Peter ; Benczur
email: benczurp@mnb.hu

Péter Benczúr

Attila Rátfai
email: ratfaia@ceu.hu

Economic Fluctuations in Central and Eastern Europe. The Facts *

Keywords: E32 -Business Fluctuations|Cycles < E3 -Prices, Business Fluctuations Business cycles, Central and Eastern Europe Business Cycles, Central and Eastern Europe JEL Classification: E32

This paper provides a detailed empirical analysis of quarterly frequency dynamics in macroeconomic aggregates in twelve countries of Central and Eastern Europe. It shows that business fluctuations in CEE countries are in general more pronounced than in developed ones, and are of similar size as in other emerging market economies. Private consumption is particularly volatile. Relative to major developed economies government spending is dominantly procyclical, and net exports are strongly countercyclical. The most frequent country outliers are the high inflation countries of Bulgaria, Romania and Russia, especially in labor market, price and exchange rate variables. Excluding these countries from the sample makes many of the observed patterns in cyclical dynamics more homogenous, and broadly similar to ones established in developed economies.

INTRODUCTION

The pure notion of the business cycle is a novelty for many observers, policymakers and citizens in the post-socialist countries of Central and Eastern Europe (CEE). Though economic fluctuations have been severely mixed with the transition bust and boom, by now it seems evident that these economies are also subject to ups and downs, regardless of the initial transition shock and the following catch-up process. While direct evidence on business fluctuations is becoming available from an increasing number of individual countries, although often using somewhat different measurements, statistics and time periods, no study has aimed at documenting business cycle facts in a major segment of emerging market countries, the economies of Central and Eastern Europe. In the current project, this is the task we pursue.

We seek to answer a number of specific questions. Is there a common pattern in CEE business cycle fluctuations? Can one treat certain variables as systematically leading or lagging the business cycle? Can one identify certain country characteristics, such as monetary policy regime, size, openness in goods and financial markets that are associated with these differences?

Are there important similarities and differences in the behavior of macroeconomic aggregates vis-à-vis developed, or other emerging countries? The findings are also meant to provide input for economic policies in these countries. For instance, in the process of joining the EU and the EMU, can policy-makers treat CEE countries as a relatively homogeneous group, or do they need to be considered on an individual basis? Understanding the cyclical frequency dynamics of key macroeconomic aggregates can also assist policymakers to identify the most important short-term policy targets, instruments and mechanisms.

To address this set of issues, we analyze the cyclical behavior of quarterly frequency time series of twenty-two major macroeconomic variables in twelve emerging market economies in the CEE region. Despite their similarity in geographical position and basic economic structure, these economies show a significant amount of variation in the strength of trading ties to the EU, policy arrangements, and country size. By studying a large group of emerging market countries with similar, still somewhat diverse institutions, we are seeking to establish regularities that are more general than pure country-specific effects, and point to insights of potential interest for business cycle theory. 2While our exploration of facts is not driven by any specific model economy, the evidence we report on is motivated by and informs modern quantitative models of the business cycle. In particular, without taking a prior stand on the particular source of shocks (e.g. Excluding pre-1993 data from the sample is driven by a number of considerations. First, some of the countries we study simply did not exist before 1993, or did not systematically collect data at the quarterly frequency. Second, major data revisions having taken place in the early 1990s render the quality of these early data highly questionable. Third, as documented in [START_REF] Artis | Characterizing the Business Cycle for Accession Countries[END_REF], the big, pre-1993 'transition shock' manifesting itself as a structural break in output series would make the interpretation of the cycle as deviation from a smooth trend questionable.

To ensure cross-country comparability in time periods, underlying shocks and data quality, we thus restrict our attention to post-1993 quarterly data.

While all variables are available in just about every country over the whole sample period, some of the countries have an imperfect record. In Hungary, Lithuania, Poland, Russia and Slovenia reliable figures for GDP and its components are available only from 1995:1, in Bulgaria, Croatia, the Czech Republic and Romania from 1994:1 onwards. Data on net capital flows in Poland is available only from 2000:1. Total employment in Latvia and industrial employment in Lithuania are missing, making the corresponding productivity variables unavailable too.

Our primary data sources are numerous, including local central banks, statistical offices and research institutes, the International Financial Statistics of the IMF, the OECD, ILO and the WIIW databases. When multiple sources exist, we always select the most credible variant. In the end, we employ a uniquely comprehensive dataset over a decade long period of economic transformation, the largest meaningful panel of such observations in terms of time frame and country coverage. We believe that the quality of the sample is as good as one can hope for in this context, even in comparison to developed economy samples. 6 Prior to the empirical analysis, the raw data are transformed in several steps. First, all variables are de-seasonalized using the X11 procedure, with multiplicative adjustment; the exceptions being inflation and the interest rate, where the adjustment is additive. For computing ratios, and other generated variables, we use the seasonally adjusted series; i.e. the ratios are not adjusted any further. As no de-trending procedure is free of criticism, we employ three alternative filtering procedures popular in the literature, such as the Hodrick-Prescott (H-P) filter (with parameter 1600), log first differencing (potentially problematic with trending variables), and fitting a quadratic time polynomial. These choices coincide with the ones used in [START_REF] Christodoulakis | Comparison of Business Cycles in Greece and the EC: Idiosyncracies and Regularities[END_REF] and [START_REF] Fiorito | Stylized Facts of Business Cycles in the G7 from a Real Business Cycle Perspective[END_REF].

rates. Other potentially relevant variables in DSGE models, for instance such as hours worked, terms of trade, FDI, or more detailed productivity figures are in general unavailable at the quarterly frequency. 6 Our project website http://www.personal.ceu.hu/departs/personal/Attila_Ratfai/data/benczur-ratfai_webpage.htm gives a full description of data definition, construction and sources. In most cases filtering is applied to the natural logarithm of the variables. Exceptions include inflation and the nominal interest rate, which are already in log-difference form so these series are directly filtered. Other exceptions are net exports and net capital flows, which can take on both negative and positive values. Similarly to [START_REF] Kydland | Is the Business Cycle of Argentina Different?[END_REF] and [START_REF] Agénor | Macroeconomic Fluctuations in Developing Countries: Some Stylized Facts[END_REF], we employ the ratio of net exports to GDP in percentage terms.7 Also, we compute the net capital flows to GDP ratio using dollar denominated data for both variables. In all other cases, taking logs and then de-trending delivers country-specific normalization. Finally, labor productivity is calculated both in economy-wide and industry-level data.

RESULTS

It is first useful to have a bird-eye view of the output data. As randomly selected examples, Figures 1 and2 show the evolution of GDP and industrial output in Estonia and Poland. The graphs confirm that GDP, and especially industrial output show notable ups and downs around a strong upward trend. One can clearly see an initial transition bust, followed by a robust expansion, in some instances broken by the apparent effect of the Russian crisis. In some quarters, growth has picked up, with an unclear cyclical behavior through the global slowdown starting around 2000. Overall, the emerging picture points to some noticeable though not systematic cyclical patterns.

We now turn to basic summary statistics of output fluctuations in CEE countries, and compare them to ones documented in other country groups. For the cyclical component in all variables, the following four statistics are reported: volatility (standard deviation) in absolute terms and relative to output, cyclicality (the size and the lead/lag position of the highest correlation in absolute value between the variable itself, and lagged and leaded output) and persistence (first-order autocorrelation coefficient). We use constant price GDP as our measure of output. 10 We derive results for three alternative filtering procedures: Hodrick-Prescott (H-P), time polynomial and first difference. In the sense that first differenced series tend to show little persistence and cyclicality, difference cycles in CEE economies are largely non-existent. To save space, we thus do not report these figures. At the same time, the H-P and the time polynomial filters tend to produce virtually identical cyclical outcomes. Consequently, we focus on results in H-P filtered data below. 11

Expenditure variables

Private consumption. The absolute and relative volatility of private consumption is exceptionally high; indeed, it is higher in all CEE countries than in the US. Some of the CEE countries have even higher consumption volatility than other emerging countries, such as 9 The 95% significance level benchmark we use throughout is approximated by 2 0.3 T ≈ . 10 The detailed results with industrial production serving as a proxy for output are available upon request. 11 The full set of results is available in the non-for-publication appendix. Argentina, Mexico and Turkey. 12 The comparison with the EU and the G7 country group studied in [START_REF] Christodoulakis | Comparison of Business Cycles in Greece and the EC: Idiosyncracies and Regularities[END_REF] and [START_REF] Fiorito | Stylized Facts of Business Cycles in the G7 from a Real Business Cycle Perspective[END_REF] also shows instructive patterns. For instance, the UK has the largest relative volatility of 1.15 in the G7 group, a figure being on the same order of magnitude as some of the smallest relative volatilities in the current sample with 1.05 in Lithuania and Poland, and 1.03 in Russia. The relative volatility figure of 0.71 in Slovenia is a clear outlier. While high consumption volatility contradicts the predictions of business cycle models with household preferences for consumption smoothing, potential explanations for this puzzle are manifold. First, one of the explanations could be the dominance of durable consumption, a particularly important and volatile component of private consumption, especially in CEE economies characterized by rapid income growth and fast drifting consumer behavior. A complementary argument is the presence of liquidity constraints in economies with underdeveloped financial systems. It might also be the case that consumers face particularly uncertain income prospects, resulting in strong precautionary motives to save and excess sensitivity in consumption. Finally, as argued in [START_REF] Aguiar | Emerging Market Business Cycles: The Cycle is the Trend[END_REF], high volatility in consumption may also stem from the dominance of permanent shocks to trend growth, a particularly pervasive feature of many emerging economies.

With the exceptions of Latvia being countercyclical and Lithuania acyclical, private consumption is also highly procyclical. The contemporaneous correlation between consumption and GDP is always positive, typically significantly so. The magnitude of the correlation coefficients appears to be similar to ones found in developed economies. Persistence in consumption is in general significant, though lower than in the US. The main outlier is Latvia, with a tiny autocorrelation coefficient.

Investment. Investment is strongly procyclical and is often coincidental. Latvia is an exception again. Investment is also the most volatile component of aggregate spending in all CEE countries. Though we measure investment as gross fixed capital formation, thereby excluding its most volatile component inventories, its absolute volatility is very high in international comparison, especially relative to developed countries. At the same time, the relative volatility figures are strikingly similar to ones found in many other samples. 13Nonetheless, excessive volatilities might stem from measurement problems such as classification of certain capital items, or simply the privatization of a large portion of previously government owned physical assets. Countries show mixed patterns in persistence. Interestingly, in some countries such as Latvia, Romania, and to a smaller degree, Hungary and Slovenia, low persistence is coupled with low synchronization. Governments still play a central role in many CEE economies. At the same time, prudential fiscal policy is one of the key criteria of EU and EMU accession. For these reasons, in these countries budget items are often moved across years or budget categories, creating extra volatility in spending, and transforming fiscal dynamics in artificial ways. Having said that, government consumption in CEE countries appears to be more volatile than in developed and about as volatile as in emerging market countries. In addition, government spending tends to be more volatile than private consumption, and less volatile than investment in the sample. While Croatia, the Czech Republic and Hungary are acyclical, and Estonia is countercyclical, government consumption in general is procyclical, somewhat more so than in developed economies. The fact that governments tend to boost spending in cyclical upturns and do the reverse in downturns suggests that fiscal policy magnifies rather than mitigates fluctuations in CEE economies. 14 The persistence in government consumption is moderate.

