Launching Export Accelerations in Latin America and the World1
Author: Ms. Valerie Cerra

Contributor Notes

Authors’ E-Mail Addresses: VCerra@imf.org; Martha.T.Woldemichael@gmail.com
Publication Date:
09 Mar 2017
eISBN:
9781475585506
Language:
English
Keywords:
WP; export acceleration; real GDP; export growth; banking sector; exchange rate; sustained export growth; export acceleration episode; export collapse turn
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This paper investigates the determinants of sustained accelerations in goods and services exports. Strong predictors of export takeoffs include domestic and structural indicators such as lower macroeconomic uncertainty, improved quality of institutions, a depreciated exchange rate, and agricultural reforms. Lower tariffs, participation in global value chains and diversification also contribute to initiating export accelerations. The paper also finds heterogeneity, with somewhat different triggers for Latin America and the Caribbean, as well as for goods and services. Finally, despite the lack of a robust effect on output, export surges tend to be associated with lower post-acceleration unemployment and income inequality.

Abstract

This paper investigates the determinants of sustained accelerations in goods and services exports. Strong predictors of export takeoffs include domestic and structural indicators such as lower macroeconomic uncertainty, improved quality of institutions, a depreciated exchange rate, and agricultural reforms. Lower tariffs, participation in global value chains and diversification also contribute to initiating export accelerations. The paper also finds heterogeneity, with somewhat different triggers for Latin America and the Caribbean, as well as for goods and services. Finally, despite the lack of a robust effect on output, export surges tend to be associated with lower post-acceleration unemployment and income inequality.

I. Introduction

1. The world economy has recently been marked by a global trade slowdown and sluggish output growth (IMF, 2016). Reinvigorating and sustaining strong export growth could be an engine of growth and productivity. But what factors lead to an export take off? This paper investigates the determinants of export accelerations by examining episodes of clear shifts in export growth. The rationale for this focus is similar to Hausmann et al. (2005) who examine the predictors of growth accelerations in GDP per capita, adopting Pritchett (2000)’s argument that output performance is not always stable, with countries alternately experiencing episodes of growth, stagnation, and decline of different durations. Beyond the abundant literature that exploits shifts in GDP per capita performance,2 other papers have focused on turning points in the savings rate (Rodrik, 2000; Ebeke, 2014), productivity growth (Cadot et al., 2015), changes in fiscal expenditure (Carrère and de Melo, 2012), and export growth (Freund and Pierola, 2012; Eichengreen and Gupta, 2013).

2. We contribute to the literature along several fronts. First, we explore a rich array of potential predictors of export accelerations instead of focusing on a single determinant. Second, we expand the analysis beyond manufacturing goods exports to include non-fuel primary commodities, which typically account for a large share of developing countries’ export baskets. Third, we further extend our analysis to services exports, given the rising importance of trade in services (Sáez et al., 2015). Fourth, we allow for heterogeneities in the determinants of export accelerations by carrying out the empirical analysis for Latin America and the Caribbean (henceforth LAC) separately, and by distinguishing between goods and services.

3. The paper finds that export accelerations are relatively frequent across the world, with a large bulk occurring in emerging market and developing economies. Several preconditions including lower macroeconomic uncertainty, improved quality of institutions, a depreciated real exchange rate, agricultural reforms and global value chain (GVC) participation make the occurrence of export accelerations more likely. However, the paper also provides evidence of heterogeneity across regions. For instance, diversification matters for export transitions; but while the positive effect materializes through the intensive margin of trade for the world, diversification at the extensive margin seems to be key to achieving high and sustained export growth in LAC. Export takeoffs in services also tend to be associated with somewhat different triggers than those in goods. For example, services export accelerations in LAC are preceded by growth in FDI inflows and domestic financial liberalization through banking sector reforms whereas goods export surges respond to capital account openness. In the majority of cases, the effects of the correlates on the initiation of accelerations in LAC turn out to be at least twice the size of the estimates for the world sample, suggesting that the region would benefit more from the implementation of export growth promoting policies.

