Macroeconomic Synchronization between Mexico and its
NAFTA Partners
Alfredo Cuevas, Miguel Messmacher and Alejandro Werner♣,⊗
June 2002
Abstract
In this study we analyze changes in the degree of macroeconomic synchronization between Mexico and
its NAFTA partners. In particular, we compare the change in the degree of synchronization before and
after NAFTA was implemented. For this, we use several samples and control variables. The first is a
sample of different countries, the second is of sectors of economic activity, the third consists of
components of aggregate demand and supply and finally we analyze the evolution of employment at the
regional level in Mexico and the US. We find that synchronization seems to have increased due to
NAFTA and this has occurred in a large number of pro-cyclical economic sectors and regions,
reinforcing traditional links between the Mexico and its NAFTA partners. In terms of policy
implications, even though optimal stabilization policies will be qualitatively more similar in the future
for the three countries, we find that idiosyncratic shocks are still important for Mexico and common
shocks also have stronger effects in this economy in a context of different policy transmission channels.
Therefore, the magnitude of this desired policy response would not be similar.
1. Introduction and Review of the Literature
In this document we try to assess whether macroeconomic synchronization of business
cycles has increased between Canada, Mexico and the USA as a result of NAFTA. Given
that a close relationship already existed between Canada and the USA, most of our focus is
on whether Mexico’s business cycle is more similar to those of its major trading partners.
In this respect, there are two different, though related, issues to consider. The first is
whether the Mexican economy has become more sensitive to developments in its NAFTA
partners, i.e. whether the business cycle in Canada or the USA generates a larger response
in the Mexican business cycle than before. The second issue is whether shocks to growth in
♣
The first two authors are Economic Research Officers and the third is Director of Economic Research,
General Direction of Economic Studies, Banco de Mexico.
⊗
The authors would like to thank Luz Marina Arias and Leonardo Armas for their assistance. We would also
like to thank Daniel Lederman, Luis Serven and the participants at the “NAFTA Brainstorming Workshop” at
the World Bank for their comments. The opinions included in this document are exclusively those of the
authors and do not represent the point of view of Banco de México. This study was prepared as a background
paper for a wide ranging analysis by the World Bank on the effects of free trade agreements, and particularly
NAFTA, on economic activity.
Canada or the USA have become a more important source of volatility relative to other
types of shocks, i.e. whether the business cycle in these countries represents a more
significant source of volatility for the Mexican economy than other types of shocks, such as
terms of trade, financial contagion or domestic aggregate demand shocks.
Why is it important to distinguish between these two issues? The sensitivity of the Mexican
economy to developments in Canada or the USA may have increased, which ceteris
paribus, would lead to higher synchronization of business cycles. However, if idiosyncratic
shocks to the Mexican economy are still quite large or increase, then the increase in the
sensitivity of the Mexican economy to developments in Canada and the USA may not be
enough to make these the main sources of volatility for the Mexican economy. In this case,
even though the degree of synchronization would increase, it wouldn’t really be fair to
speak of a synchronized business cycle. This phenomenon could also work in opposite
direction, i.e., that the Mexican economy has not suffered significant idiosyncratic shocks
in the period after the balance of payments crisis in 1995, so we find that the Mexican
business cycle is highly correlated with those of its NAFTA partners, even though this
would not be a robust finding in a larger sample that includes future idiosyncratic shocks.
Finally, if empirically we found that developments in Canada and the USA were more
important in explaining volatility in Mexico, but without an increase in sensitivity, it would
mean that the frequency and magnitude of the idiosyncratic shocks affecting Mexico are
smaller, not that the relationship between Mexico and its trading partners is larger due to
NAFTA.1 Thus, we would need to observe the two phenomenon in order to argue that there
is a stronger synchronization due to the free trade agreement.
There are also practical arguments for making the distinction. Since the implementation of
NAFTA in 1994, Mexico has been subjected to important shocks that did not affect Canada
or the USA in significant ways. The Mexican balance of payments crisis of 1994-1995 and
the Russian and Brazilian crises are “idiosyncratic” shocks to Mexico with respect to
Canada and the USA, even though some of them are common shocks from the point of
view of emerging markets. In addition, during the 1994-2001 period there has really been
only one business cycle in Canada and the USA, and not even a complete one. Thus, it is
likely that, given the absence of long frequency data needed to study business cycles after
NAFTA, we could be overestimating the importance of idiosyncratic shocks versus
common shocks in accounting for volatility in Mexican business cycles. However, finding
an increase in the sensitivity of Mexican GDP to developments in the US would suggest
that synchronization is likely to increase in the future, in the absence of an increase in the
frequency and magnitude of future idiosyncratic shocks to Mexico.
In addition, the policy implications of the two events are quite different. The presence of
important idiosyncratic shocks probably requires that Mexican authorities follow
macroeconomic policies that may be quite different from those implemented in its trading
1
Of course, it is possible that due to NAFTA, the frequency and magnitude of idiosyncratic shocks is smaller.
This could occur because of several reasons. If the trade agreement leads to a more diversified export base,
then Mexico would be less sensitive to specific terms of trade shocks such as changes in the price of oil. In
addition, the legal changes associated to NAFTA may have given greater certainty to investors about future
Mexican economic policies, thus leading to higher and more stable investment. These are different from
traditional arguments for synchronization due to higher trade flows between two countries.
2
partners, even if Mexico is more sensitive to developments in these countries. On the other
hand, if shocks within the NAFTA group are similar and are the main source of shocks,
then Mexico would benefit from the stabilization policies followed by its trading partners,
and its own desired policy adjustments would be similar to those desired for the other two
countries.2
Thus, in what follows we analyze both issues: whether Mexico is more sensitive to
developments in its trading partners, with particular emphasis on the USA, and if
developments in the USA account for a larger proportion of the variability in Mexican
growth rates. The methodology of analysis we use is explained in detail in section 2. Given
that it has been argued that the world is becoming more integrated as a whole, section 3 of
the document compares recent changes for Mexico with those for other countries, in order
to assess if we can really attribute the change to NAFTA rather than to the widespread
effect of globalization that affected every country similarly or to a general increase in
international trade. In addition, we include a section that reviews the case of Ireland,
Portugal and Spain in the European Union to see if developments in Mexico mirror those in
these three countries.
It is also important to compare the Mexican relationship with the US with that of this
country and Canada, as this last case can be used as a benchmark for very close integration.
We do this at the national level but also at the sectorial (section 4.1), aggregate demand
(section 4.2) and regional level (section 4.3). This should also allow us to be more
confident that a stronger synchronization is really due to NAFTA by comparing changes in
tradable and non-tradable goods sectors, as well as regions that should have benefited more
from NAFTA with those less likely to have experienced significant changes. In section 5
we discuss the policy implications from our analysis while section 6 concludes.
We also include a summary of the results at the end of sections 3 and 4, in case the reader
wishes to get a brief impression of the main empirical results from the extensive statistical
analysis that is presented in each of these sections.
Literature Review
There is an extensive theoretical and empirical literature on international business cycles
and comovement of growth across countries.3 However, most of it analyzes comovement
across industrial countries, given the availability of the long time series necessary to
undertake business cycle analysis. In the more scarce literature that compares industrial
with developing countries it is found that the comovement between industrial countries
tends to be much larger than that between industrial and developing countries or between
developing countries in general.4 Obviously, this does not mean that developing countries
2
The policy adjustments would probably be similar in direction, though the different structural conditions of
the Mexican economy compared with those of Canada and the USA might imply that changes need to be of
different magnitudes.
3
See for example Backus and Kehoe (1992), Backus, Kehoe and Kydland (1992, 1995), Stockman (1990).
4
See for example Hoffmaister and Roldos (1997), Loayza, Lopez and Ubide (2001), Hall, Monge and Robles
(1999), Agenor, McDermott and Prasad (1999).
3
are not affected by developments in the industrial countries or among themselves. Instead,
it probably signals that in addition to these common effects there are large idiosyncratic
shocks that mute more general shocks. This seems to be confirmed by the work of Arora
and Vamvakidis (2001) where they analyze the coefficient of US growth in long run growth
regressions.5 They find that the point estimate on US growth for a large sample of
developing countries is close to 1, while that for a sample of industrial countries is in the
range of 0.3-0.4. Thus, in the long run, growth in the US (or other industrialized countries)
is very important for developing countries, though in the short run there may be significant
idiosyncratic shocks. An alternative explanation is that idiosyncratic shocks tend to be more
transitory in character, while permanent shocks are of a more international nature.
Loayza, Lopez and Ubide (2001) do a more detailed analysis of comovement using an error
components model comparing the results from three blocks of countries: Latin America,
East Asia and Europe. They find that common shocks explain a substantial part of the
variation in growth rates in East Asia and Europe, but idiosyncratic shocks are clearly
dominant for Latin America.6 Monges, Hall and Robles (1999) find a similar
preponderance of idiosyncratic shocks in an analysis for Central American countries and
Mexico. In spite of their analysis for several Latin American countries, their results are not
directly related with the main objectives of our study because of three reasons. First, they
don’t do an analysis of the relationship between Canada, Mexico and the USA. Second,
both studies are done using a long term perspective, while for our purposes it is very
important to look at changes over time in order to assess if NAFTA had any impact.
Finally, their methodology would allow us to distinguish if in the more recent period the
effect of common shocks in Canada, Mexico and the USA account for a larger part of the
variability in Mexico’s production, but we would not be able to separate how much of this
is due to a larger shock in the USA or smaller idiosyncratic shocks than observed in the
past (two events presumably unrelated to NAFTA) and the extent to which this is due to a
higher sensitivity of Mexican production to shocks in the USA (related with NAFTA).7
Del Negro (2001) uses a technique combining factor analysis with identified VAR models
to study output comovements across US states, Mexican states and Canadian provinces,
using annual data for the period 1971-1998. He finds interesting results that suggest that in
this period there existed some comovement between Mexican states and some US states
and Canadian provinces, but this is probably due to common exogenous shocks. In
particular, his results from cluster analysis show a large degree of comovement between
Mexican states and oil producing states or provinces in Canada and the US. A second
cluster is formed by most US states and the Eastern provinces of Canada. Unfortunately, his
5
Specifically, they regress five year rates of growth of GDP per capita for the period 1980-1998 on the
contemporary growth rate of the US together with some variables that have traditionally been used in growth
regressions, such as initial level of GDP per capita and population growth rate. The number of countries
varies with the specification employed, but normally ranges between 100 and 140 countries.
6
Karras (2000) uses a similar methodology when considering if the Americas are an optimal currency area
and finds similar results.
7
As mentioned, if NAFTA leads to a change in the composition of Mexican exports, in the institutional
background for foreign investment in Mexico or in risk perception about the Mexican economy it could also
modify the distribution of idiosyncratic shocks. However, in order to assess this in a statistically significant
way we would probably need longer time series after the implementation of the trade agreement.
4
analysis is also subject to the criticisms that it is focused on long term effects and does not
allow us to differentiate between a larger sensitivity of Mexico to developments in its
partners’ economies or just that its idiosyncratic shocks are smaller (and common shocks
are larger).
The studies mentioned don’t look at the possible effect of NAFTA or other trade
agreements. However, there is an important literature on the effects of trade agreements and
business cycle consolidation. Theoretical analysis has shown that cycles and shocks could
become more or less idiosyncratic. In particular, the degree of business cycle
synchronization could fall if a free trade agreement leads countries to higher specialization,
with sectorial shocks being large (Eichengreen (1992), Kenen (1969), and Krugman
(1993)).8 On the other hand if demand or common shocks are more important or if most
trade is of intra-industry type, then business cycles would become more synchronized.
Frankel and Rose (FR, 1998) is a seminal paper that tried to assess which of the two
hypothesis is the correct one, using a sample of twenty industrialized countries over thirty
years. They found that closer trade links actually led to more highly correlated business
cycles. This is not a direct test of the effect of free trade agreements, but it is suggestive of
what to expect from them. In fact, in an estimation they use later on to construct
instrumental variables they find that, for their sample of countries, free trade agreements
lead to very significant increases in trade.
FR’s study focused on the European Union and the adoption of a common currency. Their
intention was to show that the conditions for an optimal currency area are endogenous, as a
monetary union would lead to higher trade, and higher trade in turn to higher
synchronization of business cycles. Other studies following a similar objective and using
similar methodology are: Artis and Zhang (1995), who find that European economies were
highly correlated with the US from 1961-1979 but more with Germany since joining the
ERM; Fidrmuc (2001), who estimates the relationship between trade and the correlation of
business cycles using a sample that includes Central and Eastern European countries and
also the level intra-industry trade which is found to be positive and significant; Fontagné
and Freudenberg (1999) find the same results as FR looking at more disaggregated trade
data for the European Union; Anderson, Kwark and Vahid (1999) again find similar results
using more sophisticated measures of comovement compared with the simple correlations
employed by FR.
Imbs (1998) casts an important doubt on the results of FR (1998) and those derived using
the same methodology, as he finds that their results may not be robust to the inclusion of
fixed effects or of other possible determinants of both trade and synchronization, such as
gravity effects. Thus, it seems as if FR’s results could be strongly driven by cross sectional
variation, possibly due to other effects, and not time variation in trade and synchronization.
8
It has also been argued that capital market integration that leads to higher risk sharing may also lead to
higher specialization, see for example Kalemi-Ozcan, Sorensen and Yosha (2000). However, as higher risk
sharing is the force driving the specialization it would actually increase comovement in income and
consumption, though not in production.
5
It is worth noting that in our study we put particular emphasis on the time variation, so our
results don’t seem subject to Imbs’ criticism.
With an alternative specification, Imbs (2000) finds that cycle synchronization is more
responsive to similarities in the structure of production rather than to trade intensity,
suggesting that sectorial shocks are an important factor driving comovement. Again, this is
less likely to be driving changes in comovement across the Mexican economy and those of
its NAFTA partners, given that the sectorial composition of GDP is more different between
them than in a sample of industrial countries.
This implies that even if there is a strong and positive relationship between trade intensity
and the correlation of business cycles in a sample of industrialized countries it could be
arising from the fact that they have similar factor endowments, so a higher degree of
specialization due to trade is limited. It might be the case that we only observe sufficient
marginal specialization in cases of significant trade between countries that have very
different factor endowments. In this sense, it is more likely to find less synchronization in a
case like the Mexican one, where relative factor endowments are significantly different
from those in the US and Canada, unless trade is mostly intra-industry in character.9
The empirical evidence for the effect of higher trade between industrial and emerging
market on synchronization of business cycles is mixed. The adjustment of trade patterns
following the transitional recession of the early 1990’s seems to have led to a higher
correlation of the business cycle between Germany and several Central and Eastern
European countries according to Fidrmuc (2001). Achy and Milgram (2001) argue that a
free trade agreement between Morocco and the European Union is very likely to lead to
higher specialization in Morocco and a less synchronized business cycle.
Ahumada and Martirena-Mantel (2001) do the same analysis as FR for a sample of some
Mercosur countries plus Chile in order to assess whether an increase in trade volumes
across these countries has led to higher synchronization. This is an interesting case that is
somewhat different from the European or NAFTA contexts, as the exports of these
countries are generally intensive in natural resources, at least with third countries. The
authors find suggestive evidence that higher trade has led to higher comovement, but the
strongest factor driving their results is the change in correlations between Argentina and
Brazil from 1987-1992 to 1993-1999.10 It would be interesting to assess if this result still
holds when considering the recent Argentine crisis.
Torres and Vela (2002) analyze in detail the degree of correlation of several quarterly
variables between Mexico and the US for the period 1992-2001, with particular emphasis
on the leads and lags structure of the correlations. They find a positive correlation between
GDP in the US and GDP or manufacturing production in Mexico, though their results are
puzzling as the strongest correlation is with the first lag of the Mexican variables. A
9
Another potential counter argument against finding a smaller correlation are the large differences in size,
particularly between Mexico and the US. A relatively small demand shock for the size of the US economy
could be very large for Mexico.
10
The correlations between Argentina-Uruguay, Brazil-Uruguay, Argentina-Chile, Brazil-Chile and ChileUruguay change little between both periods and in some cases fall in the second period.
6
possibility is that the demand for Mexican exports responds more quickly to the business
cycle than other components of US GDP. In terms of the relationship between Mexican
exports and GDP, they find that the highest correlation is contemporary and is substantially
larger for the period 1996-2001 than for their whole sample 1992-2001. The correlation of
imports with GDP is quite high for the whole period. Unfortunately, they don’t analyze
changes over time in the correlation between US variables and Mexican ones. The only
result that is strongly suggestive of a marginal effect from NAFTA is the substantial
increase of the correlation between exports and GDP in Mexico.
2. General Methodology
Throughout this document, we analyze economic synchronization of economic variables
for different samples and levels of aggregation (national, sectorial and regional levels). The
methodology of analysis is essentially the same for all cases, and we point out any
differences in the specific sections where this occurs. The analysis is done using annual
growth rates of the variables. These are used because the calculation of business cycles by
means of filters may depend in an important way on the use of seasonally adjusted data,
which is not available in many cases. In addition, they might be more sensitive to
measurement error problems, as the annual growth rates are incorporating information from
the last twelve months.
There is one important consideration related with the interpretation of correlations of
annual growth rates. The correlation between two series of annual growth rates would
increase if there is a larger effect from changes in one on the other, but also if the effect
takes place sooner, even if the size of the effect has not changed.11 In section 4.1 we make
an analysis of changes in the speed with which shocks in the US affect manufacturing in
Mexico.
The following methods are used in all cases:
i)
Correlations.
We do two types of analysis using correlations. The first is a comparison of correlations
between the different variables for the longest possible time period, depending on the
availability of data, and then for a shorter time period meant to capture the effect of
NAFTA. In addition, we analyze three year moving correlations. In all cases, we place
particular emphasis on the correlation with respect to the same variable in the USA. This
allows us to observe: i) if the correlation between the Mexican and USA variables has
increased more than that between other countries and the USA, in the case of international
comparisons, and ii) if the correlation between Mexican and USA sectors, components of
aggregate demand or regions has increased more for those cases where we would expect a
larger effect from NAFTA.
11
This is discussed in greater detail in Appendix 1.
7
ii)
Basic regression analysis.
We regress the annual growth rate of production, demand or employment variables against
contemporaneous and lagged observations of themselves and contemporaneous
observations of the same variable in the USA. The general form of the regressions is the
following:
∆xit = α i + β i ∆xit −1 + γ i ∆xUSt + µ i dT + λi dT ⋅ ∆xit −1 + δ i dT ⋅ ∆xUSt
(1)
where ∆xit is the annual growth rate of variable x in country, region or sector i, ∆xUSt is the
annual growth rate of the same variable in the USA,12 and dT is a time dummy to capture
whether the sensitivity of the variable to developments in the USA has changed.13 The
regressions are initially estimated on a country by country (sector, demand component or
region within a country) basis. There are two options for dT, and we report results for both
of them. The first option is a d94, which is one from 1994 to the end of period, and zero for
previous periods. The second, d97, is one from 1997 on. Even though NAFTA was
implemented in 1994, the large balance of payments crisis that took place in Mexico in
1995 and the fast subsequent recovery in 1996 are large shocks, presumably unrelated with
NAFTA and that might make it more difficult to find any significant effects from the trade
agreement.
These simple regressions allow us to compare three things. The first is how sensitive the
variable is to developments in the USA (γ), the second is how this sensitivity has changed
over time (δ), and finally the R2 can tell us how much of the variables’ variation can be
accounted for by developments in the USA (restricting β=0).14 A country may be quite
responsive to developments in the USA, but if it is also subject to other significant shocks
we could find a high γ but a low R2.
We used three specific forms of equation (1) throughout the analysis. The first is merely to
include a constant plus the contemporary growth rate of the variable in the US (with and
without interaction with time dummys). The second was to add one lag of the dependent
variable. Finally, we added more lags of the dependent variable as well as for the US
variable, so we had 2 lags of the dependent variables and the contemporary and 2 lagged
values of the variable in the US, with and without interacting with the post-NAFTA
dummys. In the case of variables with quarterly frequency, more than one lag for the
12
In the case of regions, it corresponds to total US employment growth, and in the case of sectors of
economic activity it corresponds to the growth rate of the same sector of economic activity in the USA.
13
This methodology is very similar to that used by Frankel, Schmukler and Serven (2002) to assess how
responsive interest rates are under different currency regimes to changes in rates in the USA.
14
In addition, the coefficient β tells us the degree of persistence in the variable, while α/(1-β) gives us the
long-run growth trend if growth in the USA was zero. We do not discuss the results on α or β in detail, as we
are more interested in looking at the relationship with the USA.
8
dependent variable and any lag for the variable in the US were seldom significant and did
not affect the results in terms of the overall relationship of a variable or changes in the
relationship between the pre and post-NAFTA periods. In the case of monthly industrial
production growth rates, the two lags of the dependent variable were typically significant so
we reestimated using four lags of the dependent variable plus the contemporary value and
two lags for the US variable. Thus, we discuss in detail the results when including longer
lags in the sections on multi-country industrial production data and on industrial
subcomponents in Mexico.
Given that the four quarter (or 12 month) filter implicit in working with annual growth may
not remove all sources of seasonality we also did all the regressions with seasonal dummys.
These were never significant and their inclusion did not affect in any important way the
estimated parameters nor their significance. Thus, it seems that the annual filters do a good
work in removing seasonality and in the text we report the results without including the
seasonal dummys.
iii)
Factor analysis
The factor analysis employed is a simple specification. This is again conditioned by the fact
that we specifically want to look for changes in the post-NAFTA period, which limits
sample size considerably and thus the type of procedures we can use. We use maximum
likelihood estimation to decompose growth rates of a set of i variables into k factors:
∆y i = µ + Λf + u
(2)
where ∆yi is the growth rate of our variable of interest, the subindex i will stand for
countries, sectors of activity, regions or components of aggregate demand, µ is a constant,
Λ is a matrix of factor loadings, f is a vector of factors and u is a vector of idiosyncratic
shocks. The number of factors included in each estimation is determined by a χ2 test against
the hypothesis of including more factors, and the reported factor loadings correspond to
those obtained from varimax rotations.
Factor analysis complements the correlation and regression analysis by incorporating all the
information available in the cross correlations of all the variables in the sample, in
comparison with the bivariate regression analysis, while at the same time expressing this
information in a way that is easier to analyze than the complete matrix of cross correlations.
3. National Economic Aggregates
In this section, we measure the degree of synchronization among the economies of Mexico,
the US and Canada, and compare it to that observed between each one of those countries
9
with other countries in Latin America and Europe.15 The data employed are annual rates of
growth of GDP (at quarterly frequency) and of industrial production (at monthly
frequency). The source of the data for all the countries is IFS of the IMF with the exception
of the industrial production of Argentina, Brazil and Chile. For these three countries, the
data on industrial production are from FIEL16 in Argentina and the National Institutes of
Statistics of Brazil and Chile.
In addition, we present a brief summary of the results other authors have found in the case
of Portugal and Spain in the context of the European Union and compare these with those
found for Mexico.
3.1. Results with a sample of different countries
Analysis of correlations across countries and with the USA
Table 3.1 shows the correlation coefficients between annual growth rates of GDP for the
sample of countries during the periods 1981Q1-2001Q2 and 1994Q1-2001Q2.17 In the
longer sample, the correlation coefficient between Canada and the USA is quite high,
followed by a large margin by those of the United Kingdom, Chile and Italy. The
correlation of Mexico with Canada and the USA is positive, but low. Nevertheless, they are
the highest correlation coefficients Mexico has with any of the countries in the sample with
the exception of Chile and, surprisingly, Germany.
In the shorter and more recent time period, the highest correlations with the USA are those
of Canada and Mexico, both with very similar values. However, it is interesting to notice
that while this coefficient increases for Mexico it falls for Canada compared with that for
the full sample. These are also the highest correlation coefficients of these two countries
with any other country. The correlation with the USA of all the European countries is
positive, though at an intermediate level. Argentina’s coefficient is similar to those of
European countries, Brazil’s is virtually nil, and Chile’s is negative. The correlation
between Canada and Mexico is higher than for the whole sample. However, in the
Canadian case, its correlation coefficient with several European countries is higher than
that with Mexico. In the Mexican case, its correlation is only higher with Argentina,
presumably because of the effects the Tequila crisis had on the South American country. Its
correlation also increased with most European economies in the sample, though the
increase is much smaller than that with the US. This last phenomenon is consistent with the
general opening to trade followed by Mexico since the mid eighties. Some of the
15
The American countries included in the sample are: Argentina, Brazil, Canada, Chile, Mexico and the USA.
The European countries are France, Germany, Ireland, Italy, Portugal, Spain and the United Kingdom. Ireland
and Portugal are not always included in the analysis due to data limitations.
16
The Fundación de Investigaciones Económicas Latinoamericanas is the institution that has carried out a
public monthly survey of industrial production (EMI, Encuesta Mensual Industrial) in Argentina for the
longest time.
17
The date of the second sample is conditioned by the fact that we don’t have quarterly GDP data for
Argentina until 1993. Brazil is not included in the correlation calculations for the first sample as its series of
quarterly GDP starts in 1990.
10
correlations of other countries with the US also increased in the latter sample, but this is not
a general phenomenon and the increase is clearly the largest for Mexico.
Table 3.1
Correlations in annual growth of GDP
i)1981-2001
USA
USA
CAN
FRA
GER
ITA
SPAIN
UK
CHI
MEX
CAN
1
0.874
0.152
-0.134
0.421
0.186
0.478
0.433
0.164
FRA
1
0.215
-0.207
0.485
0.235
0.496
0.374
0.158
1
0.186
0.690
0.775
0.414
-0.020
0.088
GER
1
0.288
0.276
-0.319
0.284
0.173
ITA
SPA
1
0.706
0.499
0.253
-0.036
UK
1
0.450
0.044
0.016
CHI
1
-0.065
-0.298
MEX
1
0.221
1
ii)1994-2001
USA
USA
CAN
FRA
GER
ITA
SPA
UK
CHI
MEX
BRA
ARG
1
0.655
0.313
0.307
0.179
0.267
0.303
-0.291
0.656
0.029
0.364
CAN
1
0.619
0.613
0.469
0.374
0.274
-0.319
0.452
0.315
0.106
FRA
1
0.545
0.373
0.426
0.422
-0.341
0.235
0.119
-0.167
GER
1
0.612
0.445
0.335
0.118
0.216
0.259
0.026
ITA
1
0.322
0.303
0.383
-0.015
0.211
-0.076
SPA
1
-0.087
-0.230
0.310
-0.402
-0.181
UK
1
0.287
0.310
0.549
0.584
CHI
1
-0.311
0.194
0.313
MEX
1
-0.088
0.589
BRA
1
0.244
ARG
1
Graph 3.1.i shows the evolution of three year moving correlations between the USA and
continental European economies. The level of the correlation may differ across them, but
they evolve over time in very similar ways, with the exception of the period of German
unification when this country clearly behaves like an outlier. For the other countries, the
correlation coefficient has remained positive and relatively stable with the exception of the
mid eighties and the ERM crisis of 1992-1993.
Graph 3.1.ii shows the three year moving correlations between the USA and the Latin
American countries in our sample. The correlations for Chile and Mexico have evolved in
opposite ways during most of the sample period. That for Chile is higher in the beginning
and middle of the sample, while that of Mexico has remained significantly higher and
positive since NAFTA, though the emerging market crisis of 1997-1999 might have led to a
temporary reduction in the correlation coefficient. The correlation for Brazil seems to have
responded to the East Asian crisis in 1997, with a posterior increase that took it to levels
similar to Mexico’s, though that for Mexico has become higher in the latter part of the
sample. The correlation for Argentina is very similar to Mexico’s until the emerging
markets crises that started in 1997, after which it fluctuates around zero.
Finally, Graph 3.1.iii shows the correlations for Canada and the United Kingdom. Canada’s
remains always quite high, and is the one with the lowest variability, though this seems to
11
increase slightly in the second half of the sample. That for the United Kingdom falls in the
mid-eighties, as was observed for the continental European economies, then recovers to a
substantial level by 1990, but then shows a declining trend. Thus, since the mid nineties,
the correlation between the Mexican and USA economies seems to have increased
substantially and remained quite high with the exception of the Russian and Brazilian crises
episodes. No other country in our sample seems to have evolved in such a way.
Graph 3.1
Three year moving correlations in annual growth of GDP between a set of countries and the USA
i) Continental European Economies
ii) Latin American Economies
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
-0.2
-0.2
-0.4
-0.4
FRANCE
-0.6
ARGENTINA
-0.6
GERMANY
BRAZIL
ITALY
-0.8
MEXICO
-0.8
SPAIN
CHILE
2000,4
1999,4
1998,4
1997,4
1996,4
1995,4
1994,4
1993,4
1992,4
1991,4
1990,4
1989,4
1988,4
1987,4
1986,4
1985,4
1984,4
2000,4
1999,4
1998,4
1997,4
1996,4
1995,4
1994,4
1993,4
1992,4
1991,4
1990,4
1989,4
1988,4
1987,4
1986,4
1985,4
1984,4
1983,4
1983,4
-1
-1
iii) Canada and the United Kingdom
1
0.8
0.6
0.4
0.2
0
-0.2
CANADA
-0.4
UNITEDKINGDOM
-0.6
-0.8
2000,4
1999,4
1998,4
1997,4
1996,4
1995,4
1994,4
1993,4
1992,4
1991,4
1990,4
1989,4
1988,4
1987,4
1986,4
1985,4
1984,4
1983,4
-1
12
In order to complement the results on GDP, we did the same analysis employing industrial
production data. Table 3.2 shows the correlation coefficients between annual growth rates
of industrial production for the sample of countries during the periods Jan 1987 – April
2001 and Jan 1995 – April 2001.18 The countries that have the largest correlations with the
USA during the longer sample period are Canada (significantly larger than all the rest), the
United Kingdom, Spain, Italy and France, all with correlation coefficients larger than 0.39.
On the other extreme are Chile, Mexico and Germany with correlation coefficients below
0.2. The change between these results and those for the shorter time period are striking.
There is a generalized increase in the correlation coefficient with the USA, with 4 countries
having correlation coefficients above 0.9. These countries are Canada, Brazil, Mexico and
Chile. The correlation coefficients for Italy and Spain also increase significantly, those for
Germany and the United Kingdom show minor changes, while that for France falls
substantially. Argentina, which is included only in the smaller sample, has a negative
correlation coefficient with the USA. In terms of the questions addressed by this paper, the
simple analysis of correlations does not show an exclusive increase of the correlation
between Mexico and the USA but rather a more generalized increase in the correlation of
several countries with the USA, and particularly American economies (Brazil, Canada and
Chile).