Net exports. With the exceptions of Hungary and Romania showing acyclical trade balance, all signs of the cyclicality statistics are negative, though sometimes only marginally so, as predicted by standard open economy models with technology shocks, and in line with the experience in other emerging and developed economies. 15 Russia, major exporter of raw materials shows a number of sizeable and positive lead coefficients as well. While they tend to be the least volatile component of GDP in absolute terms, net export volatilities in CEE economies are substantially higher than in developed ones. Finally, countries with the highest persistence in net exports, Russia and Slovakia show particularly high volatility as well.

Imports. The volatility of imports relative to GDP tends to be larger than the one for developed economies. In relative terms, imports are the most volatile in Slovakia, perhaps due to heavy re-exporting activities. Croatia, Lithuania and Russia show particularly high, while the Czech Republic and Slovenia particularly low absolute volatilities. Just like in G7 countries, imports are always markedly procyclical and close to being coincidental in all countries.

Exports. Again, relative export volatilities in CEE countries tend to exceed those in developed countries. Exports are least volatile in Russia and Slovenia, both in absolute and relative terms. Exports are much less procyclical than imports; indeed, they are sometimes acyclical, or even mildly countercyclical as in Bulgaria. Exports are especially procyclical and persistent in countries with the most open goods and capital markets, such as the Baltic countries and Hungary. Nonetheless, exports are also procyclical and moderately persistent in major commodity exporter countries, such as Romania and Russia. 14 For similar international evidence in annual frequency data, see [START_REF] Kaminsky | When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies[END_REF]. 15 See [START_REF] Aguiar | Emerging Market Business Cycles: The Cycle is the Trend[END_REF], [START_REF] Backus | International Business Cycles: Theory and Evidence[END_REF] and [START_REF] Neumeyer | Business Cycles in Emerging Economies: The Role of Interest Rates[END_REF]. Labor market variables Employment. We examine both total employment and employment in industry. In general, employment in CEE countries tends to be slightly more variable than in developed ones, both in absolute and relative terms. Bulgaria shows particularly high absolute volatility, while the Czech Republic and Slovenia a particularly low relative one. Cyclical patterns in employment in CEE countries are similar to G7 ones as documented in [START_REF] Fiorito | Stylized Facts of Business Cycles in the G7 from a Real Business Cycle Perspective[END_REF]; with the exception of Estonia (only industrial employment), the Czech Republic (only industrial employment, with a short time series), Slovenia and Croatia, employment is highly procyclical. Conforming again to the evidence in G7 economies, one can detect phase shifts, especially in total employment. In particular employment is often lagging the cycle in CEE economies, pointing to theories of the business cycle with labor hoarding considerations. 16 The cyclical component of employment is also highly persistent.

Real wage. The relative volatility of real wages is notably higher here than in G7 economies, particularly so in Hungary and Russia. In contrast to the acyclical or mildly procyclical pattern observed in developed economies, significant positive cyclical patterns dominate negative and zero ones, though phase shifts depict a mixed picture. Volatile and procyclical real wages, a key component of marginal costs, are consistent both with workers on their labor supply curve responding to technology shocks in RBC models, and with countercyclical markups when firms do not adjust prices to demand disturbances in monetary models. At the same time, in the presence of preference or government expenditure shocks, equilibrium models of the business cycle are more consistent with countercyclical real wages.

The differential patterns observed in real wage cyclicality points to cross-country variation in the relative importance of supply and demand shocks. Lastly, we note that real wages tend to be persistent, with the exception of Estonia.

Productivity. We study both economy-wide (total) and industrial labor productivity.

Absolute and relative volatilities in total productivity are in general fairly high in most countries, often exceeding similar statistics in developed economies. The absolute volatility of total productivity appears to be low in the 'Visegrad Group' of the Czech Republic, Hungary, Poland and Slovakia. At the same time, industrial productivity is exceptionally volatile in Bulgaria, Estonia and Romania. Productivity is strongly procyclical and typically coincidental. Exceptions include only marginally procyclical total productivity in Slovakia, countercyclical industrial productivity in Bulgaria and Slovakia, and acyclical industrial productivity in Poland. With the exception of Slovenia, the data also show medium to high persistence in cyclical productivity. 16 See for example [START_REF] Mckay | The Brevity and Violence of Contractions and Expansions[END_REF].

Monetary and financial variables

Private sector credit. Unlike [START_REF] Agénor | Macroeconomic Fluctuations in Developing Countries: Some Stylized Facts[END_REF], we find some pronounced pattern in the current sample. The relative volatilities in many countries appear to be fairly high, especially in Latvia. Absolute volatilities in Bulgaria, Latvia and Romania are truly astronomic, likely to be explained by the financial crises in the mid-to late-1990s. Private sector credit is dominantly procyclical and strongly persistent. As pointed out by [START_REF] Agénor | Macroeconomic Fluctuations in Developing Countries: Some Stylized Facts[END_REF], a strong positive coefficient could have important consequences for the cost of monetary tightening if credit leads the cycle. In the current sample however private credit is dominantly lagging the cycle. In Bulgaria, Latvia, Lithuania and Russia, significant negative lead correlation coefficients are followed by positive lag ones, again, potentially explained by the financial crisis episodes.

Money. Relative volatilities in the sample are only somewhat larger than the ones in the US or in the G7 economies. Absolute volatility in M1 is particularly high in Bulgaria, and to a lesser extent in Croatia, Russia and Slovakia. Given the high or moderate inflation history in most CEE countries, large volatility in money creation should come as no surprise. In absolute terms, M1 is least volatile in countries maintaining versions of managed float exchange rate policies, Hungary and Slovenia. Apart from Hungary and Slovakia, absolute volatilities in M2 are large, larger than for the G7 and the EU group, other than France, but never as high as say in Argentina. M2 is highly volatile in Bulgaria, Croatia, Latvia and Russia. M1 is in general persistent, procyclical, and rather leading or coincidental. At the same time, in Estonia and Latvia one can observe large cyclical coefficients in M1 of both signs at various leads and lags.

The same applies to M2 in Latvia and Romania. M1 in Hungary shows a unique pattern with correlations being insignificant at all leads and lags. By exhibiting large negative but no sizeable positive correlation between money and output, Bulgaria also stands out. [START_REF] Kydland | Is the Business Cycle of Argentina Different?[END_REF] also find M1 to be countercyclical using their "new version" of GDP in Argentina, a country plagued by a history of particularly deep financial crises. Money moving the opposite direction to output is however undocumented in other samples. Overall, M1 and M2 behave similarly; both variables tend to be procyclical or acyclical, in accordance with the evidence in G7 economies. Without imposing further structure of the data, these findings are consistent with alternative interpretations of the business cycle, including ones that posit monetary disturbances as the fundamental source of aggregate fluctuations, and ones supporting the endogenous determination of the money supply.

CPI. Since a large and changing fraction of prices is in the regulated category in CEE economies, one would not expect a very clear cyclical pattern of the CPI. Somewhat 11 surprisingly, most of the countries still exhibit a countercyclical behavior of the price level. This behavior is similar to that of the G7, and is often interpreted as supporting the classical approach to economic fluctuations with shifting aggregate supply along a stable aggregate demand curve.

Countercyclical prices are weakly leading or coincidental. The CPI is strongly procyclical in Poland and Russia, and marginally procyclical in Lithuania. Prices in Croatia, the Czech Republic, Hungary, Poland, Slovakia and Slovenia vary only moderately. Reflecting the large nominal shock associated with the financial crises periods in the second half of the 1990s, prices are particularly volatile in Bulgaria, Romania and Russia. The Baltic countries, holding close trading ties with Russia, appear to constitute another group with moderately high absolute volatility figures. Overall, the CPI in CEE economies exhibits much larger absolute volatility than in developed ones. The CPI is also in general highly persistent in most countries. Croatia and Slovenia have the least persistent and least volatile CPI.

Inflation. [START_REF] Chadha | Are Prices Countercyclical? Evidence from the G7[END_REF] argue that it is the behavior of inflation and output that reflects the relative importance of demand-versus supply-driven versus supply-driven disturbances. Though the relevant negative correlation coefficients outnumber the positive ones, the small size of the largest coefficients and the highly mixed pattern in leads and lags make inflation show no unambiguous cyclical properties. Inflation is not particularly volatile in most countries, the exceptions again being Bulgaria, Romania and Russia. Russia and Estonia also stand out by having inflation series that are quite persistent and highly negatively correlated with GDP. It is also notable that inflation is procyclical in countries with relatively more flexible exchange rate regimes, such as the Czech Republic, Hungary, Poland and Romania.

Net capital flows. Net capital flows in CEE economies are in general quite volatile, much more volatile than in developed ones, with Hungary and Slovakia exhibiting the highest volatilities. 17 The relatively large and closed economies of Poland, Romania and Russia exhibit the lowest volatilities. Also, net capital flows are universally more volatile than net exports.

Although no particularly strong patterns appear to emerge, capital flows tend to be somewhat procyclical. They are marginally countercyclical only in Bulgaria and Slovenia. Possibly explained by the impact of the financial crisis in 1998, Russia shows significant positive coincident and lagged coefficients. Consistently with the presence of significant barriers to international capital flows in CEE countries, with the exception of Russia, net capital flows show very low persistence. Indeed, other than in Lithuania, they are much less persistent than net exports.

Nominal interest rate. Interest rates proxied by the nominal lending rate are extremely variable in Bulgaria, Russia, and somewhat in Romania. In other countries they exhibit very 17 See [START_REF] Broner | Why Are Capital Flows So Much More Volatile in Emerging than in Developed Countries?[END_REF]. Volatilities do not seem to be strongly related to country size, openness or monetary regime; they are rather associated with the impact of single exchange rate episodes in particular countries. Nonetheless, economies with more volatile nominal exchange rates seem to have more volatile price levels as well. While all series are highly persistent, the cyclical correlations and phase shifts show entirely mixed patterns.

