Back Matter
Author:
Mr. Nadeem Ilahi
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Mrs. Armine Khachatryan
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William Lindquisthttps://isni.org/isni/0000000404811396, International Monetary Fund

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Ms. Nhu Nguyen
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Ms. Faezeh Raei
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Jesmin Rahman
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Publication Date:
12 Nov 2019
ISBN:
9781498314916
Language:
English
Keywords:
DPPP; DP; WB country; WB region; GVC link; WB export; export firm; services export; WB economy
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Annex I. Analysis of GVC Trade Flows

Annex Table I.1.

List of Sectors for GVC Analysis (EORA Database)

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Annex Figure I.1.
Annex Figure I.1.

Decomposition of Gross Exports into Value-added Exports

Citation: Departmental Papers 2019, 017; 10.5089/9781498314916.087.A999

Sources: Koopman and others (2011), Rahman and Zhao (2013), Aslam and others (2017).

Annex II. Regression Analysis for Determinants of GVC Flows

We use a gravity regression framework to analyze the determinants of bilateral GVC trade flows between 180 source and destination countries during 2000–13. The gravity model is mainly used to study determinants of trade flows (Lankes and Venables 1996; Baldwin and Taglioni 2006), with more recent applications to GVC and value-added export flows.

We rely on the structural gravity equation which can be written as follows:

log ⁡ ( Y i j t ) = α . G i j t + β . X i j t + ϒ . Z i t + μ i + η j + ϵ i j t

The variable of interest log (Yijt) is the bilateral GVC-related trade flows from country i to country j at time t , comprising of forward and backward GVC flows. Based on the existing literature, a set of explanatory variables are included in various specifications of the panel regression. These include both gravity variables (Gijt) as well as policy variables of bilateral nature (Xijt) and institutional and structural variables (Zit). Below is a brief description of the variables. Appendix Table III.1 provides the sources.

Variables

Gravity Variables

Five main gravity variables are used (sources in parenthesis) that capture geographic, historical, and physical relationships between the origin and destination countries. These included distance (CEPII), a dummy indicating common border (CEPII), a dummy indicating common official language (CEPII), a dummy indicating common colonial heritage (CEPII), and population of source and destination countries (world development indicators [WDI]). The latter is used to capture the market size.

Policy Variables

These fall into several policy areas and some are of a bilateral nature (trade agreements).

  • Production costs and skills include the unit labor costs relative to trading partner (ILO), the share of working age population with at least an upper secondary education (World Bank World Development Indicators (WDI), the share of vocational enrollment to total secondary enrollment (World Bank WDI), and the competitiveness index on quality of education (WDI).

  • Infrastructure and institutions include the index of quality of infrastructure (WEF), and several components of the worldwide governance indicators. The latter includes various dimensions of which we include the index of rule of law and the index of government effectiveness, as these two remain significant in most specifications.

  • Trade agreements include variables that capture the existence and depth of trade agreements between each country pairs. As discussed in Box 1, not all trade agreements are created equally. Accordingly, we explore if the existence as well as the depth of an agreement has any explanatory power for GVC flows. Two specifications are explored: one that includes a dummy variable for the existence of a preferential trade agreement (PTA) between two countries (World Trade Organization RTA Database), and a dummy for the depth of that PTA, measured by the number of legally enforceable provisions in PTA (Deep dummy = 1 if this number is larger than 20), as discussed in Box 1 and following Hofmann, Osnago, and Ruta (2017). In the second approach, instead of using dummy variables, we directly include two variables that capture the total number of provisions in PTAs and the number of legally enforceable ones (Hofmann, Osnago, and Ruta 2017). The second approach is more suitable when the sample is restricted to European countries, since the PTA depth dummy in the first approach would be closely related to EU membership, confounding the impact of the two.

Regression Results and Robustness

We ran a variety of regressions experimenting with the set of explanatory variables as well as the country samples. Appendix Table II.2 summarizes our preferred regressions that are used for calculation of policy gains in Figure 9. Appendix Table II.3 reports the results of a sample set of robustness tests. As is well known in the literature, most variables described above (skills, institutions, infrastructure) are highly correlated with each other, which complicates the identification. As such, our findings represent associations. Nonetheless, among various explanatory variables, we have tried to narrow down the ones with coefficients that appear most robust. This is achieved through a parsimonious approach by running numerous regressions and inclusion of variables one by one, and then in conjunction with others.

