The theoretical interactions between trade openness, financial integration, specialization and business cycles synchronization are complex. It is well known that openness to goods trade results in high degrees of specialization.2 Similarly, financial liberalizations may result in specialization, as access to an increasing range of state-contingent securities unhinges domestic consumption patterns from domestic production, which then becomes free to specialize according to comparative advantage, for instance.3 Both trade in goods and in financial assets, in turn, potentially affect the cross-country synchronization of business cycles. These mechanisms were exposited in a literature spawned by the work of Backus, Kehoe, and Kydland (1992), that attempts to account for the observed high international correlation of business cycles, among others. In particular, intense bilateral trade will tend to accompany highly correlated business cycles in a wide range of theoretical models, ranging from multisector international models with intermediate goods trade to one-sector versions with either technology or monetary shocks.4
The impact of financial integration on cycles synchronization is not unambiguous. On the one hand, a limited ability to borrow and lend internationally hampers the transfer of resources across countries and can increase GDP correlations. On the other hand, if investors have imperfect information or face liquidity constraints, limiting capital flows can actually decrease GDP correlations, as investors herd, or withdraw capital from many destinations simultaneously.5
Specialization, in turn, is likely to affect the international synchronization of business cycles directly. This will naturally occur in the presence of sector-specific shocks, as two economies producing the same types of goods will then be subjected to similar stochastic developments. But it may also occur in the absence of any sector-specific shock. If sectors differ in their response to monetary shocks, say, for instance because of different market structures or labor market arrangements, countries with similar production patterns will be synchronized even though shocks are purely aggregate.6
To summarize, as illustrated in Figure 1, both goods and assets trade have potentially direct as well as indirect effects on business cycles synchronization. Furthermore, both have ambiguous overall impact. Classic Ricardian or Heckscher-Ohlin specialization may mitigate the direct impact of openness to goods trade, whereas financial integration may decrease (or increase) synchronization, but will also unambiguously induce specialization. This paper introduces a simultaneous equations methodology to assess empirically the magnitudes of these channels. Disentangling the relative contributions of trade, finance and specialization is crucial simply from the point of view of business cycles research. But it is also a relevant policy question, since the international correlation of business cycles is an important metric used to measure the desirability of a currency union. Giving up independent monetary policy can be very costly when business cycles are out of phase. More generally, the channels this paper proposes to identify are directly relevant to policymakers asking if, and why, they should be concerned with foreign developments affecting domestic fluctuations.
Figure 1:Direct and Indirect Channels Equation-by-Equation Evidence in the Literature
Most, but not all, of these linkages have been investigated empirically, but never simultaneously.7 Most famously, the direct impact of trade on synchronization is documented in Frankel and Rose (1998), who estimate a strong and robust positive relationship between trade and cycle synchronization, taking particular care of the endogeneity of trade to the business cycle with appropriate instrumentation. They interpret their estimate as indicative that trade-induced specialization has but a small effect on business cycles, and is dominated by the direct positive link. Given the large evidence on the specialization effects of goods trade, it is of independent interest to quantify precisely the magnitude of this indirect effect of trade on business cycles correlations.8 This is a first justification for the simultaneous estimation method implemented in this paper.9
The impact of financial integration on specialization is, in turn, well-documented too. For instance, Kalemli-Ozcan, Sorensen and Yosha (2003) show there is a significantly positive relationship between specialization and risk-sharing. Thus presumably financial integration should affect (negatively) cycles synchronization, via its effect on specialization. The evidence on a direct link between finance and the extent of co-fluctuations is, as suggested by theory, equivocal. Heathcote and Perri (2002b) argue the U.S. business cycle has become increasingly idiosyncratic over the past thirty years, and relate this to the increasing share of international assets held in the U.S.10 However, a considerable amount of empirical work lends support to the claim that capital flows are correlated internationally, and that financial integration tends to synchronize business cycles.11 Thus, the link between finance and cycle correlations is ambiguous for two reasons: first the sign of the direct link is unclear in general, second the indirect specialization effect could either mitigate or reinforce the direct link. This is the second justification for the simultaneous approach in this paper.12
Finally, the direct effect of sectoral specialization on business cycles synchronization, although very intuitive, is perhaps the least researched empirical question amongst those addressed in this paper. Otto, Voss and Willard (2001), Kalemli-Ozcan, Sorensen and Yosha (2001) and Imbs (2001) all find a significantly positive role for an index of similarity in production structures. Clark and van Wincoop (2001) use a similar index to account for higher business cycle correlations within than between countries.13 But although they all point to a sizable direct impact of specialization on business cycles, none of these papers embed the possibility that specialization could be an indirect manifestation of trade or financial integration, and amend the estimated effects of trade, finance and specialization accordingly. This is the third justification for a simultaneous approach, that appears to be implicit in most of the existing empirical work.14
This paper’s estimates suggest simultaneity is important. The “reduced form” estimates of the effects of trade mask two distinct channels, in that a substantial share of the measured effect works through intra-industry trade. Furthermore, the evidence that trade-induced specialization affects cycles synchronization is weak at best. This suggests overall an estimated effect of inter-industry trade in line with existing models. By the same token, the effects of financial integration are two-fold. On the one hand it results in positively correlated cycles, but the correlation coefficient would be higher if finance-induced specialization were held constant. Finally, the direct effects of specialization are important economically, even once trade and financial integration are accounted for.
Results implied by cross-country and cross-state data are strikingly similar. This is important for three reasons. (i) The fact that financial integration appears to result in correlated business cycles is not an artefact of an international convergence of policy-making, most notably monetary. Similarly, we do not observe synchronized business cycles between trade partners only because they tend to follow the same monetary policy. Similar estimates obtain across U.S. states and across countries with substantially different monetary policies. (ii) The importance of specialization patterns in affecting cycles is not due to the arbitrary choice of a time-period or geographic coverage. In particular, the results cannot stem from the prevalence of one given type of shock in a given sample.15 (iii) Trade treatment is constitutionally homogenized across the States of the Union. This legitimizes focusing on bilateral trade flows, since third party treatment is the same for all pairs of states.
The results in this paper suggest theories of the international business cycle should build on the following ingredients: some sectoral heterogeneity, e.g. in the responses of different sectors to a given macroeconomic shock, trade both within and between industries and some “herding” in international capital flows, e.g. through liquidity constraints or imperfect information.16 The rest of the paper proceeds as follows. Section II describes the data, main econometric issues and general methodology. Section III illustrates the importance of simultaneity, comparing three-stage least squares and simple OLS estimates to the existing literature. Section IV explores the robustness of the conclusions to alternative specifications. Section V concludes.
II. Methodology and Econometric Issues
This section introduces the system of equations estimated in the paper, and relates each individual specification in the system with the relevant literature. It then briefly describes the variables involved, their measurement and data sources, and closes with an account of the specific heteroscedasticity problem in a cross-section of bilateral correlations.
