Policy Research Working Paper 10490 Trade Elasticities in Aggregate Models Estimates for 191 Countries Shantayanan Devarajan Delfin S. Go Sherman Robinson Development Economics Prospects Group June 2023 Policy Research Working Paper 10490 Abstract Armington’s insight that imports and domestically produced ranging from China (population of 1.4 billion) to Tuvalu goods were imperfect substitutes has unleashed extensive (11,200), including 45 of 48 Sub-Saharan African countries estimates of the associated trade elasticity, primarily for and understudied countries such as Benin, the Republic of developed countries. This notion of product differentia- Congo, Niger, Fiji, Haiti, Kiribati, and Tajikistan. Import tion, which extends symmetrically to exports and domestic and export elasticities of high-income countries average goods, has underpinned trade-focused, computable general about 1.4, reflecting the greater diversity of their econ- equilibrium models of developing countries, including the omies; developing countries’ elasticities average around aggregate, compact version, the 1–2–3 model. Noting that 0.7 for imports and 0.6 for exports. Elasticities generally estimates of trade elasticities for developing countries are rise with per capita income. That the elasticity is greater few, this paper remedies the situation. Using the vector than one for developed and less for developing countries error correction model as the primary method and con- implies asymmetric responses to shocks, which conforms trolling for global trends and other factors, the analysis to intuition and corroborates the analytical results from derives the long-run elasticity estimates for 191 countries, the 1–2–3 model. This paper is a product of the Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sd294@georgetown.edu, dgo@worldbank.org, and srobinson@piie.com. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Trade Elasticities in Aggregate Models: Estimates for 191 Countries by Shantayanan Devarajan, Delfin S. Go, and Sherman Robinson1 Keywords – trade elasticities, Armington, econometric estimates JEL codes – F14, C13, C68 1 Devarajan: Georgetown University, sd294@georgetown.edu; Go: The World Bank, dgo@worldbank.org; Robinson: Peterson Institute of International Economics, srobinson@piie.com. We thank Jongrim Ha and Ergys Islamaj for comments and suggestions and Hiroko Maeda and Eric Roland Metreau for help in downloading and interpreting the data from the World Bank World Development Indicators and the country classifications used for various country groups. We also thank all collaborators of the 1-2-3 model for their previous insights. I. Introduction Paul Armington's seminal 1969 paper, "A Theory of Demand for Products Distinguished by Place of Production," showed that imports and domestically produced goods in the same sector could be imperfect substitutes, with the degree of substitutability captured by the elasticity of substitution, or Armington elasticity. This insight helped explain why we observe domestic and foreign goods in the same sector sold at different prices in the same country. It also enabled more realistic simulation results from computable general equilibrium (CGE) models where, otherwise, small changes in policies or terms of trade would lead to huge swings in countries' trade patterns (Deardorff 2006). The notion of product differentiation, which extends symmetrically to exports and domestic goods, has since underpinned trade-focused, computable general equilibrium models,2 including the aggregate, compact version, the 1-2-3 model, and additionally recent open-economy macroeconomic models.3 In this regard, Devarajan, Lewis, and Robinson (1990) used the Armington assumption to specify the 1-2-3 model that captured most of the results from more disaggregated models in a transparent and data-economizing way.4 The 1-2-3 model has been extended to incorporate dynamics (Devarajan and Go 1998) and uncertainty (Devarajan et al. 2017) and used to analyze equilibrium exchange rates (Devarajan et al. 1993), trade policy (de Melo and Robinson 1992), welfare costs of taxation (Auriol and Warlters 2012), and poverty (Devarajan and Go 2003). Alongside the widespread use of the Armington framework, there has been considerable effort in estimating the magnitude of Armington elasticities at the sectoral level. Bajzik et al. (2020) collected 3,524 estimates in their meta-analysis on the 50th anniversary of Armington's paper. Almost all of these estimates are for developed countries at the sectoral level. Meanwhile, many CGE models of developing countries, including the 1-2-3 model, have been built with the Armington elasticities exogenously specified rather than empirically estimated. The reason is that there has not been sufficient time-series data to estimate these elasticities econometrically (some African countries gained independence only in the 1960s). Yet, as Schurenberg-Frosch (2015) shows in her sensitivity analysis of the Armington elasticity, model results can be highly sensitive to the magnitude of the elasticity. Hilberry and Hummels (2013) put it more bluntly: "It is no exaggeration to say that [the Armington elasticity] is the most important parameter in modern trade theory." Furthermore, apart from a few studies like Devarajan, Go, and Li (1999), there are hardly any direct estimates of export elasticity. A few studies take a country’s exports as imports from its major trading partners, thus estimating it indirectly as export demand from the rest of the world through an Armington function. In the 1-2-3 model, however, the export elasticity derives from a constant elasticity of transformation (CET) of supply, which, as mentioned above, is symmetric with the Armington function for imports in a general equilibrium framework. Thus, estimating it directly is also possible. In this paper, we estimate the Armington and export elasticities for the 1-2-3 model for 191 countries using data from 1970-2018. Of these, 128 are developing countries, including almost all the countries in Sub-Saharan Africa and many under-studied ones like Benin, the Republic of Congo, Niger, Fiji, Haiti, Kiribati, and Tajikistan. The list includes several microstates, such as Nauru, Tuvalu, and 2 de Melo and Robinson (1989) used an aggregate framework that anticipates the 1-2-3 model to derive the theoretical properties of product differentiation in CGE models. Later, Thierfelder and Robisnon (2003) discussed how the 1-2-3 model qualifies the results of trade theory with perfect-substitution between foreign and domestic goods. 3 The recent literature of open-economy macroeconomics using the Armington formulation includes the New Keynesian framework and the associated class of dynamic stochastic general equilibrium models. See, for example, Gali (2015), Uribe and Schmitt-Grohé (2017) and Végh (2013). 4 See also the next section and Devarajan and Robinson (2013) for a discussion of how the 1-2-3 model extends the tradability factor of the Salter-Swan framework and its relations to disaggregated CGE models in policy analysis of developing countries. Also, Devarajan, Lewis, and Robinson (1993) discussed its macro properties. 