4. The paper also assesses whether countries that experience export accelerations perform better in terms of higher GDP per capita and lower unemployment and income inequality. This contributes to the literature on the relationship between trade and growth, as well as trade and welfare (Bernard et al., 1995; Frankel and Romer, 1999; Winters, 2004). For this purpose, we resort to the synthetic control method developed by Abadie and Gardeazabal (2003) and extended by Abadie et al., (2010), and implement two illustrative case studies. We find that post-surge GDP per capita is higher in Peru, while the evidence is inconclusive for Brazil. In contrast, both countries experienced a lower unemployment rate and income inequality compared to their synthetic counterparts, highlighting the benefits of high and sustained export growth in terms of improving the income distribution and labor market conditions.

5. The paper is organized as follows. Section 2 explains the methodology used to identify export accelerations and discusses some stylized facts, including findings from event analysis. Section 3 presents the empirical analysis of the determinants of export transitions. Section 4 assesses the post-surge performance of selected LAC countries using the synthetic control method. Section 5 concludes.

II. Identification of Export Acceleration Dates

A. Methodology

6. Following Freund and Pierola (2012), we define an export acceleration as a significant increase in export growth that is sustained for at least 7 years.3 Borrowing from Cadot et al. (2015), let vit be the level of exports of country i at time t, and git = ln(Vit) – ln(Vit-1) the real growth rate of exports.4 The term takeoff refers to a seven-year period, with the surge date being its first year, and the baseline is the seven-year period immediately preceding it. Subsequently, we define git1 and git0 as the real average export growth during the takeoff and baseline periods respectively. Ultimately, the identification of the timing of export acceleration episodes relies on the simultaneous application of four criteria:

  • 1. git1>g¯

  • 2. git1>1.3*g it0 and git1>g it0+0.03

  • 3. min(Vit, Vit+1,…,Vit+6) < max(Vit-7, Vit-6,…,Vit-1)

  • 4. git1\{m⁢ax(git,git+1,…,git+6)}>git0

• Criterion 1 ensures that real average export growth during takeoff is strong and above the world median value g¯.5 Criterion 2 ensures that increases in export growth are substantial by imposing that the real average export growth during takeoff increases by one third from the baseline growth rate and exceeds it by at least 3 percentage points.6 To rule-out volatility-driven surges, criterion 3 requires that the minimum level of exports observed during takeoff be higher than the maximum level of exports observed over the baseline period. Finally, criterion 4 avoids retaining accelerations triggered by a single year of very strong growth by imposing that the real average growth rate during takeoff, excluding the year of strongest growth, be greater than real average growth during baseline.

• To identify export accelerations, only countries with export spells of at least 14 years are considered, i.e. periods with missing observations are excluded.7 In the event of contiguous eligible years, we allow countries to have several instances of export accelerations as long as the dates are at least eight years apart. We investigate the timing of export accelerations for both goods and services exports. Mirror data on merchandise exports are taken from COMTRADE over 1976-2015. We focus on aggregate exports excluding fuels (SITC rev.2 section 3) and minerals (divisions 27, 28 and 68) to avoid identifying surges that are driven by global commodity price booms. Services export series are culled from the joint ITC-UNCTAD-WTO dataset and span 1980-2013. Given data availability and the definition of the criteria, the earliest possible initiation date of a goods (services) export acceleration is 1983 (1987) and the latest 2009 (2007).

B. Stylized Facts

7. The application of the filters on a sample of 187 countries yields 175 and 162 accelerations in goods and services exports, respectively. Figure 1 shows the timing of export accelerations, distinguishing between advanced economies, and emerging market and developing countries. The latter group comprised 86 percent of accelerations in goods exports and 73 percent in services. Accelerations were more frequent in the second half of the 1980s, probably reflecting the transition from import substitution strategies to export-oriented growth. The first half of the 2000s also hosted a large number of accelerations, especially in services, possibly reflecting the rise of China and other emerging countries. More specifically, Figure 2 indicates that 78 out of the 175 goods accelerations and 88 out of the 162 services accelerations occurred in the 2000s. Figure A1 in the Appendix further depicts the geographical distribution of export accelerations across the world.

Figure 1.
Figure 1.

Timing of Export Accelerations

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE, the joint ITC-UNCTAD-WTO dataset and IMF’s classification.
Figure 2.
Figure 2.

Distribution of Export Accelerations by Decade

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE and the joint ITC-UNCTAD-WTO dataset. Given the definition of the criteria applied in the identification of export surge dates, in (a) the 1980s start in 1983 and the 2000s end in 2009; while in (b) the 1980s start in 1987 and the 2000s end in 2007.