A. The System
This paper estimates the following system of equations simultaneously
where i, j indexes country pairs, ρ denotes bilateral business cycles correlation, T is bilateral trade intensity and S is a specialization index capturing how different the sectoral allocations of resources are between countries i and j. Business cycles correlations, bilateral trade and specialization are all endogenous variables, while I1, I2 and I3 contain the vectors of their exogenous determinants, respectively. Identification of the system requires differences between at least I2 and I3. Fortunately, a substantial literature is there to provide guidance on what variables to include in the Ii vectors. I next turn to this question. To clarify matters, Figure 1 summarizes the direct and indirect channels discussed here, and relates them to the relevant literature. The reader might find it useful to refer to the Figure while going through the following review.
The dependent variable in equation (1) is one of the most topical items in the list of optimal currency area criteria.17 It is therefore of interest in its own right, and indeed its determinants have been the object of intense scrutiny. Frankel and Rose (1998) have for instance focused on α1, reasoning that if currency unions affect trade and then trade in turn boosts cycle correlations, then currency areas can endogenously become optimal.18Imbs (2001) focused on α2, arguing that measured bilateral trade may partly be a manifestation of differences in the degrees of specialization between the trading countries, which could affect ρ independently.19 The estimates of α1 can therefore be affected by inclusion of a specialization term.20 Since specialization can very well be the result of trade intensity, however, accurate identification of α1 and α2 requires exogenous determinants for both trade and specialization, which equations (2) and (3) provide. Hence an added advantage to estimating (l)-(2)-(3) simultaneously.
The typically large estimates of α1 pose a theoretical puzzle. While Baxter (1995) reviews the theories that imply a positive α1, Canova and Dellas (1993), Schmitt-Grohé (1998) and Kose and Yi (2002) have used various methods to document the inability of existing models to reproduce the magnitude of standard estimates for α1.21Imbs (2001) proposed to add the potentially omitted variable S to equation (1) and corrected the estimates accordingly. But an equally plausible explanation could be that we are not using the appropriate modelling strategy when attempting to reproduce the observed effects of trade. In particular, α1 embeds the impact of both inter- and intra-industry trade, two dimensions that the models typically do not share. The simultaneous approach makes it possible to decompose the two effects, as β1 in equation (2) captures the extent to which bilateral trade can be accounted for by the similarities in the two countries’ economic structures, i.e. an account of intra-industry trade, denoted by an upward dotted arrow in Figure 1. Thus, the total effects of trade in the simultaneous estimation equals α1. β1 + α1. β2, where the former term captures the importance of intra-industry trade. One sector models should seek to reproduce α1. β2 only.22
This leaves open the question of what additional regressors to include in (1). Since this paper is concerned with the (direct and indirect) effects of financial integration, an important variable is one capturing the extent of impediments to capital flows between each pair of countries. This is done by including a variable capturing capital account restrictions in the international dataset, and an estimate of inter-state risk-sharing in the intranational data.23 The preferred specification for (1) therefore contends that economic structure, and integration to financial and trade markets jointly explain a sufficient fraction of ρ to rule out omitted variable bias in the estimation of the coefficients αi.24 There are, however, a few additional explanations, including most prominently convergence in policies. In Section IV, I subject the specification to some sensitivity checks, introducing among others controls for monetary policy. More importantly, I implement the same estimations using information on U.S. states, which are subjected to a unique monetary policy, and find strikingly similar estimates.25,26
The specification of equation (2) is more straightforward, although also an object of debate. The empirical performance of so-called gravity variables in accounting for trade flows is a result going back at least to Tinbergen (1962), which has been used extensively subsequently.27 The set of gravity variables customarily included in I2 include measures of both countries’ GDP levels, or sometimes of their populations, the geographic distance between their capitals, and binary variables capturing the presence of a common border and linguistic similarities between them. The list is usually argued to contain clearly exogenous variables with high predictive power on trade flows, thus supplying an exceptional instrument set, used for instance to identify causality between trade and growth in Frankel and Romer (1999), or to control for other determinants of trade in Rose (2000), or both in Frankel and Rose (2002). This paper uses similar insights to isolate the exogenous impact of trade on cycles synchronization and specialization.28
The exogenous determinants of specialization, summarized in I3, are empirically less established.
Two variables do, however, spring to mind. First, access to financial markets will influence specialization patterns, and how similar they are between countries. I thus include in the vector I3 the measures of capital account restrictions and the extent of risk-sharing already used in I1. Second, Imbs and Wacziarg (2003) show economies go through two stages of specialization as income per capita grows: they initially diversify but re-specialize once a (relatively high) level of income per capita is reached. This empirical fact suggests two additional components for I3: GDP per capita levels in both economies, but also, because of the non-monotonicity, the gap between them.
In summary, since the intersection between I2 and I3 is empty, the system can be identified through a choice of instruments that is largely warranted by an existing literature. The main contribution of the present exercise is simultaneity. The main assumptions are the exogeneity of financial integration to ρ and S, and that of GDP per capita and relative GDP per capita to S.29 I now turn to a detailed description of the data and measurement of the variables included in the Ii vectors.
B. Data and Measurement
Bilateral correlations in business cycles are computed on the basis of the cyclical component of quarterly GDP, isolated using the Band-Pass Filter introduced in Baxter and King (1999).30 The quarterly data cover the 1980s and 1990s in 24 countries, and come from the International Financial Statistics issued by the IMF.31 This gives rise to a cross-section of 276 bilateral correlations.32 A concern with this cross-section is that it builds on twenty years of data, over which the U.S. for instance has experienced two, at most three, business cycles. Can one safely draw inferences on the medium to long-run characteristics of the cross-section of business cycles synchronization on such a reduced sample? This is of course a concern that pervades any such exercise, and in particular all the papers mentioned earlier.33 Here I propose to assuage somewhat this worry using two alternative data sources. First, I also compute bilateral correlation in business cycles on the basis of yearly data taken from the version 6.1 of the Penn-World Tables, where correlations are computed over the period 1960 to 2000. Second, (annual) series on Gross State Product are taken from the Bureau of Economic Analysis, and run from 1977 to 2001. It is reassuring that similar results obtain in all these datasets.