2 Liechtenstein. The results are generally consistent with macro or aggregate estimates and recent research findings. The more detailed results below show that trade elasticities typically rise with income. And consistent with the theoretical results behind the 1-2-3 model, the average elasticity is less than one (about 0.65) for developing countries and higher than one (about 1.4) for high-income countries. In the next section, we briefly review the literature on estimating trade elasticities to inform our choice of specification and technique. Section III presents the 1-2-3 model, which serves as the specification of our estimates of trade elasticities in Section IV. Section V contains some concluding remarks. II. Previous Estimates of Trade Elasticities We survey the vast literature on estimates of trade elasticities selectively to highlight three points: 1) There are few estimates of trade elasticities for developing countries, not just the Armington elasticity but also the export supply elasticity; 2) past estimates vary substantially but appear lower in recent studies; and 3) some issues, such as trends and fluctuations, are especially relevant to developing countries. Fifty years after Armington's contribution, Bajzik et al. (2020) counted 3,524 estimates of the elasticity of substitution, varying widely, mainly for developed countries and many at the sectoral level. The authors attributed the substantial differences in magnitude to differences in data: aggregation, frequency, size, and dimension. After correcting for biases against weak results and study quality, their meta-regression analysis implied a median Armington elasticity of 3.8 with a range of 2.5-5.1; the few developing countries in the survey were predominantly upper-middle-income countries by World Bank classification (such as former Soviet Republics). Anderson and Wincoop (2004) and Head and Mayer (2014) reviewed the variation in past studies from the lenses of trade costs and the gravity framework, respectively. The elasticity estimates tend to support a high value of 3 to 7, which was the conventional wisdom in the past. Within the EU market, Zofío et al. (2020) estimated the foreign trade elasticity at 2.2, lower than the national trade elasticity. Nevertheless, the estimates vary widely, and the median values are as low as 0.9 (Gallaway et al. 1997) and 0.97 (Reinert and Roland-Holst 1992) and as high as 6.5 (Hertel et al. 2007). Moreover, many estimates pertain to the sectoral level and primarily higher-income countries. Although estimates have varied widely, common patterns have emerged from reviews of past studies. Ahmad, Montgomery, and Schreiber (2020), McDaniel and Balistreri (2003), and Imbs and Mejean (2015) found trade elasticity estimates to decrease with the level of aggregation. In particular, commodities exhibit a high Armington elasticity, while differentiated products (like manufactures) tend to have a low elasticity. McDaniel and Balistreri (2003) also observed that long-run elasticities are higher than their short- run counterparts and that reduced-form time-series analyses have a lower magnitude than cross-sectional studies. Imbs and Mejean (2015) argued that aggregation restricted sector elasticities to be homogeneous and suggested using a weighted average of sector elasticities for aggregated ones to avoid heterogeneity bias. Although worth exploring further, data constraints in developing countries will make comparing macro estimates with averages of econometric estimations of sector values challenging. But are macro elasticities consistently lower than those from micro elasticity studies of more specific sectors or commodities? Feenstra et al. (2018) found the results mixed: for between two-thirds and 3 three-quarters of sample goods, there is no significant difference between the macro- and micro-elasticities, but the micro elasticity is significantly higher for the rest. There are also differences in findings at the disaggregated levels as well. Brenton and Winters (1992) do not assume separability between home and foreign goods and find low import price elasticities. In contrast, Panagariya, Shah, and Mishra (1996) employ better data, such as explicit competitors' prices (not proxies), and find high elasticities. These elasticities apply to more specific groups of commodities, i.e., not at the level of aggregation of the 1-2-3 model. At the aggregate level, foreign and domestic goods are composites of many different goods, and the substitution possibility (e.g., as found in Devarajan, Go, and Li, 1999) will lead to smaller elasticity values and ranges than more disaggregated cases (e.g., in Hillberry and Hummels, 2013; Bajzik et al. 2020). Recent estimates tend to point towards elasticities that are lower in magnitude. For example, Boehm, Levchenko, and Pandalai-Nayar (2023) compared the differential effects on imports of countries that changed Most Favored Nations (MFN) tariffs with their trading partners relative to a control of countries not subject to the MFN scheme. The authors identified the trade elasticity for the short and long horizons. They found the short-term elasticity (one year after the exogenous tariff change) to be 0.76 and the long-run elasticity ranging from 1.75 to 2.25 (typically after 7-10 years). The sample covered trade flows of goods at the disaggregation level of HS6 (Harmonized System at the 6-digit codes). Each panel of countries had over 80 countries, most of them high-income; the developing countries included were mainly upper-middle-income or large countries in global trade like China and India. The method did not differentiate by country, so the paper did not provide elasticities for developing countries. An earlier study by Whalley (1985) likewise inferred the value of the Armington elasticity from trade liberalization episodes and yielded an estimate in the neighborhood of 1.5 over 5-10 years. Past studies concentrated on the Armington elasticity between imports and domestic goods, ignoring much of the export supply side because of estimation issues. In CGE models, the export supply side is usually defined as a function with a constant elasticity of transformation between exports and domestic goods, a symmetric formulation to the Armington formulation (see further discussion below). Hillberry and Hummels (2013) suggested that future research uses firm-level heterogeneity to uncover export behavior and elasticity, which affect trade flows jointly with the demand side. Only a few studies investigated export supply explicitly. Diewert and Morrison (1988) employ a production-based approach initially developed in Kohli (1978) to obtain export supply and import demand. Faini (1994) directly estimates transformation elasticities from a CET function and considers adjustment lags, factor prices, and the importance of capacity utilization. He finds that the CET elasticity is less than one for Morocco but much greater than one for Türkiye. It would be difficult to replicate these studies for many countries without extensive microdata. One reason is the measurement problems of factor accumulations and their returns. Another issue is the adjustment