Real effective exchange rate. Relative volatilities in real exchange rates are in general lower than the ones for nominal rates. The only country in which absolute volatility in real effective exchange rates exceeds the corresponding nominal figure is the Czech Republic. While real exchange rate volatility figures show more uniformity than nominal ones, again, in absolute terms they are particularly volatile in Bulgaria, Romania and Russia. Relative volatility is quite high in Poland and Russia, indicating that the exchange rate could be rather a source than an absorber of shocks in these countries. Comparing patterns in cyclicality in real with that in nominal exchange rates, we find sign switches in Romania and to some degree, Russia; otherwise signs, and often phase shifts remain unchanged. This is the sense in which purchasing power parity is at work in cyclical exchange rate data. The small number of positive lead coefficients in this and the previous table however seem to indicate that the exchange rate channel is not particularly strong in CEE economies, relative to the interest rate channel. [START_REF] Corsetti | Productivity, External Balance and Exchange Rates: Evidence on the Transmission Mechanism among G7 Countries[END_REF] argue that in response to technological shocks, the real exchange rate is more likely to appreciate in larger and more closed economies. The results give some support to this prediction. In countries where the real wage is procyclical suggesting a dominant role for technological shocks, it is only the small and very open Lithuania exhibiting a negative correlation between output and the real exchange rate; while larger and more closed Bulgaria, Croatia, Russia, and partly Romania all show signs of appreciation (depreciation) associated with an output boom (bust). Finally, real rates are persistent, though the degree of persistence tends to be slightly lower than the one in nominal exchange rates. [START_REF] Kydland | Is the Business Cycle of Argentina Different?[END_REF] for GDP and IP in Argentina (old / new estimates); Agenor et al (2000) for IP in all other developing countries; Alper (2003) for GDP in Mexico and Turkey; Burgoeing and Soto (2000) for GDP in Chile; [START_REF] Fiorito | Stylized Facts of Business Cycles in the G7 from a Real Business Cycle Perspective[END_REF] for GDP and IP in G7 countries; Christodoulakis et al (1995) for GDP and IP in EU countries; authors' calculation for GDP and IP in CEE countries.

F

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

  Our sample ideally consists of 48 quarterly observations from 1993:01 to 2004:04.

  these findings are consistent with shocks to productivity playing an important role in driving economic fluctuations.

  Though the figures are not always significant, nominal interest rates tend to show positive lagging, and negative leading correlation coefficients. With Croatia and Russia as notable exceptions, one may interpret this pattern as evidence for the interest channel in monetary transmission, at least in the sense of Granger-causality. Nominal interest rates are also markedly persistent, with the exceptions of Croatia and Slovenia.Nominal effective exchange rate. Exchange rates in Bulgaria and Russia show exceptionally high absolute and relative volatilities. Absolute volatilities are also quite high in Estonia, Latvia, Lithuania and Romania. These observations are partly explained by the few large discrete jumps in the nominal exchange rate associated with policy regime changes, partly by high the high inflation episodes, especially in Bulgaria, Romania and Russia. On the other hand, Croatia, the Czech Republic and Slovenia show particularly low relative volatilities.

Table I

 I

	third one is sometimes so. Persistence is particularly high in G7 and some CEE economies, and
	low in Spain and Slovenia.
	As industrial production is a popular proxy for output in the related literature, we also
	examine this variable in some more detail. The first panel of Table II displays the relevant
	volatility, cyclicality and persistence figures. Comparing these figures to the corresponding ones
	in Table I indicates that industrial output is highly volatile, about as volatile as in other emerging
	countries. Volatilities are reasonably similar across CEE countries. Industrial output is in
	general strongly procyclical and often synchronous. 9 With the generally low correlation
	coefficients and persistence measures, Slovakia and Slovenia are major outliers. Interestingly,
	while the H-P filtered series show high persistence, first differenced industrial production series
	(not reported) tend to be close to white noise.
	Tables II through IV summarize the results for the three major groups of variables we F examine, constant price output components (consumption, investment, government o r consumption, net exports, imports, exports), labor market variables (employment, real wages,
	productivity), and monetary and nominal variables (private sector credit, M1, M2, CPI, P e inflation, net capital flows, nominal interest rates, nominal and real effective exchange rates).
	e r
	reports measures of volatility and persistence in H-P-filtered measures of output. Overall, output is somewhat more R e v volatile in CEE countries than in developed economies, and is about as volatile as in other emerging ones. 8 Average GDP volatility in CEE countries is a bit lower than in the small i e number of emerging market countries with data available, and higher than in EU countries. Hungary appears to be a clear outlier here, and Slovakia also features relatively low GDP w
	volatility. It is interesting to observe that the most volatile countries, Bulgaria, Romania and
	Russia are also countries with the highest annual inflation rates, with above 40 percent inflation
	on average over the sample period. The persistence in H-P filtered output is broadly similar in
	all countries listed in the table; the first two autocorrelations are typically significant, and the
	6

  The goal of the present work is to document facts of business cycle fluctuations in a major segment of emerging market economies, the countries of Central and Eastern Europe. The evidence in general suggests that real business cycle models with shocks to productivity can account for a number of fundamental features of the data. Indeed, many countries in the sample, including Croatia and the accession group (the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia) show broadly similar cyclical behavior to developed economies. The most frequent country outliers are the high inflation countries of Bulgaria, Romania and Russia, especially in labor market, price and exchange rate variables.

		4	TAKING STOCK Figure 1: Estonia Figure 2: Poland
	10.4 12.3			.08 .08
	10.3			.06 .06
	10.2 12.2			.04 .04
	10.1 12.1			.02 .02
	10.0			.00
	9.9 12.0			-.02 .00
	9.8 11.9			-.04 -.02
	9.7			-.06 -.04
	9.6 11.8	o r F o r F o r 93 94 95 96 97 98 99 00 01 02 03 04 Log GDP HP trend Fitted quadratic polynomial F o r 95 96 97 98 99 00 01 02 03 04 Log DP HP trend Fitted quadratic polynomial F o r	-.08 -.06	93 94 95 96 97 98 99 00 01 02 03 04 Deviation of LGDP from HP trend Deviation of LGDP from fitted polynomial First difference of LGDP 95 96 97 98 99 00 01 02 03 04 Deviation of LGDP from HP trend Deviation of LGDP from fitted ploynomial First difference of LGDP
	4.8 4.9 5.0 5.1 4.7 4.9 4.8	P e e r P e e r P e e r .04 .08 .12 P e e r P .08 e .04 e r
	4.2 4.4 4.5 4.6 4.7 4.3 4.4 4.5 4.6 4.3 4.2	93 94 95 96 97 98 99 00 01 02 03 04 95 96 97 98 99 00 01 02 03 04	R e v i e R e v i e R e v i e -.08 -.04 .00 R e v -.04 .00 R e v -.12 93 94 95 96 97 98 99 00 01 02 03 04 i e -.08 95 96 97 98 99 00 01 02 03 04 i e
		Log production HP trend Fitted quadratic polynomial Log production HP trend Fitted quadratic polynomial			w w w Deviation of LIND from HP trend Deviation of LIND from fitted polynomial First difference of LIND w Deviation of LIND from HP trend Deviation of LIND from fitted polynomial First difference of LIND w
				13 16

TABLE I

 I

			, SUMMARY STATISTICS FOR OUTPUT
	Country	Sample Period	GDP	IP		Autocorrelation
			Volatility Volatility	lag1	lag2	lag3	lag4
	Argentina	1970:1 / 1980:1 -	3.06 / 4.59	5.57
		1990:4
	Chile	1986:1 -1998:4	2.00	4.53	0.68	0.51	0.27	0.00
	Colombia	1978:1 -1995:4		2.33	0.51	0.27	0.17	0.02
	India	1978:1 -1995:4		2.45	0.48	0.35	0.10	0.02
	Korea	1978:1 -1995:4		3.47	0.71	0.44	0.20	-0.14
	Malaysia	1978:1 -1995:4		4.06	0.69	0.30	0.07	-0.16
	Mexico	1987:1 -2000:2	2.34	3.31	0.72	0.40	0.14	-0.13
	Morocco	1978:1 -1995:4		2.77	0.06	0.25	0.08	-0.18
	Nigeria	1978:1 -1995:4		6.69	0.45	0.09	-0.06	-0.12
	Philippines	1978:1 -1995:4		7.45	0.63	0.42	0.10	-0.15
	Tunisia	1978:1 -1995:4		2.72	0.63	0.42	0.13	0.06
	Turkey	1987:1 -2000:2	3.48	3.62	0.38	0.14	0.06	-0.12
	Uruguay	1978:1 -1995:4		4.94	0.63	0.50	0.27	-0.01
	Developing average		2.77 / 3.10	4.15	0.55	0.34	0.13	-0.08
	Bulgaria	1994:1 -2004:4	3.99	5.87	0.65	0.31	0.01	-0.18
	Croatia	1994:1 -2004:4	2.15	2.31	0.58	0.33	0.18	0.12
	Czech Republic	1994:1 -2004:4	1.63	2.67	0.89	0.74	0.48	0.24
	Estonia	1993:1 -2004:4	2.37	3.95	0.72	0.45	0.18	-0.08
	Hungary	1995:1 -2004:4	0.99	3.62	0.69	0.35	0.08	0.18
	Latvia	1993:1 -2004:4	1.81	4.27	0.63	0.33	0.09	0.06
	Lithuania	1995:1 -2004:4	2.42	5.60	0.59	0.42	0.42	0.25
	Poland	1995:1 -2004:4	1.57	3.30	0.44	0.22	0.29	0.15
	Romania	1994:1 -2004:4	3.45	7.09	0.67	0.43	0.35	0.29
	Russia	1995:1 -2004:4	2.87	3.92	0.81	0.53	0.24	0.01
	Slovakia	1993:1 -2004:4	1.22	3.85	0.63	0.55	0.50	0.51
	Slovenia	1995:1 -2004:4	2.19	2.04	0.20	0.36	0.16	-0.03
	CEE average		2.22	4.04	0.62	0.42	0.25	0.13
	US	1960:1 -1989:3	1.74	3.70	0.85	0.65	0.41	0.21
	Canada	1960:1 -1989:3	1.39	3.79	0.78	0.51	0.27	0.04
	Japan	1960:1 -1989:3	1.53	4.07	0.78	0.59	0.38	0.19
	Germany	1960:1 -1989:2	1.69	3.06	0.67	0.46	0.35	0.23
	France	1960:1 -1989:3	0.90	2.70	0.77	0.54	0.30	0.10
	UK	1960:1 -1989:1	1.54	2.85	0.55	0.37	0.20	0.07
	Italy	1960:1 -1989:3	1.70	3.58	0.80	0.52	0.22	-0.04
	G7 average		1.50	3.39	0.74	0.52	0.30	0.11
	Belgium	1960:1 -1989:4	2.68	2.75	0.72	0.49	0.22	-0.04
	Denmark	1960:1 -1989:4	2.30	2.24	0.26	0.05	0.00	0.13
	Greece	1962:1 -1990:4	2.85	3.04	0.64	0.36	0.17	-0.01
	Ireland	1976:1 -1989:4	2.31	3.11	0.35	0.03	0.10	0.05
	Luxembourg	1960:1 -1989:4	3.20	5.07	0.54	0.30	0.11	0.00
	Netherlands	1960:1 -1989:4	1.79	2.27	0.32	0.09	0.11	0.06
	Portugal	1968:1 -1989:4	3.05	3.52	0.52	0.37	0.19	0.16
	Spain	1975:1 -1989:4	1.47	1.80	0.13	0.17	0.18	0.03
	EU average		2.12	3.07	0.52	0.31	0.18	0.06
	Note: GDP and Industrial Production (IP) are all Hodrick-Prescott filtered. Autocorrelations are
	computed in IP in the developing group, and in real GDP otherwise. 'EU average' includes G7
	members of EU as well.
	Sources:

TABLE II ,

 II PRODUCTION AND EXPENDITURE VARIABLES a All data are at the quarterly frequency, de-seasonalized and de-trended by the Hodrick-Prescott filter. 'Absolute Volatility' is the standard deviation of the variable. 'Relative Volatility' is measured as the ratio of the standard deviation of the variable and that of real GDP. 'Cyclicality' is the highest correlation coefficient in absolute value between the variable and real GDP. Negative values for 'Phase shift' indicate lead, while positive ones lag to real GDP. 'Persistence' is the AR(1) coefficient.

		Bulgaria	Croatia Czech Rep Estonia	Hungary	Latvia	Lithuania Poland	Romania	Russia	Slovakia Slovenia
	Industrial Output
	Absolute Volatility	5.87	2.31	2.67	3.95	3.62	4.27	5.60	3.30	7.09	3.92	3.85	2.04
	Relative Volatility	1.47	1.08	1.64	1.74	3.66	2.52	2.31	2.10	2.06	1.37	3.16	0.93
	Cyclicality/Phase Shift -0.35 / -4 0.71 / 0 0.58 / +4 0.77 / 0	0.84 / 0	0.61 / 0	0.70 / 0	0.72 / 0	0.78 / 0 0.87 / +2 0.38 / -4 § 0.38 / +4
	Persistence	0.75	0.52	0.64	0.69	0.80	0.70	0.37	0.79	0.86	0.77	0.38	0.25
	Private Consumption
	Absolute Volatility	5.22	5.03	2.05	3.30	1.97	2.28	2.54	1.64	4.78	2.95	2.38	1.56
	Relative Volatility	1.31	2.34	1.26	1.39	2.00	1.39	1.05	1.05	1.38	1.03	1.95	0.71
	Cyclicality/Phase Shift 0.78 / 0	0.59 / 0	0.73 / 0	0.71 / 0 0.39 / +3 -0.61 / +4 0.30 / +1 0.44 / +1 0.74 / 0 0.61 / +3 0.44 / +2 0.79 / 0
	Persistence	0.56	0.81	0.65	0.71	0.72	0.20	0.40	0.43	0.56	0.75	0.47	0.36
	Investment
	Absolute Volatility 13.74	8.16	4.55	7.16	3.29	12.43	9.65	6.36	8.08	8.55	9.78	6.05
	Relative Volatility	3.44	3.80	2.80	3.02	3.32	7.60	3.98	4.05	2.34	2.98	8.01	2.77
	Cyclicality/Phase Shift 0.48 / -3	0.71 / 0	0.86 / 0 0.61 / +1 0.51 / -1 0.32 / +2 0.67 / 0	0.67 / 0	0.38 / 0 0.71 / +1 0.48 / +3 0.90 / 0
	Persistence	0.45	0.83	0.86	0.66	0.19	0.21	0.63	0.63	-0.09	0.38	0.71	0.18
	Government Consumption
	Absolute Volatility	7.65	2.94	2.37	3.62	2.68	4.02	5.45	2.54	4.63	1.27	5.68	0.76
	Relative Volatility	1.92	1.37	1.46	1.52	2.71	2.46	2.25	1.62	1.34	0.44	4.66	0.35
	Cyclicality/Phase Shift 0.61 / -1 0.23 / +4 § -0.30 / 0 -0.39 / +2 -0.19 / -3 0.77 / +3 § 0.51 / 0 0.36 / +2 0.44 / -1 0.43 / -4 0.30 / +2 0.37 / -3
	Persistence	0.47	0.42	0.39	0.43	0.33	0.66	0.04	-0.08	0.25	0.44	0.34	0.44
	Net Exports
	Absolute Volatility	4.61	3.79	1.66	2.48	2.16	2.58	2.11	1.17	2.30	3.86	4.52	1.67
	Cyclicality/Phase Shift -0.51 / 0 -0.59 / 0 -0.45 / -1 -0.32 / -1 -0.29 / -4 -0.36 / +2 -0.35 / +3 -0.57 / +2 0.17 / +2 -0.68 / +1 § -0.36 / +3 -0.90 / 0
	Persistence	0.45	0.42	0.36	0.19	0.44	0.41	0.12	0.55	0.23	0.73	0.61	0.26
	Real Imports
	Absolute Volatility	6.25	8.63	4.06	7.20	4.57	6.25	9.12	6.79	6.21	12.64	6.53	3.23
	Relative Volatility	1.57	4.02	2.50	3.03	4.62	3.82	3.77	4.32	1.80	4.40	5.35	1.48
	Cyclicality/Phase Shift 0.47 / 0	0.66 / 0 0.65 / +1 0.60 / +1 0.66 / -1 0.47 / +1 0.68 / -3	0.65 / 0	0.38 / -2 0.75 / +1 0.49 / +3 0.78 / 0
	Persistence	0.15	0.70	0.51	0.75	0.72	0.56	0.72	0.32	0.46	0.75	0.48	0.05
	Real Exports
	Absolute Volatility	7.23	7.05	4.23	7.24	4.40	5.09	9.32	6.45	6.63	3.25	4.70	3.03
	Relative Volatility	1.81	3.29	2.61	3.05	4.45	3.11	3.85	4.10	1.92	1.13	3.85	1.39
	Cyclicality/Phase Shift -0.34 / +3 0.24 / -1	0.34 / 0	0.53 / 0	0.40 / -1	0.65 / 0	0.69 / -3	0.60 / 0	0.42 / -3 0.55 / -2 -0.14 / 0 -0.28 / -1
	Persistence	0.39	0.30	0.63	0.73	0.67	0.81	0.75	0.03	0.41	0.41	0.62	0.43
	a

§ Unclear cyclical pattern. Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript F o r P e e r R e v i e w 19

TABLE III

 III

						, LABOR MARKET VARIABLES a
		Bulgaria	Croatia Czech Rep Estonia	Hungary	Latvia	Lithuania Poland	Romania	Russia	Slovakia Slovenia
	Total Employment
	Absolute Volatility	4.29	1.79	0.82	1.26	0.87		2.28	1.58	1.98	0.76	1.36	0.81
	Relative Volatility	1.08	0.84	0.51	0.53	0.88		0.94	1.01	0.57	0.26	1.14	0.37
	Cyclicality/Phase Shift 0.70 / +4 -0.61 / -4 0.34 / +1 0.55 / +2 0.47 / -2		0.46 / +3 0.59 / +2 0.56 / 0	0.52 / 0	0.71 / -1 -0.36 / -4
	Persistence	0.87	0.72	0.74	0.72	0.81		0.75	0.82	0.82	0.68	0.91	0.84
	Industrial Employment
	Absolute Volatility	6.72	2.62	1.46	4.84	1.66	3.59		3.21	2.74	2.58	2.38	1.23
	Relative Volatility	1.69	1.22	1.30	2.04	1.68	1.98		2.04	0.80	0.90	2.00	0.56
	Cyclicality/Phase Shift 0.77 / +4 -0.60 / -4 -0.57 / -4 § -0.48 / +4 0.41 / +1 0.55 / 0		0.62 / +1 0.51 / +2 0.66 / +1 0.73 / 0 0.05 / +4
	Persistence	0.83	0.77	0.71	0.53	0.76	0.84		0.87	0.82	0.94	0.89	0.85
	Real Wage
	Absolute Volatility 10.28	3.83	2.28	2.34	3.46	3.35	5.93	4.41	7.34	10.57	2.80	0.99
	Relative Volatility	2.58	1.78	1.40	0.98	3.50	1.85	2.45	2.81	2.13	3.68	2.30	0.45
	Cyclicality/Phase Shift 0.80 / 0	0.37 / 0	0.73 / 0 -0.28 / -4 -0.32 / +3 -0.46 / -4 0.70 / +3 0.25 / -2 0.75 / -1 0.69 / +2 0.68 / +1 -0.22 / +3
	Persistence	0.54	0.81	0.69	0.01	0.81	0.70	0.78	0.78	0.77	0.87	0.85	0.53
	Productivity
	Absolute Volatility	6.43	3.12	1.59	2.09	1.02		3.03	1.82	2.86	2.56	1.01	2.24
	Relative Volatility	1.61	1.45	0.98	0.88	1.04		1.25	1.16	0.83	0.89	0.85	1.02
	Cyclicality/Phase Shift 0.76 / 0	0.83 / 0	0.87 / 0	0.85 / 0	0.63 / 0		0.67 / 0	0.57 / 0	0.82 / 0	0.97 / 0	0.30 / 0	0.93 / 0
	Persistence	0.76	0.57	0.81	0.57	0.48		0.63	0.43	0.50	0.77	0.36	0.20
	Industrial Productivity
	Absolute Volatility	7.46	3.62	2.61	6.33	3.77	3.45		2.80	6.56	2.74	3.24	2.11
	Relative Volatility	1.87	1.69	2.33	2.79	3.81	2.03		1.78	1.90	0.95	2.72	0.97
	Cyclicality/Phase Shift -0.74 / +4 0.70 / 0 0.56 / +4 0.49 / 0	0.65 / 0	0.37 / 0		0.28 / -2	0.69 / 0 0.67 / +2 -0.53 / +4 0.33 / +4
	Persistence	0.60	0.65	0.71	0.58	0.72	0.52		0.62	0.78	0.61	0.72	0.23

a See notes to Table

II

. § Unclear cyclical pattern.

Editorial Office,

Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript F o r P e e r R e v i e w 20

TABLE IV ,

 IV FINANCIAL AND MONETARY VARIABLES a Unclear cyclical pattern.