Table 3.2
Correlations in annual growth of industrial production
i)1987-2001
USA
USA
CAN
GER
FRA
ITA
SPA
UK
BRA
CHI
MEX
1
0.883
0.102
0.391
0.396
0.467
0.597
0.262
-0.013
0.168
CAN
1
0.028
0.339
0.394
0.430
0.659
0.334
-0.098
0.054
GER
FRA
1
0.785
0.488
0.652
0.153
0.026
-0.145
0.312
ITA
1
0.655
0.804
0.368
0.113
-0.041
0.278
SPA
1
0.657
0.478
0.072
0.220
-0.056
UK
BRA
1
0.402
0.153
-0.056
0.160
1
0.155
0.083
-0.033
UK
BRA
CHI
1
0.135
0.051
1
-0.135
CHI
MEX
MEX
1
ii)1995-2001
USA
USA
CAN
GER
FRA
ITA
SPA
UK
BRA
CHI
MEX
ARG
1
0.961
0.156
-0.576
0.757
0.821
0.593
0.942
0.912
0.931
-0.266
CAN
1
0.423
-0.779
0.547
0.631
0.347
0.998
0.990
0.795
0.011
GER
1
-0.897
-0.527
-0.436
-0.703
0.479
0.547
-0.214
0.911
FRA
1
0.098
-0.006
0.317
-0.818
-0.860
-0.239
-0.635
ITA
1
0.995
0.975
0.493
0.423
0.943
-0.831
SPA
1
0.946
0.581
0.515
0.973
-0.769
1
0.287
0.211
0.845
-0.934
1
0.997
0.754
0.074
1
0.700
0.153
1
-0.599
ARG
1
In order to confirm the above results, we calculated three year moving correlations of each
country in the sample with the USA. These show that correlations for large periods actually
mask some important facts. Graph 3.2.i shows the correlations with the USA of France,
18
The latter sample period was chosen because it allows us to include observations for Argentina.
13
Germany, Italy and Spain. Even though the correlations presented in Table 3.2 for Italy and
Spain are larger than those for Germany and France, the evolution of the four is very
similar, suggesting that the degree of synchronization between the USA and the major
European continental economies has changed in very similar ways. The correlation is quite
high during the period 1993-1998, but then falls significantly (in the case of Italy the
reduction occurs earlier). Graph 3.2.ii shows the case of Ireland and the United Kingdom,
for which the evolution of the correlation is again similar. Ireland shows a high degree of
correlation until 1995, while that for the United Kingdom is also high from 1986 to 1997.
There is a large drop in both correlations and then a recovery in the later part of the sample.
Graph 3.2.iii shows the evolution of the correlations for Argentina, Brazil and Mexico. In
the first part of the sample, until 1993, the correlations for Brazil and Mexico show
significant volatility but, more important, they evolve in opposite directions. Then from
1995 to 2000, the correlations for Argentina, Brazil and Mexico show a very similar and
generally increasing trend. Then in 2001, the correlations for Argentina and Brazil fall
significantly while that of Mexico increases further. Finally, Graph 3.2.iv shows the
correlations for Canada, Chile and Portugal. That for Canada is always quite high and
varies much less than those for all the other countries. Those for Chile and Portugal evolve
in similar ways during the second half of the sample, though Portugal’s is always much
lower. The correlation is low from 1994 to 1998, then increases from 1999 to 2000, and
falls significantly in 2001 in the case of Chile, similar to what it observed for Argentina and
Brazil.19
Graph 3.2
Three year moving correlations in annual growth of industrial production between a set of countries
and the USA
i) Large Continental European Countries
ii) Large Island European Countries
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
-0.2
-0.2
GERMANY
-0.4
IRELAND
-0.4
UNITED
KINGDOM
FRANCE
-0.6
-0.6
ITALY
-0.8
-0.8
SPAIN
19
2001,12
2000,12
1999,12
1998,12
1997,12
1996,12
1995,12
1994,12
1993,12
1992,12
1991,12
1990,12
1989,12
1988,12
1987,12
1986,12
1985,12
1984,12
1983,12
2001,12
2000,12
1999,12
1998,12
1997,12
1996,12
1995,12
1994,12
1993,12
1992,12
1991,12
1990,12
1989,12
1988,12
1987,12
1986,12
1985,12
-1
1984,12
1983,12
-1
We have no data in 2001 for Portugal.
14
iii) Latin American Countries
iv) Canada, Chile and Portugal
1
1
BRAZIL
0.8
0.8
MEXICO
0.6
0.6
ARGENTINA
0.4
CHILE
PORTUGAL
2001,12
2000,12
1999,12
1998,12
1997,12
1996,12
1995,12
1994,12
1993,12
1992,12
1991,12
1990,12
1989,12
1988,12
1987,12
1986,12
CANADA
1985,12
1983,12
2001,12
2000,12
1999,12
1998,12
1997,12
1996,12
-1
1995,12
-1
1994,12
-0.8
1993,12
-0.8
1992,12
-0.6
1991,12
-0.6
1990,12
-0.4
1989,12
-0.4
1988,12
-0.2
1987,12
-0.2
1986,12
0
1985,12
0
1984,12
0.2
1983,12
0.2
1984,12
0.4
The above results on 3-year moving correlations suggest that the increase in correlations
and the larger synchronization of many countries with the USA is not as evident as when
we observe the correlations for longer time periods. In particular, it is no longer as clear
that the correlation between European countries and the USA has increased in the more
recent period. On the other hand, the increase in correlations for all Latin American
countries in the last 7 years seems more robust, though this relationship breaks for all
countries with the exception of Mexico in 2001.
The results from both industrial production and GDP confirm the fact that the correlation
between the Mexican and the USA economies has increased substantially since NAFTA.
However, the differences in results between GDP and industrial production point to several
interesting issues. In the case of GDP, the evidence of a uniquely stronger integration
between the Mexican and USA economies seems clear, as the increase in the correlation
has been higher and more persistent than that observed for other European or Latin
American countries. However, in the case of industrial production we see other similar
cases of large increases in correlations in the latter part of our sample. This suggests that
trade opening and greater integration might be occurring in a global scale, so there is larger
synchronization in the cycles of highly tradable and low tariff products such as some
manufacturing products. On the other hand, other additional factors seem to be leading to a
larger integration between Mexico and the USA not only reflected in tradable goods but
more generally in total GDP. Some of these factors could be higher FDI, remittances of
Mexican workers in the USA, or smaller trade restrictions for non-manufacturing products.
In addition, as was mentioned in section 2, the correlation coefficients don’t give us the
magnitude of impact of given shock, they just tell us that two series tend to move in the
same direction. It may be the case that the sensitivity of manufacturing has increased
15
generally, but much more so in the Mexican case, leading to larger effects on consumption
and investment explaining the results for correlation of GDP.
Simple regression analysis of macroeconomic synchronization with the USA
Simple regressions of the type of (1) were estimated for annual growth rates of GDP and
industrial production for each country. For those countries where the data allows it, the
regressions cover the period 1981-2001 in the case of GDP and 1987-2001 for industrial
production. For countries with shorter data series, the sample starts with the first available
observation.
Table 3.3 shows the results for GDP. The columns have each of the countries while six
sections of rows contain the results from each specification. In this way, it is
straightforward to make comparisons across countries. The results in the first section of
rows correspond to a regression with only a constant and the contemporary growth rate of
GDP in the USA. The second and third section of rows present the results for the
regressions that add interactive dummys for 1994 and 1997 to the original specification.
The fourth section of rows presents the results when we include the lagged rate of growth in
the country, without any dummys. The fifth and sixth sections have the results when the
interactive dummys are added to the regression of row 4. Countries are ordered according
to whether they are members of NAFTA, other Latin American countries and European
countries.
We first review the results when the rate of growth of GDP in a country was regressed only
on the rate of growth of GDP in the US. These regressions indicate the degree to which
developments in the US economy are related with those in the country, what is the degree
of sensitivity of the country to developments in the US, and also how much of the
variability in the growth rate is accounted for by variability in the US. Without the 1994 or
1997 dummys, there are three countries for which the coefficient on US growth is positive
and significant: Canada, Chile, Italy, Spain and the UK. The point estimate in the Mexican
regression is the fourth in magnitude (out of nine countries) after Chile, Canada and the
UK, but it is not significant. The close historical relationship between the US, Canada and
the UK is known. The Chilean case might respond to the fact that it is a small, fairly open
economy, whose growth depends importantly on its level of exports and commodity prices.
In addition, it has been the most stable economy in Latin America, so idiosyncratic shocks
might be less important. These results are confirmed by the R2 in the regressions. The
regression explains a very large proportion of the variability in Canadian GDP growth.
Next, with much smaller levels, come the UK, Chile and Italy. For the other countries, the
R2 is very low, suggesting either no or very low relationships with the USA (France,
Germany, Spain), or high volatility due to idiosyncratic shocks so a very small part of the
volatility is explained by that in the USA (Brazil and Mexico).
When we include the interactive dummys for 1994 onwards, there are several important
changes. The point estimate of the interactive dummy is negative for those countries that
had a high relationship with the USA during the whole period (Canada, Chile, Italy and the
UK, significant only for Chile). On the other hand, the coefficient for Mexico increases
16
very substantially, making it the highest of all, and is significant. The R2 and adjusted R2
statistics increase slightly for most of the countries, though Mexico’s increase very
substantially, now with similar levels to those of Chile and the UK.
There is an important caveat with the above results for Mexico. The constant dummy for
1994 is also significant, negative and large in absolute terms. A possibility is that the
dummy is really capturing the period 1994-1996 associated with the balance of payments
crisis, and that is why the R2s are so much larger. That could also be biasing the estimation
of the coefficient on US growth as the coefficient are capturing both post-NAFTA and
balance of payments crisis. The estimation using dummys for 1997 should not have these
problems, though the fewer number of observations may lead to more imprecise
estimations as well as identifying the effects of particular shocks instead of long run
changes in trends and in the relationship with the USA. We again find that the point
estimate on the interactive dummy shows a fall in the relationship with the USA in the case
of the countries that had the highest point estimates for the entire period (Canada, Chile and
the UK, significant for Canada), and it falls also for Argentina and Brazil.20 In the case of
Mexico we still observe the largest increase in the point estimate, making it again the
largest for the period after 1997, though it is not significant. Nevertheless, the fact that it
does not fall, as is the case for all other Latin American countries, in a period of high
shocks to emerging markets, suggests that in reality we might have an important increase.21
The increase in the R2 and adjusted R2 statistics is again largest for Mexico22, though it is
much smaller and is still quite below those of Canada, Chile and the UK. This suggests that
a large part of the increase in the R2 observed when using the 1994 dummy does respond to
the fact that it captures some of the volatility associated with the 1994 crisis.
The regressions when including the autoregressive coefficient separates partially between
idiosyncratic shocks and the effects coming from growth in the US. In the case without
time dummys we find that the point estimate for growth in the US is highest and significant
for Canada, Chile and Mexico, respectively. It is also significant for Italy and France, but of
much smaller magnitude. The most significant differences with respect to the case when we
did not include the autoregressive coefficient is that the Mexican point estimate is now
significant (though of similar magnitude), while those for all the other countries are
smaller, very substantially in the case of Canada and of Chile, and sometimes they are no
longer significant, as in the case of Spain and the UK. The R2’s are naturally much larger,
except in the case of Brazil, and out of all the other countries Mexico’s is the next to
lowest, again confirming the fact that Latin American economies are more volatile, with the
exception of Chile.
When we include the dummy for 1994, the interactive coefficient with US growth increases
significantly for Brazil, Mexico, Spain and France. The largest increases are for Brazil and
20
The level and change in the point estimates for Argentina suggest that we might just be capturing the
increasing and then decreasing trend in Argentina’s growth rates. In addition, the small number of
observations, both before and after 1997, might be leading to very imprecise estimates.
21
The East Asian, Russian, Brazilian, Turkish and the beginning of the Argentine crisis occurred during this
period.
22
With the exception of Argentina.
17
Mexico.23 As in the case without the autoregressive term, the point estimate falls for
Canada and Chile. Finally, positive and significant point estimates for the period previous
to 1994 are only found for Canada, Chile, Mexico and Italy, in decreasing order of
magnitude.
The results for Mexico using the 1994 dummy could again be reflecting the 1994-1996
balance of payments crisis. The results when using the 1997 dummy are similar for most
countries, though many of the estimates associated with the dummy are no longer
significant. The point estimate on the interactive dummy with US growth for Mexico is
again the largest, though now non-significant. The only significant increase is now that for
Italy.24 The estimates for Canada and Chile are again negative, though non-significant. The
point estimates on US growth for the period before 1997 are significant for Canada, Chile,
Mexico and Italy, in decreasing order.
Summarizing, the results from the regressions on economic growth suggest the following.
There has traditionally been an important relationship between the USA, Canada and Chile.
That between Mexico and the USA was important, but explained little of the variability in
Mexican GDP due to the presence of important idiosyncratic shocks. However, in more
recent periods, Mexico has become more sensitive to developments in the USA, as
evidenced by the increase in the point estimate associated with US growth in the
regressions when the dummys are included.25
Table 3.4 includes the results from the same type of regressions using the annual growth
rate of industrial production at a monthly frequency, for the period 1987-2001. In the case
without interactive dummys and the autoregressive term we find large and significant point
estimates for Canada, Ireland and Brazil. Italy, the UK, France have intermediate and
significant levels. In general, the point estimates are larger than those found for GDP, with
the exception of Chile, whose point estimate is close to zero and non-significant. The
coefficient for Mexico is larger and now significant, with a level close to those of Italy, the
UK and France. In terms of the explained volatility, Canada again has a very high R2, much
larger than that for any other country. It is followed by the UK, Ireland and Spain. The R2
for the Latin American countries is still quite low, though larger than for GDP in the case of
Brazil and Mexico.
When we include the dummy for 1994 we find significant increases in the estimates for
Mexico (with the largest increase), Germany, Spain and France. Those for Canada and the
UK fall. The results seem consistent with what was found in the GDP regressions. The
results are very similar when using the dummy for 1997, though in that case the only
significant increase is that for Mexico. Thus, the higher sensitivity of Mexico to
23
The point estimates for Brazil change significantly between the two periods, and are also quite different
when using the 1997 dummy, so this is probably a case when we are capturing a particular shock or trend with
the 1994 dummy.
24
The result for Argentina again makes no economic sense.
25
And it also explains a larger part of the volatility. The adjusted R2s of the regressions without the
autoregresive coefficient are –0.020 for the period 1981-1993 and 0.347 for the period 1997-2001.
18
developments in the US seems a robust finding, and is significantly larger than that
observed for any other country.26
When we include one or more lags of the dependent and variables and the rate of growth of
industrial production in the US, without the time dummys, we find large but temporary
effect of US growth for Mexico and Brazil (Table 3.3). The effects for Canada, UK, France,
Germany, Ireland and Italy tend to be smaller initially but more persistent. When we
include the 1994 dummy, we find a large and significant increase in the sensitivity to US
growth for Mexico and Brazil, and smaller ones for France and Spain, in the specification
with just one lag for the dependent variable and no lags for US growth. When we include
more lags, these coefficients loose significance, except in the case of Brazil. In the case of
the 1997 dummy, the only increase in sensitivity found to be significant is that for Italy
with only one lag for the dependent variable, though that for Mexico is the largest increase
in magnitude in all the lag specifications.
The above results suggest that, even though the Mexican economy has become more
sensitive to developments in the USA, and more so than any other country according to the
point estimates on the rate of growth of GDP and industrial production in the USA, it is still
subject to important idiosyncratic shocks, presumably risk aversion and contagion, terms of
trade, policy and oil price shocks, as evidenced by the still low R2 from the GDP
regressions. On the other hand, a very substantial part of the volatility of industrial
production in the more recent period is explained by developments in the US, suggesting
that synchronization in the tradable sector of the economy is already quite substantial.
These results lead to a different interpretation from that obtained solely on the basis of
correlations. While the correlations increased in the latter years to very large levels for
Latin American countries, this may simply be due to the fact that the cycles tend to move in
more similar directions but the correlations don’t tell us anything about the sensitivity of
the variables in the different countries with respect to the US.
Factor analysis
We did three factor analysis exercises with GDP growth rates by country, following
equation (2). The first was done for the whole sample, 1981-2001, the second was done for
the sample period 1994-2001, and finally we included Argentina and Brazil in this second
sample.27
For the whole period, we could not reject the hypothesis that more than four factors were
necessary for explaining the variability of the series, while we did so for five factors. The
results from the analysis using 5 factors are reported in table 3.5. The first factor seems to
be a European one, finding high factor loadings for France, Italy and Spain, and
intermediate ones for Germany and the UK. The second one seems to be a US factor, with
high loading for the US, and Chile, and intermediate ones for Canada, Italy and Mexico.
26
Volatility of industrial growth in the US also explains a much larger proportion of that in Mexico. The
adjusted R2s of the regressions without the autoregresive coefficient are –0.011 for the period 1987-1993 and
0.615 for the period 1997-2001.
27
The exercise for the sample 1997-2001 was not done due to degrees of freedom considerations.
19
The third to fifth factors seem to be capturing particular idiosyncratic shocks, or the case of
countries integrated to both of these major groups. The third factor is associated mostly
with the UK, the fourth with Mexico and the fifth with Germany.
In the shorter sample we find that we can reject the hypothesis of more factors once we
have four of them. The idiosyncratic shock that seems associated with Germany does not
appear significant any more, as would be expected once the shocks from German
reunification passed. Thus, we find a first factor that again seems to identify European
shocks, with very high coefficients for France, Germany, Italy and Spain and, somewhat
surprisingly, Canada. The loading for the US is intermediate. The loading on Mexico
associated with the second factor is large and the loading on the US is quite substantial.
This suggests that while Canada is subject to more general industrialized country shocks,
Mexico still is subject to a significant amount of idiosyncratic ones and also to idiosyncratic
US shocks. Finally the third and fourth factors seem to capture idiosyncratic shocks to the
UK and to Chile, respectively. Thus, compared with the results for the whole sample, we
now have a factor that has high loadings on both Mexico and the US.
Table 3.5
Results from factor analysis of annual GDP growth rates
Factor loadings correspond to varimax rotation
Variable
USA
CANADA
FRANCE
GERMANY
ITALY
SPAIN
UK
CHILE
MEXICO
Variable
USA
CANADA
FRANCE
GERMANY
ITALY
SPAIN
UK
CHILE
MEXICO
1
0.134
0.132
0.892
0.366
0.838
0.837
0.345
0.065
0.085
1. Period 1981-2001
Rotated Factor Loadings
2
3
0.799
0.329
0.204
0.041
-0.039
0.129
0.147
-0.494
0.291
0.154
0.042
0.174
0.146
0.925
0.668
-0.200
0.294
-0.387
2. Period 1994-2001
Rotated Factor Loadings
1
2
3
0.249
0.587
0.222
0.769
0.119
-0.195
0.596
0.065
0.461
0.822
0.147
0.243
0.678
-0.038
0.211
0.543
0.282
-0.154
0.064
0.190
0.963
-0.005
-0.192
0.157
0.065
0.974
0.153
4
0.041
-0.025
0.175
-0.188
-0.412
0.059
-0.048
-0.131
0.362
5
-0.299
-0.796
-0.046
0.436
-0.144
0.069
-0.043
0.131
-0.109
4
-0.219
-0.265
-0.411
0.115
0.356
-0.154
0.177
0.969
-0.152
20
3. Period 1994-2001. Sample includes Argentina and Brazil
Variable
Rotated Factor Loadings
1
2
3
4
USA
0.467
0.302
-0.395
0.298
CANADA
0.677
-0.164
-0.279
-0.347
FRANCE
0.385
0.421
-0.483
-0.472
GERMANY
0.704
0.335
0.022
-0.316
ITALY
0.559
0.303
0.308
-0.416
SPAIN
0.615
-0.087
-0.214
-0.132
UK
0.000
1.000
-0.002
0.000
CHILE
0.000
0.289
0.957
0.000
MEXICO
0.483
0.309
-0.418
0.615
BRAZIL
-0.143
0.549
0.037
-0.176
ARGENTINA
0.042
0.585
0.151
0.734
When we included Argentina and Brazil in the shorter time period, there are important
adjustments in the factor loadings, though not in the number of factors. As in the previous
case, we have factors that seem to capture idiosyncratic shocks to the UK and to Chile, the
second and third. The first factor again seems to capture shocks but now in a more general
way across the industrialized countries in our sample, with high coefficients for all of them
with the exception of the UK. In addition, the factor loading of Mexico is now quite
substantial for this first factor, suggesting that, compared with Argentina, Brazil, Chile and
the UK, the Mexican economy is more sensitive to these general developments. The fourth
factor seems to capture now the effects of the Tequila crisis of 1994-1995. At the time, both
Argentina and Mexico suffered from a significant albeit temporary reduction in production.
The same type of factor analysis was done using the annual growth rates of industrial
production. The estimations were done for the whole period, 1987-2001, and for 19972001, with and without the inclusion of Argentina in the sample (Table 3.6).28 Using the
sample for the whole period we find that 4 factors seem to be sufficient. The first factor
seems to capture fluctuations in continental Europe with high factor loadings for France,
Germany, Italy and Spain. The second factor is an Anglo-Saxon one, with high loadings for
Canada, the US and the UK. Finally, the third and fourth factors have high loadings for
Chile and Mexico, respectively.
In the shorter period, we reject the test for a higher number of factors only when we have 5
factors. The first factor is again a continental European one, with high loadings for France,
Germany, Italy and Spain. In addition, Chile has a high loading with this factor. The second
factor is again an Anglo-Saxon one, with high loadings for Canada, the US and the UK, and
Mexico now has a high loading with this factor. The third and fifth factors seem to capture
additional shocks to some European countries, in the first case, and to the US, Spain and
Mexico. Finally, the fourth factor seems to capture idiosyncratic Brazilian shocks. Thus, it
seems that in the more recent period, Chile has become more sensitive to factors affecting
the European Union, while Mexico is more sensitive to those affecting the US. Neither of
28
The results for the sample 1994-2001 are similar to those found for GDP growth rates. Thus, we emphasize
the results for the shorter time period, as we could not calculate these for GDP.
21
them has now a factor associated exclusively with themselves, as was the case for the
whole sample.
Finally, once Argentina is included in the sample we don’t have a change in the number of
factors, but the loadings change in an important way. The first factor seems to capture
fluctuations in Argentina, which are also affecting Chile, Mexico and Spain. The second
factor is again that driven by Canada and the USA, with a high loading for Mexico, and
smaller ones for Spain and the UK. The third factor is the European one, with high loading
for the continental European countries as well as Chile, as was found in the sample without
Argentina. The fourth factor seems to be capturing shocks to Brazil, while the fifth one has
little importance.
Table 3.6
Results from factor analysis of annual Industrial Production growth rates
Factor loadings correspond to varimax rotation
1. Period 1987-2001
Variable
1
0.199
0.121
0.863
0.910
0.680
0.797
0.266
0.023
-0.027
0.221
USA
CANADA
FRANCE
GERMANY
ITALY
SPAIN
UK
BRAZIL
CHILE
MEXICO
Rotated Factor Loadings
2
3
0.872
0.024
0.990
-0.065
-0.080
-0.118
0.234
-0.005
0.320
0.236
0.334
-0.026
0.634
0.103
0.348
0.152
-0.031
0.998
0.042
-0.100
4
0.097
-0.031
0.159
0.105
-0.311
-0.029
-0.178
0.127
-0.042
0.663
2. Period 1997-2001
Variable
USA
CANADA
FRANCE
GERMANY
ITALY
SPAIN
UK
BRAZIL
CHILE
MEXICO
1
0.105
0.092
0.503
0.978
0.711
0.546
0.326
0.047
0.657
0.320
Rotated Factor Loadings
2
3
4
0.679
0.298
0.021
0.985
-0.011
-0.006
0.151
0.487
0.408
0.119
-0.007
-0.031
0.139
0.362
0.288
0.273
-0.042
0.009
0.414
0.079
0.402
-0.012
0.060
0.942
-0.006
0.245
0.430
0.442
-0.072
0.169
5
0.606
0.142
0.001
0.169
0.122
0.625
0.055
0.052
0.375
0.659
22
3. Period 1997-2001. Sample includes Argentina
Variable
USA
CANADA
FRANCE
GERMANY
ITALY
SPAIN
UK
BRAZIL
CHILE
MEXICO
ARGENTINA
1
0.259
-0.092
0.106
0.393
0.332
0.565
0.101
0.304
0.698
0.498
0.895
Rotated Factor Loadings
2
3
4
0.946
0.078
0.042
0.850
0.148
0.074
0.184
0.529
0.565
0.138
0.902
-0.042
0.169
0.670
0.338
0.484
0.406
-0.095
0.369
0.301
0.515
-0.040
-0.065
0.793
0.119
0.481
0.377
0.631
0.193
0.079
0.152
0.325
0.209
5
-0.176
0.190
-0.260
0.104
-0.220
0.125
0.224
0.019
-0.176
0.185
0.007
Thus, the results from this shorter sample show that the factor that seems to relate Mexico
and Argentina has a lower loading for the first country than what was found in the case of
GDP for the sample 1994-2001. In addition, these results confirm the stronger sensitivity of
Mexico and the US to a common factor. A similar phenomenon seems to occur in the
Chilean case with respect to European countries.
3.2. The case of Ireland, Portugal and Spain
A natural comparison point of our results is the experience observed by relatively small
open economies that joined the European Union. Thus, in this section we review the results
obtained by other authors using similar methodologies as ours to analyze synchronization
of the business cycles of Portugal, Spain and Ireland with the rest of the European Union.
The first thing to note is that there are different timings for the case of each country, and
that the integration process has been gradually increasing in the EU leading to a degree of
integration that is stronger within the EU than in NAFTA, as it has led not only to free trade
but also to labor migration agreements and a common monetary policy for euro members.
Another point to note is that the difference in levels of development between these three
countries and the rest of the EU was smaller than that between Mexico and its NAFTA
partners.
Ireland applied for membership of the EU in May 1967, joined the Union in 1973 and
entered the EMS in 1979. The cases of Portugal and Spain were much later, applying
formally for membership to the EU in 1977 (March and July respectively) and joining the
Union the first of January of 1986.29 The Spanish peseta entered the EMS exchange rate
mechanism in June 1989, while the Escudo did so until April 1992. The single European
market was established in January of 1993, the second stage of EMU accession started in
1994, and the three countries were ratified in May 1998 to adopt the euro in January 1999.
29
It should be noted that Portugal had a free trade agreement with the EC that was in effect since 1973.
23
Artis and Zhang (1995, 1997) constitutes seminal work about changes in the degree of
macroeconomic synchronization in European countries due to closer integration.30
However, their focus was not on integration increasing due to trade but rather to monetary
and exchange rate policies, particularly the establishment of the European Monetary
System (EMS) and within it the ERM (Exchange Rate Mechanism), occurring in 1979.
Thus, they compare the correlation of business cycle measures of industrial production in
several European countries with those in Germany and the US in the pre-ERM period
(1961:1 –1979:3) and in the ERM period (1979:4 – 1993:12).
These authors find that there is a very sharp increase in the correlation of Portuguese and
Spanish business cycles with those of Germany, while this increase in not observed with
respect to US fluctuations. In addition, whilst in the pre-ERM period they lagged the
German business cycle more than that in the US, in the ERM period this is reversed.
However, as mentioned, Portugal and Spain don’t join the ERM until 1992 and 1989,
respectively. Thus, it is difficult to conclude that the higher correlation is actually due to
common monetary or exchange rate policies. This is something the authors also caution
about. It seems more likely that the increase in the correlation in the case of these countries
is due to their earlier entry into the European Union in 1986. Comparing their results with
the ones found here for industrial production, we find that the initial level of correlation
rates was actually higher for Portugal and Spain, but the increase in the correlation between
Mexico and the US in the post-NAFTA period is higher than for these countries in the postERM period with Germany.
An interesting case that puts some doubt that ERM was the cause of the higher
synchronization compared with greater trade flows is that of Ireland. As mentioned, this
country joined ERM in 1979, and thus we would expect higher increase in the degree of
synchronization than for Portugal and Spain if it was mostly driven by the EMS and ERM.
In the pre-ERM period its correlation with Germany’s industrial production is similar to
those of Portugal and Spain, but we don’t observe an increase of this correlation in the postERM period as in the other two countries. The same occurs with the UK, which
participated in the ERM longer than Portugal in the time period used by the authors. An
important, if not the main, difference between these four countries is the degree of
integration with the US economy through trade links.31 Thus, their evidence may be
interpreted as supporting the positive effect of greater international trade instead of
common monetary and exchange rate policies.32
Several other studies have followed the research by Artis and Zhang using other variables,
longer time periods, and different methodologies. We focus on those that put particular
attention to the Irish, Portuguese and Spanish experiences.
30
Frankel and Rose’s (1998) work focuses on the effect of free trade on business cycle synchronization but
they don’t analyze in special detail the case of particular European economies and, specially, the change in
synchronization over time.
31
Financial links are also important.
32
It also suggests that trade agreements are one among several determinants of trade. Ireland and the UK were
members of the EU since 1973, yet still maintain a significantly diversified trade structure between the EU
continental economies and the US.
24
Angeloni and Dedola (1999) look at the correlation, between GDP and industrial
production of these countries and that of the EU, with a longer time period and separating
their sample in four (pre ERM, soft ERM, hard ERM, pre EMU; the total time period
covered is 1965-1997). Using this finer sample partitioning and a longer post ERM period,
they find that correlations for both variables were higher for Portugal and Spain in since the
hard ERM period compared with before, though again the increase is smaller than that we
found for Mexico. There seems to be no such increase in correlations for Ireland (as for the
UK). They also find that the increase in the correlation of output was more gradual than
that of industrial production, also suggesting that part of the increase in correlation is driven
by cycles in tradable goods and not only common policies.
Belo (2001) calculates correlations, concordance and Spearman’s rank correlations of
industrial production for several countries and the Euro zone in the period 1960-1999,
splitting the sample in two in 1979, coinciding with ERM. The results using the three
measures of business cycle association are similar to those found in the other studies,
though he finds an initially lower association in the case of Ireland, and thus an increase
over time, albeit the smallest in the sample.33
Finally, Boone(1997) uses a VAR system to identify demand and supply shocks for the
countries in the European Union (and some other countries as controls), using a
methodology similar to that used by Bayoumi and Eichengreen (1996). Then he analyzes
the degree of correlation between demand and supply shocks of each country with
Germany. In the case of supply shocks, he finds it is fairly constant for Ireland and Spain in
the period 1974-1990, and increases in 1991-1994. In the case of Portugal, it is already
quite low in the period 1980-1990 but then increases additionally in 1991-1994. As for
demand shocks, the correlation increases substantially from 1974-1979 to 1980-1990, but
then falls in 1991-1994. For Ireland and Portugal, the correlation falls strongly from 19741979 to 1980-1990, and remains constant in 1991-1994.
As in the case of previous studies, this evidence seems more consistent with gradually
increasing trade integration than with common policies. The increase in correlation of
supply shocks should actually be driven by trade integration, not common demand
management policies. In terms of demand shocks, the fact that correlation does not increase
additionally after Portugal and Spain join the ERM is likely to be due to the ERM crisis of
1992-1993.
Thus, comparing these results with those found for the Mexican case it seems safe to
conclude that macroeconomic synchronization seems to increase through trade integration,
first through tradables and latter on non-tradables that are procyclical. The initial
correlations of Portugal and Spain with the EU were larger than those of Mexico with its
NAFTA partners, though the increase in these is larger in the latter case. Thus, in the
context on the discussions around the effect of trade integration on macroeconomic
synchronization, if higher trade does not lead to larger specialization in production, and
intra-industry trade increases, then the increase in synchronization may be much larger for
dissimilar countries than for those with more similar levels of development.
33
Borodo, González and Rodríguez (1998) find similar results looking at five year moving correlations.
25
3.3. Summary
The analysis of correlations between GDP growth rates shows that there was a large
increase in the post-NAFTA period in the correlation of GDP in Mexico with those of
Canada and the US, an increase not seen for any other country. In the case of industrial
production correlations, the increase in the correlation in the more recent time period is
observed for other countries, particularly in Latin America. This suggests that globalization
has led to higher correlation of a more general type in the production of tradable goods,
while the more intensive integration due to NAFTA has led to synchronization in other
sectors of economic activity. Moving 3 year correlations suggest that the higher correlation
of GDP in the Mexican case is mostly due to the recent deceleration in the NAFTA
countries.
