may thus provide new and interesting evidence on the two conflicting hypotheses. According to the explanation given by Blanchard and Katz (1999), we should expect to find a Phillips curve rather than a wage curve when looking at European data for the interwar years. On the other hand, it could be that the wage curve model emerges as the best specification. In that case, other theories are called for to explain the differences between US and European wage setting. When looking at evidence to date from the interwar period, the empirical wage equations appear to be somewhat fragile. For the United Kingdom, Hatton (1988), Dimsdale et al. (1989) and Broadberry (1986) estimate several wage equations, including a wage-bargain model and a Phillips-curve type of model, using quarterly time series data, but no empirically well-specified model was obtained. The results from other European countries reported by Newell and Symons (1988) are somewhat more in line with standard wage equations than is the case for Britain, but even here there is only a weak feedback from unemployment to the real wage. One explanation for these conflicting empirical findings may be that wage formation in interwar labor markets was indeed different from the postwar period, thus supporting Blanchard and Katz (1999). Data from the United States indicate a change in the cyclical behavior of real wages between the interwar period and the postwar years.1This fact does not necessarily imply that there were changes in the structural parameters of labor demand and supply equations. Such changes could also stem from differences in the relative magnitudes of labor demand and supply shocks in the two time periods.2 Below, we report empirical evidence on interwar wage equations for one European country, Norway, using GMM estimation methods. Our purpose is twofold: to show that theoretically plausible and 1See Bernanke and Powell (1986) and Hanes (1996) for evidence on the changing cyclicality of real wages. 2On the other hand, Hanes (1996) rejected the hypothesis of relative changes in demand and supply shocks in favour of an explanation in terms of a shift towards more finished goods in the consumption bundle of consumers, making the real consumption wage more procyclical over time. 3 empirically sound wage equations can be found for the interwar period, once a more powerful data set is available and the proper estimation methods are applied. This will then allow us to test the hypothesis of Blanchard and Katz (1999)–that the existence of a wage curve is dependent upon the presence of modern ‘European’ type labor market settings. Most previous studies have been poorly equipped to identify a stable and well identified relationship, being confined to use the relatively small samples of time series data available for the interwar years. Even quarterly data, typically over a period of at most 15 years, provide a limited basis for identifying stable relationships between key variables.3 The novel feature of our approach is to estimate standard wage equations using a panel data set recently constructed by Klovland (1999) for Norwegian manufacturing. Panel data estimation is likely to provide more information than time series estimation over a relatively short sample period, since we can draw inference from the cross-section variation in the data in addition to the time series aspects of the early 1930s. The data base contains annual values of key output and labor market variables for 55 manufacturing industries over the period 1927 to 1939: nominal average hourly earnings, producer price indices, labor productivity (real value added per hour) and, at a somewhat less disaggregated level, unemployment rates. Section 2 briefly presents the general model, which is sufficiently general to encompass wage behavior in this period. Section 3 reviews some features of interwar labor markets in Norway that are of specific relevance to the theories examined here. We report the empirical modelling of the wage equation for the years 1927 — 1939 in Section 4, focusing on the economic interpretation of the results as well as methodological issues related to estimation methods. A fuller discussion of the methodological issues is contained in Appendix A, where we present some new Monte Carlo evidence on the properties of the estimators used. 2 The wage equation A general dynamic specification, nesting equation (1), is (1 −α1L)wit =(β0+β1L)pit +(γ0+γ1L)qit +(δ0+δ1L)uit +(ζ0+ζ1L)pct+ηi+εit.(2) 3The fact that Bernanke (1986) obtained quite well-behaved real earnings equations using US monthly manufacturing data of relatively high quality from the interwar period may indicate that better data may be of some importance. 