Maxime Forster
email: forster@apollonsolar.com

Erwann Fourmond

Roland Einhaus

Hubert Lauvray

Jed Kraiem

Mustapha Lemiti

DOPING ENGINEERING TO INCREASE THE MATERIAL YIELD DURING CRYSTALLIZATION OF B AND P COMPENSATED SILICON

In this paper, we investigate gallium co-doping during crystallization of boron and phosphorus compensated Si. It is shown that the addition of gallium yields a fully p-type ingot with high resistivity despite high B and P contents in the silicon melt. Segregation of doping impurities is consistent with theory. Minority carrier lifetime and majority carrier mobility measurements indicate that this material is suitable for the realization of solar cells with comparable efficiencies to standard material. Significant light-induced degradation of minority carrier lifetime is however revealed to occur in this material as in standard boron-doped silicon.

INTRODUCTION

In the drive to reduce the cost of solar energy, one promising path is the use of low cost Si feedstock, or Upgraded Metallurgical Grade Si (UMG-Si), obtained by simplified purification processes [START_REF] Einhaus | Proc. 21st ECPVSEC[END_REF]. This kind of material was recently shown to offer a great potential in terms of cell performances when it is sufficiently purified [START_REF] Kraiem | Proc. 35th IEEE Photovoltaic Specialists Conference[END_REF]. However, it usually contains more impurities than EG (Electronic Grade) silicon that may harm the photovoltaic performance of this material. Among them, B and P are the most difficult to remove through metallurgical processes and may thus remain in concentration up to 10ppmw after purification. Such a Si, containing both B and P in significant concentration is designated as compensated Si and shows modified electrical properties compared to uncompensated Bdoped Si. These electrical properties (resistivity, minority carrier lifetime) depend critically on the net doping N A -N D . Previous results [START_REF] Dubois | [END_REF] have shown the minority carrier lifetime to be higher and thus solar cells to be more efficient at high compensation levels (C l ), i.e. for low net doping, (or high resistivity). In addition, a lower net doping seems to lead to a reduction of the light-induced degradation observed in B-doped and oxygen contaminated Si [4].

However, because of the lower segregation coefficient of phosphorus (0.35) compared to that of boron (0.8), the net doping is not uniform along the ingot height and inversion of Si polarity, from p-to n-type may occur during crystallization (Fig. 1), reducing the material yield for standard industrial solar cell fabrication. Simulations [START_REF] Enebakk | 3rd Intenational Workshop on Crystalline Silicon Solar Cells[END_REF] using Scheil's law for dopant distribution and simple models for calculating carrier mobility show that requiring a minimum resistivity of 0.5Ω.cm and an ingot yield of at least 90% compels the use of Si feedstock containing less than 0.45ppmw of B and 1.0ppmw of P. Gallium co-doping has recently been proposed [START_REF] Kraiem | [END_REF][START_REF] Kraiem | Proc. 34th IEEE Photovoltaic Specialists Conference[END_REF][START_REF] Macdonald | 19th Workshop on Crystalline Silicon Solar Cells & Modules: Materials and Processes[END_REF] as a potential solution to control the net doping along the ingot height even when using Si containing large quantities of B and P. It relies on the low segregation coefficient of Ga which enables the increased P concentration, in relation to B, to be counterbalanced during crystal growth (Fig. 2). It is thus possible to obtain low net doping and p-type Si along the full ingot height. In this work, mono-and multi-crystalline ingots were grown using intrinsic solar grade polysilicon to which was voluntarily added known concentrations of B, P and Ga before crystallization. The resulting electrical properties were studied and correlated to the doping impurities vertical distribution along the ingot height.

EXPERIMENTAL DETAILS

Crystallization and wafering

For this study, two 15kg mono-crystalline and one 35kg multi-crystalline ingots were grown. The monocrystalline ingots Cz#1 and Cz#2 were <100> oriented Si crystals of about 160mm in diameter grown with the Cz method at Siltronix. The multi-crystalline ingot Mc#1 was grown in a casting furnace developed by Cyberstar and Apollon Solar [START_REF] Lissalde | Proceedings of the 22nd European PVSEC[END_REF][START_REF] Kraiem | Proceedings of the 23rd European PVSEC[END_REF]. Each ingot was made with intrinsic solar grade poly-Si to which known

Solidified fraction

Boron (cm-3)

Phosphorus (cm-3) Gallium (cm-3)
Net doping Na -Nd (cm-3)

p-type n-type

25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, 6-10 September 2010, Valencia, Spain concentrations of B, P and Ga were added (Table 1). The concentrations of B and P added in ingot Cz#1 correspond to an UMG-Si from which B has been efficiently removed by purification but which still contains high amounts of P. The B and P concentrations in ingots Cz#2 and Mc#1 correspond to a less purified UMG-Si. In each case, the Ga concentration to be added was calculated using the Scheil's law in order to obtain a fully p-type ingot and relatively high resistivity. B and P were introduced through highly doped Si wafers in which the doping impurity concentration was evaluated by means of four-point probe resistivity measurements correlated to the Irvin curves. Ga was introduced in the form of high purity Ga pellets. All the dopant sources were precisely weighed and put into the crucible together with the Si feedstock before melting. Ingot Cz#1 was then shaped into a 125x125mm² pseudo-square brick and sliced into 240μm thick wafers which were KOH etched to remove saw damage.

Table 1: Initial dopant concentrations added into the crucible before fusion for the three studied ingots.

Characterization

Resistivity was measured by Eddy currents at various heights along the length of each ingot in order to evaluate the impact of Ga co-doping.