lags in supply that may require measuring capacity utilization. The impact of lagged variables may also require time-series estimation that accounts for nonstationarities, such as vector autoregression (VAR) or its restricted form, the vector error correction (VEC) model, which we use in our analysis below. The few studies examining developing countries' exports indirectly modeled exports as import demand from major trade partners, e.g., Reinhart (1995) and Senhadji and Montenegro (1999). Reinhart (1995) calculated the import demand for 12 developing countries and the corresponding export demand (from industrial countries). Senhadji and Montenegro (1999) estimated export price and income elasticities 4 as import demand from trading partners for 53 industrial and developing countries. As a salient feature, both studies used time-series estimation to handle nonstationarity issues and the lack of cointegration that might result in spurious relationships. Devarajan, Go, and Li (1999) also used time series and other techniques but estimated the Armington and CET elasticities directly for many developing countries. They found some export elasticities have the wrong sign, possibly because of the short data series and potential identification issues affecting export supply (see below).5 These elasticities were excluded from the final estimates. In addition to world prices, domestic prices affect imports and exports. However, the literature is divided on the impact and significance of the real exchange rate, or the ratio of domestic to world prices, on the trade balance. Earlier papers such as Branson (1972), Khan (1974), Rittenberg (1986), Bond (1987), and Marquez and McNeilly (1988) found that trade flows respond significantly to changes in relative prices. They are criticized today for inference problems associated with time-series variables with unit roots. Empirical work that considered the time-series properties of trade flows and prices, such as Rose (1990 and 1991) and Ostry and Rose (1992), found little evidence that relative prices affect trade flows. Yet, the lack of theory in time-series techniques makes the estimates difficult to interpret. Marquez (1994), for example, stressed the importance of optimizing behavior and simultaneity in determining expenditures on domestic and foreign goods. For developing countries, Faini, Pritchett, and Clavijo (1988) discussed the importance of trade policy and restrictions, which are likely to understate the structural demand elasticities. Reinhart (1995) uses dynamic optimizing behavior with time-series techniques and finds significant trade relationships. Our analysis follows this approach by combining the optimizing behavior of the 1-2-3 model with time-series techniques. Nonetheless, recent open-economy macroeconomic models use a trade elasticity below or around unity, often assumed, calibrated, or estimated for high-income countries. For example, Corsetti, Dedola, and Leduc (2008), Gust, Leduc, and Sheets (2009), and Justiniano and Preston (2010) have elasticity estimates between 0.8 and 0.86, while Galí and Monacelli (2005) used unity. Among textbooks, Végh (2013) examined the impact of devaluation under alternative elasticity of substitution between tradables and nontradables from 0.2 to 1.0;6 Uribe and Schmitt-Grohé (2017) set the elasticity to unity but tested the economic impact of terms- of-trade shocks of alternative values of 0.75 and 1.5.7 Another issue is the assumption of homotheticity of the Armington or CET function, which is violated by the time trends observed in trade shares. Import and export shares in GDP for many countries appear to be increasing, independently of relative price movements. For example, Alston et al. (1990) note that while the implicit assumption of homotheticity in the CES and CET formulations is theoretically appealing, it is highly restrictive in CGE modeling. The standard correction usually employs a scale variable, such as an income term, to denote aggregate income activity. Alternative formulations like the almost ideal demand system (AIDS) or one of the flexible functional forms are often suggested. While it is plausible that the capacity to import among countries rises with income, Petri (1984) and Ho and Jorgenson (1997) believe that the estimated high-income elasticities are probably spurious. Trade shares seem to increase over time for rich and poor countries, as would be the case with increasing 5 Several estimates in Devarajan, Go, and Li (1999) used OLS (ordinary least squares) with simple time trends. While that approach could capture rising trade shares over time, it might not alleviate autocorrelation in the residuals, making the coefficient estimates inefficient or the standard t-tests improper. Seemingly unrelated regression (SUR) that assumes a joint distribution of error terms across countries (with a small sample) was also used to improve the efficiency of the variance. That approach is not necessary with a larger sample size now available for each country. 6 p. 295, Table 6.1. 7 P. 252, Table 7.8. 5 globalization. A natural breakpoint was the 1970s when the international monetary and trading system changed substantially. Even for large industrial countries like the United States, there was a sharp acceleration in the import share in the 1970s. For developing and transitional economies, periods of rapid economic and trade liberalization (particularly in the late 1980s) are crucial factors. Compared to the earlier periods of inward orientation, changes in trade policy in the latter periods often led to structural breaks in the trade shares. We use a time trend to account for the shifts in trade shares or the ratios of factors (equations 7 and 8 below). Moreover, trade ratios might not rise steadily; they may fluctuate erratically due to policy reversals, crises, or conflicts, particularly in developing countries. For example, Arbache et al. (2008) found that growth collapses in Sub-Saharan Africa were frequent before 1995. Weather conditions could also affect exports of developing countries when they are mainly agricultural products. In these cases, the trade shares in output are likely co-dependent variables with no exogenous trends. III. Methodology: The Model, Data, and Specification The 1-2-3 Model The 1-2-3 model became a practical economic tool at the World Bank and for teaching more complex models due to its transparent algebraic and conceptual results and accessible numerical implementation using only national accounts and popular spreadsheet software (see Devarajan, Lewis, and Robinson 1990 and 1993; Devarajan, Go, Lewis, Robinson, and Sinko 1997). Since its inception, the 1-2- 3 model has addressed various policy issues in developing countries. The most common application has been the real exchange rate effects (including Dutch disease effects) of commodity price shocks or changes in capital flows (such as foreign aid and transfers). The algebraic and numerical spreadsheet solutions anticipate the relationship between external shocks and policy responses of more complex models. For example, the model was used to determine the pre-1994 overvaluation of the CFA franc (Devarajan 1997, 1999), which informed the discussion about the magnitude of the 1994 devaluation. Since the new millennium, the authors have conducted similar exercises as part of World Bank operational work in CFA countries, the Arab Republic of Egypt, Zambia, and other African countries. Another productive application has been the economic effects of trade reform, especially in the 1990s when the issue was crucial for many developing countries. Devarajan, Go, and Li (1999) quantify the fiscal effects of trade reform and show how the results depend on the substitution elasticity between foreign and domestic goods. Devarajan and Go (1998) incorporate rational-expectations dynamics to capture the intertemporal effects of trade reform and import price shocks. Relatedly, de Melo and Robinson (1992) use the framework to examine export externalities in developing countries. Taking advantage of the model’s simplicity and minimal data requirements, Auriol and Warlters (2012) use the 1 -2-3 model to calculate the marginal welfare cost of public funds in 38 African countries. Devarajan and Go (2003) link the framework to growth and poverty modules to examine the implications of growth and poverty reduction strategies, especially in those classified as Heavily Indebted Poor Countries (HIPCs) in Africa. The model has also been used to study the macroeconomic dynamics of scaling up foreign aid (Devarajan, Go, Page, Robinson, and Thierfelder, 2008). Extending the regional integration application in Devarajan, Go, Suthiwart-Narueput, and Voss (1997b), a global version called the R23 model exploits its parsimonious structure to link, through trade flows, over 200 1-2-3 models 6 (McDonald, Thierfelder, and Walmsley 2012). Finally, Devarajan, Dissou, Go, and Robinson (2017) developed a dynamic stochastic general equilibrium version to examine budget rules when the export price of a resource-rich country is uncertain. Specification of Trade Elasticities in the 1-2-3 Model The 1-2-3 model is specified for one country producing two commodities: an export good (E) traded and sold only to foreigners and a domestic good (D) that is nontraded and sold domestically. The third commodity is a traded import (M) sold in the domestic market. One representative consumer receives all income and allocates it according to preferences for the two goods sold on the domestic market, D and M. The country is small in world markets, facing exogenous world prices for exports and imports. The two traded goods (E and M) and the nontraded good (D) are imperfect substitutes, a feature found in most CGE models that follows the distinction of "tradable" (imports and exports) and "nontradable" (the domestic good) of Salter (1959) and Swan (1960). The consumer’s utility function consists of the Armington CES function of D and M. Production is specified by a CET production possibility frontier of D and E. There is no need for separate production functions for D and E—the transformation function is all that is needed for the analysis.8 The two elasticities in the model characterize the trade substitution possibilities. These are the key parameters to be estimated. The CES and CET functions have the same algebraic form and are distinguished by their parameters (convex for the CES and concave for the CET). Equation 1 represents the common CES and CET functions. Equation 2 is its corresponding dual price equation. 1 (1) = ̅[ ∙ 1 + (1 − ) ∙ 2 ] −1 ̅ 1 1⁄(1−) ⁄(−1) ⁄(−1) (2) = − [ ∙ 1 + (1 − )1⁄(1−) ∙ 2 ] where X is the CES or CET composite or ̅ ; ̅ is the shift parameter, α is the share parameter, and  is the exponent: In the CES case, 1 ≡ and 2 ≡ ; and in the CET case, 1 ≡ and 2 ≡ . The P's are the corresponding domestic prices of the inputs, , , . The CES substitution elasticity σ and CET transformation elasticity Ω are given by = 1⁄(1 − ) ; −∞ < < 1 in the CES case and Ω = 1⁄( − 1); 1 < < ∞ in the CET case. Both the CET and CES functions exhibit constant returns to scale. The allocation of the composite good into its components depends on the relative prices of the individual components. Noting that = = in equilibrium, the corresponding export supply and import demand functions are expressed as ratios from the first-order conditions for profit and utility maximization (equations 3 and 4): (1−)∙ Ω (3) =[ ] export supply, and ∙ 8 There are examples of extended 1-2-3 models that include production functions for D and E. It can be shown that if production is specified by Cobb-Douglas functions with different factor proportions, the implicit production possibility frontier is approximately CET. 7 ∙ (4) =[ ] import demand, (1−)∙ where and are the corresponding share parameters in the CET export transformation and CES import aggregation functions.9 The CET function describes the production transformation frontier between D and E for a fixed ̅ or real GDP (since there are no intermediate inputs). The assumption that level of ̅ is fixed is equivalent x to assuming full employment of all primary factors. The composite good price P corresponds to the GDP deflator. The composite price of Q corresponds to the consumer price index. Following the numerical implementation in Devarajan et al. (1997), GDP not sold to the rest of the world (i.e., E, exports of goods and services) defines the domestic good D. Given price indices, Px and Pe, the implicit price for the domestic ̅ = + − = + where Q is good, Pd, can be derived from the GDP identities: aggregate demand. The model can therefore be implemented using national data for macro aggregates (see Devarajan, Go, Lewis, Robinson, and Sinko 1997). Estimating Equation The log-linear transformation of the supply and demand equations (3) and (4) provides a convenient way to estimate the elasticities: (5) [ ] = 1 + Ω [ ] + (6) [ ] = 2 + [ ] + (1−) where 1 = Ωl [ ], 2 = [(1−)], the time subscript, and , are the error terms. Note that equations 5 and 6 extend beyond the 1-2-3 model. As mentioned in the literature review, the import equation derived from the CES function is also known as the Armington import demand in the trade literature. Exports are also often expressed as Armington import demand from the rest of the world. However, in our case, the CET function and the estimation of Ω complete the country-specific model. Data Data are from the World Bank’s World Development Indicators (WDI)10 and the United Nations National Accounts database.11 WDI is used where available. The two sources are combined to extend series or fill in missing observations. We retain the WDI levels and base year for constant prices where the two 9 Note that the CES and CET functions are not defined for elasticities that equal 1 (a Cobb-Douglas formulation can be used). However, the export supply and import demand equations are well defined for unitary elasticities and the estimation procedure will work in that case. 10 https://databank.worldbank.org/source/world-development-indicators. 11 https://unstats.un.org/unsd/snaama/Index. 