Bilateral trade intensity is computed in two ways. The first one is standard, used in Frankel and Rose (1998) among others, and writes
where Xi,j,t denotes total merchandise exports from country i to j in year t, Mi,j,t represents imports to i from j, and Yi denotes nominal GDP in country i. Bilateral trade data are from the IMF’s Direction of Trade Statistics. I use this standard measure for the benchmark case. Clark and van Wincoop (2001) use an alternative measure of trade intensity, independent of size, based on Deardorff (1998) model, which can be constructed as
There is no data available on bilateral trade flows between U.S. states. The main virtue of the gravity model, however, is that it provides a dependable predicting tool. Just as cross-country trade flows are well accounted by the gravity variables, with R2 above 0.65, so cross-state trade flows can be simulated with reasonable reliability on the basis of each state’s economic size, the distance separating each state’s main business center and whether they share a common border.34 Thus, I use the estimated coefficients from a cross-country gravity estimation of
There are no standard measure of similarity in industry specialization. Krugman (1991) and Clark and van Wincoop (2001) favour a variable akin to a Herfindahl index of concentration, whereas Imbs (2001) uses a correlation coefficient between sectoral shares in aggregate output or employment. Here, I use sectoral real value added to compute
where sn,i denotes the GDP share of industry n in country i. In words, Si,j is the time average of the discrepancies in the economic structures of countries i and j.36 Thus, S reaches its maximal value of two for two countries with no sector in common: we should therefore expect α2< 0. For the international estimation, the sectoral shares s are computed using two alternative data sources: first the United Nations Statistical Yearbook sectoral data, which provides sectoral value added at the one-digit level, for all sectors in the economy and with (incomplete) coverage from 1960 to 1998. Second, for robustness, I also use two-digit value added data issued by the UNIDO, pertaining to manufacturing sectors only.37 For the intranational estimation, I use real sectoral state value added series issued by the Bureau of Economic Analysis. These cover all economic activities, at the three-digit aggregation level.38
Bilateral financial integration is notoriously difficult to measure. I therefore maximize the number of alternative measures, computed from widely different data sources, and subject the conclusions to an extensive sensitivity analysis. The measures can be separated in two distinct groups: indices capturing restrictions to capital flows, and variables reflecting effective financial flows. The two measures are fundamentally different in that the absence of any impediments to capital flows does not necessarily result in intense financial integration. Included in the first group are the IMF’s binary index of capital account restrictions, and the extension thereof reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER).39 These indices are summed pairwise, and thus report the average number of countries with restrictions to financial flows, for each country pair.40,41
Measuring effective bilateral capital flows is an even taller order. Here two proxies are proposed. For intranational data, Kalemli-Ozcan, Sorensen and Yosha (2002) computed a state-specific index of risk-sharing by estimating
where gsp denotes Gross State Product per capita and dy is state disposable income per capita. Kalemli-Ozcan, Sorensen and Yosha interpret β as an index of risk-sharing. Indeed, if inter-state risk sharing (in the guise of income insurance) is perfect, β = 1 as disposable income is unrelated with GSP per capita, and equation (4) is simply a regression of GSP on itself. Conversely, if there is no inter-state risk-sharing β = 0 since the dependent variable becomes essentially noise. A measure of cross-state financial integration is then given by pairwise sums of the state-specific estimates of β. I use data on Gross Domestic Product and Private Consumption to extend these estimates in the international data.42
An alternative is possible thanks to the recent dataset constructed by Lane and Milesi-Ferretti (2001), that reports cumulated external positions for a large sample of developing and industrial economies. Pairs of countries with intense capital flows should display different (or even opposite) external positions. Two countries with massively positive (negative) net foreign assets holdings will both tend to be issuers (recipients) of capital flows, and should experience less bilateral flow than two economies where one is structurally in surplus and the other in deficit. Thus, the Lane - Milesi-Ferretti data can be used to construct
The gravity variables in I2 are standard, and include the following: a measure of the (log mile) distance between the countries capitals, the (log) products of each country’s GDP and binary variables indicating the presence of a common border and whether they share the same language. The vector I3 is different from I2, and includes: the proposed measure of financial integration, already in I1, the (log) product of each country’s GDP per capita, and the (log) GDP disparity, defined as
Table 1 reports summary statistics for the three endogenous variables of the system, in both datasets. Table 2 reports the corresponding unconditional correlations. As usual, and particularly in the present context of simultaneity, unconditional correlations are only informative superficially. However, several points are worth noting. First, the cross-sections of cycle synchronizations appear to be very similar regardless whether the cycles are computed on the basis of quarterly or annual data. ρQ and ρY have similar moments and extreme values, and are highly correlated. Cycle correlations between U.S. states do not seem higher on average, although the cross-section tends to have more extreme values. Second, states are more specialized than countries, as S is on average larger than both SYB and SMfg. This is a well-known result, first documented in Krugman (1991). Average specialization in manufactures is also higher than across all economic activities, and the correlation between the two cross-sections is only 0.47, which warrants some sensitivity analysis.45 In what follows, manufacturing data is privileged in computing indices of specialization because of its large coverage; the sensitivity analysis does, however, show that the results obtain as well using a coarser specialization measure, based on all economic activities and not only manufactures. Third, both measures of trade intensity are very similar, with a correlation of 0.85. They both correlate positively with ρ, although T1 does so more significantly. Fourth, specialization is correlated negatively with cycle synchronization, no matter the combination of measures used. The evidence for a trade-induced specialization is a priori weak, since the correlation between specialization and trade measures in only positive with T2, and very weakly. Unsurprisingly, this calls for appropriate conditioning, as well as a simultaneous approach.
|T1||0.004||7.22 x 10−5||0.069||0.007||276|
Finally, Table 3 reports a few extreme values for the four main variables used in this study, namely cycles correlations, trade, specialization and financial integration. This is only illustrative, but has some interesting teachings. For instance, it is remarkable that the only country pair displaying both high business cycles correlation and high trade linkages should be the U.S. with Canada, precisely where Schmitt-Grohé (1998) showed the theoretical inability of trade per se to account for cycles synchronization. Thus, even between the two countries where the case for trade would be the strongest, the variable alone appears insufficient to account for observed synchronization. Similarly, low correlations between GSP tend to involve South Dakota, whereas low inter-state trade flows (admittedly, only as predicted by a gravity model) tend to involve Vermont, a rather remote and economically small state. Florida and Virginia display the most correlated GSP, yet there is no particular reason to expect strong trade linkages between the two states, at least not on the basis of a gravity model.
