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Emerging Markets Finance and Trade

ISSN: 1540-496X (Print) 1558-0938 (Online) Journal homepage: https://www.tandfonline.com/loi/mree20

The Impact of Emerging Asia´s Demand on thePacific Alliance Countries

Lya Paola Sierra & Pavel Vidal Alejandro

To cite this article: Lya Paola Sierra & Pavel Vidal Alejandro (2019): The Impact of EmergingAsia´s Demand on the Pacific Alliance Countries, Emerging Markets Finance and Trade, DOI:10.1080/1540496X.2019.1693362

To link to this article: https://doi.org/10.1080/1540496X.2019.1693362

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The Impact of Emerging Asia´s Demand on the PacificAlliance CountriesLya Paola Sierra and Pavel Vidal Alejandro

Department of Economics, Pontificia Universidad Javeriana of Cali, Cali, Colombia

ABSTRACTThis article presents new evidence on the economic linksbetween Asia and Latin America. Estimates from a Bayesianvector autoregression model show that key macroeconomicvariables of the Pacific Alliance countries (Mexico, Peru, Chile,and Colombia) have a significant response to shocks onEmerging Asia’s demand. The spillover effects show that Peruis the country that is most vulnerable to income shocks inEmerging Asia. Nevertheless, developed economies’ shocks arestill significant when it comes to explain the macroeconomicperformances of Colombia, Chile, and especially, Mexico.

KEYWORDSPacific alliance; Asia;business cycle; tradelinkages; commodity prices

JELF44; F49; O54

1. Introduction

Given the current protectionist trends in the political landscape of variouseconomies, strengthening integration across emerging markets is more rele-vant today than ever. More integration within emerging economies providesan opportunity to continue fostering trade, drive more growth, and reducepoverty. The members of the Pacific Alliance (PA) seem willing to maintainopen borders for trade and foreign capital. Colombia, Chile, Mexico, andPeru launched the trade protocol of tariff relief on May 1, 2016. The Alliancemakes up 39% of the total GDP of Latin America and the Caribbean. Thegeographical location of these countries puts them in a privileged position toexpand trade with the Asia–Pacific region.

Overall, robust growth in Asia and the boom of commodity prices hascoincided with higher income growth and declining poverty rates in the PAcountries since the mid-2000s. However, since China´s economy has sloweddown and commodity prices have slumped, the macroeconomic environ-ment has become far more complex for Latin American economies. LatinAmerican countries’ economic policies have had to cope with large trade andfiscal deficits, sudden exchange rate depreciation, inflation above the target,and disappointing growth.

CONTACT Lya Paola Sierra lyap@javarianacali.edu.co Department of Economics, Pontificia UniversidadJaveriana of Cali, Calle 18 No. 118-250 Cali 26239, ColombiaColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/mree.

Supplemental data for this article can be accessed publisher’s website.

EMERGING MARKETS FINANCE AND TRADEhttps://doi.org/10.1080/1540496X.2019.1693362

© 2019 Taylor & Francis Group, LLC

The purpose of this article is to analyze the links between Emerging Asia´seconomies and PA countries in two dimensions. First, we attempt to evaluatethe impact of fluctuations in Emerging Asia’s demand – coming from theeconomic growth of China, South Korea, Indonesia, and India – on keymacroeconomic variables of the PA members. Second, we attempt to deter-mine the spillover effects across the economies of these two regions. Thisarticle aims to contribute to the existing business cycle literature on “decou-pling.” Researchers have written extensively about whether emerging marketsare becoming less dependent on the economic growth of advanced econo-mies, and are instead aligning with the rapid economic growth of Asiancountries (see, e.g. Pesce 2017; Kose and Prasad 2010; Cesa-Bianchi et al.2012). According to Cesa-Bianchi (2013), the emerging economies’ resilienceto the 2009 global financial crisis is a sign of decline in their dependency ondeveloped economies, which supports the decoupling hypothesis. In thisarticle, we compare the effects of income shocks from developed countriesand Emerging Asia on PA countries’ macroeconomics. To the best of ourknowledge, this is the first attempt to compare these impacts on the PAeconomies.