Additional characterizations were held on ingot Cz#1: SIMS analyses on wafers from different ingot heights to determine the vertical distribution of doping impurities. Four-point probe resistivity and Hall effect measurements were performed to study the electrical activation of dopants. Minority carrier lifetimes were measured by QSSPC after chemical polishing and surfaces passivation by Si-rich SiNx:H layer deposition by PECVD, which was shown to ensure an optimum surface passivation [START_REF] Lelièvre | [END_REF]. Lifetime measurements were done after a six day light soaking under a 70 mW/cm² halogen lamp for full B-O complexes activation, and after an annealing step during 20 min at 210°C in the dark.

RESULTS

Resistivity

Measured resistivities are shown in Figure 3. For each ingot, the Si is p-type and the resistivity higher than 0.5Ω.cm along the full ingot height, despite high concentrations of B and P. Moreover, one can see that the resistivity is comprised in a very narrow range (0.5 to 1.2Ω.cm) along the full length of ingots Cz#2 and Mc#1. This indicates that Ga co-doping was efficient for:

(1) Avoiding Si to turn n-type. Indeed, using the Scheil's law, one can expect from B and P initial concentrations that the entire ingot Cz1 would be n-type and that a type inversion should occur at around 60% of the heights of ingot Cz2 and Mc1 if no Ga had been added before crystallization.

(2) Maintaining a relatively low net doping since the resistivity is kept higher than 0.5Ω.cm. 3.2 SIMS analyses SIMS analyses (Fig. 4) confirm that the p-type resistivity is to be attributed to the addition of Ga. Indeed, one can observe that the P concentration exceeds that of B over the entire ingot height, suggesting that without Ga co-doping, the ingot would be fully n-type.

0 ) 1 (      eff k S eff S f C k C Eq. 1
The effective segregation coefficients k eff of B and P can be extracted by fitting Scheil's equation (Eq.1) to the measured concentration of these two impurities. The extracted segregation coefficients equal 0.77 and 0.32 respectively for B and P, these values being in good agreement with theoretical values (0.8 for B and 0.35 for P [13]). SIMS analyses of Ga concentrations were not accurate enough (±20%) to extract its segregation coefficient. The distribution plotted using k Ga =0.0086 remains however within the error margins of the SIMS measurements. This good agreement between theory and measured segregation coefficients is very important since the calculation of the Ga concentration to be added is based on those.

Due to its very low segregation coefficient in Si, Ga is heterogeneously distributed along the ingot height. As can be observed (Fig. 4), Ga concentration is very low in the first solidified fraction of the ingot compared to its initial concentration in the melt. The incorporated Ga concentration then increases towards the end of crystallization. This allows counterbalancing the increase of P concentration towards the end of crystal growth without resulting in a too high net doping in the first

Concentrations

(cm-3)

Solidified fraction

[B] SIMS

[P] SIMS [Ga] SIMS [B] Scheil [P] Scheil [Ga] Scheil
Ingot B 0 (cm-3) P 0 (cm-3) Ga 0 (cm-3) Cz#1 2.7x10 16 1.8x10 17 5.8x10 In order to evaluate the net doping vertical distribution in the ingot, we executed room temperature Hall effect measurements on samples taken at various ingot heights. The difficulty in interpreting Hall effect measurements arises from the fact that the measured Hall carrier density p H differs from the true carrier density p by a factor close to unity (Eq.2), called the Hall factor r H , which depends on the main scattering mechanism.

H H p r p   Eq. 2
The r H dependence on the concentration of doping impurities and on temperature has already been studied for uncompensated p-type Si [14,15]. Although no data was found in the literature for r H in highly compensated Si, recent results suggest that it is only weakly dependent on the compensation level [16]. In this work, r H is thus assumed to be equal to 0.7 at room temperature for all samples, as predicted for uncompensated p-type Si with similar doping impurities concentration. The measured hole density is plotted against the solidified fraction in Fig. 3 together with the net doping [B]-[P] measured by SIMS that would be obtained without Ga addition. Again, it is shown that without Ga co-doping, the ingot would be fully n-type since the net doping [B]-[P] is negative along the full ingot height. In addition, one can observe that while the net doping [B]-[P] without Ga co-doping varies in a wide range, the equilibrium hole density with Ga co-doping remains below 6×10 16 cm -3 over 85% of the ingot height. This thus demonstrates that Ga can be used as a way to monitor the compensation level in order to obtain a relatively low net doping along the full ingot height while crystallizing ingot using B and P compensated Si.

Carrier mobility and lifetime

The hole mobility is calculated from the Hall effect measurements previously mentioned. Again, in all samples we take r H as equal to 0.7 to deduce the hole drift mobility from the measured Hall mobility. This mobility is compared in Fig. 4 to that calculated with Arora's model [17]. No significant difference is observed between the measured carrier mobility and Arora's model in the last solidified ingot fraction which contains the largest amounts of Ga. This indicates that Ga does not affect carrier transportation in significantly different manner than boron. In the first solidified fraction of the ingot, measured mobilities are significantly below those predicted by the model. These discrepancies were already observed by Veirman et al. [16] in highly compensated Si suggesting the existence of a specific compensationrelated scattering mechanism limiting the carrier mobility. In the ingot studied in this work, the compensation level C l =(N A +N D )/p is the highest in the first solidified fraction (C l =20 at f s =0.02 compared to C l = 6.6 at f S = 0.85) which might explain the disagreement with Arora's model in this region. QSSPC lifetime measurements were carried out to evaluate the impact of doping impurities on the electrical quality of Si. Results (Fig. 7) show that relatively high lifetimes are obtained after recovery in the major part of the ingot, considering the quality of the Si feedstock. A decrease in the minority carrier lifetime is observed as the solidified fraction increases which can be attributed, as proposed by Dubois et al. [START_REF] Dubois | [END_REF] to the increase in the majority carrier density and thus of the recombination strength of the defects energy states in the band gap of the Si. The very low carrier lifetimes measured in the bottom part of the ingot may also be due to the appearance of high densities of dislocations in this part of the ingot, as observed during crystal growth with the disappearance of the four facets on the edge of the ingot, representative of the dislocation-free <100> oriented mono-crystal. A significant degradation of minority carrier lifetime is observed after light-soaking suggesting the activation of the well-known boron and oxygen related complex [START_REF] Bothe | 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion[END_REF]. Further studies have to be carried out to determine whether the presence of Ga has an impact on the formation of such complex.