4 The variables are (logs of) nominal hourly earnings w, producer prices p, labor productivity q,the unemployment rate u, and retail prices pc.4The subscript idenotes the industry, while Lis the lag operator: Lxit =xi,t−1.Thevariableswit,pit,qit and uit are industry-specific, while pctcaptures economy-wide effects that are not transmitted through the unemployment rate. Nominal wage growth responds positively to increases in producer and retail prices, labor productivity, and negatively to increased unemployment. A natural property of a wage equation is that in the long run the nominal wage level is homogenous of degree one with respect to the two price variables (industry-specific output prices and general retail prices), but that there is some degree of wage level stickiness in the short run. A key hypothesis, subjected to empirical testing below, is that productivity growth increases real wages in the same proportion in the long run. The equivalent to Blanchard and Katz’ model in (1) is the ECM reparameterization of (2): ∆wit =β0∆pit +γ0∆qit +δ0∆uit +ζ0∆pct−α1(w−w∗)i,t−1+ηi+εit,(3) where w∗ it is the steady-state wage level w∗ it =µβ0+β1 1−α1¶pit +µγ0+γ1 1−α1¶qit +µδ0+δ1 1−α1¶ut+µζ0+ζ1 1−α1¶pct =β∗pit +γ∗qit +δ∗ut+ζ∗pct.(4) Price level homogeneity requires that β∗+ζ∗=1, while the long-run proportionality of labor productivity implies γ∗=1. Institutional and structural features are reflected in the coefficients of (4). Changes in the impact of institutions on wage setting can therefore be tested by looking at the empirical stability of (4) over the sample period. It is quite likely that wages interact simultaneously with all the explanatory variables. We do, of course, take the possible simultaneity into account when estimating the model by using instrumental variables. 3 Some features of the interwar labor market in Norway After a deflationary period in the mid 1920s, Norway was back on the gold standard at the prewar parity in May 1928.5Manufacturing output, which is shown in Figure 1, was significantly affected by the international depression beginning in the autumn of 1929. The output level of 1929 was not surpassed until 1934, but even this five-year growth pause was a reasonably good performance relative 4We disregard tax rates, which were rather low during the interwar period. 5Klovland (1998) contains some background on the monetary policy in the interwar years. 5 to other countries. The fact that Norway followed pound sterling and went offthe gold standard in September 1931 may be a key factor here, as suggested by the international cross-section analysis in Eichengreen and Sachs (1985). In the second half of the 1930s manufacturing output recovered quite well, very much in line with other Scandinavian countries and other Sterling block countries.6 Increasing labor productivity and capital deepening implied that output could expand significantly without leading to any shortage of labor. Although unemployment went down somewhat in the latter half of the 1930s, it was still high among trade union members in manufacturing at the end of the decade. 1930 1935 100 120 140 Manufacturing output 1930 1935 90 100 110 Retail price index Figure 1: Indices of manufacturing output and retail prices, 1927-1939. 1929=100. A general scheme of unemployment insurance for manufacturing workers guaranteed by the government was not established until 1938.7Before that, only members of trade unions that offered unemployment schemes were entitled to unemployment benefits. About one third of trade union members did not have access to such schemes. The amounts paid were low and fairly constant in real terms, amounting only to about one third of the general wage level in manufacturing. Grytten (2000, p. 34) concludes that ‘it is not likely that the unemployment benefits paid to insured trade unionists gave any significant incentive to stay unemployed’. Furthermore, the level of unionization was relatively modest: roughly one in four workers were trade union members. Unorganized workers and members of unions that did not have unemployment schemes were forced to seek public relief work in case of unemployment. This was of short duration and poorly paid, being about the same level as unemployment benefits from trade unions. Thus reservation wages in the manufacturing industries 6See Klovland (1997) for new data on manufacturing output in Norway and some international comparisons. 7Information on labour market instutions is from Grytten (2000). 