8. Next, we assess the regional distribution of export accelerations after normalizing by the number of countries in each region (Figure 3). LAC appears as the best performer with an average of 1.19 goods accelerations per country, followed by Emerging Europe (1.18) and Middle East and North Africa (MENA) (1.14) 8 Advanced economies rank first in terms of the average number of services export accelerations per country (1.19), followed by emerging Asia (1.03), MENA (0.82) and LAC (0.81). For both types of exports, the smallest figures were recorded by Sub-Saharan Africa. The rise in the average number of export episodes with the level of income suggests a positive association between the occurrence of accelerations and the level of development (Figure 4).

Figure 3.
Figure 3.

Average Number of Export Accelerations per Country, by Region

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE, the joint ITG-UNCTAD-WTO dataset and IMF’s classification. Emerging market and developing countries are broken down into emerging Asia, emerging Europe, the Commonwealth of Independent States (CIS). Latin America and the Caribbean (LAC). Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA). MENA includes Afghanistan and Pakistan. Table A1 details the countries included in each region.
Figure 4.
Figure 4.

Average Number of Export Accelerations per Country, by Income Level

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE. the joint ITG-UNCTAD-WTO dataset and the World Bank’s 2013 income classification. Per capita GNI must bo lower than or equal to 1.045 USD to qualify a< a low-income country, comprised hot ween 1.046 and 4.125 USD for lower middle-income countries, comprised between 4.126 and 12,745 USD for upper middle-income countries; and higher than 12.745 USD for high-income countries. Table A1 details the countries included in each income group.

9. How do emerging market and developing countries compare when grouped according to their main source of export earnings?9 Figure 5 reveals that developing economies whose export revenues are sourced from manufactures and those with a diversified source of export earnings witnessed the highest average number of goods exports accelerations per country (1.44 and 1.09 respectively). In contrast, developing economies dependent on non-fuel primary commodities and services experienced the lowest average number of goods accelerations per country (0.72). A similar pattern holds for services accelerations, although in this case services-exporting economies performed better than oil-dependent countries.

Figure 5.
Figure 5.

Average Number of Export Accelerations per Country, by Main Source of Export Earnings

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE, the joint ITC-UNCTAD-WTO dataset and IMF’s classification. Non-fuel sources of export earnings include manufactures; non-fuel primary commodities; services, income and transfers, as well as a diversified source of export earnings. Table A1 details the countries included in each group.

10. Focusing on LAC, Figure 6 reveals cross-country disparities within the region. Most countries in Central and South America registered comparable performance in goods and services, as illustrated by the cases of Chile, Colombia, Peru, El Salvador and Nicaragua, among others. The number of services accelerations exceeded the number of goods accelerations in Brazil, whereas the inverse is true for Bolivia, Ecuador and Costa Rica. The latter stands out as the only country in LAC with three episodes of accelerations in goods exports. Interestingly, Caribbean countries that experienced accelerations registered a higher number of goods than services episodes. Haiti and Saint Vincent and the Grenadines are the only economies which performed better in services than in goods exports. LAC’s performance in both goods and services is driven by LA6 countries (Figure 7).10 In contrast, the average number of goods and services export accelerations stands at only 0.67 and 0.17, respectively, in the Caribbean region.

Figure 6.
Figure 6.

Distribution of Export Accelerations in Latin America and the Caribbean

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE and the joint ITC-UNCTAD-WTO dataset. Generated using STATA software.
Figure 7.
Figure 7.

Average Number of Export Accelerations per Country in LAC

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE. the joint ITC-UNCTAD-WTO dataset and IMF’s classification. LAG includes Brazil, Chile. Columbia, Mexico, Peru and Uruguay. Table A1 details the countries included in each sub-region.

C. Event Studies

11. In this section, we examine the time path of selected economic and social indicators of an average country that experienced an export acceleration. Similar to Wacziarg and Welch (2008) with trade liberalization dates, we use an event study methodology to depict the behavior of selected variables five years around the initiation date of the export surge. This exercise is carried out for both goods and services export acceleration dates with the aim of identifying potential predictors of export transitions, before turning to a more formal analysis of the determinants of export accelerations in Section 3.