CONCLUSION

Thanks to Ga co-doping we control the net doping along the ingot height during crystallization of B and P compensated Si. This method may thus be used to increase the B and P concentration limits allowed in UMG-Si to produce high quality solar cells. The axial distribution of doping species was studied and showed good agreement with theory. Carrier mobility and lifetime measured along ingot height suggest that the full ingot is appropriate for the realisation of good performance solar cells. Significant LID was however measured in these wafers. Solar cells are currently undergoing processing to assess the photovoltaic potential of this material.

Figure 1 :

 1 Figure 1: B, P and net doping distribution calculated with Scheil's law along the height of an ingot which would be crystallized with Si containing 1ppmw of B and 5ppmw of P.

Figure 2 :

 2 Figure 2: B, P, Ga and net doping distribution calculated with Scheil's law along the height of an ingot which would be crystallized with Si containing 1ppmw of B, 5ppmw of P and with the addition of 150ppmw of Ga.

Figure 3 :

 3 Figure 3: Resistivity measured at different heights along the length of ingots Cz#1, Cz#2 and Mc#1.

Figure 4 :

 4 Figure 4: B, P and Ga concentrations measured by SIMS at various heights of ingot Cz1. The solid lines are the fits to Scheil's equation.

1

 1

Figure 5

 5 Figure 5 Hole density distribution along the ingot height compared to the net doping [B]-[P] that would be obtained without Ga co-doping.

Figure 6

 6 Figure 6 Hole drift mobility deduced from measured Hall mobility considering r H =0.7 and compared to Arora's model.

Figure 7

 7 Figure 7 Evolution of the minority carrier lifetime measured for each sample at the injection level Δn=0.1×p.

  Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, 6-10 September 2010, Valencia, Spain


Lieven Baele
email: lieven.baele@uvt.nl

Pilar Soriano
email: pilar.soriano-felipe@uv.es

The Determinants of Increasing Equity Market Comovement: Economic or Financial Integration?

Keywords: JEL Classification: G11, G12, G15, C32, F37 Economic Integration, Financial Integration, Cash-flow News, Discountrate News

This paper investigates to what extent the substantial increase in exposures of local European equity market returns to global shocks is mainly due to a convergence in cash flows ("economic integration"), to a convergence in discount rates ("financial integration"), or to both. We find that this increased exposure is nearly entirely due to increasing discount-rate betas. This finding is robust to alternative ways of calculating discount-rate and cash-flow shocks.

1

Introduction

There is increasing consensus that globalization and integration lead to substantially higher equity market betas and correlations. Apart from in emerging markets, the evidence is particularly strong in Europe, a region where integration has made considerable progress over the last twenty years 3 .

A yet unresolved question is what type of integration is behind these increases in market betas and correlations. In fact, cross-country equity market correlations could increase because of economic integration through a convergence in cross-country cash flows, because of financial integration through a convergence in cross-country discount rates, or through both. The aim of this paper is to quantify the relative importance of economic and financial integration in explaining time-varying equity market betas and correlations.

Distinguishing between both effects is important for a number of reasons. First, cross-market interdependences and correlations have frequently been used as indirect measures of financial integration. By separately correcting for economic integration, we should obtain a cleaner measure of financial integration. Second, differences in the degree of and time variation in respectively economic and financial integration may explain why equity correlations vary substantially across countries and over time. For instance, is one market more correlated with the world equity market because its cash flows are more similar, because it is relatively better financially integrated, or a combination of both? Last but not least, by identifying the different sources of market comovement in "normal" times, our analysis should also provide for a better identification of the various channels through which contagion may occur.

To empirically study the relative importance of economic and financial integration, we focus on a large sample of 21 equity markets from both Western and Eastern Europe.

We do this for a number of reasons. First, over the last decades, Europe has gone through an extraordinary period of increasing integration, including the introduction of the euro in 1999 and the accession of 10 new members to the European Union in 2004.

Second, the comparison of countries in an economically homogeneous region with those that opted to stay out of the economic (and monetary) union offers an ideal test for the main hypothesis in this paper. Third, this analysis may hold important lessons for the recently emerged equity markets in Central and Eastern European Countries which have just embarked or are about to embark on the integration process.

In our analysis, we jointly determine to what extent global market betas have increased over time, and to what extent this increase is due to a convergence in cashflow expectations (related to further economic integration) or in discount rates (resulting from increasing financial integration). As in the previous literature, we use the US marketstill by far the dominant equity market in the worldas a proxy for the global market portfolio. We use the VAR methodology developed in Campbell and Shiller (1988a) and [START_REF] Campbell | A variance decomposition for stock returns[END_REF] to decompose the return on the US market into a component due to revisions in future cash flows and a part due to news about future discount rates. [START_REF] Campbell | Where do betas come from -asset price dynamics and the sources of systematic-risk[END_REF] decomposed the betas of industry and size portfolios into components attributable to news about future cash flows, real interest rates, and excess returns. In a recent paper, [START_REF] Campbell | Bad beta, good beta[END_REF] showed that the size and value anomalies in stock returns can be explained by allowing stocks to have a different exposure to cash-flow and discount-rate news.