													Page
		Bulgaria	Croatia Czech Rep Estonia	Hungary	Latvia	Lithuania Poland	Romania	Russia	Slovakia Slovenia
	Private Sector Credit
	Absolute Volatility 26.28	7.66	8.21	10.76	5.95	20.21	12.22	3.06	21.39	10.42	4.65	4.08
	Relative Volatility	6.59	3.62	5.05	4.53	6.02	11.34	5.05	1.95	6.20	3.63	3.63	1.86
	Cyclicality/Phase Shift 0.77 / +3 0.63 / +1 0.41 / 0 0.64 / +2 0.73 / 0 0.70 / +3 0.69 / +4 0.44 / -2 0.82 / +2 -0.45 / -4 § 0.63 / +1 0.37 / 0
	Persistence	0.78	0.83	0.87	0.88	0.81	0.87	0.90	0.77	0.88	0.73	0.91	0.66
	M1
	Absolute Volatility 35.37	9.25	6.59	6.43	4.30	6.17	7.69	6.28	6.12	9.39	8.44	3.04
	Relative Volatility	8.87	4.37	4.05	2.71	4.35	3.46	3.18	4.00	1.78	3.27	6.92	1.39
	Correlation -0.52	0.61	0.49	0.20	0.13	0.30	0.73	0.33	0.21	0.50	0.50	0.52
	Cyclicality/Phase Shift -0.81 / +2 0.61 / 0	0.57 / -2 -0.45 / +4 § 0.29 / -2 0.50 / +2 § 0.76 / +2 0.57 / -3 0.60 / +3 0.67 / +1 0.68 / -3	0.52 / 0
	Persistence	0.88	0.86	0.84	0.77	0.63	0.70	0.87	0.85	0.71	0.72	0.73	0.79
	M2
	Absolute Volatility 26.89	8.90	4.76	5.83	1.82	8.62	3.96	3.77	6.27	7.87	2.45	4.61
	Relative Volatility	6.74	4.21	2.93	2.45	1.84	4.84	1.64	2.40	1.82	2.74	2.01	2.11
	Cyclicality/Phase Shift -0.83 / +2 0.62 / -1	0.76 / 0 -0.26 / +4 0.45 / -4 -0.58 / -4 § 0.49 / +2 0.64 / +4 -0.53 / -4 § 0.61 / -1 0.48 / -3 0.62 / -4
	Persistence	0.88	0.90	0.85	0.77	0.58	0.81	0.73	0.88	0.79	0.81	0.64	0.90
	CPI
	Absolute Volatility 43.31	0.92	1.46	5.03	2.37	4.38	3.95	2.35	12.61	11.23	2.12	1.29
	Relative Volatility 10.86	0.43	0.90	2.12	2.40	2.42	1.63	1.49	3.65	3.91	1.74	0.59
	Cyclicality/Phase Shift -0.85 / +2 -0.35 / -4 -0.66 / -4 -0.37 / 0 -0.43 / +2 -0.47 / 0 0.32 / -2 0.57 / +4 -0.79 / -1 0.63 / -4 -0.63 / +3 -0.48 / -1
	Persistence	0.86	0.73	0.86	0.95	0.93	0.94	0.93	0.94	0.88	0.84	0.84	0.76
	Inflation
	Absolute Volatility 22.86	0.71	0.80	1.33	0.61	1.30	1.02	0.70	6.03	6.04	1.11	0.73
	Relative Volatility	5.73	0.34	0.50	0.58	0.72	0.71	0.42	0.46	1.75	2.11	0.90	0.33
	Cyclicality/Phase Shift -0.74 / 0 -0.17 / +4 0.33 / +4 -0.41 / -2 0.36 / -1 -0.36 / -1 -0.24 / -1 0.47 / -1 0.54 / +2 -0.50 / +1 0.29 / +4 -0.38 / -2
	Persistence	0.33	-0.19	0.20	0.51	0.29	0.22	0.52	0.31	0.44	0.66	0.04	0.00

a See notes to Table

II

. §

20 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript F o r P e e r R e v i e w 21

TABLE IV ,

 IV CONTINUED, FINANCIAL AND MONETARY VARIABLES a Phase Shift -0.71 / 0 § 0.38 / +4 0.72 / +4 0.66 / +3 -0.44 / -4 0.43 / +4 -0.72 / -2 -0.49 / -4 § -0.52 / -2 0.49 / -4 0.62 / +4 -0.50 / -2 Phase Shift 0.82 / +1 0.63 / +1 0.24 / +1 -0.71 / 0 -0.45 / -3 -0.51 / -2 -0.72 / -3 -0.63 / -2 0.63 / -1 -0.70 / -4 § 0.

		Bulgaria	Croatia Czech Rep Estonia	Hungary	Latvia	Lithuania Poland	Romania	Russia	Slovakia Slovenia
	Net Capital Flows
	Absolute Volatility	6.59	6.25	5.13	4.93	7.49	5.64	4.74	2.68	3.89	4.67	8.77	4.08
	Cyclicality/Phase Shift -0.33 / +2 § 0.34 / 0	0.24 / -4 0.47 / -2 0.26 / +2 0.41 / +3 0.32 / 0	0.32 / 0 0.22 / +4 0.47 / +1 § 0.18 / -1 -0.35 / -2
	Persistence -0.03	-0.10	0.22	0.14	0.07	-0.03	-0.32	0.09	0.03	0.42	-0.07	-0.17
	Nominal Interest Rate
	Absolute Volatility	9.49	1.00	0.24	0.54	0.35	1.13	0.42	0.62	3.15	9.99	0.55	0.62
	Relative Volatility	2.38	0.46	0.15	0.23	0.36	0.64	0.18	0.40	0.91	3.48	0.45	0.28
	Cyclicality/Persistence	0.75	0.25	0.86	0.72	0.77	0.83	0.85	0.83	0.62	0.87	0.83	0.38
	Nominal Effective
	Exchange Rate
	Absolute Volatility 42.03	2.41	2.88	5.93	3.50	7.56	7.63	4.77	9.96	21.47	3.31	2.29
	Relative Volatility 10.54	1.12	1.77	2.50	3.54	4.46	3.15	3.04	2.89	7.48	2.71	1.05
	Cyclicality/39 / 0	0.57 / -3
	Persistence	0.87	0.82	0.71	0.86	0.78	0.80	0.84	0.77	0.74	0.84	0.67	0.81
	Real Effective Exchange
	Rate
	Absolute Volatility	7.83	1.90	3.04	4.10	2.39	4.02	4.14	5.00	8.20	14.25	3.07	1.90
	Relative Volatility	1.96	0.89	1.87	1.73	2.42	2.35	1.71	3.18	2.38	4.96	2.52	0.87
	Cyclicality/Phase Shift 0.73 / -1 0.43 / -3 -0.25 / -2 -0.38 / +1 -0.47 / +1 -0.31 / -4 -0.61 / -4 -0.55 / -2 -0.59 / +1 § 0.70 / +2 § -0.30 / +3 0.59 / -3
	Persistence	0.64	0.64	0.71	0.76	0.70	0.80	0.74	0.76	0.80	0.83	0.59	0.71

a See notes to Table

II

. § Unclear cyclical pattern.

We discuss only briefly how cyclical regularities in CEE countries relate to those observed in other developed and emerging market economies. Providing a more comprehensive account of the international evidence is the subject of our ongoing research.

Serving as an impetus for much of the subsequent research in the RBC tradition, the classic studies examining the cyclical component of macroeconomic time series are[START_REF] Kydland | Business Cycles: Real Facts and Monetary Myth[END_REF] in the closed economy context, and[START_REF] Backus | International Business Cycles: Theory and Evidence[END_REF] in an open economy one.

Importantly, we do not study the degree of comovement of particular variables across different countries. For a study of cross-country patterns in comovement in CEE economies, see[START_REF] Darvas | Business Cycle Synchronization in the Enlarged EU[END_REF].

Private sector credit is added for comparison to[START_REF] Agénor | Macroeconomic Fluctuations in Developing Countries: Some Stylized Facts[END_REF].[START_REF] Fiorito | Stylized Facts of Business Cycles in the G7 from a Real Business Cycle Perspective[END_REF] also analyze the properties of real interest rates, defined as the difference between nominal rates and realized future inflation. Such a procedure of calculating the real interest rate would be problematic in our sample, due to high and volatile inflation

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

[START_REF] Kaminsky | When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies[END_REF] argue that the correlation between the levels of these variables, not normalized by output provides a superior measure of the cyclical stance. Using the cyclical component of the raw net export and capital flow data however makes the interpretation of the relevant volatility figures difficult as the scale is invariant within, but not across countries.

As most other results in the literature are obtained using less recent, but longer, 15-30 years of quarterly time series, some of this pattern might be due to differences in sample period and size. For recent evidence in developed economies, see[START_REF] Agresti | Some Stylized Facts on the Euro Area Business Cycle[END_REF]. The most detailed account of emerging market fluctuations are in Agenor et al(2000),[START_REF] Aguiar | Emerging Market Business Cycles: The Cycle is the Trend[END_REF] amd[START_REF] Neumeyer | Business Cycles in Emerging Economies: The Role of Interest Rates[END_REF].

See Alper (2002) for Mexico and Turkey and[START_REF] Kydland | Is the Business Cycle of Argentina Different?[END_REF] for Argentina.

See Basu and Taylor (1999).

available at http://www.personal.ceu.


Matthias Weiss
email: weiss@mea.uni-mannheim.de

Alfred Garloff
email: alfrde.garloff@iab.de

Philippe Aghion

Nicole Gürtzgen

Martin Hellwig

Melanie Lührmann

Lia Pacelli

Thomas Zwick

Skill Biased Technological Change and Endogenous Benefits: The Dynamics of Unemployment and Wage Inequality *

Keywords: Skill, Occupation, etc. < J3 -Wages, Compensation, Unemployment, Skill-Biased Technological Change, Wage Inequality Unemployment, Skill-Biased Technological Change, Wage Inequality, Benefits JEL Classification: E24, J31, O30

In this paper, we study the effect of skill-biased technological change on unemployment and wage inequality in the presence of a link between social benefits and average income. In this case, an increase in the productivity of skilled workers and hence their wage leads to an increase in average income and hence in benefits. The increased fallback income, in turn, makes unskilled workers ask for higher wages. As higher wages are not justified by respective productivity increases, unemployment rises. More generally, we show that skill-biased technological change leads to increasing unemployment of the unskilled and to a moderately increasing wage inequality when benefits are endogenous.

The model provides a theoretical explanation for diverging dynamics in wage inequality and unemployment under different social benefits regimes: Analyzing the social legislation in 14 countries, we find that benefits are linked to the evolution of average income in Continental Europe but not in the U.S. and the UK. Given this institutional difference, our model predicts that skill-biased technological change leads to rising unemployment in Continental Europe and rising wage inequality in the U.S. and the UK.