12. Figures 8 and 9 report the mean evolution of selected variables around the surge year.11 Analytical time is given on the x-axis with t = 0 being the initiation date. We split the sample of countries that experienced at least one export acceleration episode into LAC and non-LAC. The time path for an average country in LAC is illustrated by the solid red line (left-axis), whereas the dashed blue line pertains to an average country outside the region (right-axis). The dashed-dotted black line stands as a benchmark as it represents the time path for an average country in the sample considering all economies listed in Tables A1 and A2, i.e. including all countries with available data, irrespective of whether they experienced an acceleration or not.12 Axes are adjusted to reflect the same percentage change for the three series.

Figure 8.
Figure 8.

Around the Initiation Date of Goods Export Accelerations

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE, WDI, IFS, IMF Diversification Toolkit and Novta and Rodrigues Bastos (2016).
Figure 9.
Figure 9.

Around the Initiation Date of Services Export Accelerations

Citation: IMF Working Papers 2017, 043; 10.5089/9781475585506.001.A001

Notes: Authors’ calculations based on COMTRADE, the joint ITC-UNCTAD-WTO dataset, WDI, IFS, IMF Diversification Toolkit and Novta and Rodrigues Bastos (2016).

13. Graphs (a) and (b) display the mean evolution of the level and growth of exports around the initiation date. As expected, exports increase significantly at the surge time, and export growth accelerates as depicted by the sharp spike during takeoff. As in Cadot et al. (2015a, b), we observe a ratchet effect on real exports since levels seem to remain permanently higher after the initiation date, whereas mean reversion occurs in growth rates, for both goods and services. Before the surge date, the average LAC country typically records a real effective exchange rate (REER) depreciation of almost 20 percent and 12 percent for goods and services, respectively, (graph (c)); these figures are much larger than the 7 percent depreciation recorded for the benchmark. Similarly, the downward trend in the Theil index in graph (d) exhibits a larger slope for surge countries relative to the benchmark, suggesting that diversification is important for triggering export acceleration episodes. In the same vein, a reduction in tariffs occurs before the surge starts, especially for LAC, with rates falling by more than 3 percentage points while the reduction is less than 1 percentage point for the benchmark (graph (e)). A rise in GVC participation characterizes the baseline period (graph (f)).

14. Graphs (g) - (i) show the average behavior of real GDP per capita, unemployment and income inequality five years before and after a surge starts. The post-acceleration trajectories of these three variables are formally assessed in Section 4 using the synthetic control methodology, but Figures 8 and 9 offer a first look at the data. Although real GDP per capita of LAC countries rises at a similar rate as the benchmark after an export surge, non-LAC economies grow considerably faster, especially after accelerations in goods exports. Unemployment recorded a remarkable fall in both LAC and non-LAC surge countries, with a 1.5 percentage point decrease over the post-surge phase, while the benchmark rate only decreased by 0.2 percentage point over the same period. Furthermore, income inequality in surge-countries decreased by an approximate 6.7 percent during the five-year period that followed the goods export acceleration date, while the Gini index remained mostly unchanged for the benchmark. In the case of services, LAC countries enjoyed a 7.6 percent reduction in income inequality during the post-acceleration phase, while the index slightly increased for the benchmark.

III. Correlates of Export Accelerations

A. Econometric Model

15. In this section, we formally investigate the determinants of export transitions using regression analysis. Specifically, we estimate the following probit model of the timing of export accelerations:

P⁢r(EAit=1)=ϕ[δ0l⁢n(GDPcapit−2)+δ1l⁢n(GDPcapit−22)+δ2l⁢n(Populationit−2)+δ3Market⁢  Accessit−2+δ4Xit−2+ΣλtDt]

where ϕ is the cumulative normal distribution. The dependent variable EAit is a dummy that equals 1 over the three-year window centered on the initiation year of the export acceleration (i.e. for t – 1, t and t + 1). As in Hausmann, Pritchett and Rodrik (2005), Carrère and de Melo (2012) and Ebeke (2014), we impose a three-year window to reduce the likelihood of narrowly missing the timing of an acceleration through quirks in the data or in our method. The sample is not restricted to countries that have experienced export accelerations13, but we adjust it as follows: (i) we drop the first and last seven years of data as export acceleration episodes could not have been calculated for those years given the criteria we applied to identify them; (ii) since we are interested in uncovering the variables that contribute to triggering export takeoffs, we drop all data pertaining to years t + 2, …,t + 7 of an episode.