|-0.586||Norway - Portugal||7.22 x 10−5||Australia - Peru|
|-0.522||Philippines - South Africa||7.40 x 10−5||Norway - Philippines|
|-0.471||Israel - U.S.||8.43 x 10−5||Norway - Peru|
|0.839||Switzerland - Netherlands||0.029||France - Germany|
|0.862||Belgium - Italy||0.031||Germany - Netherlands|
|0.867||U.S. - Canada||0.074||Belgium - Netherlands|
|-0.896||Texas - South Dakota||17.06||Vermont - Wyoming|
|-0.894||Connecticut - South Dakota||17.94||Vermont - South Dakota|
|-0.808||Rhode Island - South Dakota||18.92||Vermont - Alaska|
|0.964||Massachusetts - Maine||64.36||New York - Pennsylvania|
|0.967||Florida - Maine||66.95||California - Texas|
|0.968||Florida - Virginia||67.54||California - New York|
|0.143||Australia - Canada||0||Mexico - U.S.|
|0.155||Netherlands - Canada||0||Canada - U.S.|
|0.157||U.K. - France||0||Mexico - Canada|
|0.653||Israel - Mexico||2||Italy - South Africa|
|0.692||Norway - Philippines||2||U.K. - South Africa|
|0.747||Israel - Philippines||2||Netherlands - Japan|
|0.201||Indiana - Ohio||0.247||Nebraska - Virginia|
|0.231||Arizona - Florida||0.287||Kentucky - South Dakota|
|0.242||Minnesota - Wisconsin||0.319||California - Rhode Island|
|1.295||D.C. - South Dakota||1.506||Alaska - Wyoming|
|1.331||D.C. - Wyoming||1.554||Alaska - Louisiana|
|1.339||D.C. - Delaware||1.638||Alaska - New Mexico|
To summarize, both international and intranational data suggest trade may be important, but is probably insufficient to explain all the cross-section of cycle correlations. Turning to measures of specialization, the three most similar country-pairs (on the basis of manufacturing data) are Australia - Canada, the UK - France and the Netherlands - Canada. The importance of sectoral specialization is therefore of special interest to inform the debate of Sterling’s entry into the EMU. Across states, the District of Columbia unsurprisingly displays the most idiosyncratic sectoral composition of value added. Finally, financial integration as measured by capital account restrictions is highest between the North American countries and lowest when such countries as South Africa are included. Across states, the extent of risk-sharing seems to be related with specialization, as already demonstrated in Kalemli-Ozcan, Sorensen and Yosha (2003), since pairs involving Alaska display high risk-sharing, as measured by income insurance.46
C. Three-Stage Least Squares and Heteroscedasticity
As should be clear from the previous discussion, the proposed estimation method must combine the features of simultaneous equations procedures, and account for the possible endogeneity of the three dependent variables ρ, T, S. Three-Stage Least Squares (TSLS) does exactly that. The estimator combines insights from Instrumental Variable and Generalized Least Squares methods, achieving consistency through instrumentation and efficiency through appropriate weighting in the variance-covariance matrix. As is well-known, the procedure consists of the following steps: (i) estimate the system equation by equation using Two-Stage Least Squares, and retrieve the covariance matrix of the equations disturbances, then (ii) perform a type of Generalized Least Squares estimation on the stacked system, using the covariance matrix from the first step.47
It is also often useful to contrast the results obtained via Three-Stage Least Squares, to those generated by the partly similar Seemingly Unrelated Regression (SUR) technique. SUR focuses on the simultaneity, but does not account for possible endogeneity of the dependent variables. In other words, the system is estimated equation-by-equation under the assumption that the variance-covariance matrix is non-diagonal. Finally, the next section also reports simple equation-by-equation Least Squares estimates, for the sake of comparison with existing estimates. Comparisons between the OLS, SUR and TSLS estimates highlight the importance of simultaneity and endogeneity, respectively.
In addition, the correlations in business cycles ρ are measured with error, in a way that is likely to create a specific type of heteroscedasticity. In particular, following Clark and van Wincoop (2001), let
Ignoring this heteroscedasticity is likely to result in understated standard errors for the estimates in (1), and since the estimation is simultaneous, in the rest of the system as well. It is, however, possible to account for this potential issue, at the cost of relatively mild assumptions. If the true vector of bilateral correlations ρ is assumed to be deterministic, (1) rewrites
The variance-covariance matrix of equation (5) involves
III. Trade, Finance, Specialization, and Synchronization
This section reports the results for four different procedures applied to the international data: Ordinary Least Squares, Two-Stage Least Squares, Seemingly Unrelated Regression and Three-Stage Least Square. It compares the Least Squares results with existing evidence, and investigates the impact of simultaneity and endogeneity. The section closes with the intranational results, and a comparison with the international evidence.
A. Equation-by-Equation Estimates
Table 4 reports the results of equation-by-equation estimations for the system (l)-(2)-(3), using the benchmark set of control variables described in Section 2.49 The purpose of Table 4 is to confirm that the well-known results pertaining to each equation in the estimated system, taken in isolation, are present in the dataset used here.50 Column (i) confirms the large and significant effect of trade in accounting for ρ in equation (1). The point estimate in column (i) means that doubling trade results in a correlation higher by 0.053, which is close to the estimate in Clark and van Wincoop (2001), and exactly equal to the revised estimates in Kose and Yi (2002). As in Frankel and Romer (1999), instrumenting trade with exogenous gravity variables results in an even higher point estimates. This probably happens because the endogeneity bias is negative, as non-synchronized economies tend to trade more. The point estimate for α1 in column (ii) means doubling trade now results in a correlation coefficient higher by 0.08, comparable in magnitude with the estimates in Frankel and Rose (1998). We conclude that the dataset used here is perfectly standard from the point of view of the relationship between ρ and T.
|(i) OLS||(ii) IV||(iii) OLS||(iv) IV|
|(1) Correlation ρ|
|T||0.077 (6.00)||0.115 (7.55)||0.064 (4.25)||0.083 (3.73)|
|S||-0.123 (1.92)||-0.217 (1.92)|
|K||-0.019 (0.57)||0.026 (0.77)|
|(2) Trade T|
|Distance||-0.975 (19.34)||-0.970 (17.43)||-0.922 (15.36)|
|Border||0.038 (0.17)||0.119 (0.48)||0.041 (0.16)|
|Language||0.325 (2.23)||0.347 (2.02)||0.248 (1.36)|
|GDP Product||0.261 (9.68)||0.231 (7.26)||0.166 (4.15)|
|S||-0.401 (2.22)||-1.197 (3.62)|
|(3) Specialization S|
|GDP per capita Product||-0.139 (6.02)||-0.119 (4.79)||-0.185 (7.74)||-0.163 (6.34)|
|GDP Gap||0.613 (2.21)||0.524 (1.86)||0.783 (2.81)||0.671 (2.36)|
|T||-0.051 (3.64)||-0.074 (4.17)||-0.047 (3.37)||-0.073 (4.14)|
|K||-0.102 (3.39)||-0.106 (3.51)|
Equation (2) in specification (i) is unsurprising: the gravity variables all have the expected signs, as is now completely standard in any empirical work concerned with explaining the geography of trade. The same is true of equation (3), with estimates of the γs in line with theory. In particular, pairs of rich countries tend to have significantly lower value of S, i.e. their economic structures are more similar, as would happen if growth resulted in diversification. But on the other hand, pairs of countries at different stages of development, as measured by the gap between their GDPs, have significantly higher S, i.e., tend to display different economic structures. Finally, the effect of trade on S is significant, but with the “wrong” sign, i.e. more bilateral trade results in lower S, or in more similar economies. This last result is even stronger when trade is instrumented in column (ii), a puzzling fact, though it is later shown to be non robust.
Columns (iii) and (iv) add the two variables specific to the simultaneous approach, namely specialization S and financial integration K. Three results are of particular interest. (a) Financial integration has the predicted specialization effect, since a low value of K is significantly associated with high S, that is, financially integrated economies have different specialization patterns. (b) Country pairs with low S have significantly higher ρ: similarities in economic structure result in correlated business cycles. (c) Estimates for α1 are now much smaller, with a point estimate such that a doubling of trade would result in a correlation higher by 0.044.51 Column (iv) implements the corresponding instrument variables estimations, with the same instrument set as in (i) and (ii). This tends to magnify all three results (a) to (c), and adds a fourth interesting one: (d) β1 becomes very significantly negative once specialization is instrumented. This makes sense as the endogeneity of S to T would if anything tend to bias β1 upwards, as trading partners specialize and thus have high S. A negative β1 can be interpreted as meaning that countries with similar economic structures trade more, a quantification of the extent of intra-industry trade. Estimates of β1 are therefore key to disentangling the impacts of inter- versus intra-industry trade in simultaneous estimations.