6 Table 3: Wage equations, GMM system estimates Dep. var: ∆wit (1) (2) ∆pit −0.10 (0.08) − ∆qit 0.12 (0.08) 0.19 (0.05) ∆uit 0.007 (0.008) − ∆pct0.83 (0.008) 0.62 (0.07) (w−w∗)i(t−1) −0.18 (0.03) −0.27 (0.04) Diagnostics Sargan: χ2() 53.39 (110) 54.09 (50) AR (1) −4.01∗∗ −3.74∗∗ AR (2) −0.07 −0.18 of a long-run wage curve being homogeneous in prices, proportional to productivity, and having an unemployment elasticity of −0.1, produces a statistic with a p-value of 0.27. Finally we impose the steady-state solution wit =0.5pit +qit −0.1ut+0.5pct(5) χ2(4) = 1.81[0.77]. The associated p-value in brackets suggests that this empirical representation of (4) cannot be rejected. It is therefore imposed when we next turn to estimating the dynamic specification in the error correction form given by (3).9 4.3 The dynamic model We have found a theoretically plausible and empirically sound wage equations for the interwar period in Norway, rejecting the hypothesis of Blanchard and Katz (1999) that the existence of a wage curve is dependent upon the presence of modern ‘European’ type labor market settings. The next question is whether the short-run adjustment of wages during the interwar period differed from what is found in empirical studies of the postwar period. We could find no such evidence. Our preferred equation is a quite standard dynamic wage equation, with properties matching those found in comparable studies 9Note that solving for the NAIRU is not possible without further identifying restrictions–see Bårdsen and Nymoen (2003) for the details. 13 of the Norwegian economy during the postwar era. The relevant evidence is reported in Table 3. Column (1) contains the general model reparameterized in error correction form, with the long-run solution (5) imposed. The short-run effects of producer prices and unemployment are insignificant and can be dropped–the joint test statistic has a p-value of 0.31. This is of course in accordance with the corresponding results in Table 1. The final model is reported in column (2).10 There is substantial nominal rigidity, as measured by the ECM coefficient with a value of −0.26.Consequently,adropin inflation is not likely to be reflected in a similar drop in wage growth, as documented by the coefficient of 0.6 on inflation. These magnitudes are similar to the evidence from time-series studies using recent Norwegian manufacturing data by Nymoen (1989) and Johansen (1995), as well as the panel studies of Johansen (1996) and Wulfsberg (1997). It might again be argued that perhaps such results dominate in the latter half of the sample, as Norway recovered from the great depression, instead of reflecting actual behavior during the depressed years in the early 1930s. To investigate this possibility we complete the analysis with recursive estimation of our preferred equation in column (2). The estimated coefficients, together with their approximate confidence bands, are shown in Figure 5, starting from 1932. The coefficients display considerable stability over time, although there is some downward drift in the coefficient on the retail price inflation until 1935. Otherwise there is little evidence of changing behavior during the sample period. 5Conclusions Our empirical analysis does not lend any support to the hypothesis of Blanchard and Katz (1999)– that the presence of a wage curve is due to relatively strong worker rights and alternative labor markets. In the case of Norwegian manufacturing industries during the interwar years, the preferred steady-state wage equation features the standard properties of homogeneity with respect to prices and productivity, and there is an unemployment elasticity of -0.1. We also find much inertia in the dynamics of nominal wages. These results contrast with much of the empirical findings from other countries; such studies often report difficulties with replicating the standard postwar wage models on interwar data. We believe this result mainly stems from the fact that we are able to use a panel data set of 55 manufacturing industries in our econometric analysis, rather than having to rely on a relatively short time series sample. 10The change in coefficients partly reflects changes in the list of instruments. 14 1932 1934 1936 1938 0.0 0.1 0.2 0.3 ∆ qit 1932 1934 1936 1938 0.6 0.8 1.0 1.2 ∆ pct 1932 1934 1936 1938 −0.35 −0.30 −0.25 −0.20 −0.15 −0.10 ecmt−1 Figure 5: Recursive estimates of the model parameters ±two standard errors. References Arellano, M. and S. R. Bond (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies,58, 277—297. Arellano, M. and O. Bover (1995). Another Look at the Instrumental-Variable Estimation of Error- Components Models. Journal of Econometrics,68 , 29—52. Bernanke, B. S. (1986). Employment, Hours, and Earnings in the Depression: An Analysis of Eight Manufacturing Industries. American Economic Review,76, 82—109. Bernanke, B. S. and J. L. Powell (1986). The Cyclical Behavior of Industrial Labor Markets: A Comparison of the Prewar and Postwar Eras. In Gordon, R. J. (ed.), The American Business Cycle, 583—621. University of Chicago Press, Chicago and London. Blanchard, O. and L. F. Katz (1997). What We Know and Do Not Know About the Natural Rate of Unemployment. Journal of Economic Perspectives,11, 51—72. 