A parsimonious baseline specification controls for country size, the level of development, by allowing for non-linear effects of income per capita, and market access. Fernandes et al. (2016) show that country size and stage of development matter as larger countries and developed economies export more because they host large firms that account for a significant share of exports. Export survival also tends to be lower at an early stage of development, suggesting a positive relationship between income and export accelerations. In addition, the baseline model also accounts for country membership in economic integration agreements, computed as the weighted sum of all economic agreements a country participates in, with the weights corresponding to the partner’s market size (Cadot et al., 2014). The literature provides mixed evidence on the relationship between economic integration agreements and trade flows. For instance, Baier and Bergstrand (2007) find a positive impact of FTAs on members’ international trade and Hannan (2016) demonstrates that trade agreements boost exports; however, other studies document limited or even negative effects on trade flows (Bergstrand, 1985; Frankel et al., 1995, 1997). Year dummies Dt are included to capture time-varying unobserved heterogeneity common to all countries, such as international commodity price shocks.

We test a large number of potential predictors after organizing them in five categories. Investigated determinants of the timing of export accelerations are captured by Xit-2. They are entered one at a time in the baseline model to avoid multicolinearity. The five categories, elaborated below, are domestic macroeconomic and governance indicators, real exchange rate and diversification, trade policy and product market reforms, financial liberalization, and globalization and GVC participation.

Domestic Macroeconomic and Governance Indicators

16. Investment growth, a sound macroeconomic environment, quality infrastructure and institutions as well as human capital are positively correlated with the probability of observing an export acceleration.14 Human capital is proxied by the secondary school enrollment rate taken from WDI. Lennon (2009) finds that secondary school enrollment positively influences services trade. More generally, the availability of skilled labor appears critical to services exports relying on IT (Sáez et al., 2015). Investment in hard and soft infrastructure including equipment purchases, land improvements and construction of roads should raise the supply capacity of a country. We complement this WDI indicator with an index of infrastructure quality taken from Carrère et al. (2009).15 The importance of trade-related infrastructure in supporting exports is highlighted by Freund and Weinhold (2002) who find that the Internet spurs growth in services trade. In the same vein, Lennon et al. (2009) show that the quality and quantity of transportation and telecommunications infrastructure matter for trade. We consider the three-year change in both variables to allow time for the effects to be felt. Similarly, a sound macroeconomic environment should raise the likelihood of export transitions. We use the REER volatility as a proxy for uncertainty. It is calculated as the standard deviation of the annual REER over the past five years using data from the IMF’s IFS. Two indicators are used for institutional quality, namely Polity 2 from Marshall and Jaggers (2002) which measures the degree of democracy, and the ICRG indicator of quality of government. The latter is computed as the average of the variables “Corruption”, “Law and Order” and “Bureaucracy Quality”.

Real Exchange Rate and Diversification

17. We also examine whether the exchange rate and export diversification help predict export takeoffs. A large body of literature has investigated the relationship between the exchange rate and international trade. Freund and Pierola (2012) find that exchange rate depreciation is positively associated with subsequent manufactures export growth in developing countries. They show that depreciation stimulates entry into new export products and markets, which account on average for 40 percent of export growth. Similarly, Eichengreen and Gupta (2013) confirm the positive and significant effect of real exchange rate depreciation on export growth, with a larger effect for services. We use the IMF REER index to test this hypothesis. We also draw data from the Penn World Tables 8.0 (Feenstra et al., 2015). Specifically, we use the real exchange rate at PPP to compute real exchange rate misalignment adjusting for the Balassa-Samuelson effect as in Rodrik (2008). Furthermore, countries with a diversified export portfolio may be more likely to experience episodes of high and sustained export growth. We exploit the IMF Diversification Toolkit where the aggregate Theil index further maps into the intensive and extensive margins of export diversification.16

Trade Policy and Product Market Reforms

18. Next, we assess whether trade openness contributes to raising the probability of experiencing export accelerations. For this purpose, we use the three-year change in the ratio of goods and services trade to GDP, and data on average applied tariff rates taken from WDI.17 In particular, we examine if lowering tariff rates on manufactures and primary products both contribute to initiating export accelerations.