Although they are largely consistent with existing work, all these results may alter once both endogeneity and simultaneity are controlled for. For instance in equation (1), the three regressors are theoretically related, and a proper account of the covariance term may very well result in different estimates throughout the system. For instance, without controlling for simultaneity, the effect of K on ρ in equation (1) is but a “reduced form” measure, which does not discriminate between indirect specialization effects (which equation (3) suggests are important) and putative direct effects. I next turn to the simultaneous estimations.
B. Simultaneity and Channels
Table 5 is symmetric to Table 4, and presents SUR and TSLS estimates, where the former only account for simultaneity while the latter also instruments the three endogenous variables ρ, S and T. Table 5 can help answer two questions: 1) are the estimates sensitive to simultaneity? 2) can we quantify the direct and indirect channels relating trade, finance, specialization and business cycles synchronization?
|(i) SUR||(ii) TSLS||(iii) SUR||(iv) TSLS|
|(1) Correlation ρ|
|T||0.102 (7.97)||0.122 (8.07)||0.083 (5.66)||0.074 (3.32)|
|S||-0.132 (2.09)||-0.298 (2.67)|
|K||0.020 (0.63)||0.011 (0.35)|
|(2) Trade T|
|Distance||-0.968 (19.67)||-0.946 (19.39)||-0.908 (16.93)||-0.815 (14.49)|
|Border||0.053 (0.25)||0.062 (0.30)||0.147 (0.62)||0.152 (0.69)|
|Language||0.359 (2.54)||0.367 (2.68)||0.394 (2.42)||0.401 (2.58)|
|GDP Product||0.275 (10.51)||0.278 (10.88)||0.232 (7.57)||0.216 (5.66)|
|S||-0.820 (4.68)||-1.470 (4.54)|
|(3) Specialization S|
|GDP/capita Product||-0.131 (5.75)||-0.122 (4.98)||-0.167 (7.24)||-0.144 (6.11)|
|GDP Gap||0.666 (2.42)||0.691 (2.49)||0.946 (3.51)||1.098 (4.26)|
|T||-0.061 (4.37)||-0.074 (4.24)||-0.071 (5.26)||-0.079 (4.68)|
|K||-0.096 (3.28)||-0.074 (2.62)|
|(i) Restrictions||(ii) Risk Sharing||(iii) Cum.CA||(iv) NFA|
|(1) Correlation ρ|
|T||0.073 (3.33)||0.090 (3.52)||0.070 (3.13)||0.074 (3.27)|
|S||-0.321 (2.93)||-0.352 (2.24)||-0.388 (2.75)||-0.294 (2.31)|
|K||-0.037 (0.92)||0.087 (2.38)||0.117 (0.75)||0.224 (3.51)|
|(2) Trade T|
|Distance||-0.828 (16.02)||-0.731 (10.55)||-0.855 (15.96)||-0.872 (16.30)|
|Border||0.061 (0.30)||0.037 (0.18)||0.037 (0.18)||0.041 (0.20)|
|Language||0.317 (2.42)||0.214 (1.59)||0.330 (2.45)||0.327 (2.44)|
|GDP Product||0.218 (5.85)||0.197 (3.29)||0.213 (5.15)||0.224 (5.44)|
|S||-1.239 (3.89)||-2.269 (3.68)||-1.079 (2.83)||-0.898 (2.34)|
|(3) Specialization S|
|GDP/capita Product||-0.166 (6.74)||-0.090 (3.70)||-0.127 (5.77)||-0.144 (6.31)|
|GDP Gap||0.971 (3.90)||0.765 (2.84)||0.687 (2.70)||1.088 (4.14)|
|T||-0.076 (4.72)||-0.085 (4.74)||-0.068 (4.29)||-0.074 (4.57)|
|K||-0.168 (4.64)||-0.020 (0.77)||0.491 (4.54)||0.213 (4.23)|
|(i) TSLS||(ii) TSLS||(iii) TSLS||(iv) HP||(v) GMM|
|(1) Correlation ρ|
|0.031 (13.74)||0.032 (14.09)||0.031 (13.84)||0.029 (12.65)||0.028 (7.44)|
|S||-0.351 (8.49)||-0.326 (7.64)||-0.571 (13.11)||-0.571 (8.52)|
|Risk Sharing||0.063 (2.28)||0.051 (1.86)||0.140 (4.95)||0.142 (4.60)|
|Size||-0.049 (3.51)||-0.056 (3.94)||-0.042 (2.47)|
|(3) Specialization S|
|GDP per capita Product||-0.185 (17.22)||-0.206 (18.21)||-0.206 (18.23)||-0.205 (18.33)||-0.460 (8.03)|
|GDP Gap||0.400 (9.55)||0.405 (9.09)||0.404 (9.08)||0.406 (9.23)||-0.131 (0.99)|
|-0.033 (17.66)||-0.035 (17.96)||-0.035 (17.97)||-0.035 (17.98)||-0.103 (6.46)|
|Risk Sharing||0.050 (2.81)||0.050 (2.81)||0.050 (2.81)||0.014 (0.44)|
The estimates in Table 5 are not dissimilar to the equation-by-equation approach, but some coefficients do change substantially in magnitude. First, estimates of α1 increase slightly in specifications (i) and (ii). Since T in equation (2) is only explained by the (exogenous) gravity variables, this may happen because the systems (i) and (ii) focus de facto on the geographic component of trade, and thus the attenuating effect of trade’s endogeneity is neutralized. This, however, disappears once S in included in equations (1) and (2) in specifications (iii) and (iv). Then, estimates of α1 revert to relatively low levels, roughly twice lower than the Frankel-Rose estimates. Of course, part of the overall effect of trade may now go through specialization S, as γ1 captures in equation (3). Furthermore, trade itself is decomposed into two components: intra-industry trade, as captured by β1 in equation (2), and the classic “geographic” component, as captured by β2.
One word of comment is in order here. A number of authors have puzzled over the inability of standard one-sector models to reproduce the large effect trade has on cycle correlations.52 Recently, Kose and Yi (2002) have calibrated and simulated an international business cycles model with transport costs and technology shocks. Depending on the parametrization, their model yield simulated values for α1 ranging from 0.009 to 0.069. It is interesting to note that the simultaneous estimates in Table 5 are well within this range: thus, once focused on the link between inter-industry trade and business cycles correlations, the data is not inconsistent with elasticities predicted by one-sector models. The estimates for α1 in the last two columns of Table 5 are low for two reasons. First, the presence of an adequate control for the influence of common sectors, where the sectoral pattern of production is in turn allowed to respond endogenously to trade intensity and other exogenous determinants. Second, an explicit account of the possibility that (at least part of) the high estimate for α1 originates in the prevalence of intra-industry trade, something not typically part of international business cycles models.53,54
Second, estimates of the effect of S on ρ become larger in Table 5, now that S is allowed to depend on both trade and financial integration. The R2 suggest S is a quantitatively important determinant of business cycles correlation. Furthermore, the estimates from equation (3) suggest its determinants are largely beyond the reach of policy makers. While financial integration does affect specialization patterns as predicted by theory, the bulk of the cross-section in S depends on the stages of diversification reached by both economies, largely a low frequency, long-run phenomenon. Finally, financial integration does still result in specialization, although no direct effect on cycle observation is apparent. Importantly, alternative measures of financial integration, and in particular the variables capturing effective rather than potential capital flows turn out to have substantial direct synchronization effects as well. I next turn to a quantification of these direct and indirect channels.
