Most economists view technological differences as an important part of the large disparities in per capita income across countries. For example, Paul Romer (1993, p. 543) argues that many nations are poor, in large part, “…because their citizens do not have access to the ideas that are used in industrial nations to generate economic value.” (see also Prescott, 1998). This view receives support from a number of recent studies, such as Klenow and Rodriguez (1997), Caselli et al. (1997), and Hall and Jones (1998), which find significant “total factor productivity” (TFP) differences across countries. Large crosscountry differences in technology are difficult to understand, however.

Ideas, perhaps the most important ingredient of technologies, can flow freely across countries, and machines, which embed better technologies, can be imported by less developed countries. This compelling argument has motivated papers such as Mankiw, Romer and Weil (1992), Mankiw (1995), Chari, Kehoe and McGrattan (1997), Parente, Rogerson and Wright (1998) and Jovanovic and Rob (1998) to model cross-country income differences as purely driven by differences in factors rather than in technology.

In this paper, we argue that even when all countries have access to the same set of technologies, there will be large productivity differences among them.1 The center-piece of our approach is that many technologies used by less developed countries (LDCs/the South) are imported from more advanced countries (the North) and, as such, are designed to make optimal use of the prevailing factors and conditions in these richer countries.
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Interest rate data were obtained from two sources. The Bank for International Settlements provided daily 3-, 6-, and 12-month Eurocurrency deposit rates for 21 countries, collected around 10 AM Swiss time. The data begin on January 3, 1977 for five currencies (German mark, Dutch guilder, Swiss franc, pound, and U.S. dollar), on September 1, 1977 for most other currencies, and run through August 31, 1998.

Swap rate data were downloaded from Datastream for nine European countries. Swap rates are the European coupon rates in a semiannual exchange of fixed European interest payments against floating dollar interest rate payments indexed to the Eurodollar interest rate; principal is also exchanged at maturity. Since the dollar floating-rate cash flows are at par at the swap’s inception, swap rates provide the European coupon rate such that a European bond with semiannual coupons would be at par — or, equivalently, the European semiannual yields to maturity, times two. Swap rate data are more readily available than country-specific bond yields. As with other Euromarket data, swap rates also alleviate concerns about country-specific default risk, capital controls, and tax issues, as well as eliminating heterogeneity across national data sources.
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FINANCIAL MARKETS’ ASSESSMENT: Prospects for the Future 2

Filtration-based assessments

As indicated above in Figure 3, forward rates for all countries participating in the currency union have currently essentially converged to a common “Euro” term structure. Some insights into financial markets’ assessment of the future interest rate policies of the ECB relative to earlier Bundesbank policies can potentially be gleaned by comparing this yield curve with historical German norms. To this end, a state space representation of the vector of Eurocurrency deposit rates and swap rates for German instruments maturing prior to January 1, 1999 was estimated via Kalman filtration on weekly data:
where y t is a 9 x 1 vector of 3-, 6-, and 12-month Euro-DM deposit rates and 1-7 year swap rates (excluding 6-year rates), expressed as continuously compounded yields; and zt is a 4 x 1 vector of underlying state variables.

The Euro-DM data were available from January 5, 1997, while most of the swap rate data began in June 24, 1991.22 Data for post-1998 maturities were treated as missing data, with only 3-and 6-month Euro-DM rates available for inference on the final date of July 1, 1998. The estimated state space model summarizes the time-series based information contained in the level and shape of the German term structure with regard to future term structure evolution, as well as the current assessment of the state vector conditional on pre-1999 maturities. The model was used to address two questions:

1. How does the current term structure of German swap yields compare with the German pre-1999 norm represented by the final filtered value yT|T ?

2. How does the current term structure of German forward rates compare with the future 3-month spot rates that would be predicted based upon pre-1999 norms?
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Some insight into the time series stability of the term structure of spreads at short maturities can be gleaned from Eurocurrency spreads, for which data over a longer time interval are more readily available. Lund’s model implies that the instantaneous spread is Markov, so the slope of the term structure of spreads contributes no additional information. A crude proxy for the former that partially avoids the impact on short maturities of speculative attacks is the six-month Eurocurrency spread; the latter is proxied by the difference between 12- and 6-month spreads. Table 2 reports estimates of the regression
for weekly data over 1977-1998, estimates for c over the 1990-98 subsample used by Lund, and heteroskedasticity-consistent Chow tests of process stability.
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With the exception of the Dutch-German spread slope, all subsample estimates of c1990^98 are statistically insignificant, justifying Lund’s specification. Over the longer 1977-98 interval, spread slopes are statistically significant for Denmark, Italy, and the Netherlands, but not for the other five countries. However, it does appear that including the spread slope captures parameter instabilities in the estimated spread process to some extent. P-values are substantially lower for subsample stability tests of Lund’s AR(1) than for stability tests of (26), for 7 out of 8 countries. Parameter stability of (26) is rejected at the 10% level for 3 countries (Belgium, Great Britain, and Sweden), whereas the subsample stability of Lund’s AR(1) is also rejected for these three, Denmark and Italy.
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Lund makes two key identifying assumptions. First, he explicitly rules out the “doubledecay” process of Jegadeesh and Pennacchi (1996) on the grounds of parsimony, and assumes that the “risk premium” state variable Vt does not affect the actual AR(1) process (25) followed by the instantaneous spread. This assumption appears empirically reasonable for the 1990-98 period considered, as will be discussed below. However, one advantage of the more general model, as recognized in Andersen and Lund (1996), is that the second state variable can capture structural shifts in the nominal interest spread processes resulting, e.g., from shifts in inflation targets.
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Second, the model implies yield spreads from bonds maturing prior to January 1, 1999 are unaffected by shifts in the EMU probability state variable 0t. This is critical, because the “market price of risk” Vt is also a free state variable that captures some movements in the shape of the term structure unjustified by the AR(1) process (25) estimated for the instantaneous spread st. However, Vt affects all maturities, whereas 0t affects only post-1999 maturities. 0t is inferred in essence by the difference between the observed post-1999 portion of the yield curve, and the post-1999 “local yield curve” predicted by extrapolating the pre-1999 yield curve for estimated state variables (s, Vt). Thus, Lund’s EMU probability assessments are essentially assuming that the non-EMU scenario for interest rate differentials is largely identified by the estimated 2-factor behavior of the term structure of yield spreads over 1990-98 for pre-1999 maturities.
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It seems likely that the reaction function estimated by Favero et al is partly capturing Italian monetary policy over 1987-1996. Achieving the Maastricht criteria certainly would create concern about inflation differentials, while the pre-1993 Exchange Rate Mechanism implies German interest rates were relevant over that period. However, whether forecasts based on this reaction function are relevant conditional upon Italy failing to enter EMU in 1999 is open to question. other

While the objective of qualifying by 2002 or later would plausibly prompt retention of the same reaction function, an alternate hypothesis is that the Bank of Italy might revert to its policies of the 1980’s. The Bank of Italy reaction function estimated by Clarida, Gall, and Gertler (1997) over 1981:6 through 1989:12 is quite different from that in Favero et al; in particular, higher steady-state real interest rates, and lower long-run sensitivity to Italian inflation, Italian output, and German interest rates. If this were used instead as the non-EMU scenario, inferred EMU probabilities would be quite different.

Bond pricing models

Lund (1998) provides a good example of the application of current bond pricing models to the issue of inferring EMU probabilities. Lund models, e.g., Italian instantaneous interest rates as the sum of the German rate and the Italian-German spread:
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