19. Export growth may also depend on product market competition and the quality of telecom and electricity services. We exploit Prati et al. (2013)’s database to investigate the role of structural reforms that stimulate product market competition. We use the agricultural reform index which measures the extent of public intervention in the market of the country’s main agricultural export commodity. The presence of export marketing boards and the incidence of administered prices are captured by the measure. We also investigate whether the degree of liberalization in the telecommunication and electricity markets - captured by the extent of competition in the provision of these services, privatization and the existence of an independent regulator - matters for export transitions. Services liberalization is found to benefit firms in deregulated sectors through a direct competition effect that induces innovation and the adoption of new technologies (Lanau and Topalova, 2016), possibly triggering export accelerations. Also, downstream firms using the output of deregulated sectors enjoy greater availability and higher quality of inputs. For instance, Arnold et al. (2008) find that reduced barriers to competition in telecommunication services in SSA boost manufacturing productivity. In the same vein, Arnold et al. (2011) and Arnold et al. (2016) show that liberalization in services industries positively impacts the productivity of manufacturing firms in the Czech Republic and India respectively.

Financial Liberalization

20. Financial openness - the deregulation of domestic financial markets and the liberalization of the capital account (Rancière et al., 2008) – may also play a role in igniting export acceleration episodes. Financial liberalization reduces the cost of capital through improved risk sharing and increased availability of foreign capital (Bekaert and Harvey, 2000; Henry, 2000; Bekaert et al., 2005). For example, Laeven (2003) finds that the liberalization of the banking sector reduces firms’ financing constraints. Financial openness bolsters trade by alleviating credit market imperfections, consistent with the micro literature that documents the adverse effects of financing constraints on export participation (see for example Minetti and Zhu, 2011 on Italy; Muûls, 2015 on Belgium; Manova et al., 2015 on China; and Kiendrebeogo and Minea, 2016 on Egyptian manufacturing firms). For instance, Manova (2008) shows that equity market liberalizations stimulate aggregate exports, especially for sectors that are more dependent on external finance.

21. On the other hand, financial liberalization may deter export accelerations. Financial liberalization may encourage excessive risk-taking, leading to more volatile capital flows that are prone to sudden reversals (IMF, 2012). Massive capital inflows following capital account liberalizations may lead to exchange rate appreciation and undermine the competitiveness of the tradable sector (Ostry et al., 2010); they may also fuel credit booms and asset price bubbles which can amplify financial fragility and crisis risk (Dell’Ariccia et al., 2012; Mendoza and Terrones, 2012; Schularick and Taylor, 2012). Kaminsky and Reinhart (1999) find that financial liberalization often precedes banking crises, which have been shown to jeopardize firms’ export activity through reduced access to credit, especially trade finance (Iacovone and Zavacka, 2009; Amiti and Weinstein, 2011; Chor and Manova, 2012; Kiendrebeogo, 2013).

22. We consider two measures of financial sector reforms, namely the index of domestic financial liberalization and capital account openness, both from Prati et al. (2013). Domestic financial liberalization covers reforms pertaining to the banking sector and the securities market. The former measures the reduction or removal of i) interest rate controls such as floors or ceilings; ii) credit controls; iii) competition restrictions such as entry barriers in the banking sector; iv) the degree of state ownership; and v) a measure of the quality of banking supervision and regulation. Financial reforms relating to the securities market capture policies designed to promote the development of bond and equity markets, and access of the domestic stock market to foreigners. The capital account openness index measures the extent to which residents and non-residents can freely move capital into and out of the country. We use the aggregate index and its two sub-components relating to residents and non-residents.