15 Blanchard, O. J. and L. Katz (1999). Wage Dynamics: Reconciling Theory and Evidence. American Economic Review,89(2), 69—74. Blanchflower, D. G. and A. J. Oswald (1994). The Wage Curve. The MIT Press, Cambridge, Massachusetts. Blundell, R. and S. R. Bond (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics,87, 115—143. Broadberry, S. N. (1986). Aggregate Supply in Interwar Britain. The Economic Journal,96, 467—481. Bårdsen, G. (1989). Estimation of Long Run Coefficients in Error Correction Models. Oxford Bulletin of Economics and Statistics,51, 345—350. Bårdsen, G. and R. Nymoen (2003). Testing Steady-State Implications for the NAIRU. Review of Economics and Statistics (forthcoming). Dimsdale, N. H., S. J. Nickell and N. Horsewood (1989). Real Wages and Unemployment in Britain During the 1930s. The Economic Journal,99 , 271—292. Doornik, J. A. (1999). Object-Oriented Matrix Programming Using Ox. London: Timberlake Consultants Press and Oxford: www.nuff.ox.ac.uk/Users/Doornik, 3rd edn. Doornik, J. A., M. Arellano and S. R. Bond (1999). Panel Data Estimation Using DPD for Ox. Mimeo. Eichengreen, B. and J. Sachs (1985). Exchange Rates and Economic Recovery in the 1930s. Journal of Economic History,45, 925—946. Grytten, O. H. (1994). En empirisk analyse av det norske arbeidsmarked 1918-1939: Arbeidsledigheten i Norge i internasjonalt perspektiv (An Empirical Analysis of the Norwegian Labour Market, 1918- 1939: Norwegian Interwar Unemployment in International Perspective). Ph.D. thesis, Norwegian School of Economics and Business Administration, Bergen. Grytten, O. H. (2000). A Comparison of the Living Standards of the Employed and Unemployed in Inter-war Norway. Unpublished manuscript, Norwegian School of Economics and Business Administration, Bergen. Hanes, C. (1996). Changes in the Cyclical Behavior of Real Wage Rates, 1870-1990. Journal of Economic History,56, 837—861. 16 Hatton, T. J. (1988). A Quarterly Model of the Labour Market in Interwar Britain. Oxford Bulletin of Economics and Statistics,50, 1—25. Johansen, K. (1995). Norwegian Wage Curves. Oxford Bulletin of Economics and Statistics,57, 229—247. Johansen, K. (1996). Insider Forces, Asymmetries, and Outsider Ineffectiveness: Empirical Evidence for Norwegian Industries 1966—87. Oxford Economic Papers,48, 89—104. Johansen, K. (1999). Insider Forces in Wage Determination: New Evidence for Norwegian Industries. Applied Economics,31, 137—147. Klovland, J. T. (1997). The Silver Age of Manufacturing in Norway: New Estimates of the Growth of Industrial Production 1927 - 1948. Scandinavian Economic History Review,45, 3—29. Klovland, J. T. (1998). Monetary Policy and Business Cycles in the Interwar Years: The Scandinavian Experience. European Review of Economic History,2, 309—344. Klovland, J. T. (1999). Accounting for Productivity Growth in Norwegian Manufacturing Industries 1927 - 1959. Discussion Paper 21/99, Norwegian School of Economics and Business Administration, Bergen. Newell, A. and J. Symons (1988). The Macroeconomics of the Interwar Years: International Comparisons. In Eichengreen, B. and T. Hatton (eds.), Interwar Unemployment in International Perspective, 61—96. Kluwer, Dorcrecht. Nymoen, R. (1989). Modelling Wages in the Small Open Economy: An Error-Correction Model of Norwegian Manufacturing Wages. Oxford Bulletin of Economics and statistics,51, 239—258. Windmeijer, F. (2000). A Finite Sample Correction for the Variance of Linear Two-Step GMM Estimators. Working Paper W00/19, The Institute for Fiscal Studies. Wulfsberg, F. (1997). An Application of Wage Bargaining Models to Norwegian Panel Data. Oxford Economic Papers,49, 419—440. 17 A A simulation experiment of the properties of the estimators The homoskedastic DGP in Arellano and Bond (1991) is: yit =αyi,t−1+βzi1+ηi+vit,ηi∼N[0,1] vit ∼N[0,1] i=1,...,N, t=1,...,T zit =ρzi,t−1+eit,e it ∼N[0,σ2 e]. This DGP is used in Doornik et al. (1999) to illustrate how the system GMM estimator (Sys)gives more precise estimates of the autoregressive parameter αthan the differenced GMM estimator (Diff) when αis close to unity. It was also noted that Diffunderestimates α,whereasSys produces an overestimate. While Doornik et al. (1999) keep βfixed at unity, we now proceed to keep αfixed at 0.9, and vary β.WesetN= 100,andT=7(5after allowing for lags and differences). The two estimators can be summarized as: transformation regressors instruments estimation Diff∆∆yi,−1,∆xi,1diag(yi,t−3yi,t−2),∆xi,11-step Sys ∆∆yi,−1,∆xidiag(yi,t−3yi,t−2),∆xi1-step levels: yi,−1,xi,1diag(∆yi,t−2),xi,1 When T=5, for example, the instruments Zin Diffestimation are: Zi= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ yi00000∆xi,21 0yi0yi100∆xi,31 000yi1yi2∆xi,41 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ . This assumes that initially the available observations are t=0,...,4. One observation is lost owing to the lagged dependent variable, and one more by differencing. For Sys estimation the instruments for the differenced equations (Z∗) and level equations (Z+)are: Z∗ i= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ yi00000∆xi,2 0yi0yi100∆xi,3 000yi1yi2∆xi,4 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ ,Z + i= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ ∆yi100xi,21 0∆yi20xi,31 00∆yi3xi,41 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ Some results for M= 1000 Monte Carlo replications are presented in Figure 6. MCSD is the standard deviation of the estimated ˆα. The results can be compared with Table 1 of Arellano and Bond (1991) (but we use instruments t−2,t−3instead of all possible lags from t−2onwards), and Table 2 of Blundell and Bond (1998) (but with larger T, and an additional regressor). 