Panel A of Table 8 reports all direct and indirect channels in the system under study, illustrated in Figure 1, as a function of the estimated parameters. Of interest is the decomposition of the coefficients αs in equation (1) into overall contributions from trade, specialization and financial integration. The direct effects of trade are either a reflection of intra-industry flows (α1β1) or of standard Ricardian trade (α1β2); the indirect effect of trade comes from the possibility that economies open to goods are also specialized, and may have a higher value of S as a result (α2γ1). The direct effects of specialization can, again, originate in trade (α2γ1), or in financial integration (α2γ2), or in exogenous stages of development reached by both economies (α2γ3); the indirect effect of specialization may, once again, consist in the manifestation of intra-industry trade (α1β1). The direct effect of financial integration is captured by α3, and its indirect effect is going via specialization (α2γ2).
|via Specialization||α2 γ1|
|Trade Induced||α2 γ1|
|Finance Induced||α2 γ2|
|Stages of Diversification||α2 γ3|
|via Specialization||α2 γ2|
|Intra-Industry Trade||-0.108 (0.008)|
|Geographic Trade||0.105 (0.000)|
|via Specialization||0.024 (0.024)|
|Trade Induced||0.024 (0.024)|
|Finance Induced (CAPA)||0.022 (0.060)|
|Stages of Diversification||-0.343 (2.72)|
|via Trade||-0.108 (0.008)|
|Capital Account||0.011 (0.727)|
|via Specialization (CAPA)||0.022 (0.060)|
|via Specialization||0.020 (0.00)|
|Trade Induced||0.020 (0.00)|
|Finance Induced||-0.029 (0.01)|
|Stages of Diversification||-0.694 (0.00)|
|Financial Integration||0.140 (0.00)|
|via Specialization||-0.029 (0.01)|
Panel B of Table 8 reports the values for these channels as implied by the Three-Stage Least Squares estimates in Table 5.55 A number of results are of interest. First, trade affects business cycles synchronization equally because of inter- and intra-industry deals.56 The impact of intra-industry trade is quantified by the result that economies sharing similar economic structure trade substantially more, and are more synchronized as a result. We conclude that the puzzlingly high estimates for α1 in standard single equation estimations are largely driven by intra-industry trade, an ingredient models seeking to reproduce these estimates should include. Models with inter-industry trade should only seek to reproduce the much smaller value of α1 = 0.074.
The indirect effect of trade, in turn, is estimated to increase business cycles correlations, if anything. This is a somewhat surprising result, as one would expect trading partners to specialize according to comparative advantage, and have higher S as a result. Here there is evidence for the opposite result. This may find an explanation in the fact that the sample includes a vast majority of developed economies. Perhaps specialization patterns amongst these countries may indeed follow standard trade theories, but the variable S does capture it only imperfectly. This result is also not robust: applying an alternative filter to the data, or measuring trade differently results in a coefficient with the opposite sign. The absence of much of an indirect effect of trade on ρ is consistent with existing evidence, for instance Frankel and Rose (1998).
Most of the direct effect of S on ρ works through the exogenous determinants of specialization, i.e. the degree of economic advancement of countries i and j. As mentioned above, trade does appear to result in less specialization (and higher ρ), while financial integration, in turn, does result in higher S, i.e. countries specialized in different range of activities. In other words, the bulk of the determinants of S is largely beyond the reach of short-term policymaking. In as much as the international correlation of business cycles is an important constraint to policy, e.g. through its relation with monetary union, this puts into perspective the significantly positive estimates for α1 arising from single equation estimations. While it may be possible to manipulate T through trade policy, there is no immediate equivalent for S. Finally, most of the effect of financial integration is indirect, as countries with liberalized capital account tend to be more specialized, and less correlated as a result. I next investigate how these conclusions are affected by alternate measures of financial integration, or by focusing on international as opposed to intranational data.
C. Measures of Effective Financial Integration
Results in the previous section were based on a particular measure of financial integration, which in effect merely quantifies the potential capital flows between economies. The results in Kalemli-Ozcan et al (2002) were confirmed, in that financially integrated economies were shown to be specialized, and less synchronized as a result. There was, however, no evidence on a direct link between business cycles synchronization and financial integration. In what follows, I use alternative measures to investigate the robustness of these conclusions.
Table 6 reports three-stage least squares estimates for the system (l)-(2)-(3), where the composite index of capital account restrictions is used in column (i), the index of risk-sharing introduced in Kalemli-Ozcan et al (2002) is used in column (ii), and two measures of LMF are used in the last two columns. Table 6 confirms overall that financial integration results in specialization, but provides further evidence suggesting that financial integration also affects cycle synchronization directly, and positively. This direct effect seems to become significant when measures of effective financial integration are used, rather than mere indices of restrictions.
Specification (i) uses the composite index of capital account restrictions issued by AREAER, similar to the index in Table 5. Results are almost identical: finance has an indirect, but no direct effect on cycles. The other channels are unchanged. When financial integration is measured by an index of (effective) risk-sharing between countries in specification (ii), however, the results are reversed: financial integration results in more synchronized cycles, but there is no substantial indirect specialization effect. Again, other results remain unchanged. The last two columns report results corresponding to LMF, which tends to be high between economies with differing net foreign positions, presumably more likely to exchange capital than two debitors’ or creditors’ countries. In specification (iii), LMF is computed on the basis of accumulated current account whereas it uses the accumulated net stock of direct investment, equity and debt in specification (iv). Finance-induced specialization is significant in both cases, while the last column provides further support for a positive direct channel. In specification (iv), both the direct and indirect effects of finance are also large economically. Other channels are unchanged relative to Table 5.
Overall, the picture painted by Table 6 is that of a substantial role for financial integration in affecting business cycles synchronization. While indices of capital account restrictions imply an important finance-induced specialization effect, measures of effective financial integration provide evidence of a direct positive channel from K to ρ. This is particularly true between economies with large differences in their net positions in FDI, equities and debt. The measures of LMF in specifications (iii) and (iv) are both warranted in theory. Nevertheless, there is anecdotal support for the view that the real effects of reversals in financial flows tend to go through asset holdings.57 In particular, (unreported) results suggest that these real effects work through FDI flows.58 In the next section, I investigate whether the importance of the financial channel is confirmed across U.S. states.
D. The Channels between U.S. States
Table 7 reports three-stage least squares estimates for U.S. states data. Since no data are available on direct trade flows between U.S. states, equation (2) is subsumed in implementing a gravity model to predict inter-state trade. This does not come at zero cost. First of all, all results depend on the reliability of a gravity model for inter-state trade. Luckily, the descriptive statistics in Table 3 do not seem out of line, as indeed the route between California and New York is probably amongst the most heavily traded within the U.S. The gravity model has reached almost universal validity, and is probably particularly appropriate for intranational data given the absence of any tariffs: it is hard to think of any impediments to commerce between U.S. states that are not related to geography. Second, not having any estimates for the coefficients β in equation (2) prevents a separate assessment of the effects of inter- and intra-industry trade on ρ.