Globalization and GVC Participation

23. The last set of variables pertains to globalization and GVC participation. We use the three-year change in foreign direct investment (FDI) inflows (percent of GDP) from UNCTAD. FDI may contribute to changing a country’s export basket composition depending on the sector to which it is directed. It is usually expected to support a transition toward higher value-added activities through technological and knowledge spillovers, hence affecting export performance (Fugazza, 2004). Van der Marel (2012) finds a positive association between inward FDI and productivity in services, while Fernandez and Paunov (2012) show that FDI inflows in services boost manufacturing firms’ productivity in Chile, therefore suggesting a possible export acceleration-triggering effect of FDI. We also use the KOF index of globalization introduced by Dreher (2006) and updated in Dreher et al. (2008). It covers the economic, social and political dimensions of globalization as captured by the flow of trade, capital, information and people.18

24. Next, we examine whether participation in cross-border production chains has a bearing on export accelerations. We use data from Novta and Rodrigues Bastos (2016) who rely on Koopman et al. (2014)’s decomposition of gross exports to distinguish between foreign and domestic value-added exports (FVA and DVA respectively).19 FVA is used as a proxy for downstream involvement of countries in GVCs since it represents the share of gross exports which consists of inputs that have been produced in other countries. In contrast, DVA pertains to the share of gross exports that is created in-country. Subsequently, we look into the export-triggering potential of ‘‘indirect value-added exports’’ or DVX, which is the portion of DVA that enters as an intermediate input in the value-added exported by other countries (Koopman et al., 2010; UNCTAD, 2015; IMF, 2015). Of particular interest is the component of DVX that is re-exported to third countries, the so-called Term 3 in Koopman et al. (2014)’s nine-term decomposition of gross exports. We use this variable as a proxy for a country’s participation in longer value chains as in IMF (2015). Our overall measure of GVC participation consists of the sum of FVA and DVX, hence reflecting both downstream and upstream involvement in multi-stage trade process.

25. Table A3 provides the description and source of variables. All explanatory variables are lagged by two years to mitigate reverse causality issues, but our analysis may not be entirely immune to endogeneity stemming from simultaneous bias, especially considering variables such as FDI inflows that may be forward-looking. The results should be interpreted accordingly and with caution.

B. Baseline Results

26. Tables 1 to 5 display the main probit results for the world sample and for LAC separately. While Panel A reports the marginal coefficients from the estimation of the probit model of goods export accelerations, Panel B shows the results for export surges in services. Additional statistics are provided at the bottom of each panel. They include the number of export acceleration episodes included in each regression, as well as the pseudo R2 and McFadden’s pseudo R2 which measure the model’s fit. The predictive ability of the probit model is gauged with the percentage of cases correctly classified, i.e. the proportion of export acceleration observations that are correctly predicted.20

Table 1.

Correlates of Export Accelerations: Domestic Macroeconomic and Governance Indicators

article image
Notes: Probit estimates. The dependent variable is a dummy for the timing of goods (Panel A) and services (Panel B) export accelerations (EA) which equals 1 over the 3-year window centered on the initiation date. Coefficients are marginal probabilities evaluated at the sample means. Robust standard errors are given in parenthesis. *, ** and *** denotes statistical significance at the 10%, 5% and 1% level respectively. All covariates are lagged by 2 years. Δ Investment and Δ Infrastructure refer to the 3-year change in gross fixed capital formation (% GDP) and in the infrastructure quality index. LAC stands for Latin America and the Caribbean. The description and source of variables are provided in Table A3.
Table 2.

Correlates of Export Accelerations: Real Exchange Rate and Diversification

article image
Notes: Probit estimates. The dependent variable is a dummy for the timing of goods (Panel A) and services (Panel B) export accelerations (EA) which equals 1 over the 3-year window centered on the initiation date. Coefficients are marginal probabilities evaluated at the sample means. Robust standard errors are given in parenthesis. *, ** and *** denotes statistical significance at the 10%, 5% and 1% level respectively. All covariates are lagged by 2 years. LAC stands for Latin America and the Caribbean. The description and source of variables are provided in Table A3.
Table 3.

Correlates of Export Accelerations: Trade Policy and Product Market Reforms

article image
Notes: Probit estimates. The dependent variable is a dummy for the timing of goods (Panel A) and services (Panel B) export accelerations (EA) which equals 1 over the 3-year window centered on the initiation date. Coefficients are marginal probabilities evaluated at the sample means. Robust standard errors are given in parenthesis. *, ** and *** denotes statistical significance at the 10%, 5% and 1% level respectively. All covariates are lagged by 2 years. Δ Trade openness refers to the 3-year change in trade in goods and services (% GDP). LAC stands for Latin America and the Caribbean. The description and source of variables are provided in Table A3.