The regression analysis points in the same direction. The coefficient on US growth
interacted with a post-NAFTA dummy is much larger for Mexico than for other countries
and its typically the only dummy that is significant. Finally, the factor analysis shows that,
for the whole sample period, Mexico typically had an idiosyncratic factor. In the later, post
NAFTA period, the highest factor loadings for Mexico correspond to those factors that
have a high loading for the US. This change in the structure of factor loadings with respect
to the US is not seen for any other country.
This increase in the sensitivity of Mexican variables to the evolution of those in its NAFTA
partners is similar to that observed for Portugal and Spain since the 1980’s. However, in the
case of the European countries, different authors have typically associated the increase in
synchronization with common policies and, in particular, the establishment of the EMS, not
so much with trade. This interpretation is dubious, as the synchronization of the Irish
economy with the EU has not increased to the same degree, even though Ireland was a
member of the EU and participated in the ERM before the Iberian countries. Probably the
main difference is due to the fact that Ireland has kept very close trade links with the UK
and the US, suggesting that trade is really the cause of the higher correlation of the Iberian
countries. More work needs to be done in the European case to choose between these
alternative interpretations.
4. Integration by Sectors and Regions
In this section we explore higher synchronization between Canada, Mexico and the USA at
the subnational, aggregate demand and sectorial levels using similar methods as those
employed in the previous section. Subsection 4.1 presents the results for the sectorial
analysis, subsection 4.2 those on aggregate demand and subsection 4.3 has the results of the
regional analysis.
26
4.1. Sectors of Economic Activity
In this section we analyze the relationship between growth rates of different sectors of
economic activity in Canada, Mexico and the USA. This allows us to see if the relationship
between Mexico and the other two countries changed due to NAFTA, and also if the
Mexico-USA relationship is becoming similar to the one between Canada and the USA. In
addition, we can compare across sectors of economic activity. Our working hypothesis is
that the increase in the degree of synchronization of tradable goods sectors that benefited
from the trade agreement should have been larger than that of other sectors.
The data employed in this section corresponds to:
i)
quarterly GDP measures by 1-digit sector of economic activity for Canada and
Mexico, while the data for the USA is national gross income, also by 1-digit sector
of economic activity.
ii)
monthly industrial production by 2-digit sector of economic activity for Mexico and
the USA. Unfortunately, Statistics Canada has not been producing monthly
industrial production data for some time.
The sources for the data are: INEGI in the Mexican case, CANSIM for Canada, and the
BEA for the USA. The sample period used for the quarterly data is 1987Q1 – 2001Q2,
while that for the monthly data is 1980M1 – 2001 M11. Both sample periods are
determined by the availability of data.
Analysis of correlations between Canada, Mexico and the USA
Table 4.1 shows the correlation coefficient between growth in a sector of economic activity
in the US with either Mexico or Canada. When using the whole sample, in those cases
when the correlation between a US and Canadian sector is high, it is larger than that
between the US and Mexican sectors. In general, these last correlations of US sectors with
Mexican sectors are low during 1988-2001. However, after NAFTA, and specially after the
balance of payments crisis that coincided with the start of the trade agreement, the
correlation between Mexican and US sectors increased significantly for several areas,
reaching levels similar to those observed for Canada. In particular, the correlation increased
quite noticeably for manufacturing, transport and communications, and general services.
27
Table 4.1
Correlation coefficients between Canada, Mexico and the USA
in growth rates of a given sector of economic activity
1988-2001
Canada Mexico
Agriculture
Mining
Manufactures
Construction
Transport and
Communications
Electricity, Gas and Water
Financial Services
Social, Communal and
Personal Services
USA correlation with:
1994-2001
1997-2001
Canada Mexico Canada Mexico
-0.079
0.589
0.657
0.604
-0.197
0.392
0.112
0.031
-0.045
0.645
0.779
0.125
-0.090
0.451
0.169
0.489
0.024
0.753
0.890
-0.542
-0.093
0.489
0.867
0.137
-0.031
0.240
0.296
0.399
0.150
0.619
0.241
-0.155
0.024
-0.189
0.575
-0.120
0.184
-0.118
0.705
0.332
0.387
0.186
0.322
-0.056
0.513
0.423
0.145
0.635
Graph 4.1 shows three year moving correlations between the growth rate of the same sector
of activity in Canada, Mexico and the USA. Thus, an observation labeled 1996.4 would be
the correlation in growth rates for the period 1994Q1 – 1996Q4. We discuss the results in
detail for each sector of economic activity given that the results differ quite substantially
across them.
In agriculture, there is a positive correlation during most of the period between Canada and
Mexico, while the correlation between each of these two countries and the USA fluctuates
significantly in a similar way for both countries, and is negative for most of the sample
period. A possible explanation for this phenomenon is that both Canada and Mexico are
marginal suppliers of agricultural products to the USA. When agricultural production is low
in the USA due to climatic conditions, then it imports from Canada and Mexico, while
good crops in the USA lead to a smaller demand from outside. This translates into higher
prices of crops during this period, though probably the contemporary response of
production is limited given the lags with which agricultural production operates. However,
if some producers interpret the increase in prices as a partly permanent phenomenon they
will increase their production for the next season.
For the mining sector, we find that the correlation between Mexico and the USA has always
been relatively large and positive. The correlations of both of these countries with Canada
has fluctuated in a similar way, around zero for the first part of the sample but becoming
high and positive slightly after NAFTA. This is the only sector of economic activity where
the correlation between Mexico and the USA is persistently higher than that between
Canada and the USA (presumably because of earlier and higher integration) or Canada and
Mexico (as marginal suppliers to the USA). A possible explanation for this finding is that
oil production accounts for a very substantial part of mining production in both Mexico and
the USA, and a smaller one for Canada.
In the case of manufacturing, the correlation between Canada and the USA has always been
positive and quite high, while that of Mexico with the other two countries has shown large
28
fluctuations around zero. In the more recent period, the correlations between Mexico and
the other two countries have become high and positive, as would be expected due to
NAFTA, though this phenomenon has already been observed previously and proved to be
temporary. A similar phenomenon is observed in the sector of Transport, Storage and
Communications, as would be expected from higher integration.
Graph 4.1
Three year moving correlations between growth rates of a given sector of activity.
Canada, Mexico and USA
ii) Mining
1
0.8
0.8
0.6
0.6
MEX-CAN
MEX-USA
MEX-CAN
USA-CAN
iii) Manufacturing
MEX-USA
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
USA-CAN
iv) Construction
1
1
0.8
0.6
0.6
-0.2
MEX-CAN
MEX-USA
USA-CAN
MEX-CAN
MEX-USA
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
1996.1
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
1996.1
1995.2
-1
1994.3
-1
1993.4
-0.8
1993.1
-0.8
1992.2
-0.6
1991.3
-0.6
1995.2
-0.4
1994.3
-0.4
0
1993.4
-0.2
0.2
1993.1
0
0.4
1992.2
0.2
1991.3
0.4
1990.4
3-year Moving Correlations
0.8
1990.4
3-year Moving Correlations
1996.4
1990.4
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
1996.1
1995.2
1994.3
1993.4
1993.1
-1
1992.2
-0.8
-1
1991.3
-0.6
-0.8
1996.1
-0.4
-0.6
1995.2
-0.4
-0.2
1994.3
-0.2
0
1993.4
0
0.2
1993.1
0.2
0.4
1992.2
0.4
1991.3
3-year Moving Correlations
1
1990.4
3-year Moving Correlations
i) Agriculture
USA-CAN
29
v) Transport and Communications
vi) Electricity, Gas and Water
1
1
0.8
0.8
0.6
-0.2
USA-CAN
MEX-CAN
0.8
0.8
0.6
0.6
MEX-USA
USA-CAN
MEX-CAN
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
MEX-USA
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
1996.1
-1
1995.2
-1
1994.3
-0.8
1993.4
-0.8
1993.1
-0.6
1992.2
-0.6
1996.1
-0.4
1995.2
-0.4
-0.2
1994.3
-0.2
0
1993.4
0
0.2
1993.1
0.2
1991.3
USA-CAN
0.4
1992.2
0.4
1991.3
3-year Moving Correlations
1
MEX-CAN
MEX-USA
viii) Social, Communal and Personal
Services
1
1990.4
3-year Moving Correlations
vii) Financial Services
1996.1
1990.4
2001.2
2000.3
1999.4
1999.1
1998.2
1997.3
1996.4
1996.1
1995.2
MEX-USA
1990.4
MEX-CAN
1994.3
1993.4
1993.1
-1
1992.2
-0.8
-1
1991.3
-0.6
-0.8
1995.2
-0.4
-0.6
1994.3
-0.4
0
1993.4
-0.2
0.2
1993.1
0
0.4
1992.2
0.2
1991.3
3-year Moving Correlations
0.4
1990.4
3-year Moving Correlations
0.6
USA-CAN
In the remaining sectors of economic activity (Construction; Electricity, Gas and Water;
Financial Services; and Social Communal and Personal Services), there have also been
increases in the correlation of growth rates across the three countries in recent times, but
they have typically been smaller than those observed in manufacturing and transport and
communications between Mexico and the USA.
Thus, the evidence from GDP sectorial components suggests that there was a generalized
increase in the correlation between Mexican and US sectors, in contrast with the Canadian
correlations that fall in several cases during the latter part of the sample. A possible
explanation is that the recovery in Mexico after the balance of payments crisis has been
driven mostly by exports, so all procyclical sectors have been closely linked with this
30
sector. On the other hand, the evolution of Canadian sectors is probably driven by a mix of
domestic and export demands.
Table 4.2 shows the correlation between Mexico’s and the US’ industrial production
growth and its components for the whole sample period 1981-2001 and subperiods 19942001 and 1997-2001. There is a significant increase in the correlation of industrial
production driven mostly by that in manufacturing. In terms of manufacturing subsectors,
the increase in the correlation is particularly large for paper and editorials, chemical
products, mineral based products, and machinery. This last sector, together with metals
already had an important correlation for the whole sample.
Table 4.2
Correlation coefficients between Mexico and the USA in growth rates
of a given sector of industrial production
Correlation between Mexico and EUA
1981-2001 1994-2001
Total
Mining
Electricity, Gas and Water
Manufacturing
Food and Beverages
Textiles
Wood industries
Paper and Editorials
Chemical Products
Minerals
Basic Metals
Machinery
Other Manufacturing Industries
0.316
0.366
-0.141
0.284
0.014
-0.039
0.020
0.083
0.098
0.071
0.561
0.396
0.166
0.519
0.368
-0.179
0.619
0.100
0.371
0.316
0.511
0.572
0.499
0.520
0.501
0.199
1997-2001
0.968
0.432
0.054
0.970
0.328
0.790
0.344
0.748
0.691
0.636
0.766
0.832
0.504
Graph 4.2 shows three year moving correlations between the growth rate of the same subsector of industrial production in Mexico and the USA. In the case of total industrial
production, the correlation during the first part of the sample fluctuates in a similar way as
that observed in the case of manufacturing production in Graph 4.1. However, in the case of
the monthly industrial production series the correlation increases in a very steady way since
1994-1996, and has remained high during the end of the period. This is essentially the same
result for the correlation of manufacturing using monthly frequencies. The results for
mining and utilities (electricity, gas and water) are essentially the same as those found
when using the data on a quarterly frequency.
In terms of the components of manufacturing, the correlation of textiles, paper and
editorials, chemical products, and non-metallic mineral products shows a clear positive
trend. Basic metals and machinery show a declining trend during the first half of the sample
which is reverted in the mid-nineties. Finally, the correlation of food, drink and tobacco and
that of other manufacturing industries are quite volatile, without clear trends, but both show
higher correlations than ever before in the later part of the sample period.
31
0.4
0.2
0
-0.2
-0.4
3-year Moving Correlations
0
-0.2
-0.4
3-year Moving Correlations
0.2
-0.6
-0.8
-1
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
A-01
M-00
F-99
J-98
D-96
N-95
O-94
S-93
A-92
J-91
J-90
M-89
A-88
M-87
F-86
J-85
D-83
3-year Moving Correlations
0.4
-0.6
iii) Electricity, Gas and Water
1
0.8
0.6
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
3-year Moving Correlations
Graph 4.2
Three year moving correlations between growth rates of components of industrial production
Mexico and USA
i) Total Industrial Production
1
ii) Mining
1
0.8
0.8
0.6
0.6
0.4
0.2
0
-0.2
-0.4
-0.8
-0.6
-1
-0.8
-1
iv) Manufacturing
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
32
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0
-0.2
-0.4
3-year Moving Correlations
1
0.8
0.8
0.6
0.6
0.4
0.4
0
-0.2
-0.4
3-year Moving Correlations
0.2
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
3-year Moving Correlations
1
-0.6
-0.6
-0.8
-0.8
-1
-1
vii) Manufacturing: Wood Industries
-0.6
-0.6
-0.8
-0.8
-1
-1
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
3-year Moving Correlations
v) Manufacturing: Food, Drinks and Tobacco
vi) Manufacturing: Textiles
0.2
0
-0.2
-0.4
viii) Manufacturing: Paper and Editorials
0.2
0
-0.2
-0.4
33
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0
-0.2
-0.4
3-year Moving Correlations
1
0.8
0.8
0.6
0.6
0.4
0.4
0
-0.2
-0.4
3-year Moving Correlations
0.2
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
3-year Moving Correlations
1
-0.6
-0.6
-0.8
-0.8
-1
-1
xi) Manufacturing: Basic Metals
-0.6
-0.6
-0.8
-0.8
-1
-1
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
3-year Moving Correlations
ix) Manufacturing: Chemical Products
x) Manufacturing: Minerals
0.2
0
-0.2
-0.4
xii) Manufacturing: Machinery
0.2
0
-0.2
-0.4
34
xiii) Manufacturing: Other Industries
1
0.8
3-year Moving Correlations
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
D-83
N-84
O-85
S-86
A-87
J-88
J-89
M-90
A-91
M-92
F-93
J-94
D-94
N-95
O-96
S-97
A-98
J-99
J-00
M-01
-1
The high volatility on the three year moving correlations for manufacturing subcomponents
suggests that in several cases the correlation between manufacturing in Mexico and the US
is quite high in the absence of strong idiosyncratic shocks. It is possible that the more
persistently high positive correlations observed in the more recent period is due precisely to
the lack of strong idiosyncratic shocks related with balance of payments crisis.
Simple regression analysis of sectorial synchronization of Canada and Mexico with the
USA
Simple regressions of the type of (1) were done for growth of the different GDP sectorial
components for Canada and Mexico. The sample covers 1990-2001. We compare the
sensitivity of the different sectors in Canada and Mexico, the different sectors between
themselves (as we would expect stronger sensitivities in sectors where tradables are
produced), and over time. They were also done for the components of industrial production,
but only for Mexico given that Canada does not have these series with monthly frequency.
Thus, in this last case we look at differences across Mexican subsectors of industrial
production, as well as changes over time.
Table 4.3 shows the results for the quarterly series on annual growth of different 1 digit
sectors of GDP when excluding the lagged value of the dependent variable. As in the case
of the regressions on national aggregates, the results in the first set of rows correspond to a
regression with only a constant and the contemporary growth rate of the same sector in the
USA. The second and third set of rows include 1994 and 1997 dummys. The fourth to sixth
rows have the results when including the lagged rate of growth of the same sector. The
results are grouped in columns by sector of economic activity, e.g. we report the results for
agriculture in Canada and Mexico contiguously.
According to the results when using only the contemporary growth rate in the USA, and
without dummys, the point estimates are larger for Canada in every sector except transport
35
and communications. In the Canadian case we find a positive and significant coefficient for
manufacturing (the largest by far); social, personal and communal services; mining; and
utilities (electricity, gas and water). Agriculture also has a high point estimate, but it is not
significant. The only two sectors for which these specifications explain a substantial
proportion of volatility are manufacturing (0.43) and mining (0.35). In the Mexican case,
the point estimate associated with US sectorial growth is significant only for transport and
communications and mining. The point estimate in the manufacturing regression is higher
than that of mining but is not significant. The R2s are quite low for all cases, with the
highest being 0.15 for mining.
The above results clearly suggest that for the period as a whole the relationship between
Canada and the USA was stronger than that between Mexico and the USA. Point estimates
and the proportion of volatility explained by US growth are larger for Canada. A puzzling
point is that the R2s and many of the estimated coefficients are lower than those found in
GDP regressions. A possible explanation is that growth in a given sector is more closely
related to overall growth in the USA than with growth in the same sector. In addition, it is
possible that specific sectors are subject to higher volatility that is compensated at the GDP
level.
When we include the 1994 dummy, the point estimates associated with the dummy and
growth of the sector in the USA are always positive for Mexico and significantly larger
than those for Canada with the exception of utilities. In terms of magnitude of the
coefficients, the largest changes in the Mexican case are for construction, transport and
communications, and social, communal and personal services.34 In the Canadian case there
are large increases in the point estimate for utilities, transport and communications and
services, but none of them are significant. The only significant coefficient for Canada is a
fall in the point estimate for construction.
When including the 1997 dummy, we find again that the implied increase in the point
estimate is either larger for Mexico, or similar for both countries. There isn’t any case when
the increase for Canada is substantially larger. As for 1994, the estimate on the interactive
dummy is always positive. It is now significant in the Mexican case for manufacturing and
utilities. The only significant positive case for Canada is financial services, while the
coefficient on construction is negative and significant. The results are very similar to those
found in the regressions using the dummy for 1994 onwards.
The results when including the lagged value of the dependent variable are very similar
(Table 4.4). The only substantial differences we find are that the interactive dummy for
manufacturing in Mexico is now significant for both 1994 and 1997, and those for
construction and mining are significant for 1997.
Summarizing, we observe a generalized increase in point estimates in the Mexican case,
though few of them are significant. As in the case of GDP, this is probably due to the fact
that idiosyncratic shocks to Mexican GDP were still important during the first half of the
34
The large change in the constant for construction and services suggest that the change in coefficients might
be contaminated by the 1994 crisis.
36
period since the implementation of NAFTA, and that we have few observations due to the
quarterly frequency of this data. In addition, we have not seen a complete cycle in the USA
since the implementation of NAFTA.
Our results also suggest an interesting issue concerning the analysis of synchronization of
business cycles at the sectorial level. If a country’s business cycle becomes more
synchronized with that of another country due to higher trade, then one might expect that,
at the sectorial level, we would observe a larger increase in synchronization of tradable than
of non-tradable goods. The results suggest that this might not always be the case. We find
more synchronization for manufacturing, but also for general services and construction. A
possible explanation is that these two non-tradable sectors are more closely related to the
business cycle of the economy and to manufacturing production than other tradable goods
sectors, such as agriculture, that are subject to higher idiosyncratic shocks. Thus, if the
business cycle between Mexico and the US is becoming more synchronized due to trade in
manufacturing and its driven by exports, the links of this sector with construction and
services imply that we also see higher synchronization in these non-tradable sectors.35
Table 4.5 shows the results of the same type of regression using the annual growth rate of
different components of industrial production for the period 1981-2001, excluding the
lagged value of the dependent variable. With respect to the analysis on more general sectors
of activity, we have a slightly shorter period but a higher frequency of data. Unfortunately,
as mentioned, Canada does not have these data with a monthly frequency, so more than
focusing on differences between the Canadian and Mexican results we look at the changes
over time. The previous results already suggest that changes were much more important for
Mexico than for Canada.
In the case of components of industrial production, we find a large positive and significant
point estimate for the whole period in the case of total industrial production, mining,
manufacturing and the following subsectors of manufacturing: basic metals, machinery and
“other manufacturing industries”. The R2s of basic metals and machinery are much larger
than any others. When including the time dummys, without the lagged value of the
dependent variable, we find positive and significant coefficients for total industrial
production, manufacturing and the following subcomponents: textiles, wood industries,
paper and editorials, chemical products, mineral based manufacturing, and basic metals.
For the other three subcomponents of manufacturing (food and beverages; machinery; and
“other manufacturing industries”) we find an increase but its not significant. The results are
essentially the same when we include one lag of the endogenous variable, as can be seen in
Table 4.6.36
35
This result is also consistent with factor price equalization across Mexican sectors.
The results are also very similar when including more lags of both variables. Tables reporting these results
are available from the authors.
36
37
Factor analysis
Factor analysis for the production components of GDP was done grouping Canada or
Mexico with the US. Including Canada and Mexico in the same sample limited our degrees
of freedom significantly given the larger number of production sectors. In addition, the
analysis was done using two time samples, the first is 1988-2001 and the second is 19972001.
The results are shown in Table 4.7, with those for the complete sample first. In the case of
Mexico and the US, we find that for the 9 factors included there is virtually no case when
we have important loadings for both Mexican and US sectors, with the exception of mining
in the third factor. The first and sixth factors seem to reflect general business cycle shocks
affecting several sectors in both the US and Mexico, respectively. In both cases, the sectors
include manufacturing, construction and transport and communications. For the US,
agriculture also has a high loading, while in the Mexican case it is mining and general
services. The eight factor seems to be capturing shocks to financial services in Mexico.
These seem related with construction and services in Mexico, as would be expected given
the non-tradable characteristics of these sectors, plus transport and communications in both
Mexico and the US. The other factors seem to be capturing more specific sectorial shocks.
In the same time period, the factor analysis with the Canadian and US sample needs at least
10 factors. As in the Mexican case, there doesn’t seem to be very strong evidence of
significant cross country sectorial integration, as most of the factors have high loadings
only for one sector in one country. As in the Mexican case, mining is an exception (factor
9), but also manufacturing (factor 2).
Table 4.7
Results from factor analysis of growth rates of components of GDP
Factor loadings correspond to varimax rotation
1. Mexico-USA. Sample 1988-2001
Country
Agriculture
Mining
Manufacturing
Construction
Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
1
-0.201
0.016
0.088
-0.001
-0.030
0.339
0.000
-0.095
2
0.140
-0.253
-0.060
-0.153
0.109
0.086
0.014
0.045
3
-0.017
0.399
-0.053
0.038
0.070
0.024
-0.116
-0.104
Agriculture
Mining
Manufacturing
United Construction
States Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
0.618
0.134
0.956
0.262
0.357
0.012
0.182
0.196
0.021
0.021
0.029
-0.139
0.469
0.985
-0.070
-0.014
0.227
0.960
0.092
-0.037
0.105
-0.001
0.086
0.153
Mexico
Sector
Rotated Factor Loadings
4
5
6
7
0.073 -0.027 0.117 0.165
0.148 -0.004 0.697 -0.093
-0.191 0.005 0.906 -0.174
-0.030 0.007 0.822 0.167
0.056 -0.369 0.797 -0.024
-0.279 -0.412 0.346 0.094
0.082 0.033 0.270 0.140
0.006 0.185 0.571 -0.137
-0.586
-0.203
-0.194
-0.074
0.066
0.001
-0.134
-0.962
-0.325
0.019
-0.161
-0.892
-0.148
0.105
0.028
-0.022
0.141
0.051
-0.024
0.066
0.069
-0.114
0.088
0.029
-0.307
-0.068
-0.104
0.022
-0.139
0.066
-0.896
-0.070
8
0.048
0.021
-0.027
-0.401
-0.346
-0.022
-0.889
-0.653
9
0.302
0.212
-0.059
-0.019
-0.123
-0.530
-0.002
0.025
0.059
0.105
0.018
0.096
-0.307
0.004
0.081
0.054
-0.024
-0.008
-0.049
-0.035
0.332
-0.023
0.025
-0.054
38
2. Canada-USA. Sample 1988-2001
Agriculture
Mining
Manufacturing
Construction
Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
1
0.159
0.006
0.844
0.544
0.843
-0.009
-0.018
0.907
2
0.129
0.159
0.400
0.068
0.143
0.073
0.122
0.020
3
0.188
-0.077
0.080
0.809
0.136
-0.187
0.162
0.098
Rotated Factor Loadings
4
5
6
7
-0.264 -0.135 0.001 -0.007
0.150 -0.018 -0.092 0.002
0.037 -0.066 0.067 -0.092
-0.015 0.176 -0.081 0.001
-0.121 0.207 0.112 0.040
-0.034 -0.202 -0.025 0.183
-0.167 -0.147 -0.630 -0.012
0.111 0.214 -0.321 0.010
8
-0.048
0.184
0.032
-0.068
-0.063
0.215
0.148
-0.064
9
-0.530
0.887
0.122
-0.016
0.049
0.328
0.295
-0.022
10
-0.092
0.017
0.095
0.039
0.105
-0.518
-0.018
0.044
Agriculture
Mining
Manufacturing
United Construction
States Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
0.440
-0.008
0.366
0.863
0.015
-0.102
0.023
0.124
0.437
0.071
0.863
0.116
0.131
0.000
0.078
0.105
0.162
0.290
0.042
0.187
-0.042
0.000
-0.008
0.070
0.292
0.070
0.084
-0.002
0.026
-0.080
0.973
0.075
0.153
0.015
0.161
0.135
0.949
0.218
0.020
-0.065
0.128
0.670
0.131
-0.200
0.144
0.002
0.142
0.043
-0.081
-0.293
-0.027
-0.270
-0.042
-0.025
0.004
0.070
Country
Canada
Sector
0.675
0.389
0.255
0.044
-0.007
-0.001
0.132
0.932
0.010
-0.063
-0.059
0.195
-0.042
0.002
0.042
0.045
-0.028
0.007
0.013
-0.188
0.234
0.967
-0.080
0.006
3. Mexico-USA. Sample 1997-2001
Country
Sector
Agriculture
Mining
Manufacturing
Construction
Mexico
Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
1
-0.127
0.721
0.984
0.924
0.654
0.369
0.102
0.844
2
0.098
0.166
0.092
-0.044
0.570
0.153
0.239
0.298
Agriculture
Mining
Manufacturing
United Construction
States Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
0.703
0.016
0.815
0.101
0.076
-0.464
0.670
0.333
0.635
0.301
0.277
0.251
0.926
0.481
0.163
0.869
Rotated Factor Loadings
3
4
5
6
-0.240 -0.196 -0.122 0.337
-0.080 0.388 0.077 0.342
0.085 -0.016 -0.049 -0.026
-0.094 -0.090 -0.087 0.054
0.075 0.223 0.083 0.064
-0.084 -0.113 -0.004 -0.087
0.964 0.001 0.011 -0.019
0.293 -0.017 0.028 -0.045
0.110
-0.155
0.078
-0.172
0.132
0.015
0.032
0.212
-0.085
0.931
-0.192
-0.922
-0.010
0.184
0.032
0.056
0.260
-0.039
0.238
-0.036
0.062
-0.019
0.456
-0.080
-0.117
0.040
-0.277
0.029
0.228
0.199
0.017
-0.262
7
0.374
-0.242
-0.062
0.179
0.394
0.826
-0.051
-0.025
0.034
-0.120
0.289
-0.089
0.064
0.594
-0.492
0.106
39
4. Canada-USA. Sample 1997-2001
Country
Sector
Agriculture
Mining
Manufacturing
Construction
Canada
Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
1
-0.829
0.832
-0.116
0.445
-0.328
-0.044
0.742
-0.153
2
0.241
0.059
0.090
0.182
0.724
0.767
-0.134
0.086
Agriculture
Mining
Manufacturing
United Construction
States Transport and Communications
Electricity, Gas and Water
Financial Services
Social and Personal Services
0.064
0.898
-0.075
-0.956
0.057
0.188
0.138
0.229
-0.237
-0.007
-0.108
0.001
0.212
0.819
-0.865
0.240
Rotated Factor Loadings
3
4
5
6
0.318 0.078 -0.219 -0.041
0.103 -0.365 0.077 0.131
0.919 0.034 -0.277 0.057
0.569 0.338 0.267 -0.208
0.295 -0.083 -0.458 -0.004
0.203 -0.415 0.120 0.070
0.476 -0.081 0.080 0.436
0.262 -0.043 -0.948 -0.009
0.653
-0.131
0.949
0.027
-0.080
-0.188
0.234
0.241
-0.703
-0.218
-0.183
-0.176
-0.961
-0.342
-0.165
-0.789
-0.082
0.223
-0.157
0.089
0.067
-0.007
0.084
-0.209
0.015
-0.110
0.027
0.095
-0.025
-0.158
-0.034
0.082
7
0.297
0.370
0.092
0.254
-0.037
0.015
0.036
0.001
8
0.028
-0.034
-0.178
0.085
-0.253
0.020
-0.010
0.007
-0.107
-0.176
-0.033
-0.120
0.029
-0.017
-0.089
-0.006
-0.002
-0.102
0.109
-0.150
0.090
0.134
0.026
-0.216
In the shorter sample period, the number of factors falls for both Mexico and Canada. In
addition, cross country sectorial integration seems more important. In the Mexican case, the
first factor seems to identify the shocks that drive the business cycle in this shorter sample.
It has high loadings with all sectors in Mexico37 and also with agriculture, manufacturing
and financial services in the US. Factor 2 seems to capture the service sector in the US,
with high loadings on transport and communications in both US and Mexico, as well as for
agriculture and general services in the US. Finally, factor 7 has high loadings on electricity,
gas and water, though this could be due to the international price of energy, not to a higher
link between Mexico and the US.
In the Canadian case, common high factor loadings are maintained for mining (factor 1)
and manufacturing (factor 3), and now we also observe one with utilities as in the MexicoUS case (factor 2). In addition, the loadings are larger than for the shorter period. The
changes in the loadings are smaller than in the Mexican case.
Factor analysis using the annual growth rates of industrial production in Mexico and the US
with monthly frequency was done for the samples 1981-2001 and 1997-2001 (Table 4.8).
For the larger sample we find that 2 factors, 1 and 5, have very high loadings on almost all
the components of manufacturing production in the US and Mexico, respectively, but we
don’t observe a single factor for which there are high loadings for sectors in both countries.
The rest of the factors appear associated with specific sectorial shocks, mostly in the US.
The change is quite dramatic in the latter sample. In that case, the first factor has high
loadings for all but one of the components of manufacturing in both Mexico and the US.
37
With the exception of agriculture, electricity, gas and water and financial services.
40
Factor 3 seems to capture sectorial shocks to wood products in both countries, while the
other are more specifically sectorial in either of the two countries.