We use Bayesian vector autoregression (BVAR) models as the empiricalstrategy to evaluate the impact of emerging Asia on PA countries. Thiseconometric approach deals with the problem of over-parameterization inconventional VAR models. We divide the variables into two blocks: an exo-genous foreign block (which captures conditions in Emerging Asia and devel-oped economies), and a domestic block that includes output, inflation, and thereal exchange rate of each PA nation. With this data in the BVAR, we simulatethe dynamic responses of macroeconomic indicators in PA countries to shocksin each block. To evaluate bi-directional spillovers across the economies ofthese two regions, we use the methodology of Diebold and Yilmaz 2009,Diebold, and Yilmaz 2012, 2014) which measures spillovers through forecasterror variance decomposition from a VAR system. This method has beenwidely used in the financial markets to evaluate stock market returns andthe transmission of volatilities within markets after a shock. An extension tothis methodology is that of Gamba-Santamaria, Hurtado-Guarin, and Melo-Velandia (2017; 2019), which uses a DCC-GARCH model to better analyze themultivariate relationships of the volatility among financial assets. As we do notdeal with volatility issues, we have estimated the extended version of Diebold,and Yilmaz (2012) method, which uses a generalized VAR to solve the impactof the variable ordering in the results, and addresses the directional spillovers(from/to a particular market).

The document is organized in five sections. The following sectiondescribes the channels through which Asia’s growth could have an impacton the PA countries according to economic literature. Within this frame-work, we evaluate relevant data regarding the possible links between the PA

2 L. P. SIERRA AND P. VIDAL ALEJANDRO

countries and Asia in the period 2001–2017. Section 3 describes the BVARmodel as well as the Diebold, and Yilmaz (2012) method. Section 4 discusseseconometric findings, and Section 5 concludes the study.

2. Exploring the Linkages between PA Economies and Emerging Asia

2.1. Transmission Channels

With the greater importance of emerging market economies in the lastdecades, the debate about whether the economic growth of these countriesis becoming less dependent on the economic growth of advanced countries isopen (Pesce 2017). According to He and Liao (2012), Kose et al. (2010), andMumtaz, Simonelli, and Surico (2010), economies are beginning to sharea strong specific economic cycle at a group level, while there is an increase indifferences between groups. They not only highlight evidence in favor ofmore economic growth similarities among advanced economies and amongemerging economies but also point out the divergence in business cyclesbetween these two groups.

Emerging economies’ resilience to shocks in advanced economiesstrengthened the decoupling hypothesis, as was exemplified in the globalfinancial crisis (Cesa-Bianchi 2013). Cesa-Bianchi et al. (2012) proposed thatLatin America’s economic cycle is decoupling from the US cycle in favor ofa greater connection with the Chinese economic cycle. By collecting datafrom five Latin American economies, the authors found that the impact ofchanges in China’s GDP on the region had more than tripled since the mid-1990s, whereas the effect of US GDP on the region had halved.

Three main channels can transmit the impact of Asia’s growth to the PAcountries. The first is through international trade and the influence of Asia´sdemand for goods and services of the PA countries. The PA nations’ exportsare concentrated in commodities. In 2017, 86% of Chile’s total exports werecommodities, whereas they represented 84% for Peru and 82% for Colombia.Within the Alliance, Mexico is the exception, as its exports are mostly man-ufactured goods, and only 16% of its exports were commodities in 2017.1 Thisis because, in the 1980s, Mexico and several countries in Central Americaadopted production models based on low-cost labor (or maquila), thus foster-ing manufactured exports (Cypher and Wilson 2015). For that reason, Mexicois facing increased competition with Asia in the international market.

According to Jenkins, Peters, and Moreira (2008) there is an indirect secondchannel. Asia’s growth, particularly China’s, through trade and investmentdiversification on a global scale, affects Latin American countries. Accordingto the authors, China’s competitive edge, based on labor-intensive goods, mayhave displaced Latin America’s exports, particularly after China’s entry into theWorld Trade Organization (WTO) in 2001. Likewise, Asia’s growth dynamics

EMERGING MARKETS FINANCE AND TRADE 3

have attracted massive foreign direct investment (FDI) from developed coun-tries since 1990, diverting capital from Latin America to Asia.