Introduction

The well documented increase in wage inequality in the U.S. and the UK in the 1980s and early 1990s is attributed to an increase in the demand for skills that has been faster than the increase in skills supply. Predominantly so-called skill-biased technological change is blamed for the rapid increase in the demand of skills. 1 In most of Continental Europe, wage inequality increased much less if at all; instead it experienced a significant increase of unemployment, especially of the low-skilled.

Mainly, three arguments have emerged in the literature to explain the differences in the evolution of wage inequality. First, some authors argue that the supply of skills increased faster in Europe than in the U.S./UK (see [START_REF] Nickell | Labor Market Institutions and Economic Performance[END_REF], and [START_REF] Leuven | Explaining International Differences in Male Skill Wage Differentials by Differences in Demand and Supply of Skills[END_REF]). This approach is not able, however, to account for the differential evolution of unemployment. The second, and maybe most important, approach argues that collective bargaining and labor market institutions kept the wage structure compressed in Europe implying that skill-biased technological change has been leading to increasing unemployment. 2 The role of labor unions has obtained considerable attention (eg, [START_REF] Lindbeck | Reorganization of Firms and Labor Market Inequality[END_REF]). Labor market institutions as the unemployment insurance system and employment protection (eg, [START_REF] Mortensen | Unemployment Responses to 'Skill-Biased' Technology Shocks: The Role of Labour Market Policy[END_REF]), or minimum wages (eg, Teulings (2003)) have also obtained attention in the literature. The major theoretical drawback of the institutional approaches is that they mostly explain differences in the level of unemployment and the level of wage dispersion. Only few 3 can explain a widening gap of wage dispersion and unemployment as long as the institutions are unchanged. 4 Thirdly, and more recently, it has been argued that the demand for high-skilled increased less in Europe, because there, high wages for the low-skilled workers create an incentive for firms to invest in unskill-biased technologies, implying that technical progress is on average less skill-biased in Europe (see [START_REF] Acemoglu | Cross-Country Inequality Trends[END_REF]).

Our paper contributes to the view that it is the institutions that matter for the diverging evolution between the U.S./UK and Continental Europe. Our model is able to reproduce the differential dynamics of unemployment and wage inequality and not only levels. We argue that in Europe, skill-biased technological change has adverse effects on employment of unskilled workers because their wages are linked to the skilled workers' wages. This link is established by the indexation of social benefits to per-capita income.

Modern welfare states usually possess social protection systems including schemes that provide needy people with subsistence benefits. Often, the level of benefits is linked to the evolution of wages or per-capita income. The reason for this is that benefits are paid to avoid poverty so that their level must be closely related to the "subsistence level". But the subsistence level is a relative concept and so is poverty. 5 Therefore benefits in general depend on the average wealth of a society. The strength of this link, however, varies across countries. We find that in most of Continental Europe the level of benefits is tied closely to per-capita income while in the Anglo-Saxon countries the benefits level has not been adjusted to per-capita income over the last 20 years. We show that this institutional difference is able to explain the transatlantic differences in wage and employment dynamics.

Endogeneity of the level of benefits with respect to the average income is important for labor market outcomes because it establishes a link from skilled workers' productivities to unskilled workers' wages: Changes in skilled workers productivities affect average income and thereby the level of benefits. This increase in the fallback income improves the bargaining position of the unskilled workers. In general this will result in higher wages and-for lack of respective productivity gains-higher unemployment. 6 To demonstrate the mechanisms, our baseline model considers a "European" economy with skilled and unskilled labor. We assume that the market for skilled labor clears, while the market for unskilled labor does not. In the baseline model the wage for unskilled labor is determined by a monopolistic labor union while employment is determined by competitive firms. 7 Thus, the focus of this paper is on the (strikingly large) extent to which the unskilled workers' rate of unemployment exceeds that of the skilled workers. The fact that unemployment also exists among the skilled workers might indeed be explained by considerations of insider-outsider relations, search frictions, efficiency wages, or the like. These theories might be seen as complementary rather than contradictory to this paper.

The findings of the baseline model (and its generalizations) are consistent with the evolution of wages and employment of unskilled workers in Europe over the past decades. Wages for all skill levels have risen over this period and, by and large, the employment prospects of the less skilled workers have deteriorated. 8 In the United States (and the UK), it seems, that a shift in labor demand has led to an increase in wage inequality while in (Continental) Europe, where the wage structure has remained fairly stable, it resulted in a rise in unemployment, in particular among unskilled workers (see, eg, [START_REF] Siebert | Labor Market Rigidities: At the Root of Unemployment in Europe[END_REF]). 9 This coincidence of rising wage inequality in the United States and rising unemployment (at rather stable relative wages) in Europe suggests that the kind of feedback mechanism described in the our baseline model has been an important feature of labor markets in Continental Europe but not in the U.S. and the UK. We show that, on the basis of only 5 See, eg, Foster (1998): "Absolute versus Relative Poverty" and the other contributions to the session on "What is Poverty and Who are the Poor?" in the AEA Papers and Proceedings Issue of the American Economic Review of May 1998. 6 In fact, most benefit systems have unemployment insurance elements that depend on the level of past earnings rather than the general income level of the economy. Note, however, the following: First, unemployment insurance benefits are generally limited in duration. So, in the long run, it is social benefits that constitute the fallback income. Second, for unskilled workers, unemployment insurance benefits may easily fall short of the level of social benefits. In this case, the payment is increased to this level.

7 Gürtzgen (forthcoming) shows that the impact of unions on wages is largest at the lower end of the skill distribution.

8 See, eg, [START_REF] Siebert | Labor Market Rigidities: At the Root of Unemployment in Europe[END_REF], [START_REF] Katz | Changes in the Wage Structure and Earnings Inequality[END_REF], or [START_REF] Acemoglu | Cross-Country Inequality Trends[END_REF]. For Germany a detailed analysis of the employment and wage development from 1975 through 1990 is performed in [START_REF] Fitzenberger | Wages and Employment Across Skill Groups: An Analysis for West Germany[END_REF]. 9 We are aware that the view that increasing unemployment in Continental Europe and increasing wage inequality in the U.S. and the UK are two sides of the same coin (namely skill-biased technological change) is not beyond controversy (see, eg, [START_REF] Nickell | Changes in the Distribution of Wages and Unemployment in OECD Countries[END_REF], and [START_REF] Gregg | Skill-Biassed Change, Unemployment and Wage Inequality[END_REF]). Yet, there seems to have emerged a large consensus among many economists that this view explains at least parts of the intercontinental differences (see, eg, [START_REF] Cahuc | Labor Economics[END_REF], Chapter 10. one institutional difference, namely the link between benefits and per-capita income, we can explain this difference in employment and wage dynamics.

The remainder of the paper is organized as follows. Section 2 deals with the question whether and how tightly different countries link their benefit payments to the average income for a selection of OECD countries. The model is set up in Section 3. Comparative static results and the implications of our model with respect to the transatlantic differences in the social legislation are discussed. Section 4 summarizes and concludes.

Transatlantic Differences in the Social Legislation

In this section we analyze the legal situation both in Continental Europe and in the U.S. and the UK to demonstrate how benefits depend on per-capita income in different welfare systems. We find that in the United States and the UK benefits have not been adjusted to average income in the last 20 years, while in most European countries this adjustment is automatic and by law. Having observed this, in the next section our model shows that it is precisely this institutional difference that can account for diverging experiences in the evolution of wage inequality and unemployment.

We are aware that this binary classification into European and Anglo-Saxon countries is crude. There is substantial variation in the social legislation within these groups of countries. 10 But, when it comes to the evolution of benefits over time, the similarities within and the disparities between these two groups of countries are striking: In most European countries, these benefits depend on per-capita income by law, while this is not the case in Anglo-Saxon countries.

Let us consider the European countries first. In some countries, the adjustment of the benefits level over time is automatic by law, ie, there is a clear adjustment frequency and there are clear rules to what the benefits level is to be adapted. In other countries the legislation gives more scope to the government or the parliament to act and adjustments are discretionary. In some countries where there exist rules for the adjustment of benefits, the evolution of benefit payments is linked to the evolution of wages and/or income while in others, benefits are linked to consumer prices. Figure 1 in the appendix contains a synopsis of the social legislation in a selection of countries. In most European countries (Austria, Denmark, Finland, Germany, Italy, Netherlands, and Portugal), welfare benefits are automatically linked to the evolution of average wages, average income or public pensions (which on their part are linked to the evolution of average wages or average income) by law. Exceptions are Belgium, France, Greece and Spain. In Greece a general income support scheme does not exist. We discuss the remaining three countries in Appendix A.1 and provide empirical evidence that is in accordance with our main hypothesis.

In Anglo-Saxon countries, on the other hand, benefits are not linked to average wages or income. In the UK, "income support" is tied to the evolution of consumer prices only. 11 In the U.S., the institutional and legal situation is more complex. 12 Summarizing 10 There have been several attempts in the European Union to harmonize social legislation -without much success, though. Two of the more successful attempts have led to the European Social Charter of 1989 and to the social protocol annexed to the Maastricht Treaty of 1992 -both not signed by the United Kingdom. If compared to other policy areas of the European Community, the treaties on social standards remain vague.

11 There were no additional discretionary increases between 1979 and 2001 (see [START_REF] Cantillon | The Evolution of Minimum Income Protection in 15 European Countries, 1992-2001[END_REF]).

12 For a concise overview, see Uccello and Gallagher (1997) from where most of the following information the discussion from our working paper (see Weiss and Garloff ( 2005)), we can conclude, that none of the U.S. income support programs links benefits to the evolution of average income or wages. In fact, in many cases, amounts are not even adjusted for inflation.

Figure 1 in the appendix summarizes the institutional setting. It shows that welfare benefits are linked to average wages or income by law in most of Continental Europe but not in the U.S. and the UK. In Appendix A.1, we take a closer look at those European countries, that have no legal automatic link between benefits and wages or income and provide empirical evidence that is in accordance with our assumptions. The next section containing the baseline version of our model demonstrates that this institutional difference in the determination of benefits can account for diverging experiences in the evolution of employment and inequality.

3 The Model

The Baseline Case: Europe

Consider an economy with a continuum of mass 1 of homogeneous firms producing a single good. The good is produced using two input factors, unskilled and skilled labor. There is a continuum of mass 1 of workers of each type. Each worker supplies one unit of labor. For simplicity, the model is essentially static. There is no capital in the model so that consumption equals production at any point in time.