Table 4.8
Results from factor analysis of growth rates of components of
industrial production in Mexico and the USA
Factor loadings correspond to varimax rotation
1. Sample 1981-2001
Country
Sector
Electricity, Gas and Water
Mining
Manufacturing
Manufacturing: Food and Drinks
Manufacturing: Wood Industries
Manufacturing: Basic Metals
Manufacturing: Machinery
Manufacturing: Minerals
Manufacturing: Other Industries
1
-0.126
-0.197
0.066
-0.169
0.077
0.326
0.066
0.118
0.053
2
0.050
-0.056
0.114
0.041
-0.019
0.077
0.114
-0.119
-0.069
3
0.091
0.127
0.035
0.030
0.146
0.147
-0.006
0.031
-0.063
Electricity, Gas and Water
Mining
Manufacturing
Manufacturing: Food and Drinks
United
Manufacturing: Wood Industries
States
Manufacturing: Basic Metals
Manufacturing: Machinery
Manufacturing: Minerals
Manufacturing: Other Industries
0.293
0.096
0.909
0.229
0.929
0.768
0.714
0.915
0.480
0.163
0.048
0.246
0.017
-0.135
-0.042
0.628
0.037
0.086
-0.056
0.075
0.163
0.970
0.099
0.113
0.048
0.054
0.083
Mexico
Rotated Factor Loadings
4
5
6
7
0.164 0.223 0.217 0.047
0.291 0.305 0.462 0.053
0.193 0.950 0.047 0.000
0.075 0.590 -0.038 -0.067
-0.029 0.587 0.141 -0.001
0.424 0.486 -0.006 0.279
0.192 0.933 -0.080 0.056
0.039 0.799 0.145 0.127
0.198 0.561 0.030 0.046
8
-0.345
-0.120
-0.030
-0.118
0.019
0.099
0.069
-0.135
0.044
9
0.126
-0.017
-0.012
0.378
0.241
0.059
-0.004
-0.003
0.109
0.307
0.122
0.164
0.034
-0.013
0.187
0.116
0.040
0.686
0.332
-0.009
-0.076
-0.009
0.031
0.042
0.018
0.098
0.001
-0.038
0.010
-0.021
0.006
-0.067
-0.066
0.011
0.091
-0.012
Rotated Factor Loadings
4
5
6
7
0.022 -0.020 -0.192 -0.040
0.155 0.001 0.231 -0.042
0.149 -0.037 0.107 0.126
0.159 0.019 -0.110 0.877
0.247 0.257 0.025 0.289
0.451 -0.182 0.389 0.217
0.218 0.133 0.157 0.167
0.727 -0.083 -0.042 0.250
0.339 0.008 0.089 0.417
8
-0.138
0.860
0.255
-0.009
0.280
0.214
0.216
0.201
-0.019
9
0.010
0.004
0.007
0.008
0.244
-0.002
0.171
0.058
0.519
0.037
0.223
0.196
0.460
0.076
0.125
0.090
0.033
-0.045
-0.020
0.047
-0.073
-0.364
-0.111
0.154
-0.016
-0.457
0.017
0.212
0.859
0.144
0.063
-0.229
0.345
0.090
0.081
0.243
0.024
0.332
0.164
0.029
-0.144
0.222
0.269
-0.011
0.111
-0.189
0.043
-0.068
0.018
0.175
-0.220
-0.021
-0.082
0.025
2. Sample 1997-2001
Country
Sector
Electricity, Gas and Water
Mining
Manufacturing
Manufacturing: Food and Drinks
Manufacturing: Wood Industries
Manufacturing: Basic Metals
Manufacturing: Machinery
Manufacturing: Minerals
Manufacturing: Other Industries
1
0.242
0.293
0.913
0.293
0.426
0.580
0.826
0.597
0.390
2
0.939
-0.168
0.044
-0.049
0.003
0.241
0.030
0.003
0.061
3
-0.002
0.010
0.148
0.011
0.271
-0.001
0.202
0.016
0.019
Electricity, Gas and Water
Mining
Manufacturing
Manufacturing: Food and Drinks
United
Manufacturing: Wood Industries
States
Manufacturing: Basic Metals
Manufacturing: Machinery
Manufacturing: Minerals
Manufacturing: Other Industries
0.124
-0.040
0.944
0.457
0.721
0.873
0.944
0.684
0.897
-0.009
-0.239
0.124
-0.131
0.269
0.159
0.049
0.099
0.140
0.069
0.002
0.100
0.014
0.180
-0.238
0.002
0.021
-0.150
Mexico
-0.152
0.005
0.160
-0.081
0.193
0.150
0.102
0.253
0.051
0.592
-0.057
-0.030
0.049
-0.419
-0.164
0.256
-0.045
0.040
-0.297
0.895
0.000
0.382
-0.316
0.122
-0.132
-0.225
-0.008
0.119
-0.107
0.106
0.284
0.211
0.228
0.001
0.198
0.288
41
Changes in lag structure
As mentioned in section 2, changes in the correlations of annual growth rates may be due to
two effects, a higher sensitivity of Mexican variables to developments in the US or a faster
transmission of shocks from the US to Mexico. In addition, for a complete analysis of the
effects of NAFTA on macroeconomic synchronization we need to evaluate its effect on the
speed of transmission of shocks from the US to Mexico.
The need to do the analysis employing a substantial number of lags limits us on the type of
data we can use for the evaluation of changes in the speed of transmission. Thus, we use the
rates of growth of manufacturing and its components given that they have a monthly
frequency. From previous results we know that the degree of synchronization of
manufacturing activities between Mexico and the US increased substantially in the period
after NAFTA and these are highly tradable goods, so the speed of transmission of shocks is
likely to represent an upper bound for the other sectors of activity that show an important
degree of synchronization with the US economy.
Table 4.9 shows correlations between annual growth rates of Mexican manufacturing
production and its components with total manufacturing growth in the US, including 6
leads and lags for the Mexican growth rates. Each column reports the correlation of a
Mexican growth rate at time t ± i with the US growth rate at time t. For the whole period,
1981-2001, we find that, even though contemporary correlations are high for several
sectors, the highest correlations occur with several lags.38 The highest correlation is the
contemporary only for clothes and textiles, while those for all the other sectors appear at
least at the third lag.
The results are very different in the samples 1994-2001 and 1997-2001. In 1994-2001,
Mexican sectors lead manufacturing production in the US by one or more periods, while
the correlations are always higher than those found for the whole sample. In the sample
1997-2001, there is a more even distribution of sectors leading and lagging manufacturing
production in the US, but the highest correlation for a sector is never higher than for three
leads or lags. In addition, the correlations are all higher than those found for 1994-2001.39
38
The changes in the correlation coefficient over different lags or leads are not very significant over time, so
the regression specifications that include only the contemporary growth rate in the US are not likely to be
strongly biased, though there could be some downward bias in our regressions for the whole period.
39
Torres and Vela (2002) do a similar analysis for the period 1992-2001 using quarterly data on GDP, GDP
production components, aggregate demand and supply and manufacturing production. However, they
generally do not compare their results with previous periods, so they don’t have changes over time in the
groups of correlations. Their results are very similar to those found in the period 1994-2001.
42
Table 4.9
Correlation between different leads and lags of the annual growth rate of manufacturing production
and its components in Mexico with total manufacturing production in the US
i. Period 1981-2001
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
t-6
t-5
t-4
t-3
t-2
t-1
0.408
0.118
0.153
0.234
0.255
0.290
0.518
0.415
0.215
0.251
0.399
0.101
0.158
0.239
0.261
0.303
0.558
0.412
0.241
0.242
0.392
0.077
0.155
0.219
0.251
0.271
0.558
0.393
0.248
0.211
0.377
0.064
0.167
0.195
0.223
0.277
0.557
0.376
0.254
0.201
0.354
0.015
0.154
0.162
0.179
0.249
0.542
0.343
0.246
0.184
t-6
t-5
t-4
t-3
t-2
t-1
0.344
0.150
0.282
0.265
0.292
0.380
0.262
0.231
0.059
0.007
0.419
0.181
0.331
0.297
0.350
0.426
0.323
0.308
0.138
0.043
0.481
0.215
0.385
0.334
0.387
0.474
0.361
0.364
0.224
0.066
0.534
0.234
0.455
0.369
0.432
0.523
0.401
0.432
0.299
0.119
0.584
0.241
0.503
0.373
0.459
0.554
0.440
0.477
0.361
0.162
t-6
t-5
t-4
t-3
t-2
t-1
0.823
0.387
0.700
0.667
0.564
0.568
0.459
0.758
0.520
0.285
0.866
0.398
0.752
0.659
0.608
0.604
0.533
0.803
0.594
0.308
0.910
0.401
0.790
0.664
0.656
0.648
0.593
0.855
0.648
0.382
0.932
0.430
0.797
0.673
0.682
0.683
0.625
0.866
0.674
0.420
0.947
0.409
0.827
0.656
0.698
0.694
0.659
0.889
0.719
0.433
0.964
0.390
0.841
0.580
0.725
0.702
0.686
0.890
0.742
0.436
0.324
-0.013
0.159
0.154
0.176
0.248
0.533
0.312
0.253
0.171
t
0.284
-0.041
0.174
0.165
0.148
0.230
0.517
0.277
0.241
0.142
t+1
0.227
-0.083
0.159
0.148
0.113
0.201
0.474
0.224
0.205
0.135
t+2
0.164
-0.141
0.134
0.108
0.095
0.167
0.416
0.164
0.176
0.111
t+3
0.090
-0.194
0.104
0.061
0.036
0.112
0.344
0.096
0.126
0.051
t+4
0.017
-0.228
0.072
0.017
-0.013
0.064
0.277
0.028
0.080
0.014
t+5
-0.061
-0.269
0.037
-0.036
-0.055
0.004
0.202
-0.045
0.035
-0.030
t+6
-0.143
-0.321
0.002
-0.066
-0.117
-0.058
0.131
-0.116
-0.020
-0.081
ii. Period 1994-2001
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
0.279
0.085
0.208
0.219
0.232
0.318
0.171
0.150
-0.038
-0.033
t
0.619
0.243
0.540
0.394
0.477
0.592
0.501
0.510
0.405
0.201
t+1
t+2
t+3
t+4
t+5
t+6
0.626
0.246
0.576
0.400
0.488
0.628
0.556
0.527
0.432
0.254
0.617
0.224
0.565
0.343
0.474
0.647
0.590
0.514
0.466
0.287
0.587
0.247
0.551
0.314
0.458
0.645
0.601
0.488
0.463
0.300
0.547
0.244
0.510
0.251
0.418
0.598
0.620
0.448
0.450
0.285
0.495
0.212
0.470
0.169
0.385
0.539
0.617
0.394
0.430
0.292
0.424
0.178
0.447
0.125
0.356
0.478
0.584
0.345
0.386
0.281
t+1
t+2
t+3
t+4
t+5
t+6
0.957
0.412
0.851
0.538
0.797
0.733
0.748
0.870
0.765
0.472
0.934
0.382
0.832
0.478
0.783
0.743
0.759
0.838
0.787
0.498
0.907
0.389
0.804
0.454
0.769
0.747
0.748
0.790
0.780
0.504
0.870
0.368
0.766
0.385
0.750
0.715
0.754
0.737
0.764
0.479
0.827
0.330
0.730
0.300
0.736
0.666
0.748
0.673
0.739
0.470
0.777
0.316
0.675
0.264
0.693
0.628
0.705
0.610
0.694
0.447
iii. Period 1997-2001
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
t
0.970
0.365
0.832
0.530
0.751
0.709
0.719
0.883
0.746
0.436
Part of the change in correlations during 1994-2001 could be due to the fact that the
correlation of the different sectors in the US with respect to total manufacturing output in
that country has changed in itself, and not to the fact that the sectors have become more
integrated between Mexico and the US. Thus, we analyzed also the correlation between the
growth rate of a given sector in both Mexico and the US. The results are shown in table
4.10. In the period 1981-2001, the correlations between most sectors of economic activity
are lower than those found with total manufacturing in the US. The only exceptions are
basic metals and machinery. In these two cases, the highest correlations again occur with
several lags, so these Mexican sectors lagged manufacturing production in the US as a
whole and also the production of the same sector of activity in the US. As to the rest of the
components of manufacturing, the correlations with respect to the same sector of economic
activity are very close to zero, suggesting that the markets for these goods were not
integrated between the two economies. Rather, the correlation with total manufacturing in
the US must be coming from an increase in aggregate demand in Mexico due to the effect
higher growth in the US had on other sectors of economic activity.
43
We find a similar change as before when inspecting the results from the period 1994-2001,
though some of the Mexican sectors that led manufacturing production in the US turn out to
have high contemporary relationships with the same sector in the US. What happens is that
these sectors also lead manufacturing production as a whole in the US. It is likely that the
lead may be spurious during this period as the Mexican economy recovers after the balance
of payments crises and then the US economy enters into a boom.
Finally, in the period 1997-2001, the number of leading Mexican sectors is much smaller (3
instead of 6 when looking at the correlation with total manufacturing production), but the
larger number of lagging sectors do so in a much shorter interval than what was found for
the period 1981-2001.
In addition, in these latter periods the correlation between given sectors of economic
activity are very similar as those found with respect to total manufacturing growth in the
US, being sometimes larger between a given sector in both countries. This suggests that the
markets for each type of good became much more integrated, compared with the results for
the whole sample period.
Table 4.10
Correlation between different leads and lags of the annual growth rate of manufacturing production
and its components in Mexico with the same sector in the US
i. Period 1981-2001
t-6
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
0.408
-0.091
-0.158
0.088
0.128
0.242
0.530
0.454
0.162
0.167
t-5
0.399
0.024
-0.143
0.067
0.120
0.239
0.563
0.463
0.184
0.184
t-4
0.392
-0.021
-0.121
0.042
0.153
0.193
0.601
0.454
0.177
0.153
t-3
0.377
0.070
-0.106
0.025
0.119
0.185
0.599
0.453
0.163
0.195
t-2
0.354
0.028
-0.083
0.013
0.116
0.149
0.592
0.436
0.127
0.187
t-1
0.324
0.030
-0.071
0.031
0.098
0.111
0.591
0.421
0.101
0.179
t
0.284
0.011
-0.039
0.020
0.083
0.098
0.561
0.396
0.071
0.166
t+1
0.227
0.081
-0.052
0.012
0.058
0.070
0.514
0.355
0.015
0.085
t+2
0.164
0.007
-0.052
-0.044
0.059
0.015
0.453
0.305
-0.033
0.040
t+3
0.090
-0.075
-0.075
-0.116
-0.010
-0.020
0.355
0.247
-0.065
0.017
t+4
0.017
-0.081
-0.083
-0.110
-0.031
-0.076
0.250
0.185
-0.111
-0.023
t+5
-0.061
-0.135
-0.109
-0.140
-0.045
-0.121
0.150
0.119
-0.157
-0.048
t+6
-0.143
-0.211
-0.108
-0.102
-0.096
-0.171
0.068
0.050
-0.190
-0.087
ii. Period 1994-2001
t-6
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
0.279
-0.264
0.024
0.198
0.314
0.338
0.283
0.233
0.138
-0.047
t-5
0.344
-0.137
0.078
0.212
0.382
0.405
0.342
0.303
0.258
0.027
t-4
0.419
-0.086
0.139
0.196
0.431
0.437
0.403
0.365
0.304
0.094
t-3
0.481
-0.046
0.199
0.213
0.471
0.479
0.426
0.403
0.434
0.150
t-2
0.534
-0.002
0.266
0.275
0.501
0.543
0.445
0.454
0.474
0.173
t-1
0.584
0.046
0.317
0.323
0.502
0.506
0.484
0.483
0.491
0.151
t
0.619
0.092
0.371
0.316
0.511
0.572
0.520
0.501
0.499
0.199
t+1
t+2
t+3
t+4
t+5
t+6
0.626
0.197
0.403
0.341
0.499
0.576
0.578
0.490
0.465
0.242
0.617
0.185
0.419
0.259
0.501
0.564
0.581
0.455
0.436
0.227
0.587
0.187
0.420
0.235
0.472
0.561
0.531
0.408
0.358
0.188
0.547
0.197
0.361
0.205
0.470
0.514
0.490
0.346
0.299
0.117
0.495
0.135
0.357
0.146
0.450
0.462
0.439
0.280
0.194
0.126
0.424
0.101
0.338
0.122
0.388
0.377
0.353
0.227
0.075
0.056
44
iii. Period 1997-2001
t-6
Manufactures MEX
Total
Food and Beverages
Clothes and Textiles
Wood Industries
Paper and Editorials
Chemical Products
Basic Metals
Machinery
Minerals
Other Industries
0.823
-0.044
0.777
0.519
0.605
0.594
0.546
0.604
0.278
0.183
t-5
t-4
t-3
t-2
t-1
0.866
0.122
0.796
0.505
0.673
0.652
0.631
0.670
0.379
0.282
0.910
0.163
0.843
0.457
0.720
0.662
0.718
0.734
0.438
0.383
0.932
0.241
0.859
0.470
0.754
0.706
0.734
0.762
0.577
0.473
0.947
0.396
0.846
0.464
0.776
0.716
0.755
0.806
0.617
0.458
0.964
0.337
0.814
0.425
0.750
0.636
0.768
0.834
0.586
0.455
t
0.970
0.317
0.790
0.344
0.748
0.691
0.766
0.832
0.636
0.504
t+1
t+2
t+3
t+4
t+5
t+6
0.957
0.419
0.765
0.325
0.759
0.639
0.755
0.821
0.624
0.508
0.934
0.314
0.737
0.305
0.729
0.612
0.737
0.775
0.638
0.491
0.907
0.259
0.726
0.309
0.701
0.619
0.644
0.700
0.630
0.476
0.870
0.229
0.660
0.292
0.661
0.571
0.597
0.623
0.636
0.410
0.827
0.102
0.643
0.224
0.607
0.536
0.557
0.523
0.602
0.401
0.777
0.184
0.618
0.234
0.505
0.455
0.459
0.423
0.521
0.387
Thus, there seems to be a very important change in the speed with which shocks to
manufacturing in the US are transmitted to the same sectors in Mexico. Currently, all
sectors but one in manufacturing have their highest correlations in a time span between 2
lead and 3 lagged months from the change in the same sector in the US. Thus, part of the
more rapid response of Mexican sectors to total US manufacturing is due changes
themselves in the correlation of sectors in the US with total manufacturing in that country,
but not exclusively. The presence of leads in the period 1997-2001 for some sectors may be
explained by the fact that Mexican manufacturing could be playing the role of marginal
supplier in certain sectors of the US, so they respond more quickly to economic shocks than
US production. Another possible explanation is that the definition of sectors is not
sufficiently disaggregated so we could still have very heterogeneous products within these
categories.
4.2. Components of Aggregate Demand and Supply
In addition to the analysis in section 4.1. focusing on different sectors of productive
activity, it is also important to analyze the comovement between different components of
aggregate demand. This is a particularly important issue in terms of policy implications, as
the different components of aggregate demand respond differently to policy variables.
Thus, in order to stabilize the economy in response to a shock from the USA, the optimal
policies to be followed could be different from those necessary to counteract other types of
shocks. This obviously has important implications on the scope of policy coordination.
The data used are annual growth rates of private consumption, public consumption,
investment, exports and imports with quarterly frequency for Canada, Mexico and the
USA, and the sample period is 1981Q1-2001Q3. The methods of analysis are the same as
those employed in section 4.1. First we look at correlations, then at simple regressions
analysis and finally perform a factor analysis.
From this analysis we expect to find a strong correlation of investment due to financial
linkages and open capital accounts and a weaker one with consumption, though in theory
this should be higher than that of output if there is consumption smoothing and integration
of financial markets while output is subject to more idiosyncratic shocks. We don’t have
any strong priors about government spending. If both countries followed counter cyclical
45
fiscal policies and the business cycles are becoming more synchronized, we should find an
increase in the correlation. Finally, the correlation between exports and imports is more
complex. Due to more integrated production processes, in which intermediate inputs are
sent to Mexico, processed and sent back to the US, then we would expect a positive
correlation between exports and imports in both countries.
Analysis of correlations between Canada, Mexico and the USA
Table 4.11 shows the correlation coefficients of the growth rates of different components of
aggregate demand of Canada and Mexico with the same component in the USA. Table 4.12
has the correlation of the same components for Canada and Mexico but now the correlation
is calculated with the growth rate of GDP in the USA. We include the correlations between
GDP’s for comparison purposes as well as that of the same components of aggregate
demand in the US with GDP in the US.40
For private consumption, the Canadian correlation for the whole period with the USA is
small but positive and similar when using consumption or GDP in the USA, though slightly
higher for the second variable. In the Mexican case, the correlation with consumption in the
USA is negative, but becomes very similar to that of Canada when using GDP growth in
the USA. There is a large increase in the correlations for both Canada and Mexico when
looking at the period 1994-2001, and they are very similar when using consumption or
GDP in the USA. For this shorter sample, the correlations of the US variables with those of
Mexico are higher than that with those of Canada. The fact that the correlation of
consumption is typically lower than that of output suggests that we don’t have strong
international risk sharing so consumption is subject to idiosyncratic shocks. In addition, the
correlation of consumption in both Canada and Mexico with GDP in the US is smaller than
that of consumption in the US.
Table 4.11
Correlation between the annual growth rate of a component of aggregate demand
or supply in Canada or Mexico and the same component in the USA
1981-2001
1994-2001
Household
Consumption
Can
Mex
0.114
-0.120
0.386
0.500
Government
Gross Fixed Capital
Consumption
Formation
Can
Mex
Can
Mex
0.039
-0.125
0.355
0.333
-0.123
-0.026
0.541
0.611
Exports
Can
0.292
0.512
Imports
Mex
0.007
0.726
Can
0.750
0.795
GDP
Mex
0.255
0.618
Can
0.435
0.401
Mex
0.185
0.701
40
The numbers are slightly different from those reported in section 3 due to one more quarter of data in the
sample.
46
Table 4.12
Correlation between the annual growth rate of a component of aggregate demand
or supply in Canada or Mexico and the annual growth rate of GDP in the USA
1981-2001
1994-2001
Household Consumption
Can
Mex
USA
0.174
0.152
0.784
0.386
0.490
0.567
1981-2001
1994-2001
Can
0.623
0.355
Exports
Mex
-0.136
0.371
USA
0.468
0.621
Government Consumption
Can
Mex
USA
-0.407
0.077
0.169
-0.277
0.334
-0.023
Can
0.823
0.648
Imports
Mex
0.457
0.721
USA
0.820
0.915
Gross Fixed Capital Formation
Can
Mex
USA
0.506
0.263
0.865
0.618
0.639
0.909
Can
0.435
0.401
GDP
Mex
0.185
0.701
USA
1
1
In the case of government consumption (G) for the whole period, the correlation of G in the
US with that of Canada is nil, while that of GDP is strongly negative. This suggests that
Canadian fiscal policy has reacted in a counter cyclical way to shocks in the US. The
results are the opposite for Mexico, though the coefficients are very small in both cases. In
the shorter sample, we find that the correlations of Canadian and Mexican with USA
government expenditures are negative but very small in magnitude. Those with GDP are
larger but of opposite signs, suggesting again that Canadian fiscal policy has been counter
cyclical, while Mexican policy has been pro cyclical to shocks in the US. The correlation of
government consumption in the US with its GDP is always quite small.
Gross fixed capital formation (I) has always had a high and positive correlation both with
the same variable and with GDP in the US. The coefficients are generally similar for both
Canada and Mexico, and we see increases, though larger for Mexico, in the shorter sample.
That of investment in the US with its GDP is always larger, and also shows a slight increase
in the shorter sample.
For the whole sample, the correlation of Canadian exports with those of the US and with its
GDP are high and positive, more so with GDP, and are actually higher than that of US
exports with its GDP. In contrast, Mexico’s are very small in both cases, even slightly
negative with US GDP. In the shorter sample, the correlations for Canada both remain high
and positive, though the one with US exports increases while that with US GDP falls.
Mexico’s increase very significantly, becoming larger than Canada’s for the sample period,
specially those with US exports.41
In the case of imports, we find large and positive correlations for both countries and with
both US variables during the whole sample, though Canada’s are much larger and similar to
those of US imports with its own GDP. In the shorter sample, Canada’s remain at similar
levels, while those of Mexico increase to levels similar as those observed for Canada.
41
In the case of all the variables for Mexico, the correlation with US GDP increases even further if we look at
the period 1997-2001. The strongest increase is for exports, with a correlation of 0.88 compared with 0.37 in
1994-2001 and –0.14 in 1981-2001.
47
The high correlations in exports and imports in the more recent period suggest a strong
integration of production processes between Canada, Mexico and the US. However,
previous to NAFTA, there already existed a high correlation of imports’ growth rates. A
possible explanation is that Canadian and Mexican exchange rates have generally been
referenced to the US dollar, so general appreciations and depreciations of the dollar against
other countries could lead to similar adjustments in the trade balance in the three trading
partners with respect to the rest of the world. 42 However, for this to be the reason driving
the correlations the volatility of the trade balance with the rest of the world would need to
be quite high given the proportion of trade between the three countries compared with that
carried out with the rest of the world. This is unlikely to be the case.
A more reasonable explanation seems to be that an important financial linkage has existed
between the three economies at least since the beginning of the eighties. This seems to be
confirmed by the results on investment presented throughout this section. Better financial
conditions in the US would affect favorably those in Canada and Mexico, leading to an
increase in investment with respect to savings in the three countries. The ensuing increase
in aggregate demand may translate into an increase of output and consumption and,
consequently into the high correlation of imports that is observed.
A comparison of the correlations of the different components of aggregate demand in
Mexico and Canada with that of GDP in the US as a whole allows us to get a first
impression of the component that is more sensitive to shocks in the US and are driving a
stronger synchronization. This is particularly the case when we look at the correlation of an
aggregate demand component with GDP in the US (table 4.12). In the case of Canada for
the whole period, investment, exports and imports have had higher correlations with US
GDP than Canadian GDP. This is maintained for investment and imports in the period
1994-2001, though there is a puzzling reduction in the correlation of exports.43 In the
Mexican case investment and imports have higher correlations than GDP for the whole
period. In the shorter sample its only imports, though the correlation with investment
remains very high. The high correlation with investment suggests there might exist not only
a trade channel but also an important financial channel through which shocks are
transmitted across the three countries. The higher availability of external financing leads to
an increase in the current account deficit and thus into larger imports. Of course, an
alternative explanation is that investment and imports are particularly pro cyclical sectors.
The above results are confirmed when analyzing three year moving correlations (Graph
4.3). The correlation of consumption with US GDP fluctuates widely for both Canada and
Mexico, with very low values for Canada and Mexico in the early 1990’s. However, after
1994, it moves in a very similar way for both countries. In the Mexican case, it is higher
than in any other previous period. The correlation associated with government expenditure
fluctuates widely for both countries, without any clear trends though it has increased in the
most recent period for both Canada and Mexico.
42
The reason why we would not see a high correlation for Mexican exports during the whole period is that oil
was a very important component during the first part of the sample.
43
As in the Mexican case, this is no longer the case in 1997-2001.
48
In the case of investment, the correlation between Canada and the US has generally been
positive and high, with the exception of a very sharp reduction in the mid 1980’s and a
smaller one from 1998 to mid 2000. In the Mexican case, a very sharp reduction is
observed in the early 1990’s. As in the case of consumption, after 1994 both correlations
have evolved quite similarly, and are generally higher than in all previous periods.
In terms of exports, the correlation between those of Canada and GDP in the US was quite
high until 1996, when it falls sharply to recover to its previous levels in late 2000 and 2001.
Mexico’s was generally nil or negative until a very sharp increase takes place in 2000 and
2001. The sharp reduction observed for Canada before these dates is also seen for Mexico,
confirming the similarity in the evolution of correlation coefficients since 1994. Finally, the
correlation of imports of both countries has been quite high for most of the period with the
exception of the first half of the 1990’s for Mexico.
Graph 4.3
Three year moving correlations between growth rates of components of aggregate demand
or supply in Canada or Mexico with the growth rate of GDP in the USA
i) Household Consumption
ii) Government Consumption
1.0
1.0
0.8
0.8
0.6
C CAN
C MEX
G CAN
2001.4
2000.2
1998.4
1997.2
1995.4
1994.2
1992.4
2001.4
2000.2
1998.4
1997.2
1995.4
1994.2
1992.4
-1.0
1991.2
-1.0
1989.4
-0.8
1988.2
-0.8
1986.4
-0.6
1985.2
-0.6
1991.2
-0.4
1989.4
-0.4
-0.2
1988.2
-0.2
0.0
1986.4
0.0
0.2
1985.2
0.2
0.4
1983.4
3-year Moving Correlations
0.4
1983.4
3-year Moving Correlations
0.6
G MEX
49
iv) Exports
1.0
0.8
0.8
0.6
0.6
1998.4
2000.2
2001.4
1998.4
2000.2
2001.4
EX MEX
vi) GDP
1.0
0.8
0.8
0.6
0.6
IM CAN
IM MEX
GDP CAN
1995.4
1994.2
2001.4
2000.2
1998.4
1997.2
1995.4
1994.2
1992.4
1991.2
1989.4
-1.0
1988.2
-0.8
-1.0
1986.4
-0.6
-0.8
1985.2
-0.6
1992.4
-0.4
1991.2
-0.4
-0.2
1989.4
-0.2
0.0
1988.2
0.0
0.2
1986.4
0.2
0.4
1985.2
0.4
1983.4
3-year Moving Correlations
1.0
1983.4
3-year Moving Correlations
v) Imports
1997.2
EX CAN
1995.4
1983.4
2001.4
2000.2
1998.4
1997.2
1995.4
I MEX
1997.2
I CAN
1994.2
1992.4
-1.0
1991.2
-1.0
1989.4
-0.8
1988.2
-0.8
1986.4
-0.6
1985.2
-0.6
1994.2
-0.4
1992.4
-0.4
-0.2
1991.2
-0.2
0.0
1989.4
0.0
0.2
1988.2
0.2
0.4
1986.4
0.4
1985.2
3-year Moving Correlations
1.0
1983.4
3-year Moving Correlations
iii) Gross Fixed Capital Formation
GDP MEX
There are three particular things to note. The correlation of Mexican consumption,
investment and imports with the growth rate of GDP in the US fell substantially in the first
half of the 1990’s. This was a period of US recession and posterior recovery while the
growth rate in Mexico was high but in a declining trend. Thus, the evolution of growth rates
was exactly the opposite. The second outstanding issue is the similarity between the
correlations of different aggregates in Canada and Mexico with US GDP growth since
1994. They are generally high from 1994 to 1997, then fall in 1998-1999 and finally
increase substantially in 2000-2001. The third is that the correlation of Mexican
consumption, investment, exports and imports is generally higher since 1994 than in
previous periods, but the economy still was subject to important non-US shocks during this
latter period.
50
Simple regression analysis of synchronization of the components of aggregate demand of
Canada and Mexico with the USA
In order to analyze changes in the sensitivity of the components of aggregate demand in
Canada and Mexico to developments in the US we did regressions of the type of (1) on
both the growth rates of the same sector or of GDP in the US, and using the different
specifications employed in previous analysis. The results are qualitatively similar when
using the growth rate of the same sector or of GDP in the US as well as when we don’t
include the lagged value of the variable so we only report those associated with GDP
growth in the USA as a regressor for space reasons.
Table 4.13 has the results when including the lagged rate of growth of the same variable in
both Canada and Mexico. When we don’t include the time dummys for the periods after
1994 or 1997, we find that the coefficient on US GDP growth is positive and significant for
investment, export and imports in the case of Canada and for private consumption,
investment and imports in the Mexican case. The coefficients are larger for Mexico in the
case of every variable with the exception of exports, when it is negative and nonsignificant. In terms of the magnitude of the coefficients, the largest for Canada are
imports, exports and investment suggesting a strong sensitivity of these components of
demand. Those for Mexico are imports, investment and consumption. The adjusted R2’s
are always lower for Mexico, suggesting a higher presence of idiosyncratic shocks, but they
are nevertheless high with the exception of exports and government consumption.
The results when including the dummys for the periods after 1994 and 1997 suggest there is
an increase in the sensitivity of Mexican variables to developments in the US as the
dummys always have large positive coefficients, though they are generally non-significant.
The only case when there is a significant change is for exports, for both dummys. For this
variable, the coefficient is found to be significant but negative in the earlier period, while
positive and quite large in the period associated with the dummys. In the Canadian case, the
dummys are also positive44 but always smaller than in the Mexican case and only
significant for exports when using the dummy for the period after 1994.
Factor analysis
In the case of aggregate demand and supply, given the reduced number of components
compared with sectorial components of production, the factor analysis was done jointly for
Canada, Mexico and the US, for the whole period 1981-2001 and for the subperiod 19972001 (Table 4.14).