The third channel reflects how Asia’s demand for commodities, driven inrecent years largely by the frenetic pace of the construction industry, hasaffected the international commodity prices upon which most LatinAmerican countries´ exports depend (Rosen and Houser 2007). As statedby Sierra (2016), prices of oil, copper, and iron are sensitive to shocks inAsian demand, which accounts for between 5% and 8% of their variability.Both Cheung and Morin (2007) and Sierra (2016) found that, for the abovecommodities, the influence of Asia’s demand on price variability exceeds theinfluence of developed countries.

This third channel connects the influence of Asia’s growth on the terms oftrade of the PA countries, which, in turn, has an impact on their dynamicsand macroeconomic balances (see, e.g. Zeev, Pappa, and Vicondoa 2016;Mendoza, 1995; Kose 2002; and, more recently, Schmitt-Grohé and Uribe2018). Asia may affect the terms of trade of the PA countries in two ways.Firstly, through the aforementioned influence of Asia’s demand on prices ofcommodities such as oil and copper, the main export products of Colombia,Chile, and Peru. The second relates to China’s influence on the falling pricesof manufactured goods since its entry into the WTO (Jenkins, Peters, andMoreira, 2008). Thus, China’s integration into the global markets providesimpetus to the terms of trade of countries importing manufactured goods.

In sum, the net effect of Asia’s demand on PA will depend on the relativemagnitude of each channel. The first and third channels will have a positiveimpact on PA macroeconomics, whereas the second channel will take a tollon the capital inflows and exports of PA countries.

2.2. Growth Patterns and Commodities

Since 2001, China, India, South Korea, and Indonesia (Emerging Asia) havecontributed the most to Asia’s growth dynamics. These four economies’GDP represented 70.3% of total Asian production in 2017. China’s growthrate before the financial crisis ranged between 7.6% (its lowest point, in thefourth quarter of 2001) and 14.4% (its highest point, in the second quarterof 2007). After recovering from the financial crisis with a growth rate of12%, China economy has experienced a steady deceleration since 2010(Figure 1).

Mexico and Chile were the countries most affected by the global financialcrisis. The effects of the financial crisis coincide with the fact that Mexico andChile are the most open PA economies, which makes them more sensitive toexternal shocks. According to World Bank data, in 2017, Chile and Mexico´sdegrees of trade openness were 56% and 78%, respectively, whereasColombia and Peru´s were 35%, and 47%, respectively.

4 L. P. SIERRA AND P. VIDAL ALEJANDRO

After China, India exhibits the highest growth rates in the period ofanalysis. The country reached a growth rate of 11% in 2003 and 2010, and,in contrast to China, its growth accelerated in 2015. The growth dynamics ofSouth Korea and Indonesia are not negligible; they increased between 3% and6% before the crisis and reached a rate of 7% in the post-2008 period. Of thePA countries, Peru has the highest growth rate, with an average of 5.2%,followed by Chile and Colombia, with an average of 4.2%, and Mexico, with2.1%, in the 2001–2017 period. Peru reached its highest growth rate, 10.7%,in the second quarter of 2008, and Chile had a record-high growth of 9.6% inthe first quarter of 2011 (see Figure 2).

Asia is a major destination for exports from Chile, Peru, and Colombia. Halfof Chilean exports went to Asian countries in 2017. For Peru and Colombia,Asia´s share in total exports is 44.2% and 15.7%, respectively. China was themain export destination for Chile and Peru in 2017 and the second mostimportant for Colombia, after the US. The importance of Asia with regard toPA imports is similar. Approximately one third of total PA imports originatedfrom Asia, with China being the leading market for Chile and Peru, andthe second most important for Colombia and Mexico (see Table S1 of theSupplementary Material on the journal’s website).

Among the PA countries, Mexico is an outlier. Asia accounted for only5.6% of its total exports in 2017. In addition, in recent years, Mexico has

6

7

8

9

10

11

12

13

14

15

2002 2004 2006 2008 2010 2012 2014 2016

China

-2

0

2

4

6

8

2002 2004 2006 2008 2010 2012 2014 2016

South Korea

1

2

3

4

5

6

7

8

9

2002 2004 2006 2008 2010 2012 2014 2016

Indonesia

2

4

6

8

10

12

2002 2004 2006 2008 2010 2012 2014 2016

India

Figure 1. Quarterly real GDP of emerging Asia (percentage change year over year). Source:Authors’ calculations based on data from the National Bureau of Statistics of China, Bank ofIndonesia, Korean Statistical Information Service, and Ministry of Statistics and ProgrammeImplementation of India.