On the upside, however, a dataset where trade treatment to all third parties can be taken as equal in the cross-section is a precious gift, since it helps in quantifying the extent of a potentially important bias. Anderson and van Wincoop (2002) have shown that bilateral trade flows are heavily influenced by the trade treatment each party is imposing on the rest of the world. Correcting for this “multilateral resistance” effect is crucial when investigating the determinants, as well as the impact of bilateral trade. The previous estimates of α1 in equation (1) may have suffered from this bias, to an extent that estimates based on intranational data can help evaluate. Finally, inter-state data provides an important robustness check. Indeed: the data is coming from completely different sources, the sectoral information is more disaggregated, the sample universe is more complete, as there is data on the whole of an economic entity, and the measure of financial integration is conceptually and practically different from an index of capital account restrictions.
Yet, as Table 7 reports, the results are largely unchanged. Perhaps the largest alteration in the results pertains to the estimates for α1, which are still significantly positive but much smaller in magnitude. Recall that, given the manner in which inter-state trade was simulated, there is no way of discriminating between inter- and intra-industry trade. Estimates for α1 are, however, almost half those implied by international data. Doubling inter-state trade (as predicted by a gravity model) results in GSP correlations being higher by 0.021, which is well into the range implied by standard (real) international business cycles models with plausible parameters. This suggests the bias demonstrated by Anderson and van Wincoop (2002) is prevalent in the international dataset. But trade does affect significantly cycles synchronization even after it is corrected for.
The other coefficients, however, remain largely unaffected by the use of an alternate dataset. In particular: (i) states with similar specialization patterns do display significantly more synchronized GSP, and quite remarkably the estimates for α2 in Table 7 are not significantly different from their equivalent in Tables 5 or 6. Specialization patterns continue to be an economically and statistically important determinant of business cycles synchronization. (ii) States with high risk-sharing, as measured by an index of income insurance, tend also to be more synchronized even though they also become more specialized. Both these channels remain significant in all specifications, in a way that is reminiscent of Table 6 when a measure of effective financial integration was used. (iii) Inter-state trade seems to result in less specialized states, a somewhat surprising outcome that already obtains in the international data. It is not robust, however, as the next section shows.
The channels between trade, finance, specialization and synchronization are stronger between states than between countries, although not qualitatively different. Panel C in Table 8 presents the estimated direct and indirect linkages between the four variables of interest, although no channel involving an estimate of the βs can be obtained given the simulated trade data. The most remarkable difference with panel B has to do with the significant channel from risk-sharing to ρ: financial integration results in specialization, and thus less correlated cycles. The significance of this coefficient summarizes the results in Kalemli-Ozcan, Sorensen and Yosha (2001 and 2003). But the approach here also shows the presence of a direct link between financial integration to cycles synchronization, which in the intranational data is positive at the one percent confidence level. Indeed the estimates in panel C suggest that financial integration has an overall positive effect on business cycles synchronization.
Finally, the lower panel of Table 7 presents some robustness analysis for inter-state data. In particular, three checks are performed. First, the possibility that the economic size of each state be independently and significantly affecting ρ is investigated, but without any sizable changes. Second, the Hodrick-Prescott filter is used to isolate the cyclical component of GSP when computing ρ (and the indices of risk-sharing), again without any sizable effects. Third, the GMM estimator described in section II is implemented instead of TSLS. This affects the estimates of financial integration’s specialization effect, γ2, which then becomes non-significantly different from zero. The next section extends this sensitivity analysis to the results pertaining to international data.
In this section, I subject the estimation to seven robustness checks: (a) all estimations are run implementing the GMM simultaneous estimator described in Section 2. (b) I use the alternative measure for bilateral trade T2, defined in Section 2. (c) I use the Hodrick-Prescott filter to isolate the cyclical component of GDP. (d) I use an alternative measure of specialization SYB, based on one-digit value added data covering all sectors in the economy, and issued by the UN. (e) I use yearly rather than quarterly data to compute business cycles correlations. (f) I control for convergence in monetary policies. (g) I control for the size of countries in equation (1). All results are reported in Table A9 in the Appendix.59
A. GMM Estimates
The first specification in Table A9 implements the exact same specification as in Section III.B, but using a GMM estimator instead of Three-Stage Least Squares. The results are very similar, usually stronger both economically and statistically, with the notable exception of α1, which is not significantly different from zero. This does not appear to be a robust fact, though, as the next sub-section argues.
B. Alternative Measure of Trade
The second specification in Table A9 contains the alternative measure of trade T2 introduced in Deardorff (1998). T2 is not scale dependent, which manifests itself in the sudden reversal of the size variable in I2 (namely GDP product). This measure of trade does once again affect directly cycles correlations, with an estimate for α1 in line with earlier results. Results pertaining to other channels are unchanged: they are, if anything, once again stronger than in the TSLS case with T1. Note also that T2 does not appear to correlate at all with specialization patterns, confirming the sensitivity of the earlier negative point estimates.
C. Hodrick-Prescott Filter
Specification (iii) in Table A9 uses the Hodrick-Prescott filter to isolate the cyclical component of GDP fluctuations. None of the results change significantly, except for the correlation between trade and specialization (β1), which becomes insignificant here as well.
D. All Sectors
The last column in Table A9 Panel a reports estimates when S is computed using sectoral value added at the one-digit level, but for all sectors in the economy, as issued by the UN. The main difference has to do with the (much) larger magnitude of α2, the effect of specialization on cycles correlation. The coefficient on K in equation (1) does also become significantly negative, suggesting the absence of a direct effect from K to ρ in Table 5 might have been due to focusing on manufactures only.
E. Yearly Data
The second panel of Table A9 reproduces the previous four checks, but using yearly data since 1960 to compute cycles correlations. The estimates are virtually identical to those in the first panel.
F. Monetary Policy
The first two specifications in Table A9 Panel C report estimates where convergence in monetary policies is proxied and controlled for in equations (1) and (2). Identifying monetary policy is the object of an enormous literature, whose purpose is to track the effect of exogenous monetary policy decisions over time. There is fortunately no purpose in being that ambitious in the present context. Rather, this sub-section purports to ensure that the channels identified in the previous section are not merely a manifestation of similar monetary policies. Note that this is unlikely, for, while converging monetary policies, manifested by a stable exchange rate for instance, are known to result in more trade and more synchronized business cycles, there is no obvious theoretical link with countries’ specialization patterns.60 In other words, these controls will most likely affect the estimates of α1 only. Furthermore, the fact that intranational results are almost identical to international ones suggests monetary policy is not driving the results in this paper.
Specifications (ix) and (x) in Table A9 control for the volatility in the (growth rate of the) nominal exchange rate, and for five-year averages of inflation differentials, respectively. Once again, the results are unchanged. It is interesting to notice that the effects of a stable exchange rate and/or small inflation differentials seem to work through trade, as trade is estimated to significantly increase in the face of stable and similar monetary policies.