8 sources are spliced. We use the series measured in current and constant U.S. dollars to derive the implicit price indices for the quantities. The base year is 2015, and WDI uses that year to calculate the world totals.12 Using uniform dollar units affords two innovations in the estimation. First, it provides consistent units across countries to estimate trade elasticities. Second, having the world's aggregate GDP and its components in comparable dollar units yields global demand variables for a country's exports, potentially correcting an identification issue in the CET estimation for some cases (see below). Where available, we obtain time series for 1970-2018, potentially having 49 data points for each country. We avoid the 1950s and 1960s because many developing countries had just become independent, which is a reason why the sample size was small for many countries in the previous estimation of Devarajan, Go, and Li (1999). Seventeen or more Sub-Saharan African countries became independent only in 1960 and after (e.g., Cameroon, Kenya, Madagascar, Senegal, Tanzania, Uganda, Zambia, etc.). Nascent developing countries often have limited statistical capacity in the early years after independence, with issues of measurement reliability. Moreover, we exclude the years after 2018 because of the coronavirus of 2019 (COVID-19) and the disruptive effects of the pandemic on supply chains and global trade. In cases where the series is shorter, we flag them. For example, for countries belonging to the former Soviet Union, data can only begin from about 1990. As a general rule, we omit countries with fewer than 20 observations. Nonstationarity Macroeconomic series and aggregate price indices (such as , , , , ) are known to be nonstationary. It typically takes differencing twice to make the series stationary, implying an integration order of two, i.e., I(2). Without further transformation, the significant time trend present in the data could lead to spurious relationships and estimates. Fortunately, the log ratios of the variables in the estimating equations above reduce the order of integration. Using the Augmented Dickey-Fuller (ADF) unit root test of each transformed variable ( [ ] , [ ] , [ ] , [ ]), we cannot reject nonstationarity in the null hypothesis. However, differencing the series just once makes each case stationary, implying an integration order of one, I(1). We confirm this finding to be true for each variable for each country. Cointegration The presence of the same level of integration at I(1) suggests a possible cointegrating relationship between [ ] and [ ] in one set and [ ] and [ ] in the other. We employ the Johansen cointegration test, including its alternative assumptions and specifications about the presence of a deterministic trend (linear or quadratic) and intercept in the cointegration equation (CE) or the VAR. We add exogenous variables such as global demand conditions for some CET cases (see identification below) or the appropriate trade share in GDP if there are unusual fluctuations in the dependent variable. 12 The WDI methods and ratios to impute missing observations are described in the link here. 9 Identification For some countries, the CET equation (5) might have identification problems as export supply could co-depend on global demand, expressed as an Armington import demand condition similar to equation 4: (7) = [(1− ] ) The demand for as a ratio to the global domestic good is a function of their relative price ; is the underlying CES share parameter. The log transformation of equation 7 is the familiar export demand equation in the literature, similar to Reinhart (1995) and Senhadji and Montenegro (1999): (8) [ ] = 3 + [ ] + Previous literature often uses just the aggregation of industrial countries for and with the bilateral trade flows as weights. However, the trade weights are shifting significantly over time, difficult to derive, or unavailable consistently for each country's entire 1970-2018 period. For this reason, we use the global aggregation of national accounts already available in the WDI database to derive and , which are consistent with the specification of the 1-2-3 model. Since the global GDP and its components are also expressed in current and constant U.S. dollars, the global variables are consistent with the country variables. However, for the 1-2-3 model, we are interested in the CET elasticity Ω from equation 5 and not the CES elasticity linked to global demand in equation 8. Whenever the identification issue arises (usually if there is an incorrect sign for the CET coefficient), we consider including the variables of equation 8 as additional cointegrating or exogenous variables in the long-term cointegration equation or part of the error correction of the VEC. Equations 5 and 8 could also be solved simultaneously, yielding equation 9 as another option for estimating Ω. We apply a time series technique like VEC to it. (9) [ ] = 4 + Ω [ ] + [ ] + Fluctuations and Breaks For many developing countries, the variables are often characterized by fluctuations rather than a single break, mainly due to policy reversals, crises, conflicts, or exogenous shocks from the weather (e.g., drought, hurricanes, etc.). Figure 1 shows the frequent fluctuations of relevant variables in Benin in contrast to the smoother movements in the United States. In these situations, we find that the growth rates of output, real exports, and real imports, as well as the appropriate share of trade in GDP, work well as extra exogenous variables in the cointegration equation or the error correction part. The growth rates are stationary, so they do not add to the number of cointegration equations for estimation if introduced as endogenous variables in rare instances. We adopt this approach for most countries. 10 Figure 1: Examples of variable fluctuations – Benin versus the United States A. Benin 0.4 0.5 0.0 0.0 -0.4 -0.5 -0.8 -1.0 -1.2 -1.5 -1.6 -2.0 -2.0 -2.5 70 75 80 85 90 95 00 05 10 15 70 75 80 85 90 95 00 05 10 15 lym lpm lye lpe B. United States 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 -3.5 70 75 80 85 90 95 00 05 10 15 lym lpm Source: Authors' calculations. Notes: lym = [ ]; lpm = [ ]; lye = [ ]; lpe = [ ]. An episodic structural change could lead to a false unit root in a stationary series with a structural break. In this possible situation, we use a breakpoint unit root test to consider a dummy variable and its timing and ensure that the null hypothesis of a unit root is not rejected by the Dickey-Fuller t-test (with or without a dummy). The dummy break often pertains to the intercept but could also involve intercept and trend. The innovation associated with a structural change could be gradual (versus a one-time break) after the event; changes could also build up in the years before a significant historical event; and the type and timing could differ among the cointegration equation variables. Because of the many factors and different possible dates for the variables in the cointegration equation, we minimize using dummy variables. Moreover, the statistical determination of a break could benefit from knowledge of the economic history of a country. However, except for well-known events, an in-depth understanding of the timing of actual events, shocks, or crises is beyond this study's scope (given the number of countries). In the estimation, we also consider a dummy when the time patterns of the right- and left-side variables begin to diverge over their past behavior, combining breakpoint unit root tests, visual inspection, and, if available, relevant economic information13 to determine possible timings. 