EMERGING MARKETS FINANCE AND TRADE 5

experienced a low level of dependence on commodities, which made up only16% of its total exports in 2017. The composition of trade between Asia andthe PA is asymmetric. Goods exported by the PA countries to Asia aremainly commodities, whereas imports are mainly manufactured goods (seeTable S2 available online).

The main good exported to Asia by Peru and Chile is copper ore. In 2017,copper ore represented 67% of Peru’s and 74% of Chile’s exports to Asia. Onthe other hand, the main good exported to Asia by Colombia and Mexico isoil, which represents 72% of total exports for Colombia and 25% for Mexico.In contrast, imports from Asia include a variety of manufactured goods, suchas electrical machinery, equipment, mechanical appliances, cars, and tractors.

Figure 3 plots the terms of trade of the PA members along with the priceof the main export commodities. The price of WTI oil is used for Colombiaand Mexico, and the price of copper is used for Chile and Peru. As can beobserved, they show a high degree of synchronization.

In all four countries, the figure shows a period of expansion from the firstquarter of 2001 to the second quarter of 2008. According to various empiricalstudies, this boom had several driving factors. The first factor was the rapidgrowth of emerging economies, such as China and India, which boostedglobal demand for commodities (Hamilton 2009; Krugman, 2009). Second,the growing importance of Chinese manufacturing exports slashed the price

0

1

2

3

4

5

6

7

8

9

2002 2004 2006 2008 2010 2012 2014 2016

Colombia

-4

-2

0

2

4

6

8

10

2002 2004 2006 2008 2010 2012 2014 2016

Chile

-10

-8

-6

-4

-2

0

2

4

6

8

2002 2004 2006 2008 2010 2012 2014 2016

México

-8

-4

0

4

8

12

2002 2004 2006 2008 2010 2012 2014 2016

Perú

Figure 2. Quarterly real GDP of the PA economies (percentage change year over year). Source:Authors’ calculations based on data from the International Monetary Fund.

6 L. P. SIERRA AND P. VIDAL ALEJANDRO

of these goods, which represent net importation for the PA countries. Theliterature also notes the impact of the so-called financialization of commodityprices (Tang and Xiong 2012) and the depreciation of the dollar (Zhang andWei 2010). A related factor is the expansionary monetary policy of developedcountries (Frankel 2008; Hayo, Kutan, and Neuenkirch 2012). Since 2004,there has also been an increase in the co-movement among prices of differentnon-emerging commodities, driven by greater uncertainty in financial mar-kets (Poncela, Senra, and Lya 2014).

After the onset of the international financial crisis, the terms of tradecollapsed in all the PA countries. Subsequently, there was a temporaryrecovery, which was followed by a downward trend that persisted until2016. Among the four countries, Colombia suffered a relatively greaterdecline in its terms of trade during the same period.

The fact that terms of trade evolve similarly to commodity prices hasmotivated several authors to attempt to model this relationship. They haveconsidered several models that use commodity prices and the terms of tradeas determinant variables to assess the impact of this kind of shock on thesmall economies open to international markets (see, e.g. Jääskelä, and Smith2013; Karagedikli and Price 2012, for Australia and New Zealand).

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Terms of trade for Chile

Price of Copper

-2

-1

0

1

2

3

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Terms of trade ofr Colombia

Oil price

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Terms of Trade for Peru

Price of Copper

-3

-2

-1

0

1

2

3

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Terms of trade for Mexico

Oil Price

Figure 3. Terms of trade of the PA members and oil and copper prices. Source: Authors’calculations based on data from the International Monetary Fund.