Here we provide a brief discussion of robustness by each category of variables. Regression results show that many control variables exhibit the expected signs and are statistically significant:

  • The first block of variables, grouped as Gravity variables in Appendix Table II.2, exhibit the right sign and are significant at all specifications when the full sample is used. The significance of colonial heritage, however, diminishes in the Europe-only sample. The coefficient on distance becomes larger in the Europe-only sample. This should be interpreted with caution as it could reflect either that distance bears a higher cost on trade in Europe but could also be a result of the high intensity of trade in European countries given their high level of integration, compared to the world.

  • Education, skill, and labor cost variables: The coefficients on vocational training and education levels were not robust across specifications (Appendix Table II.3). The quality of education appeared most robust, followed by unit labor costs, where the latter was significant only in the Europe-only sample.

  • Infrastructure quality and institutions: The quality of infrastructure appeared robust in all specifications. We tested various components of the World Bank governance indicators and among them, the rule of law and government effectiveness were the most robust, although the latter was not so in all specifications. This partly reflects strong correlation among all survey-based variables capturing perceptions; nonetheless, the index of rule of law was quite robust, despite inclusion of many correlated variables.

  • Trade agreements: these variables proved to be quite robust in various specifications in the full sample. Their importance increased for the Europe-only sample, as well as the NMS and WB samples (Appendix Table II.2), partly reflecting the importance of the EU single market in promoting trade within the block. Appendix Table II.3 (columns 10, 11, 21, and 22) show that both in the World and Europe-only sample, the depth of trade captured by the number of enforceable provisions remains significant, even after controlling for a host of other variables.

Annex Table II.1.

Data Sources for Gravity Regressions

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Note: The WGI are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms. The WEF GCI combines both official data and survey responses from business executives on several dimensions of competitiveness. WDI = world development indicators; WEF GCI = World Economic Forum Global Competitiveness Index.
Annex Table II.2.

Determinants of GVC Trade Flows

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Note: Robust standard errors in parentheses. Erros clustered at country-pairs. *** p < 0.01, ** p < 0.05, * p < 0.1.

Total number of provisions in the trade agreement ranges from 0–52 and is taken from the database by Hofmann, Osnago, and Ruta (2017). The depth of trade agreement is the number of provisons that are legally enforcible.

Annex Table II.3.

Additional Gravity Regressions for Determinants of Bilateral GVC-related Trade Flows

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Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

Dummy=1 if number of legally enforcible provisions in the trade agreement is larger than 20.

Total number of provisions in the trade agreement ranges from 0–52 and is taken from the database by Hofmann, Osnago, and Ruta (2017). The depth of trade agreement is the number of provisons that are legally enforcible.

Annex III. Regression Analysis for Determinants of Services Exports

We estimate two separate sets of regressions to analyze the policy factors that are associated with exports of: (1) travel services; and (2) ITC/other business services. The global dataset contains annual observations from 2000 to 2015. Although the panel structure lends itself to fixed effects estimation, we only employ fixed effects for the travel services regressions. Given the importance of geography in tourism, a fixed effects estimator serves to capture much of a country’s fixed (yet difficult to measure by one variable) tourism attractiveness. Because ITC and other business services depend less on geography, we employ pooled OLS for these regressions.

Travel Services Regressions

We employ a series of policy variables to explain the log of travel services exports/GDP. The fixed effects estimator creates a country-level dummy variable for each country to control for unobserved heterogeneity, which could include, for example, whether a country is a beach destination. Our policy variables include: (1) openness to trade, measured as the average number of provisions in a country’s trade agreements; (2) a measure of political stability and the absence of terrorism; (3) two measures of the business environment; (4) the quality of transport and electricity/telephone infrastructure; (5) the quality of education; and (6) cost competitiveness, as measured by wages and unit labor costs.

The regression results suggest that a country’s own characteristics (as proxied by the country dummy) are the primary determinant in the level of travel services exports as a share of GDP.1 This result is consistent with the fact that geography naturally confers advantages to some countries. We find that policy variables have a weaker association with travel services exports compared to the country fixed effect. In our regressions, only (1) openness (as proxied by the number of provisions in trade agreements), (2) political stability, and (3) electricity and telephone infrastructure are found to have statistically significant coefficients with the expected sign in most of the specifications. The ease of FDI and starting a business, quality of education, transport infrastructure, and wages/unit labor costs were not statistically significant in most specifications.

We also run regressions for samples covering only Europe, advanced countries, and emerging market/developing economies. The results indicate that the degree of openness to trade is more important for advanced economies, and political stability is a more important factor in emerging market/developing countries, and surprisingly, within Europe. In Europe and advanced economies, the number of procedures to start a business was significant with the anticipated sign, suggesting a greater importance of the business environment for tourism. The quality of electricity and telephone infrastructure also matters more in emerging market/developing countries than in Europe or advanced economies.