18 0.00 0.25 0.50 0.75 1.00 −1.0 −0.5 0.0 bias ^ α Diff β bias ^ α 0.00 0.25 0.50 0.75 1.00 −1.0 −0.5 0.0 Sys β Figure 6: Mean bias of ˆα,M= 1000,α=0.9,ρ=0.8,σ2 e=0.9;barsaretwicetheMCSD;β= 0,0.1,0.3,0.5,0.7,0.9,1. The results are dramatic. Despite the fact that the generated xis kept constant in replications, the bias of the Diffestimator is enormous for small values of β; for example when β=0.3,themean estimated ˆαis close to 0.5.Sys again overestimates α, but is much better behaved. These results shed some light on Table 2: the large discrepancy between the Diffand Sys results reported there corresponds to a low value of βin Figure 6. The bias in ˆ βis never so dramatic, ranging from about 0.01 to −0.04 for Diff,andfrom0.01 to −0.08 for Sys. BTheData The wage, price and productivity series are annual data 1927 - 1939 for 55 manufacturing industry groups, see Klovland (1999) for further details as to coverage and sources. The unemployment data are taken from Grytten (1994). These are only available at a more aggregated level; data for 11 industry groups were distributed on the 55 subgroups. The retail price index is taken from Historical Statistics 1948 (Statistics Norway, Oslo, 1949). The data definitions are: W = nominal hourly earnings Average hourly earnings of (male and female) production workers, calculated as total wage sum divided by hours worked by production workers. P = producer prices Paasche price index of industry gross output, shifting base year every third year. Q = labor productivity Real industry value added divided by total hours worked. Total hours 19 also include an estimate of hours worked by non-production workers. U = unemployment rate based on unemployed registered at public labor exchanges, classified by industry groups. PC = retail price index 20 21 WORKING PAPERS (ANO) FROM NORGES BANK 2002-2004 Working Papers were previously issued as Arbeidsnotater from Norges Bank, see Norges Bank’s website http://www.norges-bank.no 2002/1 Bache, Ida Wolden Empirical Modelling of Norwegian Import Prices Research Department 2002, 44p 2002/2 Bårdsen, Gunnar og Ragnar Nymoen Rente og inflasjon Forskningsavdelingen 2002, 24s 2002/3 Rakkestad, Ketil Johan Estimering av indikatorer for volatilitet Avdeling for Verdipapirer og internasjonal finans Norges Bank 33s 2002/4 Akram, Qaisar Farooq PPP in the medium run despite oil shocks: The case of Norway Research Department 2002, 34p 2002/5 Bårdsen, Gunnar, Eilev S. 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Humphrey and Bent Vale Scale economies, bank mergers, and electronic payments: A spline function approach Research Department 2003, 34p 2003/6 Harald Moen Nåverdien av statens investeringer i og støtte til norske banker Avdeling for finansiell analyse og struktur 2003, 24s 2003/7 Geir H. Bjønnes, Dagfinn Rime and Haakon O.Aa. Solheim Volume and volatility in the FX market: Does it matter who you are? Research Department 2003, 24p 2003/8 Olaf Gresvik and Grete Øwre Costs and Income in the Norwegian Payment System 2001. An application of the Activity Based Costing framework Financial Infrastructure and Payment Systems Department 2003, 51p 2003/9 Randi Næs and Johannes A.Skjeltorp Volume Strategic Investor Behaviour and the Volume-Volatility Relation in Equity Markets Research Department 2003, 43p 2003/10 Geir Høidal Bjønnes and Dagfinn Rime Dealer Behavior and Trading Systems in Foreign Exchange Markets Research Department 2003, 32p 2003/11 Kjersti-Gro Lindquist Banks’ buffer capital: How important is risk Research Department 2003, 31p 2004/1 Tommy Sveen and Lutz Weinke Pitfalls in the Modelling of Forward-Looking Price Setting and Investment Decisions Research Department 2004, 27p 2004/2 Olga Andreeva Aggregate bankruptcy probabilities and their role in explaining banks’ loan losses Research Department 2004, 44p 2004/3 Tommy Sveen and Lutz Weinke New Perspectives on Capital and Sticky Prices Research Department 2004, 23p 2004/4 Gunnar Bårdsen, Jurgen Doornik and Jan Tore Klovland A European-type wage equation from an American-style labor market: Evidence from a panel of Norwegian manufacturing industries in the 1930s Research Department 2004, 22p