13 Such as country reports from the World Bank, IMF, Economist Intelligence Unit (EIU), or Wikepedia. 11 Figure 2: Examples of Breakpoints – Germany and South Africa A. Germany – lpe Dickey-Fuller t-statistics Dickey-Fuller autoregressive coefficients 0.5 1.04 0.0 1.00 -0.5 0.96 -1.0 0.92 -1.5 0.88 -2.0 0.84 -2.5 1975 1980 1985 1990 1995 2000 2005 2010 2015 1975 1980 1985 1990 1995 2000 2005 2010 2015 B. South Africa – lye Dickey-Fuller t-statistics Dickey-Fuller autoregressive coefficients -0.5 .96 -1.0 .92 -1.5 -2.0 .88 -2.5 .84 -3.0 .80 -3.5 -4.0 .76 1975 1980 1985 1990 1995 2000 2005 2010 2015 1975 1980 1985 1990 1995 2000 2005 2010 2015 Source: Authors' calculations. Notes: lye = [ ]; lpe = [ ]. The graphs are from Eviews’ breakpoint unit root test for intercept break and innovation outlier. Take two well-known cases, Germany and South Africa. Figure 2A indicates a break for [ ] in 1989 for Germany, coinciding with the fall of the Berlin Wall that led to the unification of East and West Germany in 1990.14 Figure 2B shows that South Africa had a break for [ ] in 1991, the year trade sanctions began to end when the country repealed its Apartheid legislation, leading to a new democratic government in 1995. In the case of Germany, however, the tests also suggest 1989 for [ ] but 1994 for [ ] and 1985 for [ ]. In the case of South Africa, the breakpoint tests also indicate 1987 for [ ], 1990 for [ ], and 1989 for both intercept and trend of [ ]. After testing alternatives and considering the history, we choose 1990 for Germany and 1991 (versus 1995) for South Africa for an intercept break. As a final check, we confirm the existence of a cointegration equation with the Johansen test (using both the Trace and Max-eigenvalues rank tests). Conflicts, regime changes, and crises could also affect data quality. In this regard, we follow WDI's data vetting process about the first year to use while allowing for data splicing from the U.N. national accounts for one or two data series of a country. We only include countries with at least 20 years of time series data. 14 Unlike former Soviet republics and the allied communist states, Germany has data prior to the unification in 1990 and that goes back to 1970. 12 Re-exports or Sudden Surges in Exports Significant surges in re-exports are related to fluctuations and breaks, where foreign goods (imports, then exports) pass through from one country to another. Examples include Hong Kong SAR, China, after China's economic liberalization in 1978 and Ireland after the Good Friday Agreement for Northern Ireland in 1998. Singapore is another case with growing re-exports. With no good data yet to separate re-exports, the total value of exports will far exceed GDP, leaving a negative difference for an estimate of the domestic good. The log factor ratios of equations 7 and 8 will become indeterminate. In these cases, we avoid imputing domestic goods from historical shares since the proportions could fluctuate even before the surge in re-exports. Instead, we use the input demand and supply relative to output (GDP) for equations 7 and 8. In place of and , we use and , respectively, in both equations (using a country's aggregate output to approximate its aggregate demand on the import side). We use the same approach for equation (9), reasoning that a country's output is also a good approximation of domestic goods relative to the global variables. The solutions appear to work well for these few countries. Sudden export surges or changes due to oil finds, mineral exports, or tourism could have the same effects. Examples include Antigua and Barbuda, Grenada, and Iraq. Estimation Methods As an estimation priority, we employ the vector error correction (VEC) model, a restricted form of the vector autoregression (VAR) model.15 The method's output has two parts. The first part produces the cointegration equation (CE) or the long-term equilibrium (equations 5, 6, or 9), providing the desired elasticity estimates. It may include possible adjustments or exogenous variables like trends and global variables, which, as a general rule, we restrict to a relevant few. We use the Dickey-Fuller distribution that corrects for the fact that the p-value for the standard t-statistic is skewed to the left. The second part of the output is the error correction. The latter contains the impact of lagged variables that ensure perturbations or deviations will return to the long-term relationship estimated in the first part. We check the system for stability but only report the estimated elasticities and the corrected p-value and t-tests in the long-run cointegration relationship. Alternatively, we try single cointegrated regressions using the Fully Modified OLS (FMOLS), which allows for various trends and additional regressors. The Wald test for simple linear restriction checks that the elasticities are not zeroes. For the CET case, we also apply VEC to equation 9. If VEC and FMOLS do not yield satisfactory results, we use the Generalized Method of Moments (GMM)). At least one of these methods almost always seems to produce reasonable estimates, so there was no need to look beyond them.16 Each country presents unique circumstances suggesting a potential for self-contained estimation (for example, see discussion about Figures 2 and 3). However, we limit the interventions to a minimum consistent set across countries. In addition to possible intercepts, trends (linear or quadratic), and lag structure, we confine the introduction of other variables to those already defined in the equations or derived from them, such as trade shares, growth rates of underlying variables in real terms (like exports, imports, 15 We use the software Eviews for the estimation. 16 Except in one case where we employed the limited information maximum likelihood method. 13 and outputs), or global demand and price ratios. If included, they are usually done as exogenous variables in CE or VAR in VEC; the CE deterministic regressors or additional deterministic regressors in FMOLS; or instrument variables in GMM. Whatever the interventions, we further check the Johansen cointegration test summary and ensure that the chosen estimation has a cointegration equation in the case of VEC. In FMOLS, we look at tests such as Hansen Instability, Engle-Granger, etc., for confirmation. If the Ω estimation is the reduced form of the export system in equation 9, there could be up to two cointegration equations. No such proof is needed in the GMM case. IV. Estimates Using the methodology described above, we estimated elasticities for as many as 191 countries.17 Tables A1 and A2 in the appendix present the estimated elasticities, the method used, the t-tests, and a summary of the interventions introduced. Table A3 lists the data source and years covered for each country. The elasticities appear reasonable, and the t-tests (except for a handful) are significant. As the summary