ITC/Other Business Services Regressions

The dependent variable is the log of ITC and other business service exports as a percentage of GDP. We also included measures of education and fixed broadband Internet subscription penetration (as a measure of technological advancement) in addition to the explanatory variables used in the tourism regression.

We find that most of the policy variables generally exhibit their expected signs and are statistically significant, except for tertiary education enrollment and the perceived quality of management schools in some specifications. Unlike for travel services, unit labor costs are negatively associated with these exports, suggesting that cost competitiveness matters for ITC exports. Openness measured by the depth of trade agreements in significant is some specifications, but not when ULC are added to the regression.

We split the dataset into samples covering only Europe, advanced economies, and emerging market countries. Within Europe, the most important policy variables include broadband Internet penetration, the perceived quality of math and science education, political stability, ULC, and whether FDI rules are business friendly. The quality of science/math education and management appear to be more important in emerging market/developing countries.

Annex Table III.1.

Data Sources for Regressions

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Note: ILO = International Labour Organization; WB = World Bank; WEF GCI = World Economic Forum Global Competitiveness Index.
Annex Table III.2.

Regression Results from Tourism Exports Dependent Variable: Log of Travel Services Exports/GDP

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Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Annex Table III.3.

Regression Results from ITC and Other Business Services Exports

Dependent Variable: Log of ITC and Other Business Services Exports/GDP

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Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

Annex IV. Regression Analysis for Imports

Given the unconventional approach to establishing association between capital goods imports and growth, we use different types of econometric analysis to support our hypothesis and application of threshold regression methods to our analysis. First, we use a fixed-effect panel regression to test for potential association of capital goods imports with growth. To deal with endogeneity and omitted variables issues, we complement it with instrumental panel regression analysis with labor skill and investment as instruments. Further we complement our analysis with a dynamic panel data model, which also contains lags of the dependent variable as regressors, accounting for issues such as momentum and inertia by using Arellano-Bond dynamic panel data estimation. We also test for dependence between residuals and ran Wald test for group-wise heteroscedasticity. Our results reported in Table IV.1 show a positive association between capital goods imports and real per capital GDP growth after controlling for domestic investment, labor skills, and FDI inflows.

We conduct threshold regression analysis to explore necessary conditions for the positive impact of capital goods imports on growth. The empirical literature points to increasing use of threshold regression analysis in understanding the impact of different threshold variables on macroeconomic variables, including growth, FDI, the size of government expenditures, etc. Developed initially as an extension of simple univariate OLS regression, threshold regressions are also used for panel data. Hansen (1999) describe the use of threshold regressions with panel data to investigate whether financial constraints affect the investment practices of firms. Caner and Hansen (2004) extend their initial model to application of analysis of macroeconomic variables by using 2SLS and IV estimation techniques to threshold analysis.

Threshold regression methods are developed for nondynamic panels with individual-specific fixed effects. Threshold regression models specify that individual observations can be divided into classes (so-called regions) based on the value of an observed variable (threshold). In principle, thresholds help to delineate one state of relationship from another. Unlike full sample, in threshold regression, there is one effect, meaning one set of coefficients up to the threshold and another effect (another set of coefficients) beyond it. Those regions are identified by a threshold variable being above or below a threshold value.

Annex Table IV.1.

Regression Results: Growth and Capital Imports Dependent Variable—GDPPC Growth

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Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

Formally, a threshold regression with two regions is defined by a threshold g and presented as

y t = β x t + δ 1 z t + e t i f − ∞ < w t ≤ γ
y t = β x t + δ 2 z t + e t i f − γ < w t < ∞

Considering the role of capital goods imports in empirical literature, we investigate whether the composition of labor skill (used as a proxy for country’s absorptive capacity) can affect the significance of the impact of capital goods on real growth. In other words, what is the threshold of the labor skill that makes imports of capital goods “meaningful” for economic growth.

Using panel threshold model, we estimate the impact of capital goods imports (Capgoodsimp), domestic investment (DI) and absorption capacity of the country proxied with labor skill mix (Labskill) on GDPPC growth in 45 countries, including OECD, emerging Europe, NMS, and WB countries (results in Table IV.2.).

d G D P P C = α . C a p g o o d s i m p j t + β . D I j t + γ . L a b s k i l l i t + ϵ i t

The threshold variable used is labor skill which is measured as the ratio of low and medium skilled labor in total labor (a lower value indicates high labor skill). The threshold is estimated at 61.3 percent at 95 percent significance. We define in region 1 as high labor skill, indicating that the sum of low and medium labor as a share in total is below the threshold. Consequently, the region 2 is defined as low skill, indicating that the sum of low and medium labor share in total is above the threshold.