To our knowledge, this paper is the first to decompose country betas with respect to a common global market shock (here proxied by the US market) in a discount-rate and cash-flow beta. This allows us to quantify whether the increase in the total market beta is mainly due to an increase in the cash-flow beta (economic integration) or in the discount-rate beta (financial integration). We find that this increase is nearly fully the consequence of an increase in the discount-rate beta. This paper is most closely related to the work of [START_REF] Ammer | Measuring international economic linkages with stock market data[END_REF], [START_REF] Phylaktis | Measuring financial and economic integration with equity prices in emerging markets[END_REF], and [START_REF] Engsted | The comovement of US and UKstock markets[END_REF]. [START_REF] Ammer | Measuring international economic linkages with stock market data[END_REF] decompose the returns on the equity markets of 15 industrialized countries in a cashflow and discount-rate component over the period [1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989][1990]. Consequently, they interpret the cross-country correlations between discount and cash-flow news as measures of respectively financial and economic integration. [START_REF] Phylaktis | Measuring financial and economic integration with equity prices in emerging markets[END_REF] perform a similar analysis on a set of Pacific-Basin equity markets. [START_REF] Engsted | The comovement of US and UKstock markets[END_REF] is also similar in spirit to [START_REF] Ammer | Measuring international economic linkages with stock market data[END_REF]. They find that news about future excess returns is the main determinant of stock market volatility in both the US and the UK, and that this news component is highly cross-country correlated.

The main difference between these studies and ours is that we look at exposures to cash-flow and discount-rate shocks as measures of economic and financial integration instead of correlations in respectively cash-flow and discount-rate shocks. The main advantage of looking at exposures rather than at correlations is that the former are not vulnerable to the conditioning bias of [START_REF] Forbes | No contagion, only interdependence: Measuring stock market comovements[END_REF]. More specifically, rising cross-country correlations may be purely the result of an increase in the volatility of cash-flow / discount-rate shocks rather than of increasing integration. For instance, the high correlations observed at the end of the 1990s / early 2000s are most likely more the result of high common factor volatility following the build-up and burst of the technology bubble rather than of a sudden increase in integration. An additional difference is that we consider a broad range of both Western and Eastern European countries, and that our sample period covers a wider range of data including the early 2000s, where the process of further European integration was still taking place.

The remainder of this paper is organized as follows. Section 2 describes, first, how global market shocks can be decomposed in news about future cash flows and discount rates and, second, how to measure cash-flow and discount-rate exposures. Section 3 reports the empirical results and some robustness checks. Finally, Section 4 concludes.

Decomposing Global Risk into Cash-flow and Discount-rate News

Cash-flow and Discount-rate News

As in Campbell and Shiller (1988a) and [START_REF] Campbell | A variance decomposition for stock returns[END_REF], we use the log-linear approximate decomposition of unexpected returns4 : 9) and (10) will only hold in the absence of bubbles.

1 1 0 1 1 1 1 1 ) ( ) ( j j t j j t t j t j t t t t t r E E d E E r E r (9)
To implement this decomposition, we follow [START_REF] Campbell | A variance decomposition for stock returns[END_REF] and estimate the cashflow news and discount-rate news series using a vector autoregressive (VAR) model.

This VAR methodology first estimates the terms

1 t t r E and j t j j t t r E E 1 1 1 ) ( and
then uses 1 t r and equation ( 9) to back out the cash-flow news. As pointed out by [START_REF] Campbell | Bad beta, good beta[END_REF], this practice has an important advantage -one does not necessarily have to understand the short-run dynamics of dividends. Understanding the dynamics of expected returns is enough, as cash-flow and discount-rate news can be obtained through the linear functions described in equations ( 12) and ( 13).

We assume that the data are generated by a first-order VAR model

1 t 1 t t u Γz a z (11)
where 1 t z is a m-by-1 state vector with 1 t r as its first element, a and Г are an m-by-1

vector and m-by-m matrix of constant parameters, and 1 t u an i.i.d. m-by-1 vector of shocks. Of course, this formulation also allows for higher-order VAR models via a simple redefinition of the state vector to include lagged values.

Provided that the process in equation ( 11) generates the data, t+1 cash-flow and discount-rate news are linear functions of the t+1 shock vector:

1 1 , ) ' ' ( t t CF N u e1 e1 (12) 1 1 , ' t t DR N u e1 (13)
The VAR shocks are mapped to news by λ, defined as

1 ) ( Γ I Γ
. The longrun significance of each individual VAR shock to discount-rate expectations is captured by ' e1 , where e1 is a vector whose first element is equal to one and zero otherwise.

The greater the absolute value of a variable's coefficient in the return prediction equation (the top row of Г), the greater the weight the variable receives in the discountrate-news formula. More persistent variables should also receive more weight, which is captured by the term 1 ) ( Γ I .

Measuring Global Cash-flow and Discount-rate Exposures

We showed in the previous section how unexpected returns can be decomposed into two news components. An interesting question is whether increasing exposure to global shocks is a result of increasing exposure to cash-flow news or increasing exposure to discount-rate news. Moreover, different countries may have different betas or exposures to these two components of the global market. Following [START_REF] Campbell | Bad beta, good beta[END_REF], using the excess returns ( e t i r , ) of a particular country i, we define the cash-flow beta as Therefore, the global market beta can be decomposed into components in a simple way:

DR i CF i US i , , , (16)
We define betas by using unconditional variances and covariances. However, we will report betas using the whole sample period and also betas using the same subperiods as before, in order to get an idea of their evolution in time. An increase in economic and financial integration would be consistent with an increase in respectively This framework enables us to analyse the variation across countries and across time in the two components of the market beta.