Specifications (xi) and (xii) in Table A9 report estimates once the relative size of the two economies is controlled for in equation (1). The idea behind this control is to check whether country size continues to have a direct impact on business cycles correlation, beyond the indirect channels via trade or specialization, and to verify that the results carry through. Once again, the results are unchanged: large countries are indeed more synchronized, but it is happening indirectly because they tend to trade more.61
This paper estimates a system of simultaneous equations to disentangle the complex interactions between trade, finance, sectoral specialization and business cycles synchronization. A large theoretical and empirical literature is referred to in choosing the sets of instruments necessary to achieve identification. Simultaneity, implicit in most theories, is also revealing empirically, as summarized in Figure 2. The overall effect of trade on business cycles synchronization is confirmed to be strong, but a sizeable portion is found to actually work through intra-industry trade (denoted by a negative sign going from specialization to trade integration on Figure 2). Estimates of the link between inter-industry trade and cycles correlations, substantially smaller in magnitude, are consistent with existing models, thus arguably solving the trade-comovement anomaly. As previously documented, though never directly, trade-induced specialization has but a weak effect on cycles synchronization.
Figure 2:Direct and Indirect Channels Simultaneous Evidence
Patterns of specialization have a sizeable direct effect on business cycles correlation, as two economies with a similar economic structure are significantly more correlated ceteris paribus. This is shown to happen mostly because economies grow through evolving stages of diversification, and in spite of the specialization effects induced by financial integration. Finally, business cycles in financially integrated regions are significantly more synchronized, ceteris paribus. This remains true even though financial integration tends to result in more specialized economies, and less synchronized cycles as a consequence.
The results obtain across countries and U.S. states. They hold for a variety of sectoral datasets, collected at different aggregation levels, for various measures of financial integration and trade linkages, and for various filtering methods. They suggest an additional item on the list of criteria characterizing optimal currency areas, namely the economic structure of the putative member countries. They also provide some guidance on desirable strategies to model international business cycles, namely allowances for trade within the same industry, and international capital flows that are coordinated internationally.
|(i)T1||(ii)T2||(iii) HP Filter||(iv) SYB|
|(1) Correlation ρ|
|T||0.021 (0.90)||0.062 (2.37)||0.048 (3.01)||0.070 (2.66)|
|S||-0.636 (5.92)||-0.935 (11.29)||-0.275 (4.31)||-0.771 (9.47)|
|Capital Account||0.032 (0.91)||-0.030 (0.83)||-0.018 (0.71)||-0.084 (2.25)|
|(2) Trade T|
|Distance||-0.669 (12.41)||-0.864 (19.20)||-0.866 (19.72)||-0.906 (14.58)|
|Border||0.283 (2.62)||0.177 (1.28)||0.330 (2.42)||0.262 (1.33)|
|Language||0.341 (3.24)||0.646 (5.12)||0.670 (5.27)||0.761 (5.01)|
|GDP Product||0.263 (6.69)||-0.144 (4.73)||-0.162 (5.31)||-0.143 (3.57)|
|S||-2.000 (5.97)||0.266 (1.03)||0.408 (1.58)||-0.028 (0.08)|
|(3) Specialization S|
|GDP/cap Product||-0.090 (4.95)||-0.153 (8.72)||-0.186 (9.27)||-0.201 (6.73)|
|GDP Gap||0.962 (4.87)||0.385 (1.79)||0.691 (2.70)||1.313 (4.57)|
|T||-0.098 (6.70)||0.000 (0.03)||0.017 (0.97)||0.042 (1.54)|
|Capital Account||-0.071 (3.39)||-0.089 (3.48)||-0.088 (3.55)||-0.086 (2.27)|
|(v) T1||(vi) T2||(vii) HP Filter||(viii) SYB|
|(1) Correlation ρ|
|T||0.028 (1.64)||0.064 (3.47)||0.079 (3.41)||0.099 (2.42)|
|S||-0.536 (6.68)||-0.742 (13.39)||-0.819 (11.77)||-1.427 (12.08)|
|K||-0.039 (1.60)||-0.085 (3.43)||-0.026 (0.92)||-0.064 (1.06)|
|(2) Trade T|
|Distance||-0.689 (13.03)||-0.848 (18.89)||-0.859 (19.22)||-0.918 (14.46)|
|Border||0.282 (2.51)||0.209 (1.52)||0.192 (1.39)||0.533 (2.65)|
|Language||0.338 (3.18)||0.649 (5.35)||0.617 (5.01)||0.811 (5.52)|
|GDP Product||0.258 (6.66)||-0.142 (4.64)||-0.141 (4.62)||-0.133 (3.28)|
|S||-1.958 (5.95)||0.275 (1.06)||0.378 (1.46)||-0.141 (0.41)|
|(3) Specialization S|
|GDP/cap Product||-0.094 (5.14)||-0.149 (8.11)||-0.169 (8.78)||-0.150 (5.51)|
|GDP Gap||1.005 (5.15)||0.422 (1.92)||0.635 (2.71)||0.969 (4.13)|
|T||-0.094 (6.57)||0.009 (0.48)||-0.003 (0.16)||0.019 (0.70)|
|K||-0.069 (3.34)||-0.092 (3.54)||-0.088 (3.47)||-0.081 (2.16)|
|(ix) NER||(x) Inflation||(xi) T1||(xii) T2|
|(1) Correlation ρ|
|T||0.037 (1.27)||0.020 (0.50)||0.053 (2.38)||0.086 (3.74)|
|S||-0.378 (3.37)||-0.492 (4.09)||-0.268 (1.73)||-0.438 (3.23)|
|K||0.062 (1.43)||0.041 (0.96)||0.094 (2.05)||0.054 (1.25)|
|Monetary Policy||-0.031 (0.93)||-0.015 (0.63)|
|Size||0.028 (1.44)||0.045 (2.17)|
|(2) Trade T|
|Distance||-0.524 (3.42)||-0.380 (1.80)||-0.703 (13.33)||-0.877 (19.56)|
|Border||0.725 (1.80)||0.845 (1.93)||0.288 (2.59)||0.112 (0.79)|
|Language||0.383 (1.43)||0.344 (1.21)||0.366 (3.41)||0.659 (5.14)|
|GDP Product||-0.022 (0.19)||0.014 (0.12)||0.254 (6.57)||-0.159 (5.11)|
|S||-1.540 (3.37)||-1.507 (2.72)||-1.842 (5.60)||0.366 (1.41)|
|Monetary Policy||-0.358 (2.05)||-0.223 (1.81)|
|(3) Specialization S|
|GDP/cap Product||-0.120 (5.56)||-0.115 (5.43)||-0.106 (5.40)||-0.181 (9.48)|
|GDP Gap||0.383 (1.52)||0.355 (1.46)||1.137 (5.25)||0.672 (2.67)|
|T||-0.077 (4.91)||-0.081 (5.21)||-0.091 (6.24)||0.009 (0.50)|
|K||-0.104 (4.29)||-0.104 (4.30)||-0.070 (3.34)||-0.092 (3.73)|
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