EMERGING MARKETS FINANCE AND TRADE 7

3. Econometric Approach

Our empirical strategy consists of estimate two types of models: BVAR modeland Diebold, and Yilmaz (2012). To evaluate the effect of Emerging Asia´sdemand on key macroeconomic variables of the PA members, we useimpulse response functions (IRF) of a BVAR model as is done inKaragedikli and Price (2012) and Jääskelä, and Smith (2013). IRF can tracethe effects over time on a variable of an exogenous shock from anothervariable. On the other hand, we use Diebold and Yilmaz´s (2012) methodol-ogy to analyze the spillover effect between the countries of both regions. Theanalysis of these two econometric approaches are presented in this section.

Bayesian VARs (BVARs) find alternatives to some of the problems pre-sented in classical VARs. The need to capture the seasonal serial correlations,and to approximate the moving average processes with autoregressive speci-fications (Wold decomposition) leads to the inclusion of numerous lags inthe classic VARs, which result in the estimation of too many parameters withfew degrees of freedom. Unrestricted OLS estimates of the parameters areoften not very sufficiently determined in a finite set of T sample points. If theparameters are imprecisely estimated because of limited data, the impulseresponse functions and forecast error variance decompositions that are basedon them will also be imprecisely estimated (Blake and Mumtaz 2012).

Unrestricted VARs employ a-priori information very sparsely by choosingthe variables, selecting the lag length and imposing identification restrictions.Our database consists of quarterly series (6 for each PA country), startingonly in 2001. Furthermore, as we pointed out in the following paragraphs,the models we propose have 3 to 6 lags. Under this scenario, the task ofestimating a VAR model with classical methods is not trivial. In order tosolve this problem, for the estimation of the model we rely on a Bayesianapproach, which combines the information that results from the data,together with certain restrictions on the parameters based on prior informa-tion. More precisely, we use the so-called Minnesota (Litterman) priors. Themain idea of the Minnesota approach is to “shrink” the coefficients so that allequations center on a random walk with drift (for more details about theBVAR model and the type of restriction we used, see Appendix S1 of theSupplementary Material on the journal’s website).

The models are specified as follows:

gtdt

� �¼ αxt

Xpi¼1

Zigt�i

dt�i

� �þ B

εgtεdt

� �(1)

where gt and dt correspond to vectors of two blocks of variables, global anddomestic, for each PA country; xt is a vector of exogenous variables; and B isthe matrix of contemporaneous impacts of innovation vectors εgt and εdt . The

8 L. P. SIERRA AND P. VIDAL ALEJANDRO

BVAR for Colombia includes five lags, and the BVAR for Chile, Peru, andMexico includes three lags, following the Likelihood Ratio criteria. To iden-tify the BVAR and compute the impulse-response functions we use contem-poraneous restrictions on matrix B following a Cholesky form. B isa triangular matrix with the following order in the variables: demand fromindustrialized countries, Asian demand, terms of trade, domestic demandgrowth, real exchange rate, and inflation.

The block of global variables, gt ¼ gdpat ; gdpit; totpat

� �, captures economic

conditions external to the PA economies. gdpat is an index computing theweighted averages of the economic growth of China, South Korea, Indonesia,and India. We use this variable to approximate Asian demand, given thatthese economies represented 70.3% of the total GDP of East Asia and thePacific in 2017. The block of global variables also includes gdpit as theeconomic growth in G7 countries and reerdt as the terms of trade of the PAcountries.2

The block of domestic variables, dt ¼ gdpdt ; reerdt ; π

dt

� �, captures domestic

economic conditions in each PA economy. gdpdt is the GDP growth of eachPA country; reerdt is the real exchange rate; and πdt is domestic inflation.

Data to evaluate the effect of Emerging Asia´s demand on the macroeco-nomic variables of the PA members is made available on a quarterly basisand covers a period from the first quarter of 2001 to the fourth quarter of2017. This period began when China joined the WTO and Asia began to gainmore prominence in world trade. Appendix 1 details the characteristics andsources of all variables employed. We use all variable data in first differences(except for inflation) to induce stationarity. We perform the augmentedDickey–Fuller unit root tests and the Phillips–Perron test to check thenumber of unit roots in each time series (the results of the tests are shownin Table S3 available online).