Empirical Results

US Cash-flow and Discount-rate News

Section 2 explained how unexpected stock returns can be decomposed into a component due to revisions in expectations of future cash flows and a part due to revisions in expectations of future discount rates within a straightforward first-order VAR framework. To operationalize this VAR approach, we need to specify the variables to be included into the state vector ( 1 t z ). Following [START_REF] Campbell | Bad beta, good beta[END_REF], we choose the following four state variables: the excess market return (measured as the log excess return on the CRSP value-weighted index over Treasury bills), the yield spread between long-term and short-term bonds (measured as the yield difference between ten-year constant-maturity taxable bonds and three-month taxable notes, in annualized percentage points), the market's smoothed price-earnings ratio (measured as the log ratio of the S&P500 price index to a ten-year moving average of S&P500 earnings), and the small-stock value spread (measured as the difference between the log book-to-market ratios of small value and small growth stocks). Our monthly data covers the period January 1929 -December 2005. For January 1929 -December 2001, data is taken from Tuomo Vuolteenaho's website. For the rest of the sample period, we obtain the variables following [START_REF] Campbell | Bad beta, good beta[END_REF].

Thus, excess market return data is from CRSP, yield spread data is from FRED (Federal Reserve Economic Data), the price-earnings ratio is from [START_REF] Shiller | Irrational Exuberance[END_REF], and the smallstock value spread is constructed from the data made available by Professor Kenneth French on his web site6 . Summary statistics are reported in Table 1.

The first two predictor variables have become standard instruments in the return predictability literature. The term spread variable is consistently shown to be a leading 8 indicator of real economic activity, and hence stock prices. [START_REF] Estrella | The term structure as a predictor of real economic activity[END_REF] and [START_REF] Estrella | Predicting US recessions: Financial variables as leading indicators[END_REF] show that for the United States the yield spread significantly outperforms other financial and macroeconomic indicators in forecasting recessions. [START_REF] Bernard | Does the term structure predict recessions? the international evidence[END_REF], Estrella and[START_REF] Estrella | The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank[END_REF][START_REF] Ahrens | Predicting recessions with interest rate spreads: a multicountry regime-switching analysis[END_REF] present similar results for other countries. In addition, several papers (e.g., [START_REF] Campbell | Stock returns and the term structure[END_REF][START_REF] Fama | Business conditions and expected returns on stocks and bonds[END_REF][START_REF] Campbell | Efficient tests of stock returns predictability[END_REF] have found a positive relation between the term structure and equity returns. Second, high priceearnings ratios are associated with low long-run expected returns, at least to the extent that earnings growth is constant. For instance, [START_REF] Fama | Dividend yields and expected stock returns[END_REF] and Campbell and Shiller (1988b) find that price-dividend and price-earnings ratios predict future real equity returns, and, more recently, [START_REF] Campbell | Bad beta, good beta[END_REF] and [START_REF] Hecht | Explaining returns with cash-flow proxies[END_REF] also provide evidence on how log price-earnings ratios negatively predict returns. The third, less standard, variable is the small-stock value spread. [START_REF] Campbell | Bad beta, good beta[END_REF] offer a number of reasons for why this variable may be linked to expected returns. First, small growth stocks may generate cash flows in the more distant future and therefore their prices are more sensitive to changes in discount rates. Second, small growth companies may be particularly dependent on external financing and thus are sensitive to equity market and broader financial conditions. Finally, they argue that episodes of irrational investor optimism are likely to have a particularly powerful effect on small growth shocks.

Table 2 reports the parameter estimates for the VAR model. Row 1 to 4 correspond to respectively the equations for the excess equity market returns, the term spread, the price-earnings ratio, and the small-stock value spread. The first five columns report coefficients on the five explanatory variables: a constant, and lags of the excess market return, term yield spread, price-earnings ratio, and small-stock value spread. OLS standard errors and Bootstrap standard errors are also reported. The final two columns report the R² and F statistics for each regression. The first row of Table 2 shows that all predictor variables have a statistically significant relation with the excess market returns.

The coefficient on the lagged market return amounts to 0.0949, consistent with a modest degree of momentum. The term yield spread positively predicts the market return. The term spread accounts for a term or maturity risk premium, therefore leading to that positive relation (see [START_REF] Fama | Business conditions and expected returns on stocks and bonds[END_REF]. The smoothed price-earnings ratio isconsistent with previous findings -negatively related to expected returns. Finally, the small-stock value spread negatively predicts stock returns, consistent with findings in [START_REF] Eleswarapu | The predictability of aggregate stock market returns: Evidence based on glamour stocks[END_REF] and [START_REF] Brennan | Estimation and test of a simple model of intertemporal capital asset pricing[END_REF]. The R² is reasonable for a monthly expected return model. Rows 2 till 4 summarize the dynamics of the explanatory variables. The term spread has a high degree of autocorrelation (AR(1) coefficient of 0.9138). Interestingly, also the small-stock value spread has some predictive power for the term spread. Finally, the price-earnings ratio and the smallstock value spread ratio are both highly persistent, with roots (very) close to unity.