For the whole sample, the hypothesis test of more factors is rejected when we have nine
factors. The first one seems to identify the general business cycle in Mexico, with positive
high loadings on private consumption, investment and imports, with its counterpart in
exports in the US. In addition, it seems to be related with positive financial conditions in
44
With two exceptions, government consumption with the 1994 dummy and investment with the 1997
dummy. In both cases, the coefficients are not significant.
51
other countries, as there are intermediate positive loadings on investment in Canada and the
US. The second factor seems to identify the business cycle in the US, with high and
positive factor loadings in private consumption, investment and imports in the US, as well
as Canadian exports and imports. The higher Canadian imports might be due to higher
intermediate goods imports that go through a production process in Canada and are then
exported to the US. Several aggregate demand components of Canada are related with
factors 6 and 7 with high positive loadings on private consumption, investment and public
consumption. These are not associated with important loadings in either Mexico or the US.
Finally, the fourth factor suggests that Mexican exports were subject to important
idiosyncratic shocks, presumably capturing the fluctuations in oil prices.
In the period 1997-2001 the number of factors is significantly reduced from 9 to 5. The first
factor again captures fluctuations in Mexico, but there are important differences with
respect to what was found for 1992-2001. Mexican exports now also have a high loading as
well as imports in Canada and the US. This suggests that the export component has become
a more important element driving the Mexican business cycle. The high factor loading on
investment in Canada and the US remains, suggesting the effect of favorable financial
conditions on the Mexican business cycle. This factor could also be capturing the US
business cycle, with the low loading on consumption explained by the fact that it has
responded slowly to the recent downturn.
Table 4.14
Results from factor analysis of growth rates of components of aggregate demand or supply
Factor loadings correspond to varimax rotation
1. Period 1981-2001
Rotated Factor Loadings
4
5
6
7
-0.176 -0.020 0.078 0.033
-0.183 -0.180 -0.105 0.311
-0.108 0.145 0.141 -0.052
0.955 -0.009 -0.044 -0.011
-0.170 0.428 0.121 -0.007
Country
Sector
Private Consumption
Government Consumption
Mexico Investment
Exports
Imports
1
0.850
0.274
0.963
-0.250
0.753
2
0.022
0.250
0.098
0.013
0.235
3
0.037
0.021
0.004
0.006
-0.100
Private Consumption
Government Consumption
Canada Investment
Exports
Imports
0.002
-0.098
0.296
0.024
0.139
0.071
-0.386
0.214
0.766
0.872
-0.038
0.047
-0.013
-0.211
-0.329
-0.052
0.051
-0.053
0.125
-0.109
-0.101
-0.068
0.176
0.113
0.220
0.434
-0.076
0.857
0.029
0.205
Private Consumption
Government Consumption
Investment
Exports
Imports
-0.167
-0.072
0.212
0.224
0.053
0.642
-0.041
0.906
0.207
0.945
0.010
0.014
0.066
-0.007
0.216
-0.038
-0.174
-0.050
0.005
0.070
-0.040
-0.043
0.054
0.934
0.093
0.212
0.106
0.112
0.114
-0.020
US
8
0.013
-0.163
-0.028
-0.142
-0.167
9
-0.033
-0.226
-0.085
-0.005
0.115
0.792
0.699
0.185
-0.118
0.057
0.256
-0.068
0.104
-0.251
-0.067
-0.016
-0.070
0.066
0.075
-0.046
-0.146
0.130
0.025
-0.091
-0.092
0.374
0.891
-0.141
-0.030
0.181
0.546
0.071
0.026
-0.013
0.035
52
2. Period 1997-2001
Rotated Factor Loadings
Sector
1
2
Private Consumption
0.452 0.041
Government Consumption
0.242 0.286
Mexico Investment
0.870 0.091
Exports
0.854 -0.255
Imports
0.905 -0.065
3
0.222
0.166
0.423
0.123
0.327
4
-0.188
-0.494
0.159
-0.415
-0.116
5
0.702
-0.456
0.057
0.136
0.190
Private Consumption
Government Consumption
Canada Investment
Exports
Imports
0.246
-0.422
0.532
0.309
0.725
0.049
-0.168
-0.031
0.058
0.432
0.904
0.189
0.787
0.111
0.359
-0.004
-0.635
0.072
-0.892
-0.284
0.227
0.258
-0.303
0.092
0.076
Private Consumption
Government Consumption
Investment
Exports
Imports
0.256
-0.421
0.885
0.640
0.830
0.034
-0.243
0.298
0.097
0.212
-0.281
0.229
0.105
0.624
0.247
-0.851
-0.245
-0.148
-0.157
-0.444
-0.055
-0.100
-0.169
0.285
0.094
Country
US
Factor 4 seems to capture additional shocks to consumption in the US, with loadings of the
same sign and high in absolute value on US imports and exports of Canada and Mexico.
This factor also has high loadings in absolute value with government consumption in
Canada and Mexico, maybe because of the effect higher consumption has on prices of raw
materials, to which government income responds, though more in the Mexican case. The
Canadian business cycle, which seems related with factor three, now has also significant
loadings on investment in Mexico and on exports of the US.
The factor analysis suggests that financial linkages have traditionally been an important
way in which shocks have been transmitted across the three economies. Trade linkages,
particularly through exports, seem more recent.
4.3. Regions in Mexico and the US
The last analysis that was done regards changes in the degree of regional integration of
business cycles. If NAFTA had differentiated effects across states and regions in Mexico
and in the US, we would expect to see changes in the patterns of regional business cycles
inside each country and between them.
As mentioned, Del Negro (2001) finds that, using annual state GDP data for the period
1971-1998, there is a significant amount of comovements between all Mexican states and
certain states in the US and provinces in Canada. The states and provinces were actually oil
producers, so the comovement in this case is probably due to exogenous shocks related
with the price of oil. In the more recent period after NAFTA it is possible that the degree of
comovement has changed, and now we have a more differentiated business cycle across
different states in Mexico.
53
In our analysis, we would expect to see an increase in the degree of synchronization due to
NAFTA in two types of regions. The first regions are the northern states of the country,
given their lower transport costs. The second regions would be those producing tradable
goods. Of course, it is possible that the northern regions were already very integrated with
the US and thus the marginal change is relatively small. In that case, we could find a larger
increase for other regions that were less integrated but produce tradable goods.
Unfortunately, the data restrictions in the case of regional analysis are stronger than those
for national or sectorial analysis given the low frequency of the data in the Mexican case.
While data on state or provincial Gross State Product (GSP) is available for Canada and the
US with quarterly frequencies and a long time series, that is not the case for Mexico. The
INEGI has calculated data on state GSP for the years 1970, 1975, 1980, 1985, 1993-1999.
Thus, we have very few observations before and after NAFTA. In addition, the frequency is
clearly inadequate in the period before NAFTA to assess business cycle fluctuations.
Manufacturing production exists for 17 Mexican states with monthly frequency for the
period 1993-2001. However, the states for which these are reported are the states where
manufacturing represents a higher proportion of GSP. Thus, we don’t really have a control
group in this sample to assess whether NAFTA had differentiated effects in regional
business cycles in Mexico.
The best option seems to be the analysis of employment. The Social Security Institute
(IMSS) has information on the number of insured workers at the state level, with monthly
frequency from 1990 to 2001. Statewide employment is reported by the BLS for the US
with monthly frequency from 1991 to 2001. Thus, in the following analysis we will focus
on these two series. We will also group the state data into 7 regions in Mexico and the 8
BEA regions in the US.45 As in the case of the previous series, the analysis will be made
using annual growth rates of the series.
Before proceeding to more formal analysis, Graph 4.4 shows the growth rate of
employment in the different regions of Mexico, in Mexico and the USA as a whole. The
South region shows the most stable rate of growth of employment out of all the Mexican
regions, responding very slightly to the national cycle. In terms of the other regions,
employment growth seems to be related to the national cycle but with important
differences. In one extreme, employment growth in the North region shows an important
deceleration during the 1995 crisis, but employment growth does not become significantly
negative and there is a fast recovery. On the other extreme, the Capital region shows a very
large reduction of employment during the crisis, and also a slower recovery, with growth
below the national average until mid 1997. In the more recent period of deceleration that
started in the second half of 2000, employment falls sharply in the North region, while the
rate of growth of employment in the Center has fallen, but has remained positive. Growth
rates for the other regions, except for the South, have generally been between one of these
two extreme cases.
45
The list of the states in each region is included in Appendix 2. In the Mexican case, the regions are: North,
Pacific, Center North, Center, Capital, Gulf and South. The regions in the US are New England, Mideast,
Great Lakes, Plains, Southeast, Southwest, Rocky Mountain and Pacific.
54
This suggests the existence of three types of areas. Employment in the South seems to
behave in a largely independent way from developments in the rest of the country. In
second place we have the center, more sensitive to fiscal, oil price and other idiosyncratic
shocks, and in third place a northern region more integrated with developments in the US.
The rest of the regions are both intermediately sensitive to domestic and idiosyncratic
developments as well as developments in the US.
Graph 4.4
Annual growth rates of employment in different regions of Mexico and
of total employment in Mexico and the US
0.15
0.1
North
Pacific
North Center
Center
Total
Total USA
0.05
1998.12
1999.12
1998.12
1999.12
2001.12
1997.12
1997.12
2000.12
1996.12
1996.12
1995.12
1994.12
1993.12
1992.12
1991.12
1990.12
0
-0.05
-0.1
0.15
Capital
Gulf
South
Total
Total USA
0.1
0.05
2001.12
2000.12
1995.12
1994.12
1993.12
1992.12
1991.12
1990.12
0
-0.05
-0.1
55
Analysis of correlations between regions in Mexico and the USA
Appendix 3 has the tables of correlations across all Mexican and US regions for the periods
1992-2001 and 1997-2001. Table 4.15 shows the correlations of employment across
Mexican regions, of these with total Mexican and US employment as well as the
correlations with US regions that turn out to be higher than with the US as a whole. We
also include correlations of employment growth in US regions with US total employment
for comparison purposes.
In the period 1992-2001, employment growth in all regions of Mexico has very high
correlations with that of total employment in Mexico, though the South region has lower
correlations. The correlations with total employment growth in Mexico are significantly
higher than those with the US as a whole, although there are three regions in the US with
which correlations are high: New England, Mideast and Pacific. In fact, the correlation
between the Pacific region of the US and the Mexican regions North, Pacific, Gulf and
South is quite often higher than the one these have with other Mexican regions. These
results suggest that for the sample as a whole there were already Mexican regions with
substantial links with the US. The highest correlations with total US employment growth
are found with the North, Gulf and North Center.
Table 4.15
Correlations of annual employment growth between Mexican regions,
Mexico, the US and US regions
i. Period 1992-2001
Total
Total MEX
North MEX
Pacific MEX
North Center MEX
Center MEX
Capital MEX
Gulf MEX
South MEX
1
0.965
0.939
0.969
0.966
0.870
0.915
0.648
Total US
New England US
Mideast Region US
Pacific US
0.221
0.487
0.502
0.798
Mexico
North Pacific North Center Center Capital
0.965
0.939
0.969
0.966
0.870
1
0.847
0.970
0.910
0.728
0.847
1
0.906
0.904
0.885
0.970
0.906
1
0.930
0.759
0.910
0.904
0.930
1
0.864
0.728
0.885
0.759
0.864
1
0.881
0.863
0.878
0.871
0.750
0.671
0.523
0.647
0.645
0.373
0.366
0.464
0.526
0.819
0.137
0.481
0.433
0.690
0.212
0.485
0.490
0.700
0.070
0.280
0.199
0.532
0.119
0.468
0.439
0.704
United States
Gulf
South
0.915
0.648
0.881
0.671
0.863
0.523
0.878
0.647
0.871
0.645
0.750
0.373
1
0.670
0.670
1
0.281
0.323
0.476
0.764
New England US
Mideast Region US
Great Lakes US
Plains US
Southeast US
Southwest US
Rocky Mountain US
Pacific US
Total
0.529
0.659
0.592
0.494
0.953
0.565
0.674
0.587
0.119
0.173
0.361
0.550
56
ii. Period 1997-2001
Total
Mexico
North Pacific North Center Center Capital
0.965
0.939
0.969
0.966
0.870
1
0.847
0.970
0.910
0.728
0.847
1
0.906
0.904
0.885
0.970
0.906
1
0.930
0.759
0.910
0.904
0.930
1
0.864
0.728
0.885
0.759
0.864
1
0.881
0.863
0.878
0.871
0.750
0.671
0.523
0.647
0.645
0.373
Total MEX
North MEX
Pacific MEX
North Center MEX
Center MEX
Capital MEX
Gulf MEX
South MEX
1
0.965
0.939
0.969
0.966
0.870
0.915
0.648
Total US
Mideast Region US
Great Lakes US
Southeast US
Pacific US
0.626
0.550
0.686
0.632
0.677
0.684
0.562
0.884
0.720
0.720
0.934
0.864
0.885
0.593
0.677
United States
Gulf
South
0.915
0.648
0.881
0.671
0.863
0.523
0.878
0.647
0.871
0.645
0.750
0.373
1
0.670
0.670
1
0.459
0.553
0.596
0.914
0.859
0.794
New England US
Mideast Region US
Great Lakes US
Plains US
Southeast US
Southwest US
Rocky Mountain US
Pacific US
Total
0.921
0.801
0.814
0.446
0.978
0.348
0.804
0.755
0.024
0.118
0.259
0.049
0.517
Comparing the results with the correlations of US regions with total employment growth in
the US, we find that the correlations of Mexican regions with total employment growth in
Mexico are generally higher, suggesting that US regions are less sensitive to nationwide
shocks. In addition, the correlation between the US Pacific region and most Mexican
regions is higher than that between US Pacific and total US employment growth.
The correlations for the period 1997-2001 between Mexican regions and total employment
growth in the country are very similar to those found in 1992-2001, with no clear pattern of
increase or decrease. In contrast, the correlation with total employment growth in the US
shows important increases for all regions with the exception of the South, for which it falls.
This last region still has the lowest correlation coefficient with any other Mexican regions.
The correlation with the US Pacific region also increases, and remains the most important
regional linkage between Mexican and US regions. In terms of other regions in the US, the
correlations with New England are no longer larger than that with the US as a whole, and
neither are those with the Mideast, with the exception of the South region in Mexico,
though this correlation is small. Larger correlations than with the US are found with the
Great Lakes and Southeast regions. These two regions have two of the three higher
correlations with total US employment growth, suggesting that they might be proxying for
a strong cyclicality of Mexican regions to US growth. On the other hand, the relationship
with the Pacific region of the US seems to be independent of that, as it has an intermediate
level of correlation with US employment growth. In this case, the Pacific region has a
higher correlation with all Mexican regions but the South than it has with the US as a
whole.
Simple regression analysis of synchronization between regions in Mexico and the USA
In order to analyze changes in the sensitivity of employment in the different Mexican
regions to developments in the US we did regressions of the type of (1) with the dependent
variable being the rate of growth of employment in a Mexican region and the independent
57
variable being that for the US as a whole. We did the same regressions including
employment growth in US regions as the dependent variable for comparison purposes.
Table 4.16 shows the results for Mexican regions. Without including dummys for the
periods after 1994 or 1997 we find high positive and significant coefficients for Mexico as
a whole and for the North, North Center and Gulf regions. The estimated parameters are
larger than one in these four cases, and higher than two in the case of the North region. The
coefficient for the Capital region is close to one, but not significant, while those for Pacific,
Center and South are much smaller. In all cases, the adjusted R2s are quite small,
suggesting the presence of important idiosyncratic shocks in the sample.
When the dummys for the period after 1994 and 1997 are included we can distinguish an
important change over time between the period before and after NAFTA. However, in the
case of 1994, it is likely that some of the results are driven by the balance of payments
crisis of that year, and thus are not clear reflections of any structural change. In particular,
the dummys have large negative values for the period after 1994, while the coefficient on
the US is large and negative before 1994 and large and positive in its interaction with the
1994 dummy.
The results using the dummy for 1997 and after seem more robust. In that case the dummy
variable is less significant and is both positive and negative. The coefficient on the US is
both positive and negative in the period before 1997, and is normally non-significant. The
coefficient on US growth is close to one for the North and Gulf regions in the period before
1997, though only significant for the Gulf. In the period after 1997, we find a very large
and significant increase in the coefficient associated with employment growth in the US for
all regions except the Gulf and the South. The change is the largest for the Center and
Pacific regions, and similar for North, North Center and Capital regions. This suggests that
employment in several regions of Mexico has effectively become more sensitive to
developments in the US after NAFTA, with the exception of the Gulf and the South. In
order to identify the most sensitive regions to developments in the US in the post-NAFTA
period, we added both coefficients on the US, with and without the dummy. The ordering
depending on the size of the sum of the coefficients is: North, Pacific, North Center,
Center, Capital, Gulf and South, with North being much higher than the rest, then Pacific,
North Center, Center and Capital with similar high values, and finally Gulf and South with
much lower coefficients. Finally, even though the adjusted R2s are much higher with than
without the dummys, there remains a very substantial degree of variability unexplained by
the regressions. This suggests that there is a strong effect from US shocks, but idiosyncratic
shocks also remain important.
Table 4.17 shows the results for US regions. For the sample as a whole we find positive and
significant point estimates, always close to 1. Thus, regions as the North in Mexico seem
more sensitive to nationwide developments in the US than some regions in that country.
The adjusted R2s are higher than those for any Mexican region, but are not that high, so
there seem to be important idiosyncratic shocks to regions in the US. When we include the
NAFTA dummys, the interpretation of changes is different than in the Mexican case when
considering whether there is a higher degree of synchronization between US regions. After
all, the national growth rate is approximately a weighted average of the region growth rates,
58
so on average they can not all increase. What we should see is that the coefficients for all
regions become closer to one if indeed there is higher integration and the R2s should
increase. We do find that, in both cases of dummys, when the initial coefficient is below
one that associated with the dummy is positive, and vice versa. Thus, there seems to have
been an increase in the degree of synchronization even within US regions, though there are
still important differences from a coefficient of 1 in most cases. The R2s also increase
significantly for most regions.
Factor analysis
Factor analysis for the period 1992-2001 shows that we should include at least 10 factors.
The first factor seems to capture a shock that affects a set of regions in the US, though not
all of them. In particular, New England, Mideast and Pacific seem to respond to different
shocks. The second factor affects all regions in Mexico in a strong way, again with the
lowest loading for the South. The Pacific region of the US has a significant loading with
respect to this factor.
All the other factors seem to be capturing more localized regional shocks, whether for a
group of regions or for one in particular. The third factor has high loading for the Pacific
region in the US and the North region in Mexico, being likely that it captures some regional
border effect. The fourth has high factor loadings for the New England and Mideast regions
of the US. The other factors are less important, and are generally loaded in one specific
region.
Table 4.18
Results from factor analysis of growth rates of employment in Mexican and US regions
Factor loadings correspond to varimax rotation
i. 1992-2001
Country
Region
North
Pacific
North Center
Mexico Center
Capital
Gulf
South
New England
Mideast
Great Lakes
United Plains
States Southeast
Southwest
Rocky Mountain
Pacific
1
-0.002
-0.188
-0.071
-0.029
-0.208
-0.057
-0.112
2
0.665
0.916
0.904
0.945
0.858
0.738
0.519
3
0.327
0.080
0.042
-0.015
0.178
0.294
0.104
0.018
0.127
0.861
0.587
0.887
0.689
0.953
0.099
0.268
0.219
-0.133
-0.357
0.068
-0.317
-0.083
0.555
0.095
0.088
-0.124
-0.044
0.138
0.248
-0.037
0.655
Rotated Factor Loadings
4
5
6
7
0.292 -0.494 0.137 -0.133
0.252 -0.150 0.047 -0.058
0.272 -0.239 0.006 -0.146
0.009 -0.106 0.046 -0.133
0.264 0.030 -0.227 -0.080
0.180 -0.300 -0.121 -0.004
0.089 -0.749 0.003 -0.080
0.926
0.890
-0.036
-0.010
0.377
-0.107
-0.042
0.447
0.076
-0.229
-0.093
0.101
-0.132
0.204
0.185
-0.217
0.048
-0.069
0.456
0.016
-0.118
-0.082
-0.068
-0.062
-0.087
0.070
0.039
0.718
0.117
0.299
0.045
-0.035
8
0.048
0.036
0.050
-0.183
0.091
0.374
0.023
9
-0.118
-0.114
0.044
0.103
0.090
-0.056
0.051
10
-0.178
0.055
-0.139
-0.002
0.132
-0.006
0.028
-0.115
0.169
-0.065
0.000
0.011
0.081
0.004
0.037
-0.060
0.078
0.037
-0.013
-0.009
-0.326
0.038
-0.016
-0.014
0.008
-0.002
0.005
-0.044
-0.019
0.032
0.009
59
ii: 1997-2001
Country
Region
North
Pacific
North Center
Mexico Center
Capital
Gulf
South
New England
Mideast
Great Lakes
United Plains
States Southeast
Southwest
Rocky Mountain
Pacific
1
0.227
0.406
0.347
0.162
0.232
0.139
-0.018
2
-0.140
0.046
-0.084
0.019
0.241
-0.095
-0.644
0.916
0.957
0.764
0.469
0.687
0.015
0.371
0.331
0.245
-0.017
0.091
0.534
0.405
0.987
0.832
0.052
Rotated Factor Loadings
3
4
5
6
0.925 -0.081 0.144 -0.176
0.844 -0.206 -0.225 0.046
0.911 -0.117 0.131 -0.017
0.965 0.047 -0.074 0.027
0.799 0.035 -0.444 0.034
0.890 -0.164 -0.022 -0.022
0.665 0.031 0.039 -0.054
0.267
0.199
0.470
-0.428
0.497
-0.120
0.330
0.890
0.067
0.048
-0.181
0.514
0.286
0.025
0.072
0.192
-0.097
-0.034
0.047
-0.033
0.060
0.064
-0.203
-0.118
0.056
0.052
-0.390
0.090
-0.049
-0.049
0.048
-0.061
7
-0.098
0.051
0.003
0.114
-0.010
0.009
0.214
-0.037
0.034
0.009
0.014
-0.093
0.069
-0.066
-0.154
In the analysis for the period 1997-2001 we found that we need at least 7 factors. There
seems to be an important change in terms of integration within the US and between the US
and Mexico. The first factor seems to capture shocks to New England and Mideast, but now
other regions in the US also have a significant factor loading, and that of Mexican regions
also increased. The second factor is quite similar to the first factor for the period 19922001, with high factor loading only for some regions in the US. An important difference is
that the Great Lakes region previously had a high factor loading when these regions had it.
This is no longer the case. The third factor captures shocks to Mexican employment and is
similar to factor 2 for the period 1992-2001. However, the loadings tend to be slightly
higher for Mexican regions, while the loading of the Pacific US region is considerably
higher. The rest of the factors typically have a high loading for only one region, but the
reduction in the number of factors needed is an important one, suggesting that these type of
idiosyncratic shocks are less important in the sample, both for Mexico and for the US.
4.4. Summary
Production components
The intention of the more detailed analysis of components of production was to compare
changes in synchronization across tradable and non-tradable goods, and also to identify if
the relationship between Mexico and the US is becoming more like that observed between
Canada and the US. We found that the correlation of manufacturing growth increased
substantially between Mexico and the US, to a level similar to that observed between this
last country and Canada. In contrast, correlations for both Canada and Mexico in sectors
driven by shocks to commodity prices, such as agriculture and mining, remained at similar
levels during the entire period. The main difference between the Canadian and Mexican
60
cases is that in the more recent period, the correlation of services between Mexico and the
US increased to a level higher than that observed with Canada at any period of time. This
suggests that the Mexican business cycle in the more recent period has been mostly driven
by the export sector, and thus we also see a strong increase of the correlation in pro-cyclical
non-tradable sectors.
In the regression analysis, depending on the specification and time period, we find
increases in the post-NAFTA sensitivity to developments in the US for the following
sectors of activity in Mexico: Manufacturing, Transport and Communications, Services and
Construction. These increases are typically larger than those found for Canada. These
results confirm the interpretation given to those from the correlation analysis. Finally, the
result from factor analysis show the presence of separate idiosyncratic factors for Mexico
and the US when using the whole sample period, while in the post-NAFTA period there is
one main common factor for both economies.
If we look in detail at the correlation between more disaggregated components of
manufacturing, we find that there were some sectors that had a high correlation with the US
before NAFTA, such as basic metal products and machinery. However, in the post-NAFTA
period there is a generalized increase for all components of manufacturing. Regression
analysis shows the same results. Basic metal products, Machinery and
Other
manufacturing products have a high and significant sensitivity to developments in the US
for the whole period. In the post-NAFTA period, this is found to be the case for all subcomponents of manufacturing with the exception of foods and beverages. Factor analysis
shows the same results, with a single factor with high loadings for the components of
manufacturing in both countries during the post-NAFTA period.
Lag structure
In terms of the analysis of the lead and lag structure of the components of manufacturing,
we found that the correlation of a Mexican sector with total manufacturing production was
typically very small at all leads and lags, with the exception of basic metals and machinery.
In the case of these two variables, the highest correlation is found at a lag between 4 and 6
months. However, in the post-NAFTA period, the correlation is much higher for all
components of manufacturing and the highest one is now between a lead or a lag of three
months. A similar change occurs when we look at the correlation of a Mexican sector with
the same sector in the US. Thus, this evidence, together with that of other sectors of
economic activity, suggests that the Mexican economy has become more sensitive to
developments in the US and also shocks are propagated with a smaller lag between both
economies.
Components of aggregate demand and supply
The analysis of components of aggregate demand and supply is important to identify if the
increase in synchronization is mostly due to an increase in trade or if there are other factors
that are also affecting aggregate demand in both countries, in particular financial links.
61
The correlation analysis suggests that there were strong financial and trade links between
Canada and the US at least since 1980, as evidenced by the high correlations of investment,
imports and exports. In the Mexican case, there is also a high correlation but only for
investment and imports, suggesting the presence of a financial link so more favorable
financial conditions in the US led to higher investment there and in Mexico, which in turn
translated into higher imports. In the post-NAFTA period, the correlation of Mexican
exports increases and becomes similar to Canada’s so the trade channel seems to become
important in the Mexican case. The regression analysis confirms these results, as the only
component of aggregate demand for which we find a significant change in the post-NAFTA
period are exports in Mexico.46
Factor analysis leads to the same conclusion. For the whole period, we find there is factor
with high loadings for C, I and M in Mexico, I in Canada and I and M in the US. In the
shorter, post-NAFTA sample, the factor has high loadings with C, I, M and X in Mexico, I
and M in Canada and I, X and M in the US.
Regional analysis of employment
The final subsection analyzes the relationship between regional employment in Mexico and
the US. We find that there was a high correlation of all Mexican regions except the south
with the Pacific region in the US. In addition, employment the North region of Mexico had
an intermediate positive correlation with total employment in the US. In the post-NAFTA
period, the correlation with the Pacific region of the US is reinforced, but there a very
generalized increase in the correlation of all Mexican regions, but the south, with total
employment in the US.
The same results are found in the regression analysis. In this case, the North, North Center
and Gulf regions have significant relationships with the US for the whole sample period,
but then in the post-NAFTA period we see a generalized increase in the sensitivity to
developments in the US, again with the exception of the South. The increase in the
sensitivity is generally smaller for those regions that already had a significant relationship
with the US.
The results from factor analysis show the presence of two main factors, one for most US
regions and another for Mexican regions plus US Pacific. In the post-NAFTA period, the
loading on Mexican regions associated with the factor for most US regions is higher, but
the Mexican-US Pacific factor remains. This suggests that, even though the sensitivity of
employment to developments in the US has increased, there still remains an important
segmentation in terms of the evolution of regional labor markets.
46
In addition, consumption in Mexico has a significant sensitivity to growth in the US, and a coefficient
higher than Canada’s. This is probably due to the presence of stronger liquidity constraints in Mexico than in
Canada. Thus, an improvement in the availability of funds due to better financial conditions in the US leads to
both higher investment and consumption in Mexico.
62
5. Policy Coordination
In terms of the implications of higher or lower business cycle synchronization and the need
for policy coordination, there is a very large literature related theoretically with the
discussion on optimal currency areas, and empirically with the establishment of the
European Union, the EMU and adoption of the Euro, and the Maastricht criteria. Here we
only highlight some of the main discussions.
Presumably, the starting point should be what we consider to be policy coordination, as
there are several possibilities. On the one hand, we could have implicit or explicit
agreements between authorities about how they will be following their own separate
policies against shocks determined solely by national considerations. This is important in
the context of free trade agreements, as the perception that a country is persistently
maintaining a depreciated real exchange rate to export more and import less from its trading
partners could lead to political pressures to abandon the trade agreement. In a sense, its
really a compromise between countries not to follow destabilizing policies. This is in each
countries’ self-interest and thus does not depend on the degree of macroeconomic
synchronization across them.
A second dimension is that policy makers take into account the effects of their actions on
the other countries. For example, if the US would decide to follow a less restrictive
monetary policy not because of a deceleration of growth and low inflation in the US but
because Mexico was subject to a strong and negative idiosyncratic shock. Obviously, there
is a self-interested reason to do so, as otherwise the shock in Mexico could lead to lower
US exports, aggregate demand and production. This would implicitly lead to policy
coordination. The probability that we see such a phenomenon probably depends on
observing similar sized countries and that they trade extensively with each other.
Finally, the most extreme case is that of a common policy that will be followed for the
different countries due to the adoption of a common currency or agreements through which
fiscal resources are shared. In addition, a set of common rules may be established to
determine the type of policies that can be followed depending on different shocks, an
example of which are the fiscal rules established in the European Union’s stability pact.
Our focus will be on this last type of policy coordination, as its desirability clearly depends
on the degree of business cycle synchronization across countries. If macroeconomic
synchronization is high then it is more likely that a given common policy fits all countries
relatively well. However, even with high synchronization it may not be optimal to adopt a
common policy if the business cycle leads to larger fluctuations in one country compared
with the others or if the sensitivity of the economy to variations in the policy instruments
differ.
63
5.1. Higher policy coordination: the case of Mexico within NAFTA
The evidence from the previous sections shows that the degree of macroeconomic
synchronization of Mexico with its NAFTA partners has increased significantly and the
sensitivity of the Mexican economy to developments in the US has also increased.
However, these results are probably not sufficient to argue that Mexico should reduce its
ability to carry out independent monetary and fiscal policies.
In first place, even though the amount of variability of Mexican economic variables
explained by developments in the US has increased in the 1997-2001 period, there remains
a significant amount of variability explained by other types of shocks and this is clearly
higher than in the Canadian case. Thus, even though idiosyncratic shocks are less
important, they remain sufficiently so as to justify maintaining independent policies.
In second place, the increase in the sensitivity of Mexican variables to its US counterparts
has been such that the estimated coefficient is quite often larger than one, implying that the
Mexican economy reacts more than one-to-one to developments in the US economy. Thus,
unless the Mexican economy is more sensitive to changes in policy variables, these need to
react in a stronger way for stabilization purposes than in the US and Canada.
There are good reasons that suggest that the effect of different policy instruments and
channels differ across the three economies. Regarding monetary policy, the lower level of
financial development and credit to the private sector by domestic financial institutions in
Mexico implies that the interest rate and credit channels are likely to be weaker than in the
case of the US and Canada. Given that trade to GDP is much higher for Canada and Mexico
than the US, the exchange rate channel is likely to be more important for the first two
countries. Even though we don’t have substantial evidence suggesting that the channels
through which fiscal policy works in the three countries may be different, higher liquidity
constraints in the Mexican economy would suggest that the effect of counter cyclical fiscal
policies could have stronger effects in Mexico than in the other countries due to the fact
that Ricardian Equivalence is less likely to hold in the Mexican case.