The firm produces according to the production function

Y = (a u • l u ) ρ +(a s • l s ) ρ , 0 < ρ < 1, 0 < a u <
a s where Y is the quantity of the final good, l u and l s are the levels of employment of unskilled and skilled labor respectively, and ρ, a u , and a s are productivity parameters. This specification has the following properties. First, the elasticity of substitution between unskilled and skilled labor is σ = 1/ (1 -ρ) > 1. We restrict the analysis to substitution elasticities larger than one because only in this case does skillbiased technological change have adverse effects on the relative position of the unskilled workers. Furthermore, the majority of the empirical estimates are between 1 and 2 (see, eg, [START_REF] Autor | Computing Inequality: Have Computers Changed the Labor Market?[END_REF]). Second, the marginal productivities of unskilled and skilled workers are independent of each other and the cross wage elasticities of the factor demands are zero. We make this arguably strong assumption to guarantee that any relation between the wages for the two kinds of labor that arises in the model can be attributed solely to the institutional peculiarities.

These restrictions are also for simplicity. In Weiss and Garloff (2005), we consider the case of a more general CES (constant elasticity of substitution) technology. The results are shown to be independent of these different specifications.

Firms sell their products on the world market at the world market price P = 1 (by choice of the numéraire). At given wage levels, firms choose the level of employment so as to maximize their profits π = Y -w u • l u -w s • l s . The demand for unskilled and skilled labor is respectively

l d u (w u ) = ρ • a ρ u w u 1 1-ρ and l d s (w s ) = ρ • a ρ s w s 1 1-ρ , ( 1
)
where w u and w s are the wages for unskilled and skilled labor respectively.

is taken. The model involves unemployment of unskilled workers. All unemployed individuals are assumed to receive benefits, w. In accordance with the reasoning in the introduction, the benefits are assumed to depend on the net average income w

= µ • (1 -t) • Y
2 , where Y /2 is the per-capita income, t is the income tax rate, and µ ∈ [0, 1] is a proportionality factor. The benefits are financed through a proportional income tax. The tax rate t is endogenously determined by the government's budget constraint (1

-l u ) • w = t • Y .
All unskilled workers are assumed to be members of a labor union. The union chooses the wage to maximize the expected labor income of its members.

U = E [net labor income|w u ] = l u (w u ) • (1 -t) • w u + [1 -l u (w u )] • w (2)
The first term in expression (2) represents the probability for any union member to become (or remain) employed (conditional on the wage level) times the net wage of employed unskilled workers. The second term represents the conditional probability to become unemployed times the alternative income (ie, benefits).

We assume that the market for skilled labor clears. This determines the wage for skilled labor w s = ρ • a s . The wage for unskilled labor is assumed to be determined by a monopolistic labor union whereas the firm has the "right to manage". The union maximizes its objective function taking into account the effect of the wage level on employment. We assume that, out of idleness or lack of comprehension of the economic system, the union does not consider the second-round effects the wage has on the level of benefits and on the tax rate. 13 In the formal model, this means that the objective function ( 2) is maximized subject to (1) but taking the level of benefits w and the tax rate t as exogenously given. Solving the maximization problem yields the following result which is familiar from the literature.14 Lemma 1 Under the above assumptions, the wage for unskilled labor, w u , is an increasing function of the level of benefits, w :

w u = w ρ • (1 -t) . (3)
In contrast to standard union models (and in contrast to what the union takes into account), in this model, the level of benefits is a function of the net average income which, in turn, is a function of the wage for unskilled labor. Accounting for this endogeneity in (3) yields15

w * u = 1 2 • µ ρ • ρ • a u w * u ρ 1-ρ + a ρ s . (4)
The equilibrium wage for unskilled labor, w * u , is implicitly given by this equation. 16 It is easily verified that under the above assumptions, an equilibrium, w * u (a u , a s , µ, ρ), exists and is unique.17

Comparative Statics

The comparative static properties of the equilibrium allocation are presented in the following propositions:

Proposition 1 An increase [respectively decrease] in the unskilled workers' productivity, as measured by the productivity parameter a u , leads to an increase [respectively decrease] in both, the equilibrium wage and the level of employment of unskilled labor.

∂w * u ∂a u • a u w * u = η Y,lu 1 -ρ + η Y,lu > 0 (5) ∂l * u ∂a u • a u l * u = ρ -η Y,lu 1 -ρ + η Y,lu > 0 (6) where η Y,l i = ∂Y ∂lu • lu Y .
A decrease in the unskilled workers' productivity leads-via a decrease in the average income-to a decrease in the unskilled workers' wage. But this decrease is less than would be required by the productivity loss because the wage is linked to the average income level which decreases by less than the unskilled workers productivity. Therefore unemployment of the unskilled increases. This failure of the wage to fully adjust to changes in productivity can be seen as a rigidity in the relative wage w u /w s .

While the wage for skilled labor always adjusts to clear the market, the wage for unskilled labor depends on the productivities of both, unskilled and skilled workers. In other words, the wage for unskilled labor is linked to the wage for skilled labor. The relative wage cannot fully adjust to changes in the relative productivity. This rigidity leads to an increase in unemployment in response to a decrease in the productivity for the unskilled workers. Similar results are obtained in standard union models where the reservation wage of the workers is exogenous.

Proposition 2 An increase [respectively decrease] in the skilled workers' productivity, as measured by the productivity parameter a s , leads to an increase [respectively decrease] in the wage for unskilled labor and a decrease [respectively increase] in the level of employment for unskilled workers.

∂w

* u ∂a s • a s w * u = (1 -ρ) • η Y,ls 1 -ρ + η Y,lu > 0 (7) ∂l * u ∂a s • a s l * u = - η Y,ls 1 -ρ + η Y,lu < 0 (8)
The increased productivity of the skilled workers leads to a rise in the average income. This in turn increases-through higher benefits-the unskilled workers' reservation wage and thereby their wage. Since the productivity of the unskilled workers remains unchanged, unemployment increases.

While the result in Proposition 1-that the wage falls too little in response to a fall in the productivity of the unskilled-is also obtained in standard union models, the result condition (4) is larger (resp. smaller) than the left hand side. As both sides of the equation are continuous in wu there must exist at least one value of wu, w * u , for which both sides are equal. Uniqueness: The left hand side of ( 4) is strictly increasing in wu whereas the right hand side is strictly decreasing in wu. Therefore, if a solution to (4), w * u , exists, it must be unique. in Proposition 2-that the wage increases too much in response to a productivity gain of the skilled workers-is unique to this model where the feedback mechanism from income levels to wages is accounted for. In this model, the driving force behind both effects is the above mentioned rigidity in the relative wage.

Propositions 1 and 2 consider cases where only one type of labor becomes more productive. Depending on whose productivity increases, unemployment increases or decreases. Since in reality, technological change tends to affect the productivities of different types of labor at the same time, the question naturally arises which of the two opposite effects dominates. The following Proposition answers this question. Skill-biased technological progress favoring the skilled workers' productivity in a way that leads to an increase in a s /a u has a negative effect on the relative demand for unskilled labor, l d u /l d s . Since the relative wage for unskilled labor, w u /w s , cannot fully adjust to this shift in labor demand, unemployment of unskilled workers increases. 18 On the other hand, if the productivity of unskilled workers grows faster [or falls more slowly] than the skilled workers' productivity, the wage for unskilled labor increases [respectively falls], but by less [respectively more] than would be justified by the shift in the relative productivity so that the employment of unskilled workers increases. Any technological change that leaves the ratio a s /a u unaffected has no effect on the level of employment. This result is consistent with the view that it is the same factors that boost wage inequality in the U.S. and the UK and result in higher unemployment in Continental Europe. In a model in which the welfare system is less generous and wages are to a greater extent market-determined-the alleged features of U.S. and UK labor marketsskill-biased technological change (in the form of an increase in a s /a u ) leads to a stronger increase in wage inequality while unemployment is affected less or not all. Appendix A.2 provides some descriptive evidence for this result.

Increasing trade with and outsourcing to low-wage countries have been cited as a second culprit of the rise in wage inequality in the United States. 19 In fact, in a two-sector version of this model, it can be shown that increasing trade with low-wage countries (as modeled by a decrease in the relative price of the import good-whose production is assumed to be intensive in the use of unskilled labor) has exactly the same effect on wages and employment as skill-biased technological change (as modeled by an increase in a s /a u ).

18 It is easily verified that is unambiguously smaller than ρ, the respective elasticity that prevailed if wages were perfectly flexible.

19 See, eg, [START_REF] Fenstra | Globalization, Outsourcing, and Wage Inequality[END_REF], and [START_REF] Baldwin | Shifts in Relative U.S. Wages: The Role of Trade, Technology, and Factor Endowments[END_REF]. Increasing trade with low-wage countries also leads to a (downward) shift in the relative demand for unskilled labor. As the relative wage for unskilled labor does not fully adjust, unemployment of unskilled labor rises. 20

Wage and Employment Dynamics: Anglo-Saxony vs. Continental Europe

In this section, we explore the implications of our model for the differences in wage and employment dynamics between Anglo-Saxon countries (AS) and Continental European countries (EU). For ease of presentation, we denote as au ≡ α and ws wu ≡ ω and normalize the productivity parameter of unskilled labor to 1. The technology is thus given by Y = l ρ u + (α • l s ) ρ . Benefits are given by

w = µ • (1 -t) • Y 2 ξ ( 11
)
where ξ = 1 in Europe and ξ = 0 in Anglo-Saxon countries. As expounded in Section 2, benefits are tied to the evolution of average income in Europe, but not in the U.S. and the UK.

In both regimes, the wages for unskilled and skilled labor are given by w u =

w ρ•(1-t)
and w s = ρ • α ρ . Taking into account the differences in the determination of benefits the relative wage for skilled labor is given by

ω EU = 2 ω EU α ρ 1-ρ + 1 • ρ 2 µ and ω AS = α ρ • ρ 2 µ ( 12
)
in European and Anglo-Saxon countries respectively. The effect of skill-biased technological change on wage inequality is given by

∂ω ∂α • α ω EU = ρ • ω α ρ 1-ρ 1 -ρ + ω α ρ 1-ρ and ∂ω ∂α • α ω AS = ρ (13)
It is easily shown that ∂ω ∂α • α ω EU < ∂ω ∂α • α ω AS as long as ρ < 1. The effect of skill-biased technological change on wage inequality is smaller in European countries, where the linkage of benefits to the evolution of average income keeps the wage distribution compressed.