Table 3 reports summary statistics of the cash-flow and discount-rate news variables as implied by the VAR estimates. A first observation is that discount-rate news is double as volatile as cash-flow news (a monthly volatility of respectively 4.84% and 2.62%). This confirms the finding of [START_REF] Campbell | A variance decomposition for stock returns[END_REF] that discount-rate news is the dominant component of the market return. The table also shows that the two components of return are almost uncorrelated with one another. Following [START_REF] Campbell | Bad beta, good beta[END_REF], Table 3 also reports the correlations of each state variable innovation with the estimated news terms, and the coefficients ) ' ' ( e1 e1

and ' e1 that map innovations to cash-flow and discount-rate news. Innovations to returns are highly negatively correlated with discount-rate news, reflecting the mean reversion in stock prices that is implied by our VAR system. Market-return innovations are weakly positively correlated with cash-flow news, indicating that some part of a market rise is typically justified by underlying improvements in expected future cash flows.

Innovations to the price-earnings ratio, however, are weakly negatively correlated with cash-flow news, suggesting that price increases relative to earnings are not usually justified by improvements in future earnings growth.

Cash-flow and Discount-rate Betas

In this section, we investigate whether the 21 local European equity returns considered have become more exposed to US equity market shocks, and to what extent this increased exposure is due to a convergence in cash-flow and/or discount-rate news.

We use Datastream's Total Market indices as proxies for country returns. All returns are expressed in US dollars7 and include dividends. Datastream indices have as an advantage over MSCI indices that they cover a larger part of the market (up to about 80 percent of total market capitalization). For most countries, returns are available as from January 1973.

Table 4 reports estimates of the total, cash-flow and discount-rate beta with respect to the US market for all countries over the full period and the subperiods 1973-1979, 1980-1989, 1990-1999, and 2000-2005. Figure 1 4 and Figure 2 clearly show that this increase is nearly entirely the result of an increase in discount-rate betas. Cash-flow betas are generally very small, statistically insignificant, and if anything, decreasing over time. We conclude from this analysis that the increased exposure of local European equity markets to the US market is largely the result of increased European financial market integration. This analysis also shows that global (regional) market exposures are a useful measure of financial market integration in a sense that the effect of further economic integration on market betas is only of second order.

Robustness Checks

Post-1952 data

According to [START_REF] Chen | Return decomposition[END_REF], an interesting robustness check is to estimate cash-flow and discount-rate news using only postwar data. They suggest it is worth analysing this because [START_REF] Campbell | A variance decomposition for stock returns[END_REF] documents a shift in variance from cash-flow news to discount-rate news after 1952 and CAPM breaks down only in the postwar period. In Table 5, model 2, we report the results for the benchmark case when only postwar data is used. In this case, discount-rate news continues to be more important than cash-flow news, though, surprisingly, there is now less difference between both.

Discount-rate betas continue to be more important than cash-flow betas and their evolution in time is similar to the benchmark case. We do find though an increasing trend in the average cash flow beta across EMU countries. Cross-sectional differences are, however, large. While we see substantial increases in Germany, Greece, Luxembourg, and Italy, cash flow betas decrease in the UK, Norway, Sweden, and even (slightly) in France. Moreover, while increases are often large in percentage terms, they are relatively small in absolute terms.

Sensitivity to changes in VAR state variables

Following [START_REF] Campbell | Bad beta, good beta[END_REF], our benchmark VAR model includes the excess market return, the term spread, the market's smoothed price-earnings ratio, and the small-stock value spread. However, there are other variables that are often used to predict stock returns. In Table 5 we report some of the results obtained in this study when we include other variables in the VAR system. We report the variance of cashflow news and discount-rate news, their covariance, cash-flow betas, discount-rate betas, and their evolution in time. We report average betas for: i) the 12 EMU members, ii) the 3 non-EMU but EU members and, iii) 3 non-EMU and new EU members.

In the first column, model 1, where the benchmark case is used, the cash-flow variance is 0.07% and the discount-rate variance is 0.23%. Therefore, consistent with [START_REF] Campbell | What moves the stock and bond markets? a variance decomposition for long-term asset returns[END_REF] and [START_REF] Campbell | Bad beta, good beta[END_REF], discount-rate news far exceeds cash-flow news in driving US equity returns. In model 3, following [START_REF] Chen | Return decomposition[END_REF], we replace the price-earnings ratio from the benchmark case by a similar variable that also works as a proxy for expected returns8 , the dividend yield.

We find that the cash-flow variance is 0.16% and the discount-rate variance is 0.10%. This is, the trend is reversed. In model 4, we use the average value spread instead of the small-stock value spread. The results are very similar to those reported for the benchmark model. Following [START_REF] Liu | Is the value spread a useful predictor of stock returns?[END_REF], in models 5 and 6, we use the book-to-market spread and market-to-book spread instead of the value spread as useful predictors of returns. The results are also similar to the benchmark case. In model 7, we follow [START_REF] Campbell | Bad beta, good beta[END_REF] and add to the benchmark case two variables that are often used to predict stock returns: the dividend yield and the Treasury bill rate.

With this combination of variables, results are also very similar to those reported for the benchmark case. Finally, model 8 includes the set of variables from [START_REF] Petkova | Do the Fama-French factors proxy for innovations in predictive variables?[END_REF]: the excess market return, the term spread, the dividend yield, the default spread (Baa yield over Aaa yield), and the Treasury bill rate. As it happened in model 3, replacing the price-earnings ratio by the dividend yield, will make the cash-flow news more important.

If we focus on betas and we exclude models 3 and 8 from our analysis, all models seem to point out that discount-rate betas are higher than cash-flow betas. This result is also robust across countries. Moreover, both betas are higher for less EU-integrated countries. For instance, the 3 new EU members have always higher betas than the 12 EMU members. If we focus on the evolution of betas in time, discount-rate betas have increased both in the 12 EMU members and in the 3 non-EMU but EU members.