in Table 1 shows, one hundred twenty-six countries (66.0%) have the full sample size of 1970-2018; 153 countries (80.1%) have at least 30 years of observations in their data. Of the 38 cases (19.9%) with less than 30 observations, 58% (22 cases) are borderline, with 29 observations covering 1990-2018 and mostly former Soviet republics and allied communist states. The estimates cover 45 of 48 countries in Sub-Saharan Africa, including low-income countries like Benin, the Republic of Congo, Niger, etc. Island economies of various sizes tally as many as 47, including Fiji, Kiribati, Marshall Islands, etc. The list includes seven microstates, economies with fewer than 1,000 residents, and 49 hectares of land, such as Nauru, Tuvalu, Liechtenstein, etc. Many of these countries have severe data constraints that would have made estimation difficult with more elaborate formulations and data requirements.18 Table 1: Sample size distribution in the estimation Sample Size No. of countries Percent distribution 49 (full sample) 126 66.0% 40 - 48 8 4.2 30 - 39 19 9.9 20 - 29 38 19.9 Total 191 100.0% Source: Authors' calculation. Note: Full sample = 1970-2018. The terms – countries, territories, and economies – are used interchangeably. We include any that have national accounts data in the WDI and 17 UN sources. 18 Even so, it was not possible to estimate elasticities of some countries. A few of these are countries that have experienced severe conflicts – such as Afghanistan, Somalia, Sudan, and South Sudan. For some small countries - like Curaçao, Monaco, New Caledonia, Palau, Nepal etc., flat relative prices are registered for an extended or entire period. In those instances, the countries employed exactly the same implicit price indices for GDP, exports, imports, and (therefore) domestic goods. Hence, the estimated elasticities were essentially zeros. These countries were excluded. 14 Table 2: Average elasticities by income and regional classification (simple averages) Group Classification Armington (σ) CET (Ω) Obs 1. Incomce groups All developing countries 0.707 0.593 128 Low income 0.686 0.566 26 Lower middle income 0.692 0.583 50 Upper middle income 0.731 0.616 52 High income 1.417 1.465 63 2. Regional groups East Asia and the Pacsific 0.967 0.818 31 High income 1.515 1.353 11 Developing countries 0.666 0.523 20 Europe and Central Asia 1.142 1.206 52 High income 1.463 1.601 31 Developing countries 0.668 0.623 21 Latin America and the Caribbean 0.837 0.751 33 High income 1.080 1.271 9 Developing countries 0.746 0.555 24 Middle East and North Africa 1.008 0.966 21 High income 1.432 1.345 8 Developing countries 0.747 0.733 13 North America 1.668 1.442 3 South Asia 0.726 0.638 6 Sub-Saharan Africa 0.716 0.598 45 High income 1.071 1.240 1 Developing countries 0.708 0.584 44 Source: Authors' calculations. Notes: Groupings follow World Bank income and regional classifications. Income groups are defined by GNI per capita in US$ (Atlas methodology) in 2015: Low income <= 1025; lower middle income 1026-4035; upper middle income 4036-12,475; high income > 12,475. Table 2 presents the simple averages of the Armington (import) and CET Ω (export) elasticities for various income and regional groups according to World Bank classification. The income classification is based on GNI (gross national income) per capita in U.S. dollars (Atlas methodology) for 2015, the base year of the constant price series in the estimation. The table averages could provide "rules of thumb" estimates for missing countries. They also show interesting tendencies by broad income categories. The average and Ω of high-income countries tend to be higher than 1.0, about 1.4, while those of the lower- income groups are less than 1.0. The average elasticities for low-income and lower-middle-income countries tend to be close to one another, around 0.7 for and slightly less than 0.6 for Ω. The upper- middle-income countries tend to have slightly higher values but still less than 1.0, around 0.7 for , and 15 somewhat more than 0.6 for Ω. For practical purposes, the elasticities in developing countries could be approximated as 0.65. Regarding regional averages, the North America region (NAR) and the Europe and Central Asia (ECA) groups have the highest elasticities. They are followed by the Middle East and North Africa (MENA), East Asia and the Pacific (EAP), Latin America and the Caribbean (LAC), South Asia region (SAR), and Sub-Saharan Africa (SSA). The order generally follows the average regional income. Where some regions have mixed incomes, the table shows the average differences between high-income and developing countries. The only high-income country in SSA in 2015 was Seychelles, while Mauritius is approaching this category. The pattern generally supports the hypothesis that trade elasticities increase with per-capita income. Figure 3 shows the scatter plots of the elasticities against GDP per capita in 2015 U.S. dollars, the constant price series base year in the estimation and income group classification.19 The simple correlation of the variables is about 0.71 for both the and the Ω cases. The regression line in each plot confirms an approximately positive relationship.20 The graphs could also easily be non-linear, flat for much of the lower income levels, then dispersing and rising rapidly at higher income at around $22,000 (log of 10). The dichotomy of elasticities at about 1.0 is consistent with the summary in Table 2. The split of elasticities at 1.0 between low and high-income countries is also consistent with the trade theory behind the 1-2-3 model (see Devarajan et al. 1997 and other studies in the sub-section on the 1-2-3 model). When the world price of imports (say) rises in an economy, there are two effects: an income effect (as the consumer's real income is now lower) and a substitution effect (as the domestic good now becomes more attractive). The resulting equilibrium will depend on which effect dominates. When < 1, the income effect dominates. The economy contracts the output of the domestic good and expands that of the export commodity. To pay for the needed, imperfectly substitutable import, the real exchange rate depreciates. However, when > 1, the substitution effect dominates. The economy's long-term response is to contract exports (and hence also imports) and produce more of the domestic substitute. For most developing countries, it is likely that < 1, so that the standard policy advice to depreciate the real exchange rate in the wake of an adverse terms-of-trade shock is correct.21 For developed economies, one might reasonably expect substitution elasticities to be high. In this case, the response to a terms-of-trade shock is a real appreciation, substitution of domestic goods for the more expensive (and non-critical) imports, and a contraction in the aggregate volume of trade. In all countries, one would expect substitution elasticities to be higher in the long run. The long-run effect of the real exchange rate will thus differ, and may be of the opposite sign from the short-run effect. Another example relates to the revenue effects of tariff reforms. Devarajan, Go, and Li (1999) show how the fiscal impact of tariff liberalization also depends on the substitution elasticity between foreign and domestic goods. Unless there is an upward shift in output productivity, a reduction in tariffs will invariably involve losses in revenue for much of the plausible range of the trade elasticities unless compensated by increases in domestic taxes. There is no Laffer Curve for import tariffs. 