The effects of skill-biased technological change on unemployment in European countries have been discussed in Subsection 3.2. In the Anglo-Saxon regime, employment of unskilled workers is given by l u (w u ) = ρ wu

1 1-ρ = ρ 2 µ 1 1-ρ .
Thus (un-)employment does not depend on the relative productivity parameter α. In summary, skill-biased technological change leads to an increase in unemployment in European countries and has no effect on unemployment in Anglo-Saxon countries: We end this section by noting that a difference in bargaining power on the side of labor unions does not suffice to explain the differential unemployment and wage inequality dynamics between the two sets of countries in the presence of skill-biased technological change. If as in the model AS, the outside option does not react in response to an increasing demand for high-skilled labor, unemployment is unchanged irrespectively of the market power of labor unions. An explanation of the transatlantic difference that is linked to the bargaining power of labor unions requires a change in the bargaining power. In our model, it is the interaction of an increasing outside option and (an unchanged) bargaining power of the workers which cause the unemployment to increase. Of course, we do not negate the importance of the observed deunionization in the U.S. and UK and see our paper as complementary to studies that link the transatlantic differences to a change in bargaining power.

∂l u ∂α • α l u EU = - η Y,ls 1 -ρ + η Y,lu < 0 and ∂l u ∂α • α l u AS = 0 (

Summary and Conclusion

In this paper, we study the effects of skill-biased technological change on unemployment and wage inequality when benefits are linked to per-capita income. This link to per-capita income introduces a tie between the wages for different skill groups.

In standard models of union wage setting, wages-especially at the lower end of the wage distribution-depend on the level of unemployment or social security benefits (which constitute the workers' reservation wage). As a consequence, these wages are downwardly rigid. This rigidity causes unemployment when productivity falls. In our paper, benefits are endogenous and depend on wages. The interdependence between wages and benefits yields an allocation where the wage for unskilled labor depends positively on the wage for skilled labor. The obtained wage rigidity is a rigidity in the relation between the wages for unskilled and skilled labor. The wage for unskilled labor is too rigid with respect to the unskilled workers' productivity and it is overly sensitive to changes in the skilled workers' productivity.

If-as a result of skill-biased technological change-the productivity of the skilled workers rises faster than that of the unskilled workers, the wage of the latter increases by more than would be justified by their productivity gains because it is linked to the skilled workers' wage via the benefits. As a result, unemployment of unskilled labor increases. The matter of concern here is not that the unskilled workers' wage falls too little-as in standard union models-but that it rises too much.

The findings of this paper are consistent with the evolution of wages and employment of unskilled workers in Europe over the past decades. Wages for all skill levels have risen over this period and, by and large, the employment prospects of the less skilled workers have deteriorated.

Comparing the social legislation in the U.S. and many European countries, we find that benefits are linked to the evolution of average income or wages in Continental Europe but not in the U.S. and the UK. Given this institutional difference, our model predicts that skill-biased technological change leads to rising unemployment in Continental Europe and rising wage dispersion in the U.S. and the UK.

We can deduce interesting policy implications from the model. Any increase in the relative productivity (or more generally in the relative "market value") of skilled workers leads to a higher rate of unemployment the European model-even if the absolute produc- tivity of unskilled workers increases as well, but less than proportionately. From the point of view of the model, we can blame two factors for the high unemployment of the unskilled. First, benefits are tied to the average income and second, benefits are a determinant of the wage of the unskilled. So, any policy measure that aims at weakening either of these links will decrease unemployment. It is to be noted, however, that our model does not challenge the principal insight from the literature that there is a tradeoff between wage inequality and unemployment. A decrease in unemployment would come at the cost of higher wage inequality. There might be possibilities, however, to overcome this dilemma. One way might be the introduction of a negative income tax. Such a tax scheme allows the uncoupling of gross from net wages. Gross wages (and thus wage costs for firms) are determined by market forces and reflect productivities and at the same time, inequality in net wages can be kept from growing. These wage subsidies to unskilled workers would have to be financed, but as these workers would not earn benefits anymore, the government's budget might even be relieved.

A Appendix: Empirical Evidence

A.1 The Link Between Benefits and Per-Capita Income

To confirm our main hypothesis, in Section 2 we look at the legislation of 14 countries (see Figure 1). In addition, here we use an empirical assessment to complement the results of the legal analysis. For 10 countries the legal situation is clear and confirms our crude classification in "European" and "Anglo-Saxon" countries. In three "European" countries Belgium, France, and Spain, benefits are not linked automatically to the average income or wages. Partly the law itself envisions that there are additional discrete adjustments. This is the case for example in Belgium. There, the law explicitly allows the king to adjust the benefit payments to the development of the living standards. As the legal situation allows these countries to be "European" and "Anglo-Saxon", we choose the empirical results to uncover the connection between average wages and benefit levels. For various reasons direct data on benefit levels are not available: In general, benefit payments depend on individual characteristics (wealth, income, household size, etc.) and differ across regions. Furthermore, in-kind transfers often make up an important part of total benefits. Therefore, we use data on (real) social expenditures on unemployment per unemployed from the OECD to approximate the benefit payments. We take the social expenditures on unemployment as a proxy for expenditures on benefits and take the number of unemployed individuals (from the OECD) as a proxy for the number of benefit recipients. 21 The last row of Figure 1 reports results from regressions of changes in real social expenditures on unemployment per unemployed on real GDP per capita changes. 22 The influence is significantly positive and roughly of the same magnitude for the three countries, where the legal situation is ambiguous.To assess the quality of our proxy social expenditures for unemployment per unemployed, we run the same regression for the other countries where we know the administrative rules. Most results are as we expect. In particular, the relation between GDP p.c. and social expenditures per unemployed is insignificant in the 21 We use social expenditures on unemployment, since a category for benefits alone does not exist so that this category comes closest to our needs. Using in addition social expenditures on housing and incapacity-related benefits does not change the principal conclusions. Results are available upon request.

22 It is likely that both real GDP per capita and real social expenditures per unemployed are trended. We use first differences in order not to run into the problem of a spurious regression. U.S. and the UK while it is significant in most European countries. 23 The purpose of these regressions is modest, however: We use the best information we could assemble. That is, we use the legal situation for the countries where the situation is unambiguous and use the regression results for the other countries, where the results are at least in accordance with our classification.

F

A minimum wage that depends on average wages has a similar effect as benefits that depend on average wages. 24 This is the case in France and Spain, where the minimum wage is tied to average income by law. So, for these two countries there is an additional link between the wage of the unskilled and the wage of high-skilled, even when, as in France, the benefits are not tied to the average income by law. A minimum wage that is tied to average income also induces adverse employment effects of skill-biased technological change. Again, for the U.S. and the UK this link does not exist. In the U.S., the minimum wage has not even been adjusted to consumer prices. In 2000 the minimum wage was 25% lower in real terms than in 1978 (see [START_REF] Card | Skill Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles[END_REF], Figure 22). In the UK, a national minimum wage has only been introduced in 1999 and can therefore not account for changes in inequality and employment in the 1980s and 90s.

A.2 Descriptive Evidence to Proposition 3

Proposition 3 states that skill-biased technological change leads to rising wage inequality and rising unemployment in European countries because the relative wage cannot fully adjust to changes in relative labor demand. Put differently: If the wage dispersion in Europe rises, it does not rise enough and unemployment rises as well. The unemployment rate should thus be positively correlated with the skill-premium in Europe. For example, in Germany 1975Germany -2004, the correlation between the unemployment rate of the unskilled and the skill premium has been 0.703. 25 This substantiates the theoretical result in Proposition 3. 26 For the "Anglo-Saxon" model, we expect a zero correlation between wage dispersion and unemployment of the unskilled, since wages of the unskilled do not react to changes in the wages of the skilled. If the wage dispersion increases, it increases enough to adjust to changes in relative labor demand. Unemployment remains unchanged while wage dispersion increases. For the U. S. 1975 -2003, the correlation between the unemployment rate of the unskilled and the skill premium has been 0.004. 27 23 Austria, Italy, the Netherlands, and Portugal are the exceptions to the rule. 24 See Weiss and Garloff (2005). 25 Unemployment rates by qualification are from [START_REF] Reinberg | Vertrauter Befund: Höhere Bildung schützt auch in der Krise vor Arbeitslosigkeit[END_REF]. The skill premium has been calculated from administrative data where the wage for unskilled workers is proxied by the lowest performance group of blue-collar workers ("Leistungsgruppe 3, Arbeiter") and the wage for skilled workers is proxied by the highest performance group of white-collar workers ("Leistungsgruppe 2, Angestellte"), Source: German Federal Statistical Office. 26 The finding that higher wage dispersion and unemployment are positively correlated (across age-byeducation cells) in Germany is also found and discussed by Fitzenberger and Garloff (forthcoming).

27 Unemployment rates by educational attainment stem from U.S. Census Bureau (1975Bureau ( -2004) ) and [START_REF] Francesconi | Education and the Natural Rate of Unemployment[END_REF]. Wages stem from the CPS (U.S. Census Bureau, internet release, www.census.gov/hhes/www/income/histinc/incpertoc.html). Unemployment of the unskilled and wage dispersion are unlikely to contain a trend. While this is obvious for the unemployment rate at least in an asymptotic sense, it is plausible for wage dispersion, too. Nevertheless, we performed the same regression in first differences. Albeit less strongly, the results of a regression of the unemployment rate of the low-skilled on the wage premium in levels is significant for Germany (and insignificant for the United States).

Proposition 3

 3 Technological change leads to a decrease [respectively increase] in employment of the unskilled and an increase[decrease] in wage inequality whenever it leads to an increase [decrease] in as au .

  14)20 In several further extensions of the model, we assess the robustness of our results and show that the central results are kept. These robustness checks are performed with respect to the bargaining regime, the risk neutrality of workers, and the production technology. (Compare the working paper version of this article,Weiss and Garloff (2005))

  . E., and L. J. Gallagher (1997): "General Assistance Programs: The State-Based Part of the Safety Net," New Federalism: Issues and Options for States, No. A-4, http://www.urban.org/url.cfm?ID=307036. U.S. Census Bureau (1975 -2004): Statistical Abstracts for the United States. Weiss, M., and A. Garloff (2005): "Skill-Biased Technological Change and Endogenous Benefits: The Dynamics of Unemployment and Wage Inequality," MEA Discussion Paper, 100-05, http://www.mea.uni-mannheim.de/.

This assumption is also for simplicity. InWeiss and Garloff (2005), we consider the case where the union takes into full account the effects of the wage level on the level of benefits and the tax rate. The results are virtually unaffected.

See, eg, Cahuc and Zylberberg (2004), Chapter 7.

Equations (3) and (4) are two different ways of writing down the same result. In equation (3) the focus is on the dependency of the unskilled workers' wage on the level of (endogenous) benefits while in equation (4) the unskilled workers' wage is shown as a function of the exogenous parameters of the model.

Throughout the paper, the term "equilibrium" will be used to refer to the allocation which results from union wage setting, given the other institutional features of the model.

Existence: For wu sufficiently small (resp. sufficiently large), the right hand side of the equilibrium

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