However, they have decreased in the 3 new EU member states. These results are robust across models. Regarding cash-flow betas, there is a general decreasing trend across models if we look at the 3 non-EMU but EU members and the 3 new EU members, but there is not homogeneity in results across models if we look at the 12 EMU members (some models account for a decrease in cash-flow betas and some of them for an increase in betas).

The results are robust to adding many other known return predictors to the VAR system as long as the price-earnings ratio is included in the system. Therefore, it should be noted that our results depend critically on the inclusion of the price-earnings ratio in our aggregate VAR system. If we exclude the price-earnings ratio from the system (models 3 and 8) we no longer find that discount-rate betas are higher than cash-flow betas. As [START_REF] Campbell | Bad beta, good beta[END_REF] and [START_REF] Chen | Return decomposition[END_REF] point out, the importance of any state variable depends on the coefficient in the VAR estimation and its persistence. In our benchmark case, the price-earnings ratio is the dominant factor due to its persistence. [START_REF] Campbell | Bad beta, good beta[END_REF] contains a detailed discussion of various reasons why this variable should predict stock returns and should, therefore, be included in the VAR. In fact, the benchmark case gives the best predictive power (adjusted R 2 at 2.10%), if we compare it with those of models 3 (adjusted R 2 at 1.67%) and 8 (adjusted R 2 at 1.14%).

Finally, the results are also robust to estimating the VAR using real (instead of excess) market returns.

Conclusions

This paper investigates to what extent the increase in global market betas is mostly due to a convergence of cash flows, of discount rates, or both. We interpret the first as being the result of economic integration, the second as a consequence of financial integration.

We use the framework of Campbell and Shiller (1988a) and [START_REF] Campbell | A variance decomposition for stock returns[END_REF] to decompose the return on the US equity marketour proxy for global newsinto its cash-flow and discount-rate news components. Next, following [START_REF] Campbell | Bad beta, good beta[END_REF], we decompose the total beta of a country with respect to the US into a cash-flow and a discount-rate beta. This paper isto our knowledgethe first to decompose country betas in these two components.

We show that increasing global market betas is nearly fully the result of an increase in discount-rate betas. Therefore, we conclude that the increase in total market betas is mainly the result of increased financial integration. Finally, we show that this result is robust to alternative ways of calculating cash-flow and discount-rate news.

This paper suggests a number of paths for future research. First, it would be interesting to also decompose the beta with respect to the European equity market into its cash-flow and discount-rate components. Given the substantial degree of economic integration within Europe, one would expect that the cash-flow beta would be a more important contributor to the rising equity market beta. Such an analysis requires, r , is the excess log return on the CRSP value-weight index.

t TY is the term yield spread in percentage points, measured as the yield difference between ten-year constant-maturity taxable bonds and short-term taxable notes. t PE is the log ratio of S&P 500"s price to S&P 500"s ten-year moving average of earnings. t VS is the small-stock value spread, the difference in the log book-to-market ratios of small value and small growth stocks. "Stdev." denotes standard deviation and "Autocorr." the firstorder autocorrelation of the series.

  plots the average total, cash-flow and discount-rate betas over the four subperiods, while Figure 2 compares the cash-flow and discount-rate betas across countries. Consistent with Baele (2005) and Baele and Inghelbrecht (2006), we find a substantial increase in the exposure of local European equity markets to US equity market shocks. More specifically, the average US market exposure increased from about 0.48 in the second half of the 1970s to 0.61 in the 1980s, 0.68 in the 1990s, and 0.88 in the period 2000-2005. Panel B and C of Table

Figure 2 :

 2 Figure 2: Cash Flow and Discount Rate betas over time

Table 1 : Descriptive statistics of the VAR state variables

 1 The table shows the descriptive statistics of the VAR state variables estimated from the full sample period 1928:12-2005:12, 925 monthly data points. e

	M	t

Table 2 : VAR parameter estimates

 2 The table shows the OLS parameter estimates for a first-order VAR model including a constant, the log excess market return ( e Each set of three rows corresponds to a different dependent variable. The first five columns report coefficients on the five explanatory variables, and the remaining columns show R 2 and F statistics. The F statistic tests joint significance of all regressors. OLS standard errors are in square brackets and bootstrap standard errors are in parentheses. Bootstrap standard errors are computed from 2500 simulated realizations. We select 2500 independent bootstrap samples, each consisting of a block of consecutive observations drawn with replacement from our data set. We then evaluate the bootstrap replication corresponding to each bootstrap sample and estimate the standard error by the sample standard deviation of the 2500 replications. Sample period for the dependent variables is 1928:12-2005:12, 925 monthly data points.

	Variable	Mean	Median	Stdev.	Min	Max	Autocorr.
	e
	M r ,	t	0.0043	0.0093	0.0548	-0.3442	0.3222	0.1022
	t TY	0.7059	0.5700	0.7373	-1.3500	3.1400	0.9268
	PE	t	2.8878	2.8868	0.3742	1.5006	3.8906	0.9914
	VS	t	1.6511	1.5250	0.3668	1.1922	2.7134	0.9909
				e
	Correlations	M r ,	t	t TY	PE	t	VS	t
	e
	M r ,	t	1
	t TY	0.0580	1
	PE	t	-0.0064 -0.1134	1
	VS	t	-0.0314 -0.3679 -0.3154	1

t VS

).

Table 3 : Cash Flow and Discount Rate news for the market portfolio

 3 The table shows the properties of cash-flow news ( CF N ) and discount-rate news ( ) implied by the VAR model of Table2. The upper-left section of the table shows the covariance matrix of the news terms. The upper-right section shows the correlation matrix of the news terms with standard deviations on the diagonal. The lower-left section shows the correlation of shocks to individual state variables with the news terms. The lower right section shows the functions , is the excess log return on the CRSP value-weight index, PE is the price-earnings ratio, and t VS is the small-stock value spread.Bootstrap standard errors (in parentheses) are computed from 2500 simulated realizations.