The details of skill definition as described in ILO database are presented in Annex Table IV.3.

Annex Table IV.2.

Threshold Regression Results

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Note: *** p < 0.01, ** p < 0.05, * p < 0.1.
Annex Table IV.3.

Skill Level

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Source: ILOSTAT.

Annex V. Questionnaire for Survey of Export and GVC Companies in WB

Annex Figure V.1.
Annex Figure V.1.
Annex Figure V.1.

Questionare for Survey of Exports and GVC Companies in WB

Citation: Departmental Papers 2019, 017; 10.5089/9781498314916.087.A999

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1

NMS of the EU include Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia.

2

WB comprises Albania, Bosnia and Herzegovina, Kosovo, Montenegro, Republic of North Macedonia, and Serbia.

3

There is an upward bias in the standard trade openness measure—the ratio of sum of exports and imports to GDP—for small and developing economies as the nominal prices used to estimate GDP are lower than purchasing power parity (PPP)-adjusted prices, and small countries trade more internationally compared to economies with a bigger domestic market.

1

Our survey included the largest export firms in WB countries. We received responses from five out of six WB countries. Although the survey didn’t ask the respondents to specify their share in total exports of the country, we estimate that the firms in our sample cover 20–40 percent of their country’s exports.

2

This is due to absence of modern facilities for measurement, weighing, packing, sanitary, and phytosani-tary facilities.

3

UN ESCAP Survey on International Trade and Global Value Chains 2015; Trade by Enterprise Characteristics database used in “Inclusive Global Value Chains Policy options in trade and complementary areas for GVC Integration by small and medium enterprises and low-income developing countries,” OECD and The World Bank Group Report (2015); EBRD Business Environment and Enterprise Performance Survey (2012, 2014); and Mekong Development Research Institute 2017.

1

Our simulation entails calculating thresholds for the observed upper quartiles in the global sample for the relevant policy variables and comparing them to the observed values for each WB country. For travel services, these policy variables include openness to trade and the perceived quality of electricity/telephone infrastructure and for ITC/business services openness to FDI, quality of math/science education, and Internet penetration. We use specification 1 for travel services in Appendix Table III.2 and specification 1 for ITC/other business services exports in Appendix Table III.3.

2

Note that these simulations do not attempt to establish an upper limit but rather model the impact of specific policy improvements. Other measures that could boost travel services exports such as marketing, for example, are not modeled.

1

Our analysis is based on the labor skill categorization provided by the ILO database, which includes the following: low, low-medium, medium-high, and high (see Annex IV for details). To gauge relative skill level, we calculate the share of labor in the two lowest skill categories (low and low-medium skill) in total labor. This share variable has a high value in the WB countries compared to the NMS-7 peers, indicating a prevalence of relatively low-skilled labor. It ranges between 70 and 95 percent, except in Montenegro where it is 61 percent, reflecting a high concentration of labor in skill-intensive services such as IT and business (see Annex IV and Figure 20).

2

The 0.5 percentage points is the combined regression coefficients for explanatory variables of capital goods imports in GDP and labor skill ratio.

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Cover Departmental Papers
Lifting Growth in the Western Balkans: The Role of Global Value Chains and Services Exports
Author:
Mr. Nadeem Ilahi
,
Mrs. Armine Khachatryan
,
William Lindquist
,
Ms. Nhu Nguyen
,
Ms. Faezeh Raei
, and
Jesmin Rahman
Volume/Issue:
Volume 2019: Issue 017
Publisher:
International Monetary Fund
ISBN:
9781498314916
ISSN:
2616-5333
Pages:
75
DOI:
https://doi.org/10.5089/9781498314916.087
  • Annex Figure I.1.
    View in gallery
    Annex Figure I.1.

    Decomposition of Gross Exports into Value-added Exports

  • Annex Figure V.1. Annex Figure V.1.
    View in gallery
    Annex Figure V.1.

    Questionare for Survey of Exports and GVC Companies in WB

Annex Figure I.1.

Decomposition of Gross Exports into Value-added Exports
Annex Figure V.1.

Questionare for Survey of Exports and GVC Companies in WB
Annex Figure V.1.

Questionare for Survey of Exports and GVC Companies in WB