Another issue is whether Mexico can effectively carry out independent counter cyclical
policies. In terms of monetary policy, Frankel, Schmukler and Serven (2002) do a
regression analysis of interest rates in a large sample of countries against the interest rate of
the US, Germany or Japan. They find that, in the medium term, the evolution of interest
rates for all the other countries is determined by rates in either one of these three,
irrespectively of the exchange rate regime in the country. Thus, the ability of the rest of the
world to carry a strongly independent monetary policy is presumably limited. However, in
the short term there may be significant deviations, and a flexible exchange rate regime
allows these deviations to last for longer.47 Thus, the adoption of a flexible exchange rate
has increased Mexico’s ability to carry out an independent monetary policy at least
temporarily, as has clearly been the case during the current disinflationary episode.
47
In a strict sense, monetary policy can only affect the level of real rates in the short run in any country.
64
In terms of fiscal policy, it is likely that Mexico is less able to carry out counter cyclical
policies. Past periods of strong fiscal imbalances and balance of payments crises imply that
a larger deficit is more likely to be interpreted as a permanent increase in expenditure that
may lead to a deterioration of the public sector’s solvency instead of as a temporary counter
cyclical measure. Thus, it could lead to large increases in the interest rate, with a significant
crowding out effect, and in an extreme case leading to a confidence crisis. This potential
loss of credibility is also a factor that limits the ability to carry counter cyclical monetary
policy given past episodes of disinflation that failed, particularly that preceding the 1995
crisis. Thus, for Mexico to be able to carry stronger counter cyclical policies credibility
would need to be reinforced by attaining a period of low and stable inflation and balanced
budgets, together with a fiscal reform that reduces the vulnerability of government income
to fluctuations in the price of oil. To the extent that sound macroeconomic policies are
followed reinforced by additional structural reforms that reinforce credibility, it would be
expected that the ability to carry out counter cyclical policies would increase in the future.
Thus, the evidence of larger synchronization suggest that Mexico’s optimal policies will be
qualitatively more similar to those followed by its NAFTA partners than in the past.
However, Mexico still seems subject to important idiosyncratic shocks, particularly when
compared with Canada, but even in the absence of these the magnitude of policy
adjustments would probably need to be higher in Mexico than in the other countries.
5.2. Has there been higher synchronization of policies?
Nevertheless, it is worthwhile to review if the policies in Mexico’s NAFTA partners have
become more similar to those followed by Mexican authorities, with particular emphasis on
the US, and what has been the correlation of US policy with the Mexican business cycle.48
Thus, we look at the evolution of fiscal balances and government current expenditures to
GDP as measures of fiscal policy as well as the growth rate of real money balances (M2)
and the evolution of real interest rates as a measure of monetary policy.49
Graph 5.1 shows the evolution of these policy variables for Mexico and the US. When
looking at the fiscal variables, the first thing to note is that the fiscal balance is much more
volatile in Mexico than in the US. Both countries have important fiscal deficits during most
of the eighties that are corrected in the nineties. The correction in Mexico is a fast and large
one in the last years of the eighties, leading to a large surplus in the early nineties. Since
then, the surplus falls gradually, becoming a small deficit since mid-nineties. In contrast,
that in the US is much more gradual, achieving a surplus in 1997 that peaks in 2000,
48
This comparison is also important to gauge if the higher synchronization might be due to more similar
policies instead of higher trade between the countries.
49
The fiscal balance to GDP is obtained from the IFS Statistics prepared by the IMF. Government current
expenditures are obtained from each country’s Ministry of Finance (SHCP for Mexico, Department of the
Treasury for the US). M2 is obtained from each central bank. The sources for price indices are Banco de
México and the Department of Commerce. Finally, to construct real interest rates we used the nominal rates
on three month government bonds and divided those by the actual inflation in the three months following the
date of issue. Unfortunately, we do not have data on inflation expectations in both countries for a sufficiently
long period of time.
65
reverting to a small deficit. That in Mexico has remained fairly constant during the last five
years. Overall, there seems to be no clear relationship in terms of fiscal balances in both
countries.
However, fiscal balances have the problem of different definitions across both countries,
even though the IMF tries to standardize the data, and they include interest payments,
which might explain a large part of the volatility in Mexican variables. Thus, it is useful to
look at government current expenditures as a potentially more accurate measure of the
effect of fiscal policy on aggregate demand. In contrast with fiscal balances, the more
volatile series as a proportion of GDP is that in the US, though the fact that it also
represents a much higher proportion of GDP implies that the volatility in percentage terms
is significantly smaller in the US.50 As is clear from the Graph, there does not seem to be
any significant relationship between both variables, before or after NAFTA.
Graph 5.1
Evolution of different fiscal and monetary policy variables in Mexico and the US
0.02
0.33
0.01
0.06
0.32
0.05
0.31
0.04
0.3
0.03
0.29
0.00
-0.01
-0.02
-0.03
-0.04
MEX
0.02
0.01
USA
0.28
MEX
USA
2001.2
2000.1
1998.4
1997.3
1996.2
1995.1
1993.4
1992.3
1991.2
1990.1
1988.4
1987.3
1986.2
1985.1
1983.4
1982.3
1981.2
1980.1
0.27
0.26
1980.1
1981.2
1982.3
1983.4
1985.1
1986.2
1987.3
1988.4
1990.1
1991.2
1992.3
1993.4
1995.1
1996.2
1997.3
1998.4
2000.1
2001.2
0.00
-0.05
current expenditure (%GDP) USA
ii) Government current expenditures
0.07
current expenditure (%GDP) MEX
budget surplus (%GDP)
i) Fiscal Balance to GDP
50
The percentage difference between the maximum and minimum values of government expenditures to GDP
are 70.5% for Mexico and 16% for the US. This difference in percentage points of GDP is 2.5 for Mexico and
4.5 for the US.
66
iv) Real interest rates
14
100
35
30
MEX
USA
MEX
80
12
USA
real interest rate (MEX)
25
annual growth
20
15
10
5
10
60
8
40
6
20
4
2
0
real interest rate (USA)
iii) Real growth rate of M2
0
0
-5
-20
-2
-10
D-01
D-00
D-99
D-98
D-97
D-96
D-95
D-94
D-93
D-92
D-91
D-90
D-89
D-88
D-87
D-86
D-01
D-00
D-99
D-98
D-97
D-96
D-95
D-94
D-93
D-92
D-91
D-90
D-89
D-88
D-87
D-86
-4
D-85
-40
-15
The real growth rate of M2 shown in Graph 5.1.iii shows that this has generally been much
higher and fluctuated more in Mexico than in the US. The growth rate of real money
balances in the US was very low in the pre-NAFTA period and generally followed a slight
declining trend. That in Mexico did not show any clear trend during the same period. In the
period post-NAFTA, growth rates in Mexico and the US showed in general positive trends,
though the fluctuations around these trends seem to have happened at very different times
in both countries.
In terms of real interest rates, they were considerably more volatile in Mexico, but general
trends were very similar in both Mexico and the US from 1985 to 1994. Given that Mexico
maintained a fixed or semi-fixed exchange rate with respect to the US during a substantial
part of this period, it was to be expected that rates in Mexico would respond strongly to the
evolution of those in the US. In the period post-NAFTA, rates were generally at
intermediate levels compared with those seen in the previous period. There are no
discernible trends in either of the two interest rate series, and there does not seem to be as
close a relationship as in the previous period. This is to be expected in a flexible exchange
rate regime under which Mexico is more able to carry out and independent monetary
policy.
Table 5.1 shows the correlation coefficients between these four variables in Mexico and the
US. In the case of the fiscal balance, the correlation turns from positive in the whole period
to strongly negative in the post-NAFTA sample, as suggested by the graphical evidence.
Presumably, this negative correlation is due to the fact that the high growth observed in the
US during the second half of the nineties led to a reduction in the proportion of expenditure
to GDP, while the initial increase in Mexico’s financial balance is related with the debt
renegotiation process and the corresponding reduction in service payments. In terms of
67
government current expenditures to GDP, the correlation is close to zero for any of the
periods.
The real growth rate of M2 shows a zero correlation for the whole period, but in the postNAFTA period we observe a positive and intermediate degree of correlation. However,
given the instability of money demand in the short run, M2 is hardly a good measure of the
monetary policy stance. In addition, it is quite possible that the evolution of money is
determined by the effect of changes in income on money demand, and the higher
correlation could turn out to be really a reflection of the higher correlation in output
between the two countries. Thus, real interest rates are a better measure of the monetary
policy stance. The correlation between these is slightly positive for the whole period,
consistent with a more rigid exchange rate regime, and negative after 1997. In any case, the
coefficients are extremely close to zero, suggesting there is no clear relationship between
the two variables.51
Table 5.1
Correlations between different fiscal and monetary policy variables in Mexico and the US
(Correlation between the same variable in Mexico and the US)
i) Fiscal variables
Government
Current
Expenditure
0.282
-0.044
-0.514
0.005
-0.497
-0.087
Financial
Balance
1980:1 - 2001:4
1994:1 - 2001:4
1997:1 - 2001:4
ii) Monetary variables
Growth in Real Real Interest
Money
Rate (accurate
Balances*
expectations)*
Whole period
0.066
0.174
1994:01 - 2001:12
0.328
0.034
1997:01 - 2001:12
0.401
-0.110
* The whole period is 1986:12-2001:12 for real money
balances and 1985:12-2001:12 for the real interest rate.
Thus, the evidence presented above suggests that even though there is a higher degree of
macroeconomic synchronization, this has not been reflected in similar movements of policy
variables. In this respect, Mexico’s ability to carry out counter-cyclical fiscal policies has
probably been limited by three main factors: i) the perception that an increase in the budget
deficit in bad times could translate into a loss of confidence in the country’s economic
policies and thus make a bad situation turn worse; ii) oil related revenues still represent
around a third of government income; and iii) debt interest payments accounted for 15.5%
of total expenditures on average in the period 1997-2001, being an expenditure that can not
be adjusted in the short term and has high volatility. The second and third factors imply that
non-interest expenditures are very sensitive to the price of oil and the level of interest rates.
In terms of monetary policy, while the Federal Reserve has adjusted interest rates
depending on the fluctuations in the US seen in the last ten years, monetary policy in
51
Given that the period before 1997 was one of higher volatility and probably stronger surprises in the
observed rate of inflation, which is the one used to calculate real interest rates, the correlation between real
interest rates might be biased towards zero during this period.
68
Mexico has been conditioned by two disinflation processes, one strong balance of payments
crisis between them and a slighter one associated with the Russian and Brazilian crises.
Thus, in the post NAFTA period the objective of Banco de México has been to reduce the
rate of inflation. It’s not surprising that we don’t find any short run relationship between
rates in this period.
Finally, while policies in Mexico did not coincide with those in the US, given different
abilities to carry out counter-cyclical policies, it may be that policies in the US nevertheless
had a stabilizing impact in the Mexican economy given the higher correlation in output.
Table 5.2 shows the correlation between the four policy variables for the US and the growth
rate of Mexican GDP. The correlation between GDP and financial public balance is
positive for the whole period, though it becomes less so during the latter part of the sample.
That of government current expenditure changes from negative for the whole period to
positive for 1997-2001, though the coefficient is not very high. These two results suggest
that fiscal policy in the US did not adjust in such a way that it would have had a counter
cyclical effect with regards to the Mexican economy.
Table 5.2
Correlations between the rate of growth of Mexican GDP and different fiscal and
monetary policy variables in the US
Government Growth in Real Real Interest Rate
Current
Money
(rational
Expenditure
Balances*
expectations)*
0.328
-0.380
-0.009
0.007
0.302
-0.340
0.134
0.057
0.121
0.202
-0.746
0.632
Financial
Balance
Whole period
1994:1 - 2001:4
1997:1 - 2001:4
* The whole period is 1981:1-2001:4 for the financial balance and government current
expenditure, 1987:1-2001-4 for real money balances and 1986:1-2001:4 for the real interest
rate.
On the other hand, the correlations with money growth and real interest rates changed from
zero in the whole period to strongly negative and positive, respectively, since 1997. Given
that interest rates are more accurate indicators of the stance of monetary policy, this implies
that US monetary policy has tended to have a stronger counter-cyclical effect on the
Mexican economy. This implies that the main counter cyclical policy in the US probably
did contribute to promote higher stability of Mexican GDP during the recent period.
6. Conclusions
This study has several important implications as to the effects of free trade agreements both
in general and between more and less developed countries. The first is that, in spite of the
important differences between Mexico and its NAFTA partners, the free trade agreement
translated into higher synchronization, as has been observed in the case of trade agreements
between mostly industrialized countries. Thus, even in a case of larger differences in factor
endowments, we don’t see an increase in specialization that would translate into a higher
sensitivity to idiosyncratic shocks. This does not mean that this would never happen, but it
69
indicates that factor endowment differences probably need to be even larger than that
observed within NAFTA in order to detect less synchronization as a result of closer trade.
The second is that Mexico already had important linkages with the US before NAFTA.
Some subsectors of manufacturing production, employment in some regions and
investment and imports had a high sensitivity to developments in the US. In particular,
there seems to have been an important financial link through which better financial
conditions in the US translated into higher investment, imports and, to a lesser degree,
consumption. What NAFTA did was to reinforce the relationship through a strong link with
more sectors of economic activity, more regions in Mexico and the establishment of a
stronger trade link through which shocks are also transmitted. Two important findings are
that non-tradable pro-cyclical sectors also have a stronger relationship with developments
in the US, and that the largest increases in sensitivity were observed in those sectors that
were not strongly linked to the US before NAFTA.
In terms of policy implications, the higher degree of synchronization implies that optimal
counter-cyclical policies will be qualitatively more similar between Mexico and its NAFTA
partners. In fact, there is evidence that monetary policy in the US probably contributed to
smooth out Mexico’s business cycle in the more recent period. However, in terms of
extreme policy coordination, such as the adoption of common stabilization policies there
are two issues that point towards the convenience of maintaining independent policies. The
first is that, even though sensitivity to developments in the US has increased, there still
remains a significant amount of idiosyncratic volatility in Mexico compared with Canada.
The second is that the respond of Mexican variables to US shocks is larger than one for
one, so in the absence of stronger effects in Mexico of the same policy adjustment, those
optimal to be carried out in its NAFTA partners are likely to be insufficient. In fact, we
suspect that the same policy adjustment in the US would have smaller effects in Mexico,
particularly monetary policy given the lower degree of financial development in this last
country.
70
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73
Appendix 1. Interpretation of the increase in the correlation of annual growth rates
We show with an example how a higher correlation across two series of annual growth
rates can be the result of both higher sensitivity or faster transmission of shocks. First we
denote the log of 1 plus the annual growth rate of country i in period t with gi,t. The
corresponding log of 1 plus the quarterly growth rate is denoted with qi,t. Thus:
4
g i ,t = ∑ q i ,t − n
n =0
Next we calculate the covariances between gi,t and that of country j, gj,t. The quarterly
growth rate of country j is assumed to follow an iid process with mean q j and variance σ2.
We calculate the covariances between these under two different hypothesis about the
relationship between the quarterly growth rates in the two countries:
H 1 : q i ,t = α ⋅ q j , t
H 2 : q i ,t = α ⋅ q j ,t −1
The covariances under each of the two hypothesis are:
[( )
H 1 : cov( g i ,t , g j ,t ) = 4α E q j − (q j )
2
[ ( )
2
]
H 2 : cov( g i ,t , g j ,t ) = α 3 ⋅ E q j − 4 ⋅ (q j )
2
2
]
Thus, the covariance is larger if the sensitivity (α) is higher or under a faster transmission
of shocks.
74
Appendix 2. Classification of Mexican and US States into regions
Mexican Regions
USA Regions
North
Baja California
Chihuahua
Coahuila
Nuevo Leon
Sonora
Tamaulipas
New England
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
Pacific
Baja California Sur
Colima
Jalisco
Nayarit
Sinaloa
Mideast Region
Delaware
District of Columbia
Maryland
New Jersey
New York
Pennsylvania
North Center
Aguascalientes
Durango
Guanajuato
Queretaro
San Luis Potosí
Zacatecas
Great Lakes
Illinois
Indiana
Michigan
Ohio
Wisconsin
Plains
Iowa
Kansas
Minnesota
Missouri
Nebraska
North Dakota
South Dakota
Southeast
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Virginia
West Virginia
Southwest
Arizona
New Mexico
Oklahoma
Texas
Center
Hidalgo
Morelos
Puebla
Tlaxcala
Capital
DF
Mexico
Gulf
Campeche
Quintana Roo
Tabasco
Veracruz
Yucatán
South
Chiapas
Guerrero
Michoacán
Oaxaca
Rocky Mountain Colorado
Idaho
Montana
Utah
Wyoming
Pacific
Alaska
California
Hawaii
Nevada
Oregon
Washington
75
Table 3.3
Results from regressions of the annual growth rate of GDP in a given country on the annual growth
rate of GDP in the USA
Can
dxUSt
2
R
2
Adj R
Sample
-0.007 ***
(0.003)
1.092 ***
(0.068)
0.764
0.761
81:1-01:2
d94
dxUSt
d94*dxUSt
2
R
2
Adj R
Sample
-0.008 ***
(0.003)
0.014
(0.009)
1.107 ***
(0.073)
-0.302
(0.236)
0.772
0.763
81:1-01:2
d97
dxUSt
d97*dxUSt
2
R
2
Adj R
Sample
Chi
Fra
Ger
Ita
0.016 *
(0.008)
0.323
(0.217)
0.027
0.015
81:1-01:2
0.015
(0.012)
0.314
(0.336)
0.021
-0.003
91:1-01:2
0.003
(0.012)
1.340 ***
(0.312)
0.188
0.177
81:1-01:2
0.016 ***
(0.003)
0.114
(0.083)
0.023
0.011
81:1-01:2
0.030 ***
(0.006)
-0.187
(0.155)
0.018
0.006
81:1-01:2
0.010 ***
(0.003)
0.288 ***
(0.069)
0.178
0.167
81:1-01:2
0.022 ***
(0.008)
-0.100 ***
(0.026)
-0.014
(0.210)
3.036 ***
(0.682)
0.235
0.205
81:1-01:2
-0.043
(0.032)
1.712 **
(0.828)
0.133
0.102
94:1-01:2
0.015
(0.014)
0.005
(0.027)
0.161
(0.582)
0.067
(0.850)
0.028
-0.049
91:1-01:2
-0.004
(0.012)
0.097 **
(0.040)
1.602 ***
(0.326)
-2.712 **
(1.059)
0.251
0.222
81:1-01:2
0.016 ***
(0.003)
-0.003
(0.011)
0.051
(0.087)
0.288
(0.284)
0.085
0.049
81:1-01:2
0.033 ***
(0.006)
-0.025
(0.021)
-0.187
(0.166)
0.451
(0.540)
0.046
0.010
81:1-01:2
0.009 ***
(0.003)
0.004
(0.009)
0.302 ***
(0.075)
-0.116
(0.245)
0.180
0.149
81:1-01:2
0.002
(0.012)
0.036
(0.050)
1.562 ***
(0.324)
-1.635
(1.255)
0.235
0.206
81:1-01:2
0.016 ***
(0.003)
0.007
(0.013)
0.076
(0.086)
0.043
(0.335)
0.075
0.039
81:1-01:2
0.031 ***
(0.006)
-0.021
(0.026)
-0.194
(0.165)
0.421
(0.640)
0.028
-0.009
81:1-01:2
0.010 ***
(0.003)
-0.005
(0.012)
0.292 ***
(0.074)
0.076
(0.287)
0.182
0.150
81:1-01:2
dx it = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
iii) Regression:
Cons
Bra
dx it = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