	1' 1' , 1' e e e	that map the state-variable shocks to cash-flow and discount-rate news. We
	define	I	1	, where Γ is the estimated VAR transition matrix from Table 2 and ρ
	is set to 0.95 per annum. e M r t t TY is
	the term yield spread,
	News covariance		N	US	N	US	News corr/std	N	US	N	US
					CF		DR				CF	DR
		US							US
	N	CF		0.0007	0.0000	N	CF	0.0262	0.0359
				(0.0001)	(0.0001)			(0.0012)	(0.0600)
		US							US
	N	DR		0.0000	0.0023	N	DR	0.0359	0.0484
				(0.0001)	(0.0002)			(0.0600)	(0.0019)
	Shock correlations		N	US	N	US	Functions		N	US	N	US
					CF		DR				CF	DR
	e M r , shock t		0.4451	-0.8647	e M r , shock t	0.6358	-0.3642
				(0.0515)	(0.0118)
	t TY shock		0.1138	0.0540	t TY shock	0.0284	0.0284
				(0.0345)	(0.0359)
	t PE shock		-0.0081 -0.0885	t PE shock	-0.8293 -0.8293
				(0.0509)	(0.0474)
	t VS shock		-0.0581 -0.0253	t VS shock	-0.2688 -0.2688
				(0.0444)	(0.0436)

DR N t

Average Cash Flow and Discount Rate betas over time

	00s
	90s
						Cash Flow Beta
						Discount Rate Beta
						Total Beta
	80s
	70s
	0	0,2	0,4	0,6	0,8	1

See e.g.[START_REF] Longin | Is the correlation in international equity returns constant: 1960-1990?[END_REF],[START_REF] Bekaert | Foreign speculators and emerging equity markets[END_REF],[START_REF] Baele | Measuring European financial integration[END_REF],[START_REF] Baele | Volatility spillover effects in European equity markets[END_REF],[START_REF] Baele | Volatility spillover effects in European equity markets[END_REF][START_REF] Baele | Structural versus temporary drivers of country and industry risk[END_REF][START_REF] Bekaert | International stock return comovements[END_REF].

We considered including exchange rates in this decomposition, but decided not to do so. The main reason is that exchange rates are virtually unpredictable. Consequently, shocks to instruments have no long-lasting effects on future exchange rates, and hence on returns. There could be a contemporaneous but relatively small effect though.

As in[START_REF] Campbell | Bad beta, good beta[END_REF], we set ρ= 0.95 1/12 . Recall that it can be related to either the average dividend yield or the average consumption wealth ratio.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

In unreported results, we investigate the robustness of our findings to using local instead of US dollar returns. We find that results are very similar for nearly all countries. Results are available upon request from the authors.

See Campbell and Shiller (1988a),[START_REF] Campbell | What moves the stock and bond markets? a variance decomposition for long-term asset returns[END_REF],[START_REF] Campbell | Where do betas come from -asset price dynamics and the sources of systematic-risk[END_REF], and[START_REF] Campbell | Bad beta, good beta[END_REF].

Acknowledgements

The authors greatly benefited from discussions with Pilar Abad, Geert Bekaert, Francisco Climent, Esther Eiling, Gerhard Illing, Koen Inghelbrecht, Ralph Koijen, Rafael Santamaria, and Rudi Vander Vennet, as well as seminar participants at the DIW in Berlin, the European Commission in Brussels, the University of Valencia, the University of the Balearic Islands, and the XV Foro de Finanzas in Palma de Mallorca. We also thank two anonymous referees for very helpful comments. Financial support from the European Commission: DG Research in cooperation with DG ECFIN and DG ESTAT (Contract No. SCS8-CT-2004-502642); ERDF and Spanish Ministry of Education and Science (Contract No. ECO2009-14457-C04-04); Generalitat Valenciana (Contract No. GV/2007/082); and Catedra Finanzas Internacionales -Banco Santander is gratefully acknowledged.

however, a correct identification of cash-flow and discount-rate news for the aggregate European market. A second task for future research is to develop such a model. The Campbell -Shiller framework is less suitable in this case, because it does not account for time-varying integration and potential regime changes and structural breaks. The approach recently developed by [START_REF] Van Binsbergen | Predictive regressions: a present-value approach[END_REF] looks like a more promising starting point, as it does not rely on instruments to identify cash-flow and discount-rate shocks, and it is more easily adaptable to account for structural changes. We study news and betas when alternative VAR specifications are used. We report the variances of the cash-flow news and discount-rate news, and their covariances for the equity market portfolio. We also report the magnitude and time variation of betas. In order to do so, we report average betas for the: i) 12 EMU countries, ii) 3 non-EMU but EU countries and, iii) 3 new EU countries. The plus signs indicate the state variables and sample period included in the VAR model. Excess return refers to the excess log return on the CRSP value-weight index; Term spread is the term yield spread, measured as the yield difference between ten-year constant-maturity taxable bonds and short-term taxable notes; PE ratio is the log ratio of S&P 500"s price to S&P 500"s ten-year moving average of earnings; Small-stock value spread is the difference in the log book-to-market ratios of small value and small growth stocks; Dividend yield is the dividend-price ratio of the market portfolio; Value spread is the difference in the log book-to-market ratios of value and growth stocks; Book-to-market spread and Market-to-book spread are calculated following [START_REF] Liu | Is the value spread a useful predictor of stock returns?[END_REF]; Default spread is Baa yield over Aaa yield; Treasury bill rate is the 1-month Treasury bill yield.