To evaluate the spillover effects between the two regions, we used themethodology of Diebold, and Yilmaz (2012), which used the generalizedVAR framework proposed by (Pesaran and Shin 1998). Consider

a covariance stationary N variable VAR (p), xi ¼Ppi¼1

Φixt�i þ εi;where xi is

a vector containing the economic growth of the PA countries, Emerging Asia,and the US and OCDE – Europe. The vector of IID disturbances is ∽(0,Σ).The previous equation can be expressed in a moving average (MA) repre-

sentation xt ¼P1i¼0

Aiεt�i, where Ai ¼ Φ1Ai�1 þΦ2Ai�2 þ . . .þΦpAi�p;

which is used to forecast the future H-step-ahead error variance in forecast-ing. The methodology of Diebold, and Yilmaz (2012) relies on variancedecomposition, which is invariant to the order of the variables in the VARsystem. This Generalizad VAR framework follows the studies of Pesaran and

EMERGING MARKETS FINANCE AND TRADE 9

Shin (1998). Diebold and Yilmaz (2009), Diebold, and Yilmaz (2012) define“own variance shares” and “cross variance shares” as H-step-ahead errorvariances in forecasting xi that are due to shocks to xi and respectively, fori, j = 1,2,…,N, such that i�j. The following equation is stated in their article:

θ gij Hð Þ ¼ σ�1

jj

PH�1h¼0 ðe0iAhΣejÞ2PH�1h¼1 ðe0iAheiÞ

; (2)

Where θ gij Hð Þ is the H-step-ahead forecast error variance decomposition, for

H = 1,2;P

is the variance matrix for the error vector ε; σjj is the standard

deviation of the error term for hth equation, and ei is the section vector, withone as the ith element and zeros otherwise (Diebold, and Yilmaz 2012). AsPNj¼1

θ gij Hð Þ�1, the authors normalized each entry of the variance decomposi-

tion matrix by the sum of the:

~θ gij Hð Þ ¼ θ g

ij Hð ÞPNj¼1 θ

gij Hð Þ : (3)

Equation (3) considers that the decomposition including own shocks in each

market is equivalent to onePNj¼1

~θ gij Hð Þ ¼ 1

!; and the total decomposition

over all markets sums to NPNi;j¼1

~θ gij Hð Þ ¼ N

!. Diebold, and Yilmaz (2012)

construct several interesting measures, such as the total spillover index andthe directional spillovers. The first of these measures the extent of spilloveramong markets and the second represents the spillover transmitted from onemarket to the others. For more detail of the methodology see, for Dieboldand Yilmaz (2009), Diebold, and Yilmaz (2012; 2014), and for extensions tothe methodology Gamba-Santamaria, Hurtado-Guarin, and Melo-Velandia(2017, 2019).

4. Estimation Results

The results of the BVAR are used to compute the impulse-response functions(IRFs), with credible intervals of 95%, to display the impacts of a shock ofone standard deviation over a ten-quarter horizon. The analysis focuses onthe response of each PA country’s domestic variables to a positive shock inEmerging Asia’s economic growth. Furthermore, the variance decompositionis computed to determine the relative importance of Asian demand.

Figures 4 and 5 show the IRFs. Figure 4 highlights the significant andpositive response of the PA countries’ economic growth to a shock in

10 L. P. SIERRA AND P. VIDAL ALEJANDRO

Emerging Asia’s demand. This income shock has an immediate effect on theeconomic growth of Chile and Peru, while the effect starts to be significantafter the second quarter for Colombia and Mexico. The significant responseof Chile is brief and only takes place in one quarter, whereas the significantresponse in the other three economies lasts for more than four quarters.

Given that the four responses to the Asian demand are positive, this isevidence that the first and third channels offset and surpass the negativeimpact of the second channel (see previous section). The significance of thethird channel is confirmed in the response of the terms of trade to a positiveshock in Emerging Asia´s demand (see Figure 5). We found that this shockproduces an immediate positive effect on the terms of trade of all the PAcountries. Therefore, IRFs shows that the benefits of Asian growth on thegrowth of PA countries through export expansion and higher terms of tradeoutweigh the negative impact of Asian growth in displacing the exports of PAcountries and diverting foreign investment. Thanks to this net positiveimpact, we add new evidence in favor of the synchronization between thebusiness cycle of the PA countries and Emerging Asia, sticking out Peru withthe biggest and lasting reaction to a shock in Emerging Asia´s demand.