ii) Regression:
Cons
Arg
dx it = a 0 + a 1 *dx USt
i) Regression:
Cons
Mex
-0.008
(0.003)
0.023
(0.011)
1.111
(0.071)
-0.515
(0.273)
0.778
0.769
81:1-01:2
***
**
***
*
0.016 *
(0.008)
-0.016
(0.034)
0.126
(0.220)
1.163
(0.852)
0.132
0.099
81:1-01:2
-0.161
(0.054)
0.143
(0.064)
5.582
(1.553)
-4.731
(1.786)
0.357
0.283
94:1-01:2
***
**
***
**
0.009
(0.013)
0.020
(0.032)
0.753 *
(0.435)
-0.939
(0.839)
0.083
0.010
91:1-01:2
76
Table 3.3 (Continued)
Can
iv) Regression:
Cons
dx it-1
dx US t
2
R
2
Adj R
Sam ple
v) R egression:
Cons
d94
dx it-1
d94*dx it-1
dx US t
d94*dx U St
2
R
2
Adj R
Sam ple
vi) Regression:
Cons
d97
dx it-1
d97*dx it-1
dx US t
d97*dx U St
2
R
2
Adj R
Sam ple
dx it
-0.006 ***
(0.002)
0.524 ***
(0.058)
0.604 ***
(0.072)
0.891
0.889
81:2-01:2
M ex
Arg
= a 0 + a 1 *dx it-1 + a 2 *dx U St
Bra
-0.006
(0.005)
0.779 ***
(0.066)
0.358 ***
(0.131)
0.650
0.641
81:2-01:2
Chi
0.025 **
(0.011)
0.361 ***
(0.130)
-0.209
(0.312)
0.176
0.132
91:2-01:2
-0.006
(0.006)
0.805 ***
(0.060)
0.457 **
(0.185)
0.759
0.753
81:2-01:2
Fra
G er
Ita
0.002
(0.002)
0.819 ***
(0.055)
0.072 *
(0.042)
0.743
0.736
81:2-01:2
0.004
(0.004)
0.843 ***
(0.060)
-0.008
(0.084)
0.721
0.714
81:2-01:2
0.001
(0.002)
0.719 ***
(0.071)
0.127 ***
(0.047)
0.643
0.634
81:2-01:2
8
0.001
(0.002)
-0.001
(0.006)
0.874 ***
(0.063)
-0.264 **
(0.126)
0.044
(0.043)
0.244 *
(0.141)
0.767
0.751
81:2-01:2
0.005
(0.004)
-0.009
(0.013)
0.851 ***
(0.062)
-0.301
(0.301)
-0.039
(0.090)
0.368
(0.292)
0.732
0.715
81:2-01:2
0.002
(0.002)
-0.006
(0.008)
0.781 ***
(0.080)
-0.186
(0.181)
0.095 ***
(0.052)
0.230
(0.162)
0.661
0.639
81:2-01:2
8
0.002
(0.002)
0.000
(0.009)
0.825 ***
(0.061)
-0.080
(0.201)
0.067
(0.045)
0.061
(0.173)
0.744
0.727
81:2-01:2
0.005
(0.004)
-0.018
(0.019)
0.843 ***
(0.061)
-0.045
(0.443)
-0.031
(0.090)
0.441
(0.357)
0.728
0.710
81:2-01:2
0.002
(0.002)
-0.009
(0.009)
0.767 ***
(0.075)
-0.258
(0.216)
0.102 **
(0.050)
0.325 *
(0.183)
0.669
0.647
81:2-01:2
8
dx it = a 0 + a 1 *dx it-1 + a 2 *dx U St + a 3 *d94 + a 4 *d94*dx it-1 + a 5 *d94*dx U St
-0.007 ***
(0.002)
-0.003
(0.007)
0.474 ***
(0.066)
0.241
(0.152)
0.650 ***
(0.083)
-0.133
(0.184)
0.895
0.888
81:2-01:2
-0.005
(0.006)
-0.042
(0.018)
0.809
(0.095)
-0.217
(0.143)
0.279
(0.140)
1.350
(0.512)
0.681
0.660
81:2-01:2
-0.014
(0.018)
**
***
*
0.813 ***
(0.104)
0.414
(0.487)
**
0.746
0.726
94:2-01:2
0.046
(0.012)
-0.061
(0.023)
0.027
(0.170)
0.691
(0.236)
-1.027
(0.496)
1.627
(0.672)
0.404
0.319
91:2-01:2
***
**
***
**
**
-0.008
(0.007)
0.019
(0.028)
0.780 ***
(0.068)
0.050
(0.174)
0.558 ***
(0.209)
-0.632
(0.645)
0.763
0.748
81:2-01:2
dx it = a 0 + a 1 *dx it-1 + a 2 *dx U St + a 3 *d97 + a 4 *d97*dx it-1 + a 5 *d97*dx U St
-0.007 ***
(0.002)
0.008
(0.011)
0.508 ***
(0.062)
0.005
(0.295)
0.627 ***
(0.078)
-0.191
(0.222)
0.893
0.886
81:2-01:2
-0.005
(0.005)
-0.016
(0.024)
0.780 ***
(0.075)
-0.203
(0.311)
0.314 **
(0.140)
0.712
(0.557)
0.658
0.635
81:2-01:2
-0.142
(0.043)
0.134
(0.047)
0.163
(0.232)
0.697
(0.255)
4.787
(1.382)
-4.662
(1.462)
0.832
0.796
94:2-01:2
***
***
**
***
***
0.026 *
(0.013)
-0.022
(0.032)
0.311 **
(0.146)
0.301
(0.383)
-0.092
(0.439)
0.187
(0.771)
0.205
0.092
91:2-01:2
-0.006
(0.007)
0.006
(0.030)
0.794 ***
(0.065)
-0.027
(0.203)
0.531 **
(0.203)
-0.367
(0.718)
0.763
0.748
81:2-01:2
Table 3.4
Results from regressions of the annual growth rate of Industrial Production in a given country
and the annual growth rate of Industrial Production in the USA
77
Can
dxUSt
2
R
2
Adj R
Sample
-0.017 ***
(0.002)
1.268 ***
(0.052)
0.778
0.777
87:01-01:07
d94
dxUSt
d94*dxUSt
2
R
2
Adj R
Sample
-0.020
(0.002)
0.011
(0.073)
1.455
(0.005)
-0.394
(0.115)
0.793
0.790
87:01-01:07
d97
dxUSt
d97*dxUSt
2
R
2
Adj R
Sample
Chi
Fra
Ger
Ire
0.022 ***
(0.007)
0.443 ***
(0.153)
0.046
0.041
87:02-01:07
-0.016
(0.010)
0.733 ***
(0.224)
0.058
0.052
87:01-01:07
0.062 ***
(0.005)
0.050
(0.111)
0.001
-0.005
87:01-01:07
0.004
(0.004)
0.440 ***
(0.083)
0.140
0.135
87:01-01:06
0.011 **
(0.005)
0.153
(0.115)
0.010
0.005
87:01-01:04
0.059 ***
(0.007)
1.195 ***
(0.164)
0.251
0.246
87:01-01:04
***
**
***
***
0.034 ***
(0.007)
-0.053 ***
(0.015)
-0.045
(0.216)
1.321 ***
(0.338)
0.126
0.111
87:01-01:07
-0.040 **
(0.016)
0.980 ***
(0.980)
0.108
0.097
95:01-01:08
-0.018
(0.011)
0.013
(0.021)
0.322
(0.335)
0.424
(0.494)
0.084
0.068
87:01-01:08
0.071 ***
(0.005)
-0.035 ***
(0.010)
0.230
(0.158)
0.141
0.004
(0.004)
0.000
(0.009)
0.198 *
(0.117)
0.331 *
(0.193)
0.188
0.174
87:01-01:06
0.016
(0.005)
-0.029
(0.013)
-0.346
(0.149)
1.144
(0.289)
0.132
0.117
87:01-01:04
0.055 ***
(0.007)
0.054 **
(0.027)
1.092 ***
(0.216)
-0.766
(0.547)
0.278
0.264
87:01-01:04
(0.233)
0.126
0.110
87:01-01:08
0.001
(0.004)
0.022 **
(0.009)
0.388 ***
(0.097)
-0.136
(0.194)
0.202
0.187
87:01-01:06
0.012 **
(0.005)
0.000
(0.016)
-0.058
(0.130)
0.473
(0.322)
0.081
0.065
87:01-01:04
***
**
**
***
dx it = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
iii) Regression:
Cons
Bra
dx it = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
ii) Regression:
Cons
Arg
dx it = a 0 + a 1 *dx USt
i) Regression:
Cons
Mex
-0.022
(0.002)
0.020
(0.005)
1.414
(0.060)
-0.499
(0.111)
0.801
0.798
87:01-01:07
***
***
***
***
0.031 ***
(0.007)
-0.038
(0.015)
-0.051 **
(0.178)
1.408 ***
(0.329)
0.157
0.143
87:01-01:07
-0.116 **
(0.057)
0.082
(0.059)
2.581 **
(1.225)
-1.711
(1.269)
0.130
0.096
95:01-01:08
-0.025
(0.011)
0.040
(0.022)
0.968
(0.283)
-0.796
(0.482)
0.076
0.059
87:01-01:08
**
*
***
*
0.072 ***
(0.005)
-0.050 ***
(0.010)
0.132
(0.124)
0.233
(0.211)
0.239
0.226
87:01-01:08
78
0.058 ***
(0.007)
0.025
(0.040)
1.176 ***
(0.185)
-0.379
(0.763)
0.254
0.239
87:01-01:04
Table 3.4 (Continued)
Can
dx it
iv) Regression:
Cons
dx it-1
dx USt
2
R
2
Adj R
Sample
-0.006 ***
(0.002)
0.726 ***
(0.046)
0.372 ***
(0.065)
0.910
0.909
87:02-01:07
d94
dx it-1
d94*dx it-1
dx USt
d94*dx USt
2
R
2
Adj R
Sample
-0.005 **
(0.002)
0.373
(0.004)
0.751 ***
(0.068)
-0.063
(0.096)
0.373 ***
(0.139)
0.040
(0.096)
0.912
0.909
87:02-01:07
d97
dx it-1
d97*dx it-1
dx USt
d97*dx USt
2
R
2
Adj R
Sample
-0.007
(0.008)
0.621 ***
(0.060)
0.289
(0.182)
0.424
0.417
87:02-01:08
Chi
0.009 **
(0.004)
0.848 ***
(0.041)
0.009
(0.060)
0.717
0.713
87:02-01:08
Fra
Ger
Ire
0.000
(0.002)
0.820 ***
(0.041)
0.112
(0.049) **
0.739
0.736
87:02-01:06
0.000
(0.003)
0.822 ***
(0.043)
0.081
(0.065)
0.688
0.684
87:02-01:04
0.033 ***
(0.008)
0.425 ***
(0.069)
0.728 ***
(0.164)
0.393
0.385
87:02-01:04
87
0.019
(0.006)
-0.019
(0.011)
0.442
(0.098)
0.389
(0.116)
-0.003
(0.147)
0.145
(0.245)
0.600
0.588
87:02-01:07
***
-0.015
(0.011)
*
***
0.709 ***
(0.079)
***
0.367
(0.235)
0.575
0.564
95:01-01:08
-0.007
(0.009)
-0.001
(0.017)
0.573 ***
(0.073)
0.120
(0.131)
0.079
(0.268)
0.278
(0.399)
0.432
0.415
87:02-01:08
0.016 ***
(0.006)
-0.014 *
(0.008)
0.779 ***
(0.067)
0.081
(0.088)
0.021
(0.091)
0.081
(0.088)
0.723
0.715
87:02-01:08
0.000
(0.002)
-0.001
(0.005)
0.832 ***
(0.054)
-0.054
(0.089)
0.068
(0.067)
0.102
(0.115)
0.740
0.733
87:02-01:06
0.001
(0.003)
-0.010
(0.008)
0.868 ***
(0.053)
-0.244 **
(0.101)
0.024
(0.091)
0.359 **
(0.181)
0.703
0.694
87:02-01:04
0.045 ***
(0.009)
-0.018
(0.028)
0.197 **
(0.106)
0.377
(0.142)
0.883 ***
(0.218)
-0.340
(0.499)
0.424
0.405
87:02-01:04
87
-0.001
(0.002)
0.003
(0.006)
0.813 ***
(0.047)
-0.017
(0.125)
0.106 *
(0.058)
-0.009
(0.115)
0.740
0.732
87:02-01:06
0.001
(0.003)
-0.004
(0.010)
0.844 ***
(0.047)
-0.274 *
(0.140)
0.029
(0.075)
0.309
(0.186)
0.700
0.691
87:02-01:04
0.032 ***
(0.008)
0.007
(0.041)
0.450 ***
(0.080)
-0.106
(0.167)
0.668 ***
(0.187)
0.179
(0.691)
0.396
0.376
87:02-01:04
87
dx it = a 0 + a 1 *dx it-1 + a 2 *dx USt + a 3 *d97 + a 4 *d97*dx it-1 + a 5 *d97*dx USt
vi) Regression:
Cons
0.006
(0.005)
0.740 ***
(0.051)
0.114
(0.105)
0.571
0.566
87:02-01:07
Bra
dx it = a 0 + a 1 *dx it-1 + a 2 *dx USt + a 3 *d94 + a 4 *d94*dx it-1 + a 5 *d94*dx USt
v) Regression:
Cons
Mex
Arg
= a 0 + a 1 *dx it-1 + a 2 *dx USt
-0.005
(0.002)
0.001
(0.004)
0.779
(0.054)
-0.336
(0.122)
0.323
(0.086)
0.238
(0.137)
0.915
0.912
87:02-01:07
**
***
***
*
0.009
(0.005)
-0.016
(0.011)
0.757
(0.055)
-0.589
(0.188)
-0.012
(0.123)
1.151
(0.325)
0.603
0.591
87:02-01:07
*
***
***
***
-0.093
(0.048)
0.082
(0.049)
0.433
(0.139)
0.387
(0.170)
2.239
(1.054)
-2.015
(1.081)
0.610
0.583
95:01-01:08
-0.010
(0.009)
0.011
(0.018)
0.611 ***
(0.065)
0.025
(0.186)
0.365
(0.235)
-0.236
(0.390)
0.425
0.408
87:02-01:08
0.018 ***
(0.005)
-0.017 **
(0.007)
0.746 ***
(0.059)
0.121
(0.093)
0.034
(0.075)
0.029
(0.130)
0.729
0.721
87:02-01:08
Table 4.3
Results from regressions of the annual growth rate of different sectors of economic activity in Canada
and Mexico
on the annual growth rate of the same sector in the USA
79
C an
Agr
dx it
i) R egression:
C ons
dx U St
2
R
2
Adj R
Sam ple
0.027
(0.030)
-0.295
(0.517)
0.006
-0.013
88:01-01:02
d94
dx U St
d94*dx U St
2
R
2
Adj R
Sam ple
0.025
(0.035)
0.000
(0.082)
-0.320
(0.579)
0.095
(1.397)
0.009
-0.051
88:01-01:02
d97
dx U St
d97*dx U St
2
R
2
Adj R
Sam ple
0.007
(0.006)
0.255 ***
(0.048)
0.347
0.334
88:01-01:02
M ex
M in
0.010 **
(0.004)
0.109 ***
(0.035)
0.154
0.138
88:01-01:03
C an
M nf
-0.006
(0.007)
0.884 ***
(0.141)
0.432
0.421
88:01-01:02
M ex
M nf
C an
C nt
0.038
(0.009)
0.147
(0.178)
0.013
-0.006
88:01-01:03
0.01
(0.029
0.13
(0.308
0.02
-0.09
99:01-01:0
0.051
(0.033)
-0.007
(0.064)
-0.771
(0.556)
0.298
(1.098)
0.049
-0.007
88:01-01:03
0.009
(0.009)
-0.004
(0.012)
0.241 ***
(0.072)
0.026
(0.099)
0.348
0.309
88:01-01:02
0.008
(0.007)
0.003
(0.009)
0.061
(0.051)
0.092
(0.070)
0.200
0.153
88:01-01:03
(0.228)
0.661
0.640
88:01-01:02
0.040 ***
(0.014)
-0.005
(0.018)
0.029
(0.247)
0.260
(0.362)
0.024
-0.033
88:01-01:03
-0.03
(0.009
0.04
(0.027
0.71
(0.165
-0.55
(0.321
0.40
0.36
88:01-01:0
-0.026 ***
(0.007)
0.048 ***
(0.013)
1.028 ***
(0.133)
-0.171
(0.309)
0.618
0.595
88:01-01:02
0.045 ***
(0.011)
-0.019
(0.016)
-0.170
(0.192)
1.355 ***
(0.365)
0.248
0.204
88:01-01:03
-0.03
(0.008
0.11
(0.030
0.70
(0.118
-1.23
(0.322
0.51
0.48
88:01-01:0
-0.033 ***
(0.008)
0.049 ***
(0.011)
0.952 ***
(0.142)
-0.122
dx it = a 0 + a 1 *dx U St + a 2 *d97 + a 3 *d97*dx U St
iii) R egression:
C ons
0.053 *
(0.028)
-0.693
(0.473)
0.039
0.021
88:01-01:03
C an
M in
dx it = a 0 + a 1 *dx U St + a 2 *d94 + a 3 *d94*dx U St
ii) R egression:
C ons
M ex
Agr
= a 0 + a 1 *dx U St
0.031
(0.033)
-0.031
(0.095)
-0.350
(0.569)
0.487
(1.581)
0.008
-0.051
88:01-01:02
0.058 *
(0.032)
-0.023
(0.067)
-0.829
(0.546)
0.542
(1.148)
0.048
-0.008
88:01-01:03
0.011
(0.008)
-0.011
(0.013)
0.220 ***
(0.070)
0.063
(0.098)
0.358
0.319
88:01-01:02
0.010
(0.006)
0.002
(0.009)
0.101 *
(0.051)
0.019
(0.072)
0.157
0.108
88:01-01:03
80
Table 4.3 (Continued)
Can
Egw
dx it
i) Regression:
Cons
dx USt
2
R
2
Adj R
Sam ple
0.002
(0.007)
0.212 *
(0.118)
0.058
0.040
88:01-01:02
d94
dx USt
d94*dx USt
2
R
2
Adj R
Sam ple
0.005
(0.013)
-0.002
(0.015)
-0.021
(0.203)
0.403
(0.247)
0.170
0.121
88:01-01:02
d97
dx USt
d97*dx USt
2
R
2
Adj R
Sam ple
0.042 ***
(0.010)
-0.042
(0.187)
0.001
-0.018
88:01-01:02
Mex
Tcm
0.028 *
(0.014)
0.477 *
(0.265)
0.058
0.040
88:01-01:03
Can
Scs
Mex
Scs
Can
Sfi
0.006
(0.009)
0.281 **
(0.115)
0.103
0.086
88:01-01:02
0.024 **
(0.010)
-0.051
(0.127)
0.003
-0.016
88:01-01:03
0.0
(0.00
-0.0
(0.06
0.0
0.0
88:01-01
0.037 ***
(0.009)
0.001
(0.011)
-0.100
(0.144)
0.207
(0.175)
0.088
0.034
88:01-01:03
0.043 ***
(0.014)
-0.005
(0.018)
-0.355
(0.248)
0.630 *
(0.329)
0.281
0.238
88:01-01:02
0.041 *
(0.022)
-0.023
(0.028)
-0.063
(0.391)
1.008 *
(0.503)
0.225
0.179
88:01-01:03
-0.014
(0.009)
0.002
(0.019)
0.378 ***
(0.099)
0.293
(0.245)
0.496
0.465
88:01-01:02
0.055
(0.011)
-0.097
(0.022)
-0.340
(0.123)
1.119
(0.292)
0.312
0.272
88:01-01:03
0.0
(0.00
0.0
(0.00
-0.0
(0.09
0.0
(0.12
0.0
-0.0
88:01-01
-0.001
(0.007)
0.037
(0.034)
0.257 ***
(0.086)
-0.144
(0.424)
0.537
0.510
88:01-01:02
0.026 **
(0.010)
-0.057
(0.039)
-0.109
(0.129)
0.817
(0.501)
0.073
0.019
88:01-01:03
***
***
***
***
dx it = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
iii) Regression:
Cons
0.037 ***
(0.005)
0.015
(0.082)
0.001
-0.018
88:01-01:03
Can
Tcm
dx it = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
ii) Regression:
Cons
Mex
Egw
= a 0 + a 1 *dx USt
0.014
(0.011)
-0.017
(0.014)
-0.019
(0.180)
0.424
(0.241)
0.114
0.061
88:01-01:02
0.041 ***
(0.007)
-0.003
(0.009)
-0.147
(0.119)
0.348 **
(0.159)
0.149
0.099
88:01-01:03
0.029 **
(0.012)
0.024
(0.019)
0.024
(0.216)
0.091
(0.354)
0.222
0.175
88:01-01:02
0.006
(0.017)
0.038
(0.025)
0.604 **
(0.300)
0.214
(0.467)
0.344
0.305
88:01-01:03
Table 4.4
Results from regressions of the annual growth rate of different sectors of economic activity in Canada
and Mexico
on its lagged value and the annual growth rate of the same sector in the USA
81
0.0
(0.00
-0.0
(0.0
-0.1
(0.06
0.3
(0.13
0.1
0.0
88:01-01
Can
Agr
iv ) R e g r e s s io n :
C ons
d x it- 1
dxUSt
2
R
2
Adj R
S a m p le
v ) R e g r e s s io n :
C ons
d94
d x it- 1
d 9 4 * d x it- 1
dxUSt
d 9 4 *d x U St
2
R
2
Adj R
S a m p le
v i) R e g r e s s io n :
C ons
d97
d x it- 1
d 9 7 * d x it- 1
dxUSt
d 9 7 *d x U St
2
R
2
Adj R
S a m p le
dx
it
- 0 .0 0 1
( 0 .0 2 1 )
0 .7 2 5 * * *
( 0 .0 9 4 )
0 .0 7 6
0 .3 7 0
0 .5 4 2
0 .5 2 3
8 8 :0 2 - 0 1 :0 2
dx
- 0 .0 0 4
( 0 .0 2 3 )
0 .0 3 2
( 0 .0 6 4 )
0 .6 6 2 * * *
( 0 .1 1 2 )
0 .2 5 1
( 0 .2 1 6 )
0 .1 8 9
( 0 .4 1 2 )
- 0 .6 9 9
( 1 .0 7 1 )
0 .5 6 1
0 .5 1 4
8 8 :0 2 - 0 1 :0 2
1
*d x
it- 1
0 .0 6 4 * *
( 0 .0 3 0 )
- 0 .2 5 9 *
( 0 .1 3 6 )
- 0 .8 2 5
( 0 .5 0 7 )
0 .0 9 2
0 .0 5 6
8 8 :0 2 - 0 1 :0 3
it
- 0 .0 0 2
( 0 .0 2 4 )
0 .0 1 3
( 0 .0 5 5 )
0 .7 1 4 * * *
( 0 .1 1 8 )
0 .0 7 2
( 0 .2 0 7 )
0 .1 9 6
( 0 .4 2 4 )
- 0 .4 1 7
( 0 .9 5 1 )
0 .5 5 3
0 .5 0 6
8 8 :0 2 - 0 1 :0 2
dx
M ex
Agr
= a 0 + a
= a
0
+ a
1
*d x
it- 1
0 .0 4 9
( 0 .0 3 8 )
0 .0 0 2
( 0 .0 6 5 )
- 0 .0 1 8
( 0 .2 2 5 )
- 0 .4 2 7
( 0 .2 8 5 )
- 0 .7 1 9
( 0 .6 4 7 )
0 .2 6 2
( 1 .1 2 1 )
0 .1 4 8
0 .0 6 0
8 8 :0 2 - 0 1 :0 3
it
= a
0
+ a
1
*d x
+ a
2
Can
M in
*d x USt
M ex
M in
0 .0 0 1
( 0 .0 0 5 )
0 .6 5 8 * * *
( 0 .0 9 5 )
0 .0 7 8
( 0 .0 4 7 )
0 .6 3 0
0 .6 1 5
8 8 :0 2 - 0 1 :0 2
+ a
2
*d x
USt
+ a
3
it- 1
0 .0 8 1 * *
( 0 .0 3 6 )
- 0 .0 4 7
( 0 .0 6 8 )
- 0 .3 3 1 * *
( 0 .1 5 7 )
0 .2 4 1
( 0 .3 6 6 )
- 1 .1 7 4 *
( 0 .6 1 8 )
0 .9 1 9
( 1 .1 7 7 )
0 .1 1 7
0 .0 2 5
8 8 :0 2 - 0 1 :0 3
+ a
2
*d x
USt
+ a
3
0 .0 0 4
( 0 .0 0 4 )
0 .5 5 2 * * *
( 0 .1 1 5 )
0 .0 5 5
( 0 .0 3 7 )
0 .4 1 0
0 .3 8 7
8 8 :0 2 - 0 1 :0 3
*d 9 4 + a
0 .0 0 1
( 0 .0 0 7 )
0 .0 0 1
( 0 .0 0 9 )
0 .7 0 2 * * *
( 0 .1 2 3 )
- 0 .1 1 7
( 0 .2 0 1 )
0 .0 4 9
( 0 .0 8 1 )
0 .0 5 7
( 0 .1 0 5 )
0 .6 3 4
0 .5 9 5
8 8 :0 2 - 0 1 :0 2
4
*d 9 4 *d x
it- 1
4
*d 9 7 *d x
it- 1
0 .0 0 5
( 0 .0 0 5 )
5 .2 7 6
( 2 4 .0 4 5 )
0 .5 5 7 * * *
( 0 .1 2 0 )
0 .0 0 5
( 0 .0 7 5 )
0 .0 5 2
( 0 .0 6 3 )
0 .0 0 5
( 0 .0 7 5 )
0 .4 1 2
0 .3 5 1
8 8 :0 2 - 0 1 :0 3
M ex
M nf
- 0 .0 0 1
( 0 .0 0 4 )
0 .8 2 9 * * *
( 0 .0 8 6 )
0 .1 2 6
( 0 .1 2 1 )
0 .7 9 8
0 .7 9 0
8 8 :0 2 - 0 1 :0 2
+ a
0 .0 0 6
( 0 .0 0 6 )
3 .2 4 4
( 7 .4 5 7 )
0 .5 5 4 * * *
( 0 .1 1 9 )
- 0 .0 3 2
( 0 .0 7 4 )
- 0 .0 3 0
( 0 .0 6 5 )
0 .1 1 8
( 0 .0 7 6 )
0 .4 4 4
0 .3 8 6
8 8 :0 2 - 0 1 :0 3
*d 9 7 + a
0 .0 0 2
( 0 .0 0 7 )
- 0 .0 0 1
( 0 .0 1 0 )
0 .6 8 1 * * *
( 0 .1 1 1 )
- 0 .1 3 8
( 0 .2 4 8 )
0 .0 5 7
( 0 .0 7 4 )
- 0 .1 3 8
( 0 .2 4 8 )
0 .6 3 3
0 .5 9 4
8 8 :0 2 - 0 1 :0 2
Can
M nf
5
*d 9 4 *d x
5
*d 9 7 *d x
0 .0 0 2
( 0 .0 0 7 )
0 .8 0 0 * * *
( 0 .0 9 2 )
0 .1 3 4
( 0 .1 2 0 )
0 .6 0 3
0 .5 8 8
8 8 :0 2 - 0 1 :0 3
- 0 .0 1 3 * *
( 0 .0 0 5 )
0 .7 1 7 * * *
( 0 .0 7 9 )
0 .2 3 1 * * *
( 0 .0 7 8 )
0 .7 6 5
0 .7 5 5
8 8 :0 2 - 0 1 :0 2
0 .0 0 4
( 0 .0 1 4 )
- 0 .0 0 1
( 0 .0 1 7 )
0 .7 2 3 * * *
( 0 .2 1 5 )
0 .1 0 3
( 0 .2 4 0 )
0 .1 6 1
( 0 .1 8 4 )
- 0 .0 7 8
( 0 .2 5 4 )
0 .6 0 6
0 .5 6 5
8 8 :0 2 - 0 1 :0 3
- 0 .0 1 5 * *
( 0 .0 0 7 )
0 .0 0 4
( 0 .0 1 7 )
0 .6 5 4 * * *
( 0 .1 0 9 )
0 .1 3 8
( 0 .1 7 0 )
0 .2 9 3 * *
( 0 .1 2 3 )
- 0 .1 0 9
( 0 .2 1 3 )
0 .7 6 9
0 .7 4 4
8 8 :0 2 - 0 1 :0 2
0 .0 1 2
( 0 .0 0 9 )
- 0 .0 1 0
( 0 .0 1 7 )
0 .7 5 5 * * *
( 0 .1 1 0 )
- 0 .1 9 8
( 0 .2 9 2 )
- 0 .0 4 3
( 0 .1 4 7 )
0 .7 3 9
( 0 .3 5 6 )
0 .6 4 0
0 .6 0 3
8 8 :0 2 - 0 1 :0 3
- 0 .0 1 7 * * *
( 0 .0 0 6 )
0 .0 2 6
( 0 .0 3 3 )
0 .6 4 1 * * *
( 0 .0 9 5 )
0 .1 1 9
( 0 .2 8 7 )
0 .3 3 3 * *
( 0 .0 9 7 )
0 .1 1 9
( 0 .2 8 7 )
0 .7 8 0
0 .7 5 7
8 8 :0 2 - 0 1 :0 2
USt
0 .0 0 4
( 0 .0 0 8 )
0 .0 0 1
( 0 .0 1 1 )
0 .9 7 9 * * *
( 0 .1 4 7 )
- 0 .5 4 3 * *
( 0 .2 3 1 )
- 0 .1 5 6
( 0 .2 0 1 )
0 .6 9 2 * *
( 0 .2 6 7 )
0 .8 3 2
0 .8 1 4
8 8 :0 2 - 0 1 :0 2
+ a
Can
C nt
USt
- 0 .0 0 1
( 0 .0 0 6 )
0 .0 2 3
( 0 .0 1 6 )
0 .8 5 7 * * *
( 0 .1 1 3 )
- 0 .8 5 0 * *
( 0 .4 0 4 )
0 .0 4 7
( 0 .1 6 5 )
0 .8 0 7 * *
( 0 .3 4 0 )
0 .8 2 5
0 .8 0 7
8 8 :0 2 - 0 1 :0 2
82
Table 4.4 (Continued)
Can
Egw
iv ) R e g r e s s io n :
Cons
d x it- 1
dxUSt
2
R
2
Adj R
S a m p le
v ) R e g r e s s io n :
Cons
d94
d x it- 1
d 9 4 * d x it- 1
dxUSt
d 9 4 *d x U St
2
R
2
Adj R
S a m p le
v i) R e g r e s s io n :
Cons
d97
d x it- 1
d 9 7 * d x it- 1
dxUSt
d 9 7 *d x U St
2
R
2
Adj R
S a m p le
dx
it
0 .0 0 3
( 0 .0 0 6 )
0 .6 2 6 ** *
( 0 .1 1 5 )
0 .0 2 3
( 0 .1 0 1 )
0 .4 0 8
0 .3 8 4
8 8 :0 2 -0 1 :0 2
dx
0 .0 1 2
( 0 .0 0 9 )
-0 .0 1 5
( 0 .0 1 2 )
0 .6 4 7 ** *
( 0 .1 2 5 )
-0 .2 7 7
( 0 .3 3 0 )
-0 .1 6 1
( 0 .1 4 8 )
0 .4 2 5 **
( 0 .2 2 9 )
0 .4 4 8
0 .3 9 0
8 8 :0 2 -0 1 :0 2
1
*d x
it- 1
0 .0 1 1
( 0 .0 0 6 )
0 .6 5 5 * * *
( 0 .1 0 5 )
0 .0 2 7
( 0 .0 6 3 )
0 .4 3 1
0 .4 0 9
8 8 : 0 2 - 0 1 :0 3
it
0 .0 0 8
( 0 .0 1 1 )
-0 .0 0 6
( 0 .0 1 3 )
0 .6 2 9 ** *
( 0 .1 4 4 )
-0 .1 9 6
( 0 .2 6 5 )
-0 .1 5 3
( 0 .1 7 2 )
0 .3 6 0
( 0 .2 2 6 )
0 .4 5 0
0 .3 9 2
8 8 :0 2 -0 1 :0 2
dx
M ex
Egw
= a 0 + a
= a
0
+ a
1
*d x
it- 1
0 .0 1 2
( 0 .0 1 0 )
0 .0 0 1
( 0 .0 1 2 )
0 .6 4 5 * * *
( 0 .1 9 2 )
- 0 .0 4 3
( 0 .2 3 3 )
- 0 .0 4 9
( 0 .1 1 4 )
0 .1 3 4
( 0 .1 3 8 )
0 .4 6 1
0 .4 0 5
8 8 : 0 2 - 0 1 :0 3
it
= a
0
+ a
1
*d x
it- 1
0 .0 1 7 * *
( 0 .0 0 8 )
- 0 .0 0 3
( 0 .0 1 2 )
0 .6 4 8 * * *
( 0 .1 4 6 )
- 0 .1 0 5
( 0 .2 2 3 )
- 0 .1 0 5
( 0 .0 9 5 )
0 .2 6 0 * *
( 0 .1 2 8 )
0 .4 8 2
0 .4 2 8
8 8 : 0 2 - 0 1 :0 3
+ a
2
C an
T cm
*d x USt
M ex
T cm
0 .0 0 9
(0 .0 0 6 )
0 .8 4 4 * * *
(0 .0 7 3 )
- 0 .0 7 2
(0 .0 9 9 )
0 .7 2 9
0 .7 1 8
8 8 :0 2 - 0 1 :0 2
+ a
2
*d x
USt
+ a
3
+ a
2
*d x
USt
+ a
3
-0 .0 0 3
(0 .0 1 0 )
0 .7 7 4 * **
(0 .0 8 5 )
0 .2 9 9 *
(0 .1 6 7 )
0 .6 4 2
0 .6 2 8
8 8 :0 2 - 0 1 : 0 3
*d 9 4 + a
0 .0 1 2
(0 .0 1 0 )
- 0 .0 0 2
(0 .0 1 3 )
0 .7 3 0 * * *
(0 .1 0 6 )
0 .1 4 0
(0 .1 8 7 )
- 0 .1 2 0
(0 .1 5 4 )
0 .0 6 8
(0 .2 1 0 )
0 .7 4 5
0 .7 1 7
8 8 :0 2 - 0 1 :0 2
4
*d 9 4 *d x
it - 1
0 .0 1 5
(0 .0 3 3 )
-0 .0 1 6
(0 .0 3 5 )
0 .4 0 7
(0 .4 5 9 )
0 .3 5 7
(0 .4 7 0 )
0 .1 4 2
(0 .3 5 6 )
0 .1 7 0
(0 .4 2 6 )
0 .6 5 1
0 .6 1 4
8 8 :0 2 - 0 1 : 0 3
*d 9 7 + a
0 .0 0 4
(0 .0 0 8 )
0 .0 1 3
(0 .0 1 5 )
0 .7 8 9 * * *
(0 .0 8 9 )
0 .0 7 4
(0 .2 4 2 )
0 .0 2 6
(0 .1 2 6 )
0 .0 7 4
(0 .2 4 2 )
0 .7 4 5
0 .7 1 8
8 8 :0 2 - 0 1 :0 2
Can
Scs
4
*d 9 7 *d x
M ex
Scs
0 .0 0 6
( 0 .0 0 4 )
0 .9 2 1 ** *
( 0 .0 6 5 )
-0 .0 5 8
( 0 .0 5 8 )
0 .8 1 8
0 .8 1 0
8 8 :0 2 -0 1 :0 2
+ a
5
*d 9 4 *d x
-0 .0 0 8
(0 .0 1 3 )
0 .0 1 7
(0 .0 2 4 )
0 .7 0 5 * **
(0 .1 1 4 )
-0 .0 6 7
(0 .3 0 4 )
0 .3 8 2 *
(0 .2 2 7 )
0 .0 1 0
(0 .3 9 9 )
0 .6 5 4
0 .6 1 8
8 8 :0 2 - 0 1 : 0 3
+ a
5
*d 9 7 *d x
0 .0 0 2
( 0 .0 0 7 )
0 .7 7 9 * * *
( 0 .0 9 1 )
0 .0 3 1
( 0 .0 8 5 )
0 .5 8 9
0 .5 7 3
8 8 : 0 2 - 0 1 :0 3
0 .0 2 1 * **
(0 .0 0 5 )
0 .5 4 1 * **
(0 .1 1 8 )
-0 .0 9 7 * *
(0 .0 5 4 )
0 .3 0 6
0 .2 7 8
8 8 :0 2 - 0 1 :0 2
0 .0 1 7 *
( 0 .0 1 0 )
1 .1 8 4
( 6 .4 9 0 )
0 .6 7 1 * * *
( 0 .1 1 1 )
- 0 .0 1 2
( 0 .0 6 5 )
- 0 .0 8 8
( 0 .1 0 3 )
0 .4 1 1
( 0 .2 6 8 )
0 .6 2 7
0 .5 8 8
8 8 : 0 2 - 0 1 :0 3
0 .0 2 4 * **
(0 .0 0 8 )
-0 .0 0 4
(0 .0 1 1 )
0 .4 0 0 *
(0 .2 0 2 )
0 .2 2 0
(0 .2 5 3 )
-0 .0 8 0
(0 .0 8 4 )
-0 .0 3 5
(0 .1 1 2 )
0 .3 1 8
0 .2 4 5
8 8 :0 2 - 0 1 :0 2
0 .0 0 4
( 0 .0 0 8 )
1 1 .2 8 0
( 9 .1 2 8 )
0 .7 5 2 * * *
( 0 .0 9 7 )
- 0 .1 1 3
0 .0 9 1
0 .0 1 0
( 0 .0 8 9 )
0 .3 5 5
( 0 .3 4 9 )
0 .6 0 7
0 .5 6 6
8 8 : 0 2 - 0 1 :0 3
0 .0 2 8 * **
(0 .0 0 7 )
-0 .0 1 4
(0 .0 1 1 )
0 .4 2 9 * **
(0 .1 4 7 )
0 .2 2 4
(0 .2 9 3 )
-0 .1 4 4 * *
(0 .0 6 5 )
0 .1 0 9
(0 .1 4 7 )
0 .3 3 8
0 .2 6 8
8 8 :0 2 - 0 1 :0 2
USt
0 .0 0 1
( 0 .0 0 6 )
( 0 .0 0 6 )
0 .0 1 2
0 .7 6 4 ** *
( 0 .1 2 8 )
0 .1 1 5
( 0 .1 7 7 )
0 .0 2 7
( 0 .0 8 1 )
-0 .0 4 9
( 0 .1 8 3 )
0 .8 2 9
0 .8 1 1
8 8 :0 2 -0 1 :0 2
it - 1
Can
S fi
USt
0 .0 0 5
( 0 .0 0 4 )
0 .0 1 8
( 0 .0 2 1 )
0 .8 4 0 ** *
( 0 .0 9 2 )
-0 .4 0 4
( 0 .2 6 1 )
-0 .0 3 9
( 0 .0 6 0 )
0 .0 7 4
( 0 .2 5 9 )
0 .8 4 2
0 .8 2 6
8 8 :0 2 -0 1 :0 2
Table 4.5
Results from regressions of the annual growth rate of different components of industrial production in
Mexico
on the annual growth rate of the same component in the USA
83
T otal
i) R egression:
C ons
dx USt
R2
Adj R 2
M ining
EG W
M anufacturing
0.012 **
(0.005)
0.513 ***
(0.097)
0.100
0.096
0.024 ***
(0.003)
0.448 ***
(0.072)
0.134
0.130
0.044 ***
(0.002)
-0.124 **
(0.055)
0.020
0.016
0.016 ***
(0.005)
0.407 ***
(0.087)
0.081
0.077
ii) R egression:
dx it = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
C ons
***
d94
dx USt
d94*dx USt
R2
Adj R 2
0.014
(0.005)
-0.017
(0.011)
0.290
(0.116)
0.712
(0.216)
0.140
0.129
iii) R egression:
C ons
d97
dx USt
d97*dx USt
R2
Adj R 2
Food and Bev.
T extiles
dx it = a 0 + a 1 *dx USt
*
**
***
0.027 ***
(0.004)
-0.008
(0.007)
0.431 ***
(0.079)
0.220
(0.211)
0.143
0.133
0.048 ***
(0.003)
-0.009 *
(0.005)
-0.104
(0.063)
-0.045
(0.127)
0.041
0.030
0.018 ***
(0.005)
-0.019 *
(0.010)
0.112
(0.102)
0.874 ***
(0.184)
0.167
0.157
0.028 ***
(0.003)
0.032
(0.149)
0.000
-0.004
0.010 **
(0.005)
-0.066
(0.107)
0.002
-0.002
0.027 ***
(0.005)
0.003
(0.007)
-0.058
(0.219)
0.201
(0.301)
0.007
-0.005
0.003
(0.006)
0.035 ***
(0.010)
-0.459 ***
(0.130)
1.196 ***
(0.212)
0.138
0.128
0.026 ***
(0.004)
0.010
(0.007)
-0.009
(0.007)
0.452
(0.344)
0.026
0.014
0.009
(0.005)
0.060
(0.013)
-0.432
(0.013)
1.896
(0.245)
0.204
0.194
dx it = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
0.012 **
(0.005)
-0.004
(0.011)
0.296 ***
(0.011)
0.820 ***
(0.219)
0.167
0.157
0.026 ***
(0.004)
-0.006
(0.007)
0.439 ***
(0.007)
0.101
(0.238)
0.137
0.127
0.047 ***
(0.003)
-0.014 **
(0.006)
-0.156 ***
(0.006)
0.206
(0.155)
0.044
0.032
0.018 ***
(0.005)
-0.010
(0.010)
0.151
(0.010)
0.945 ***
(0.188)
0.189
0.179
84
*
***
***
***
Table 4.5 (Continued)
Paper and Ed.
i) R e g re s s io n :
C ons
d x USt
R2
Adj R 2
ii) R e g re s s io n :
C ons
d94
d x USt
d 9 4 *d x U S t
R2
Adj R 2
iii) R e g re s s io n :
C ons
d97
d x USt
d 9 7 *d x U S t
R2
Adj R 2
C h e m ic a l P ro d
dx
it
= a
0
+ a
0 .0 2 1 ***
(0 .0 0 6 )
0 .2 0 7
(0 .1 5 8 )
0 .0 0 7
0 .0 0 3
d x it = a
0 .0 2 2 ***
(0 .0 0 7 )
0 .0 1 7
(0 .0 1 2 )
-0 .1 6 8
(0 .0 1 2 )
1 .5 6 9 ***
(0 .3 3 7 )
0 .1 0 8
0 .0 9 7
*d x
0
+ a
1
*d x
USt
+ a
0 .0 1 9 ***
(0 .0 0 5 )
0 .0 9 9
(0 .0 8 8 )
0 .0 0 5
0 .0 0 1
2
0 .0 3 2 ***
(0 .0 0 5 )
-0 .0 3 0 ***
(0 .0 0 8 )
-0 .1 0 2
(0 .0 9 7 )
1 .1 9 2 ***
(0 .2 1 4 )
0 .1 2 0
0 .1 1 0
0
+ a
1
*d x
B a s ic M e ta ls
M a c h in e ry
USt
0 .0 2 6 ***
(0 .0 0 4 )
0 .1 4 2
(0 .0 9 1 )
0 .0 1 0
0 .0 0 6
0 .0 3 6 ***
(0 .0 0 8 )
-0 .0 1 8
(0 .0 1 1 )
-0 .3 8 0 *
(0 .1 9 9 )
1 .6 2 3 ***
(0 .3 3 1 )
0 .0 9 5
0 .0 8 4
d x it = a
1
M in e ra ls
USt
+ a
0 .0 3 1 ***
(0 .0 0 4 )
-0 .0 2 9 ***
(0 .0 0 9 )
-0 .0 6 5
(0 .0 0 9 )
1 .3 1 1 ***
(0 .2 3 8 )
0 .1 1 8
0 .1 0 8
*d 9 4 + a
3
*d 9 4 *d x
*d 9 7 + a
3
*d 9 7 *d x
0 .0 0 0
(0 .0 1 0 )
0 .6 9 2 ***
(0 .1 0 2 )
0 .1 5 7
0 .1 5 3
0 .0 1 6 **
(0 .0 0 6 )
0 .0 2 4 **
(0 .0 1 1 )
0 .4 3 9 ***
(0 .0 4 6 )
0 .2 1 9
(0 .1 4 2 )
0 .3 3 9
0 .3 3 1
0 .0 0 1
(0 .0 1 1 )
0 .0 0 0
(0 .0 2 3 )
0 .6 2 0 ***
(0 .1 6 7 )
0 .1 0 2
(0 .2 2 8 )
0 .1 5 8
0 .1 4 7
0 .0 2 6 ***
(0 .0 0 6 )
0 .0 0 0
(0 .0 1 2 )
0 .4 4 3 ***
(0 .0 1 2 )
0 .3 5 9 **
(0 .1 6 6 )
0 .3 2 8
0 .3 2 0
0 .0 0 0
(0 .0 1 1 )
0 .0 1 6
(0 .0 2 4 )
0 .5 7 3 ***
(0 .0 2 4 )
0 .1 6 9
(0 .2 1 2 )
0 .1 6 6
0 .1 5 6
USt
0 .0 2 3 ***
(0 .0 0 6 )
-0 .0 5 2 ***
(0 .0 1 2 )
-0 .0 5 8
(0 .0 9 2 )
1 .4 3 5 ***
(0 .2 5 8 )
0 .1 2 0
0 .1 0 9
2
0 .0 2 6 ***
(0 .0 0 5 )
0 .4 7 0 ***
(0 .0 4 4 )
0 .3 1 5
0 .3 1 2
USt
0 .0 1 8 ***
(0 .0 0 5 )
-0 .0 1 6
(0 .0 1 3 )
0 .0 1 9
(0 .0 1 3 )
1 .0 1 0 ***
(0 .3 3 0 )
0 .0 4 2
0 .0 3 1
Table 4.6
Results from regressions of the annual growth rate of different components of industrial production in
Mexico
on its lagged value and the annual growth rate of the same sector in the USA
85
T o ta l
iv ) R e g r e s s io n :
- 0 .0 0 2 *
( 0 .0 0 1 )
0 .9 3 8 * * *
( 0 .0 1 8 )
0 .1 2 6 * * *
( 0 .0 3 0 )
0 .9 2 3
0 .9 2 2
C ons
d x it-1
d x USt
R2
Adj R
2
v ) R e g r e s s io n :
C ons
d94
d x it-1
d 9 4 * d x it-1
d x USt
d 9 4 *d x USt
R2
Adj R
2
C ons
d97
d x it-1
it- 1
d x USt
d 9 7 *d x USt
R2
Adj R
2
d x it = a
- 0 .0 0 3
( 0 .0 0 2 )
0 .0 0 3
( 0 .0 0 3 )
0 .9 4 1 * * *
( 0 .0 2 3 )
0 .0 2 5
( 0 .0 4 1 )
0 .1 6 8 * * *
( 0 .0 3 5 )
- 0 .1 5 7 * *
( 0 .0 7 3 )
0 .9 2 4
0 .9 2 3
v i) R e g r e s s io n :
d 9 7 *d x
M in in g
d x it = a
- 0 .0 0 2
( 0 .0 0 2 )
0 .0 0 6
( 0 .0 0 4 )
0 .9 4 8
( 0 .0 1 9 )
- 0 .5 1 3
( 0 .2 0 8 )
0 .1 3 9
( 0 .0 3 3 )
0 .4 9 8
( 0 .2 3 7 )
0 .9 2 5
0 .9 2 3
d x it = a
***
**
***
**
0
+ a
EGW
1
*d x
it-1
0 .0 0 8 * * *
(0 .0 0 3 )
0 .6 4 9 * * *
(0 .0 4 7 )
0 .1 5 7 * * *
(0 .0 5 8 )
0 .5 1 0
0 .5 0 6
0
+ a
1
*d x
0 .0 0 7
(0 .0 0 3 )
0 .0 0 2
(0 .0 0 5 )
0 .6 9 8
(0 .0 5 6 )
- 0 .1 8 5
(0 .1 0 4 )
0 .1 2 7
(0 .0 6 3 )
0 .2 0 0
(0 .1 6 8 )
0 .5 1 7
0 .5 0 7
0
+ a
1
it-1
**
***
*
**
*d x
it-1
0 .0 0 8 * * *
(0 .0 0 3 )
- 0 .0 0 1
(0 .0 0 6 )
0 .6 5 4 * * *
(0 .0 5 0 )
- 0 .0 6 2
(0 .1 5 4 )
0 .1 4 9 * *
(0 .0 6 1 )
0 .0 9 2
(0 .1 9 4 )
0 .5 1 1
0 .5 0 1
+ a
2
*d x
M a n u f a c t u r in g
2
*d x
USt
+ a
- 0 .0 0 2
(0 .0 0 2 )
0 .9 2 1 * * *
(0 .0 2 2 )
0 .1 1 5 * * *
(0 .0 3 2 )
0 .8 8 4
0 .8 8 3
3
0 .0 1 1 * * *
(0 .0 0 3 )
-0 .0 0 3
(0 .0 0 6 )
0 .7 5 4 * * *
(0 .0 4 9 )
0 .0 0 2
(0 .1 0 5 )
0 .0 3 2
(0 .0 4 4 )
-0 .0 1 5
(0 .0 8 8 )
0 .5 7 0
0 .5 6 1
+ a
2
*d x
T e x t ile s
USt
0 .0 1 0 * * *
(0 .0 0 2 )
0 .7 6 0 * * *
(0 .0 4 3 )
0 .0 2 7
(0 .0 3 8 )
0 .5 6 8
0 .5 6 5
+ a
Food and B ev.
USt
+ a
0 .0 1 1 * * *
(0 .0 0 3 )
-0 .0 0 3
(0 .0 0 6 )
0 .7 5 7 * * *
(0 .0 4 8 )
-0 .0 2 2
(0 .1 1 5 )
0 .0 1 5
(0 .0 4 1 )
0 .0 6 9
(0 .1 0 5 )
0 .5 7 0
0 .5 6 2
*d 9 4 + a
4
*d 9 4 *d x
it-1
0 .0 1 8 * * *
( 0 .0 0 3 )
0 .4 1 1 * * *
( 0 .0 5 8 )
- 0 .0 4 0
( 0 .1 3 7 )
0 .1 6 8
0 .1 6 1
+ a
5
- 0 .0 0 2
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 4 )
0 .9 2 6 * * *
(0 .0 2 8 )
0 .0 1 0
(0 .0 5 5 )
0 .1 3 4 * * *
(0 .0 3 8 )
- 0 .0 7 0
(0 .0 8 3 )
0 .8 8 4
0 .8 8 2
3
*d 9 7 + a
4
*d 9 7 *d x
- 0 .0 0 2
(0 .0 0 2 )
0 .0 0 6
(0 .0 0 5 )
0 .9 3 0
(0 .0 2 4 )
- 0 .5 7 1
(0 .1 9 1 )
0 .1 1 2
(0 .0 3 6 )
0 .6 0 4
(0 .2 1 3 )
0 .8 8 8
0 .8 8 6
it-1
***
***
***
***
*d 9 4 *d x
0 .0 0 4
( 0 .0 0 4 )
0 .5 5 8 * * *
( 0 .0 5 3 )
0 .0 0 0
( 0 .0 9 0 )
0 .3 0 7
0 .3 0 1
USt
0 .0 1 7 * * *
( 0 .0 0 5 )
0 .0 0 3
( 0 .0 0 8 )
0 .4 2 7 * * *
( 0 .0 6 6 )
- 0 .0 9 5
( 0 .1 4 4 )
- 0 .1 0 6
( 0 .2 0 1 )
0 .1 5 6
( 0 .2 7 9 )
0 .1 7 1
0 .1 5 4
+ a
5
*d 9 7 *d x
0 .0 0 2
( 0 .0 0 5 )
0 .0 0 7
( 0 .0 0 9 )
0 .2 6 5
( 0 .0 7 8 )
0 .4 4 9
( 0 .1 1 1 )
- 0 .3 1 5
( 0 .1 2 0 )
0 .5 2 5
( 0 .1 9 5 )
0 .3 7 5
0 .3 6 3
***
***
***
***
USt
0 .0 1 7 * * *
( 0 .0 0 4 )
0 .0 0 9
( 0 .0 1 0 )
0 .4 0 5 * * *
( 0 .0 6 1 )
- 0 .1 2 9
( 0 .2 2 8 )
- 0 .0 8 2
( 0 .1 6 5 )
0 .3 7 7
( 0 .3 3 9 )
0 .1 7 7
0 .1 6 0
0 .0 0 5
( 0 .0 0 5 )
0 .0 5 3
( 0 .0 1 6 )
0 .4 8 4
( 0 .0 6 1 )
- 0 .3 2 1
( 0 .1 7 3 )
- 0 .2 1 3
( 0 .1 0 8 )
1 .4 5 6
( 0 .3 1 2 )
0 .3 6 9
0 .3 5 6
86
***
***
**
***
Table 4.6 (Continued)
Paper and Ed.
iv ) R e g r e s s io n :
C ons
d x USt
2
v ) R e g r e s s io n :
C ons
d94
d x it- 1
d 9 4 * d x it- 1
d x USt
d 9 4 *d x USt
R2
Adj R
2
v i) R e g r e s s io n :
C ons
d97
d x it- 1
d 9 7 *d x
it- 1
d x USt
d 9 7 *d x USt
R2
Adj R
d x it = a
0
+ a
0 .0 1 1 *
(0 .0 0 5 )
0 .4 5 9 * * *
(0 .0 5 6 )
0 .1 4 1
(0 .1 4 0 )
0 .2 1 7
0 .2 1 1
d x it- 1
R2
Adj R
C h e m ic a l P r o d
2
d x it = a
d x it = a
it-1
M i n e r a ls
+ a
2
*d x
0
+ a
1
*d x
it-1
0 .0 1 1
(0 .0 0 4 )
-0 .0 0 9
(0 .0 0 7 )
0 .5 9 9
(0 .0 6 3 )
-0 .3 0 3
(0 .1 2 1 )
0 .0 1 6
(0 .0 8 3 )
0 .7 5 3
(0 .2 1 3 )
0 .3 7 2
0 .3 5 9
0
+ a
1
*d x
it-1
0 .0 1 1
(0 .0 0 4 )
-0 .0 0 9
(0 .0 0 8 )
0 .6 0 8
(0 .0 5 7 )
-0 .5 6 6
(0 .1 4 1 )
0 .0 1 7
(0 .0 7 8 )
1 .1 8 2
(0 .2 4 5 )
0 .4 0 1
0 .3 8 8
+ a
0 .0 0 2
(0 .0 0 3 )
0 .8 3 4 * * *
(0 .0 3 5 )
0 .0 8 1 *
(0 .0 4 9 )
0 .7 0 0
0 .6 9 8
2
*d x
USt
**
*
***
+ a
***
***
***
2
*d x
M a c h in e r y
+ a
3
*d 9 4 + a
0 .0 0 8 * *
(0 .0 0 4 )
0 .6 8 8 * * *
(0 .0 4 3 )
0 .1 7 4 * * *
(0 .0 3 6 )
0 .6 6 3
0 .6 6 0
4
0 .0 0 4
(0 .0 0 3 )
-0 .0 1 2
(0 .0 0 7 )
0 .8 1 4 * * *
(0 .0 4 8 )
-0 .0 0 3
(0 .0 7 5 )
0 .0 5 7
(0 .0 5 4 )
0 .2 6 9
(0 .1 6 9 )
0 .7 0 4
0 .6 9 8
***
***
B a s ic M e t a ls
USt
0 .0 0 9 * * *
(0 .0 0 3 )
0 .5 6 8 * * *
(0 .0 5 2 )
0 .1 0 7
(0 .0 7 5 )
0 .3 3 7
0 .3 3 1
0 .0 2 2 * * *
(0 .0 0 8 )
-0 .0 1 2
(0 .0 1 1 )
0 .3 6 0 * * *
(0 .0 7 0 )
0 .1 5 3
(0 .1 3 0 )
-0 .1 9 5
(0 .1 8 8 )
0 .7 8 5 * *
(0 .3 3 6 )
0 .2 4 6
0 .2 3 0
0 .0 1 4 * *
(0 .0 0 6 )
0 .0 0 8
(0 .0 1 4 )
0 .3 9 4 * * *
(0 .0 6 1 )
0 .0 3 4
(0 .2 2 1 )
-0 .0 7 1
(0 .1 7 1 )
0 .8 7 1 * *
(0 .4 3 2 )
0 .2 4 8
0 .2 3 2
1
*d x
USt
+ a
3
*d 9 7 + a
0 .0 0 2
(0 .0 0 3 )
-0 .0 0 2
(0 .0 0 7 )
0 .8 5 5 * * *
(0 .0 3 7 )
-0 .3 6 2 * *
(0 .1 4 2 )
0 .0 7 6
(0 .0 5 1 )
0 .4 4 9 *
(0 .2 3 1 )
0 .7 0 8
0 .7 0 3
*d 9 4 *d x
it-1
+ a
5
*d 9 4 *d x
-0 .0 0 4
(0 .0 0 5 )
0 .8 2 9 * * *
(0 .0 3 4 )
0 .1 8 5 * * *
(0 .0 5 9 )
0 .7 5 8
0 .7 5 6
USt
0 .0 0 5
(0 .0 0 5 )
0 .0 0 3
(0 .0 0 9 )
0 .6 4 7 * * *
(0 .0 5 0 )
0 .1 5 6
(0 .1 0 6 )
0 .1 9 0 * * *
(0 .0 3 8 )
-0 .1 2 1
(0 .1 2 4 )
0 .6 6 8
0 .6 6 1
4
*d 9 7 *d x
it-1
+ a
5
*d 9 7 *d x
0 .0 0 8 *
(0 .0 0 4 )
0 .0 0 3
(0 .0 1 0 )
0 .6 9 4 * * *
(0 .0 4 5 )
-0 .1 4 8
(0 .1 7 3 )
0 .1 6 6 * * *
(0 .0 3 7 )
0 .1 8 8
(0 .1 8 2 )
0 .6 6 5
0 .6 5 8
Table 4.13
Results from regressions of the annual growth rate of different components of aggregate demand
in Canada and Mexico on its lagged value and the annual growth rate of GDP in the USA
87
-0 .0 0 5
(0 .0 0 6 )
0 .0 0 2
(0 .0 1 2 )
0 .8 4 2 * * *
(0 .0 3 9 )
-0 .0 3 8
(0 .0 7 8 )
0 .2 6 3 * * *
(0 .0 9 1 )
-0 .1 0 3
(0 .1 3 3 )
0 .7 5 9
0 .7 5 4
USt
-0 .0 0 6
(0 .0 0 6 )
0 .0 1 3
(0 .0 1 4 )
0 .8 5 0 * * *
(0 .0 3 4 )
-0 .4 7 5 * * *
(0 .1 7 3 )
0 .2 1 0 * * *
(0 .0 7 8 )
0 .2 6 5
(0 .1 6 7 )
0 .7 6 5
0 .7 6 0
Household consumption
expenditure
CAN
i) Regression:
Cons
dxit-1
dGDP USt
2
R
2
Adj R
ii) Regression:
Cons
d94
dxit-1
d94*dx it-1
dGDP USt
d94*dGDP USt
2
R
2
Adj R
iii) Regression:
Cons
d97
dxit-1
d97*dx it-1
dGDP USt
d97*dGDP USt
2
R
2
Adj R
Government consumption
MEX
MEX
CAN
dx it = a 0 + a 1 *dx it-1 + a 2 *dGDP USt
0.004
(0.003)
0.858 ***
(0.043)
0.102
(0.064)
0.843
0.839
-0.008
(0.006)
0.804 ***
(0.063)
0.402 ***
(0.145)
0.681
0.673
0.014 *
(0.008)
0.798 ***
(0.067)
-0.082
(0.149)
0.697
0.689
0.008
(0.011)
0.256 **
(0.106)
0.273
(0.274)
0.076
0.053
Gross fixed capital
formation
Exports of goods and
services
Imports of goods and
services
CAN
MEX
CAN
MEX
CAN
MEX
-0.012
(0.008)
0.789 ***
(0.056)
0.704 ***
(0.224)
0.798
0.793
-0.042 ***
(0.016)
0.822 ***
(0.057)
1.432 ***
(0.416)
0.745
0.738
-0.008
(0.009)
0.644 ***
(0.072)
1.205 ***
(0.258)
0.700
0.692
0.067 ***
(0.023)
0.522 ***
(0.097)
-0.642
(0.537)
0.280
0.262
-0.021 ***
(0.008)
0.474 ***
(0.070)
2.039 ***
(0.278)
0.801
0.795
-0.065 ***
(0.023)
0.729 ***
(0.061)
2.728 ***
(0.626)
0.723
0.716
-0.017
(0.008)
-0.044
(0.025)
0.403
(0.080)
0.322
(0.159)
2.224
(0.310)
0.014
(0.726)
0.817
0.805
-0.066 ***
(0.025)
-0.037
(0.075)
0.755 ***
(0.066)
-0.267
(0.210)
2.664 ***
(0.671)
2.028
(2.416)
0.729
0.711
dx it = a 0 + a 1 *dx it-1 + a 2 *dGDP USt + a 3 *d94
0.007
-0.008
0.003
0.011
(0.004)
(0.007)
(0.011)
(0.012)
-0.005
-0.017
0.029
-0.040
(0.010)
(0.017)
(0.021)
(0.033)
0.833 ***
0.838 ***
0.908 ***
0.290 **
(0.054)
(0.095)
(0.098)
(0.125)
-0.070
-0.116
-0.320 **
-0.321
(0.157)
(0.133)
(0.150)
(0.280)
0.113
0.347 **
0.046
0.264
(0.071)
(0.162)
(0.161)
(0.302)
0.123
0.619
-0.511
0.966
(0.218)
(0.471)
(0.454)
(0.936)
0.846
0.690
0.717
0.105
0.836
0.670
0.699
0.047
+ a 4 *d94*dx it-1 + a 5 *d94*dGDP USt
-0.012
-0.041 **
-0.003
(0.008)
(0.017)
(0.009)
-0.008
-0.051
-0.078 **
(0.027)
(0.054)
(0.032)
0.793 ***
0.867 ***
0.553 ***
(0.063)
(0.077)
(0.096)
-0.040
-0.174
0.296 *
(0.155)
(0.133)
(0.153)
0.672 ***
1.386 ***
1.201 ***
(0.241)
(0.455)
(0.290)
0.331
1.621
1.347 *
(0.826)
(1.537)
(0.749)
0.799
0.751
0.728
0.785
0.735
0.710
0.094
(0.023)
-0.139
(0.060)
0.302
(0.111)
0.562
(0.225)
-1.374
(0.553)
2.973
(1.545)
0.391
0.352
dx it = a 0 + a 1 *dx it-1 + a 2 *dGDP USt + a 3 *d97
0.005
-0.008
0.015 *
0.009
(0.004)
(0.006)
(0.009)
(0.012)
0.005
0.006
-0.006
-0.031
(0.018)
(0.021)
(0.026)
(0.037)
0.856 ***
0.802 ***
0.792 ***
0.276 **
(0.045)
(0.071)
(0.074)
(0.115)
-0.191
-0.231
-0.185
-0.405
(0.307)
(0.266)
(0.342)
(0.406)
0.105
0.369 **
-0.102
0.231
(0.069)
(0.157)
(0.158)
(0.298)
0.084
0.326
0.247
1.036
(0.240)
(0.495)
(0.518)
(1.063)
0.844
0.687
0.700
0.091
0.834
0.667
0.680
0.032
+ a 4 *d97*dx it-1 + a 5 *d97*dGDP USt
-0.013
-0.040 **
-0.004
(0.008)
(0.017)
(0.009)
0.004
-0.031
-0.058
(0.030)
(0.056)
(0.037)
0.790 ***
0.843 ***
0.644 ***
(0.060)
(0.063)
(0.075)
0.011
-0.230
0.065
(0.222)
(0.271)
(0.269)
0.718 ***
1.448 ***
1.147 ***
(0.237)
(0.448)
(0.275)
-0.172
1.190
1.109
(0.909)
(1.887)
(0.890)
0.798
0.748
0.711
0.785
0.732
0.692
0.091
(0.024)
-0.186
(0.068)
0.468
(0.099)
-0.245
(0.448)
-1.141
(0.560)
5.681
(2.386)
0.348
0.305
***
**
***
**
**
*
***
***
***
**
**
**
*
***
**
***
-0.017 **
(0.008)
-0.041
(0.030)
0.458 ***
(0.072)
0.018
(0.283)
2.102 ***
(0.293)
0.542
(1.026)
0.812
0.799
Table 4.16
Results from regressions of the annual growth rate of employment in different regions in Mexico
on the annual growth rate of employment in the USA
88
-0.063 **
(0.024)
-0.020
(0.077)
0.740 ***
(0.064)
-0.280
(0.390)
2.724 ***
(0.669)
1.509
(2.816)
0.725
0.708
Total
i) Regression:
Cons
dxUSt
R2
Adj R2
ii) Regression:
Cons
d94
dxUSt
d94*dxUSt
R2
Adj R2
iii) Regression:
Cons
d97
dxUSt
d97*dxUSt
R2
Adj R2
North
dx it = a 0 + a 1 *dx USt
0.013
(0.008)
1.251 *
(0.514)
0.049
0.041
0.002
(0.009)
2.337 ***
(0.554)
0.134
0.126
Pacific
0.016 **
(0.008)
0.751
(0.506)
0.019
0.010
North Center
Center
Capital
0.024 ***
(0.008)
1.161 **
(0.500)
0.045
0.036
0.037 ***
(0.009)
0.413
(0.550)
0.005
-0.004
0.002
(0.012)
0.919
(0.717)
0.014
0.006
dxit = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
0.039
0.026 *
0.055 ***
(0.014)
(0.015)
(0.014)
-0.026
-0.020
-0.047 ***
(0.017)
(0.019)
(0.017)
-2.385
-1.426
-3.652 ***
(1.184)
(1.268)
(1.153)
3.836 ***
3.800 ***
5.058 ***
(1.324)
(1.418)
(1.290)
0.164
0.249
0.156
0.142
0.229
0.134
0.053 ***
(0.014)
-0.037 **
(0.017)
-1.954
(1.185)
3.666 ***
(1.326)
0.111
0.087
0.091
(0.015)
-0.082
(0.018)
-3.695
(1.244)
5.669
(1.391)
0.156
0.133
0.025
(0.020)
-0.019
(0.025)
-2.947 *
(1.683)
3.870 **
(1.883)
0.100
0.076
dxit = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
0.014
0.010
0.026 ***
(0.008)
(0.010)
(0.008)
-0.011
-0.026
-0.032 **
(0.013)
(0.016)
(0.013)
-0.027
1.002
-0.930 *
(0.499)
(0.627)
(0.494)
3.138 **
3.366 ***
4.235 ***
(0.812)
(1.020)
(0.804)
0.453
0.323
0.429
0.438
0.305
0.414
0.031 ***
(0.009)
-0.023
(0.014)
-0.184
(0.529)
3.370 ***
(0.861)
0.347
0.330
0.053
(0.010)
-0.047
(0.016)
-(1.341)
(0.612)
4.503
(0.996)
0.246
0.226
***
***
***
***
***
***
**
***
Table 4.17
Results from regressions of the annual growth rate of employment in different regions in the USA
on the annual growth rate of national employment in the USA
89
-0.005
(0.010)
0.005
(0.016)
-0.618
(0.613)
3.654 ***
(0.997)
0.560
0.548
New England
i) Regression:
Mideast Region
Great Lakes
Plains
Southeast
Southwest
Rock
dx it = a 0 + a 1 *dx USt
Cons
0.002
(0.001)
0.722 ***
(0.091)
0.351
0.345
0.003
(0.002)
0.750 ***
(0.122)
0.244
0.237
0.001 ***
(0.001)
1.063 ***
(0.031)
0.908
0.908
0.006 ***
(0.002)
1.041 ***
(0.141)
0.319
0.313
ii) Regression: dxit = a 0 + a 1 *dx USt + a 2 *d94 + a 3 *d94*dx USt
Cons
-0.014 ***
-0.020 ***
(0.003)
(0.003)
d94
0.016 ***
0.020 ***
(0.004)
(0.003)
dxUSt
1.540 ***
1.604 ***
(0.276)
(0.218)
-1.045 ***
-1.027 ***
d94*dxUSt
(0.308)
(0.243)
R2
0.366
0.603
Adj R2
0.350
0.593
0.009
(0.002)
-0.015
(0.002)
0.725
(0.158)
0.370
(0.177)
0.676
0.668
0.012 ***
(0.003)
-0.015 ***
(0.004)
0.177
(0.284)
0.882 ***
(0.317)
0.329
0.311
0.001
(0.001)
-0.001
(0.001)
1.288 ***
(0.059)
-0.168 **
(0.066)
0.945
0.944
0.011 ***
(0.004)
-0.007
(0.005)
0.642 *
(0.344)
0.538
(0.384)
0.332
0.314
iii) Regression: dxit = a 0 + a 1 *dx USt + a 2 *d97 + a 3 *d97*dx USt
Cons
-0.001
-0.010 ***
(0.002)
(0.002)
d97
-0.009 ***
0.004 ***
(0.003)
(0.003)
dxUSt
0.355 ***
0.955
(0.114)
(0.123)
1.107 ***
0.069
d97*dxUSt
(0.184)
(0.198)
R2
0.602
0.535
Adj R2
0.591
0.523
0.007 ***
(0.001)
-0.010 ***
(0.002)
0.716 ***
(0.071)
0.089
(0.115)
0.758
0.751
0.006 ***
(0.002)
-0.007 **
(0.003)
0.841 ***
(0.122)
-0.160
(0.196)
0.546
0.534
0.001
(0.001)
0.002 **
(0.001)
1.157 ***
(0.033)
-0.231 ***
(0.054)
0.937
0.935
0.004
(0.003)
0.007
(0.004)
1.358 ***
(0.166)
-0.786 ***
(0.268)
0.426
0.411
dxUSt
R2
Adj R2
-0.004 **
(0.002)
0.803 ***
(0.119)
0.280
0.274
-0.008 ***
(0.002)
0.999 ***
(0.105)
0.434
0.429
***
***
***
**
Appendix 3. Correlation of annual employment growth rates between regions in
Mexico and the US
(monthly frequency data)
i. 1992-2001
North MEX
Pacific MEX
North Center MEX
Center MEX
Capital MEX
Gulf MEX
South MEX
Total MEX
New England USA
Mideast Region USA
Great Lakes USA
Plains USA
Southeast USA
Southwest USA
Rocky Mountain USA
Pacific USA
Total USA
No MEX Pa MEX NC MEX Ce MEX Ca MEX Gu MEX So MEX Tot MEX NE USA ME USA GL USA PL USA SE US
1.000
0.751
1.000
0.836
0.944
1.000
0.680
0.879
0.900
1.000
0.591
0.908
0.861
0.814
1.000
0.768
0.800
0.816
0.647
0.771
1.000
0.753
0.623
0.688
0.572
0.492
0.671
1.000
0.845
0.960
0.969
0.874
0.924
0.870
0.696
1.000
0.464
0.481
0.485
0.280
0.468
0.323
0.173
0.487
1.000
0.526
0.433
0.490
0.199
0.439
0.476
0.361
0.502
0.843
1.000
-0.045
-0.276
-0.179
-0.108
-0.437
-0.245
-0.113
-0.280
-0.041
0.032
1.000
-0.398
-0.497
-0.499
-0.460
-0.497
-0.346
-0.390
-0.507
-0.161
0.003
0.579
1.000
0.242
0.008
0.127
0.030
0.006
0.166
0.072
0.100
0.372
0.523
0.678
0.548
1.0
-0.267
-0.439
-0.454
-0.437
-0.422
-0.224
-0.417
-0.416
-0.160
-0.096
0.523
0.741
0.5
-0.197
-0.304
-0.209
-0.131
-0.261
-0.182
-0.296
-0.264
-0.039
0.035
0.784
0.636
0.8
0.819
0.690
0.700
0.532
0.704
0.764
0.550
0.798
0.616
0.645
-0.099
-0.234
0.4
0.366
0.137
0.212
0.070
0.119
0.281
0.119
0.221
0.529
0.659
0.592
0.494
0.9
90
ii. 1997-2001
North MEX
Pacific MEX
North Center MEX
Center MEX
Capital MEX
Gulf MEX
South MEX
Total MEX
New England EU
Mideast Region EU
Great Lakes EU
Plains EU
Southeast EU
Southwest EU
Rocky Mountain EU
Pacific EU
Total EU
No MEX Pa MEX NC MEX Ce MEX Ca MEX Gu MEX So MEX Tot MEX NE USA ME USA GL USA PL USA SE US
1.000
0.847
1.000
0.970
0.906
1.000
0.910
0.904
0.930
1.000
0.728
0.885
0.759
0.864
1.000
0.881
0.863
0.878
0.871
0.750
1.000
0.671
0.523
0.647
0.645
0.373
0.670
1.000
0.965
0.939
0.969
0.966
0.870
0.915
0.648
1.000
0.395
0.617
0.520
0.418
0.532
0.332
-0.007
0.481
1.000
0.382
0.555
0.504
0.360
0.401
0.302
0.118
0.434
0.933
1.000
0.684
0.720
0.720
0.558
0.535
0.553
0.259
0.677
0.813
0.802
1.000
-0.428
-0.240
-0.338
-0.299
-0.065
-0.435
-0.616
-0.345
0.471
0.369
0.063
1.000
0.562
0.639
0.631
0.596
0.633
0.450
0.049
0.616
0.869
0.748
0.747
0.457
1.0
-0.236
-0.068
-0.181
-0.091
0.116
-0.202
-0.694
-0.155
0.178
-0.064
0.011
0.586
0.3
0.236
0.497
0.324
0.407
0.646
0.258
-0.344
0.385
0.659
0.407
0.459
0.508
0.7
0.884
0.864
0.885
0.914
0.859
0.794
0.517
0.934
0.593
0.499
0.657
-0.111
0.7
0.550
0.686
0.632
0.593
0.677
0.459
0.024
0.626
0.921
0.801
0.814
0.446
0.9
91