The macroeconomic sensitivity of PA countries to changes in Asian demandis also reflected in the inflation dynamic. The IRFs in Figure 5 show that inflationincreases significantly in Peru and Chile after a positive shock in Emerging Asia´s demand. China is the main export destination for these two countries.Therefore, fiscal income soars through taxes and royalties from the tradablesector, which activates a transmission mechanism that expands aggregatedemand and drives up inflation. We found that the magnitude of the inflation

Figure 4. Impact of emerging Asia’s demand on PA countries’ growth.

EMERGING MARKETS FINANCE AND TRADE 11

Figure 5. Impact of emerging Asia’s growth on PA countries’ terms of trade, inflation, and realexchange.

12 L. P. SIERRA AND P. VIDAL ALEJANDRO

reaction is larger in Chile than in Peru. In Colombia, inflation only increasessignificantly in the fifth quarter after the shock.

With regard to the exchange rate, IRFs in Figure 5 display a significant realappreciation for Peru after a positive shock in Emerging Asia´s demand. Thiseffect may have resulted from a nominal appreciation of the national cur-rency following a larger influx of foreign currency from the commoditysector.3 The positive response of inflation to the same shock (abovemen-tioned) also contributes to the appreciation of the real exchange rate in Peru,challenging the competitiveness of the tradable sector and increasing the riskof suffering the consequences of the so-called Dutch disease.

On the contrary, the real exchange rate of Colombia and Mexico reacts ina different way, making it easier for policy makers to manage a positiveincome shock from Asia. A positive shock in Emerging Asia’s demanddepreciates the real exchange rate in both countries, suggesting the preva-lence of the Balassa–Samuelson effect4. It is consistent with the widespreadobservation that there is a strong positive relationship between price levelsand GDP per capita. Here, a positive shock in Asia´s economic growth,enhanced perhaps for an increase in their productivity, boosts their pricesand generates a real appreciation of their currencies (real devaluation of PAcurrencies). For Mexico, the peak response is instantaneous, whereas theeffect takes more time to materialize for Colombia. Therefore, in terms ofcompetitiveness, higher economic growth in Asia is good news for bothcountries, as it would induce a temporary improvement in the trade balance.For Chile, we found no significant effect on the real exchange rate.

Following the methodology of Diebold, and Yilmaz (2012) Table 1 showsthe bi-directional spillovers effects for the nine countries (US, China, India,Indonesia, Korea, Colombia, Chile, Mexico, Peru) and OCDE -Europe.5 Theijth entry is the estimated contribution to the forecast error variance of theeconomic cycle of country i resulting from innovations to the economic cyclein country j. Therefore, the table shows an “input-output” decomposition ofthe total connectedness: the sums of off-diagonal columns and rows are thedirectional spillover contributions TO others and FROM others, respectively.Additionally the table shows the total spillover index in the lower rightcorner of the table. This index is computed by dividing the sum of thefinal column (Directional FROM others) by the sum of the final row(Directional including own).

Compelling findings emerge from Table 1. The spillover effects fromEmerging Asia’s economic growth to the PA countries’ economic growth arelarger than those from the PA countries to emerging Asia. Within EmergingAsia, China´s demand has the largest spillover effect on Peru and Colombia’seconomic growth, while India’s demand is more relevant to Mexico’s economicgrowth. Korea´s demand explains 13.1% of the variance of Peru’ economicgrowth. The mayor transmission from the PA to emerging Asia comes from

EMERGING MARKETS FINANCE AND TRADE 13

Table1.

Spillover

effects.

US

China

India

Indo

nesia

Korea

Colombia

Mexico

Peru

Chile

OECD-Europ

eDirectionalFRO

MOthers

US

47.3

6.3

13.1

2.8

5.8

4.1

6.9

10.2

12.5

52.7

China

3.9

58.2

10.7

0.1

2.8

1.3

7.4

3.5

4.3

7.8

41.8

India

10.9

16.9

46.4

5.6

2.4

0.1

1.8

2.6

1.4

11.6

53.6

Indo

nesia

47

12.1

52.4

4.2

5.4

82

2.5

2.4

47.6

Korea

14.6

12.3

16.1

5.3

30.4

3.1

4.5

2.5

0.6

10.6

69.6

Colombia

7.4

12.9

7.4

1.5

4.1

44.9

1.4

0.9

6.6

13.1

55.1

Mexico

21.5

6.2

16.8

1.9

5.7

5.7

27.9

0.7

0.4

13.1

72.1

Peru

5.8

16.5

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14 L. P. SIERRA AND P. VIDAL ALEJANDRO

Mexico to Indonesia and China, but only accounts for 8% and 7.4% of theireconomic growth variance.

The results show that spillovers among PA countries are less importantthan spillovers from emerging Asian or developed economies to PA coun-tries, which are consistent with the lower level of intra-regional trade whencompared with the importance of China and the US. The biggest intra-regional spillover effect across PA countries appears from Mexico to Chile,explaining 6.8% of the economic growth variance. According to these spil-lover effects, Chile and Mexico’s economic growth are the most dependentupon shocks in the demand from others, and Colombia the least dependent.

Even though income shocks from Emerging Asia are important sources ofoutput fluctuation in PA countries, developed economies’ demand still playa relevant role. The US demand explains 21.5% and 18.5% of the Mexico andChile’s economic growth variance. The OECD-Europe demand explains 13.1%of the Mexico and Colombia’s economic growth variance. Within PA countries,Mexico’s economic growth is the most dependent upon shocks in the demand ofdeveloped countries, and that which has the highest spillover effect to others.

5. Conclusions

This article assesses the effects of the demand of Emerging Asia on the fourLatin American economies that form the Pacific Alliance (PA) bloc (Mexico,Peru, Chile, and Colombia). To this end, we use an empirical strategy basedon a Bayesian vector autoregression (BVAR) model to evaluate the effect ofEmerging Asia´s demand on key macroeconomic variables of the PA mem-bers. Additionally, we employ the generalized vector autoregressive frame-work of Diebold, and Yilmaz (2012) to evaluate spillover effects.

Estimates of the BVAR show that economic growth in PA countries hasa significant positive response to shocks on Emerging Asia’s demand. Asiangrowth benefits PA countries’ growth through export expansion and morefavorable terms of trade. In fact, IRFs show that Emerging Asia’s demand hasa significant impact on the terms of trade of the PA countries. This is consistentwith the high degree of synchronization between the terms of trade and prices ofthe main commodities exported by the PA countries (oil for Colombia andMexico and copper for Chile and Peru) and helps to explain why the rising Asiandemand intensified the region’s commodity concentration during the period2001–2015. Commodity export concentration leaves the region vulnerable toexternal shocks, such as rawmaterial price swings and changes in Asian demand.

The spillover effects show that Peru is the country that is most vulnerableto Emerging Asia’s income shocks. China and India´s demand explain 16.5%and 14.8% of the variance of Peru’s economic growth. Peru proved to be theeconomy that was least dependent on developed economies’ income shocks.It is the PA economy that is most decoupled from the business cycle in

EMERGING MARKETS FINANCE AND TRADE 15

advanced economies. We did not find strong evidence of the so-calleddecoupling hypothesis for the rest of the PA countries. Advanced economies’shocks still matter when it comes to explaining macroeconomic performanceof Colombia, Chile, and especially, Mexico. Developed economies are stilla key driver of business cycle fluctuation for most Pacific Alliance members.

Notes

1. Data from TRADEMAP.2. Zeev, Pappa, and Vicondoa (2016) validate the idea of exogeneity of terms of trade for

small countries open to international trade in a restricted VAR for Latin America.3. We assume that there is capital inflow caused by a shock in Asia, cause by the demand

channel (Asia´s demand for goods and services of the PA countries), which generatea real appreciation of the local currency.

4. The Balassa–Samuelson model (Balassa, 1964) attributes movements in real exchangerates over time and across countries to cross-country differentials in sectoral total-factor productivities.

5. Data from OECD.STAT.

ORCID

Pavel Vidal Alejandro http://orcid.org/0000-0001-8278-3122

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18 L. P. SIERRA AND P. VIDAL ALEJANDRO

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EMERGING MARKETS FINANCE AND TRADE 19