19 We also tried two altermative income per capita measurements from the World Bank WDI – the 2015 gross national income per capita using the Atlas and purchasing power parity methods. The patterns of the scatter plots remain largely the same. Because some countries have missing observations using these methods, we did not choose them. 20 The R-squared of the regression line is about 0.50 in both cases. The regression coefficient in each case is positive and significant at prob=0.05. 21 This is confirmed in the empirical estimation of substitution elasticities in Devarajan, Go and Li (1994). 16 Figure 3: Scatter plots of the elasticities against income per capita A. Sigma () 2.8 2.4 2.0 1.6 Sigma 1.2 0.8 0.4 0.0 5 6 7 8 9 10 11 12 13 lny B. Omega (Ω) 3.0 2.5 2.0 Omega 1.5 1.0 0.5 0.0 5 6 7 8 9 10 11 12 13 lny Source: Authors' calculations. Note: lny = natural log of GDP per capita, U.S. dollars, 2015. Table 3 provides additional summary descriptive statistics for the two main income groups. The median trade elasticities are close to the simple averages in all cases. In developing countries, the upper range is also less but close to one for both the Armington and CET elasticities. Countries with elasticities near the value of one are usually those close to the boundaries of the high-income group, such as emerging economies like Brazil and South Africa and some Latin American countries like Costa Rica and 17 Argentina.22 Although the average and median elasticity of high-income countries is higher than one, the lower range is less than one, overlapping with the upper range of the developing countries. The reasons are many: the boundary is ad hoc, and the income range for high income is wide; at the lower range are new entrants, such as former communist countries like Latvia and Poland, and island and Latin American countries like Barbados, Bermuda, and Chile. If we restrict the group to early OECD countries, the lower value is greater than 1.10 for both elasticities. Table 3: Descriptive Statistics for developing and high-income countries Armington (σ) CET (Ω) Developing High-income Developing High-income countries countries countries countries Average 0.71 1.42 0.59 1.46 Median 0.74 1.39 0.59 1.33 Standard deviation 0.16 0.42 0.19 0.50 Range 0.32-0.99 0.78-2.49 0.22-0.96 0.74-2.87 Source: Authors' calculations. Our results are consistent with recent findings of lower elasticities for macro or aggregate elasticities than those of micro elasticity studies of specific sectors or commodities. Because a cointegration equation corresponds to a long-term equilibrium, the results are comparable to the range of 1.75 to 2.25 for the long-term elasticity estimates of Boehm, Levchenko, and Pandalai-Nayar (2023), especially for the high-income group. Past results that do not correct for nonstationarity issues in time-series estimation will also be spuriously high. The survey and analysis by Bajzik et al. (2020), including many past studies at sectoral levels, show a broader range in magnitude. Plausible explanations mentioned before include aggregated imports, exports, and domestic goods are composites of many goods; developing countries are less diversified, and the composition of these goods tends to differ; and the external balance of payments constraint at the macro level could limit substitution possibilities. Finally, this study provides country- specific estimates of trade elasticities for many countries, 128 developing countries and 63 high-income countries. Not only were estimates for developing countries lacking in the literature, but estimates of the export supply side were also lacking. V. Conclusion This paper tries to fill a lacuna in the literature. While the Armington elasticity has been estimated at the sectoral level for a number of (mostly) developed countries, the equivalent elasticity in the widely used aggregate, 1-2-3 model of developing countries has typically been assumed. We provide empirical estimates of the import and export elasticities of the 1-2-3 model for 191 countries. Using data from 1970-2019 and the Vector Error Correction model as the dominant technique, we derive robust estimates that also square with intuition. Elasticities for high-income countries are generally greater than one, averaging around 1.4, while those of lower-income countries are below one, averaging around 0.65. 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Joint Research Center Working Papers on Territorial Modelling and Analysis No. 05/2020, the European Commission. 24 Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications CE deterministic regressors= C, T, xgr; additional Albania 0.551 0.235 2.342 28 FMOLS deterministic regressors: T^2 Algeria 0.801 0.110 7.282 46 VEC 1,2 C in CE mshare exogenous in VAR Angola 0.716 0.064 11.165 36 VEC 1,2 C in CE, VAR eshare exogenous in CE, VAR factor demand spec; CE deterministic regressors = Antigua and Barbuda 1.054 0.304 3.467 41 FMOLS C, T, eshare, xgr; additional deterministic regressors = T^2 mshare exogenous in CE; a dummy for 1998-2002 Argentina 0.993 0.055 18.109 43 VEC 1,5 C, T in CE,VAR (depression) Armenia 0.709 0.094 7.532 26 VEC 1,2 C in CE,VAR mshare exogenous in VAR Aruba 1.393 0.294 4.743 24 VEC 1,3 C in CE,VAR xgr exogenous in CE; mshare exogenous in VAR Australia 2.490 0.810 3.075 43 VEC 1,5 xgr exogenous in CE; eshare, egr exogenous in VAR Austria 1.128 0.070 16.068 46 VEC 1,2 T in CE; C in VAR mshare exogenous in VAR Azerbaijan 0.503 0.241 2.087 23 VEC 1,3 C, T in CE; C in VAR xgr exogenous in CE; mshare exogenous in VAR Bahamas, The 0.931 0.047 20.006 26 VEC 1,3 dummy after 1996 exogenous in CE, VAR Bahrain 1.283 0.437 2.937 45 VEC 1,3 C in CE factor demand spec; eshare exogenous in VAR mgr, xgr exogenous in CE; mshare, xgr exogenous Bangladesh 0.732 0.097 7.560 43 VEC 1,5 in VAR Barbados 0.902 0.035 25.850 46 VEC 1,2 C in CE,VAR mshare in VAR Belarus 0.536 0.101 5.312 28 FMOLS CE deterministic regressor= C Belgium 1.637 0.394 4.151 46 VEC 1,2 C, T in CE, VAR CE deterministic regressors= C, T, xgr; additional Belize 0.544 0.171 3.174 48 FMOLS deterministic regressors: T^2, mshare Benin 0.764 0.032 23.935 42 VEC 1,6 C, T in CE; C in VAR mshare exogenous in VAR Bermuda 0.915 0.077 11.963 46 VEC 1,2 C in CE, VAR mshare exogenous in VAR Bhutan 0.836 0.160 5.232 38 FMOLS CE deterministic regressors= C, mshare,mgr CE deterministic regressors= C, T, mshare, xgr; Bolivia 0.668 0.116 5.756 48 FMOLS additional deterministic regressors: T^2 Bosnia and 0.339 0.032 10.671 23 VEC 1,3 C, T in VAR xgr as extra endogenous var in CE Herzegovina factor demand spec; CE deterministic regressors= Botswana 0.905 0.353 2.563 48 FMOLS C, T, lyx; additional deterministic regressors: T^2, mshare Brazil 0.939 0.027 35.023 46 VEC 1,2 C in CE xgr exogenous in CE; mshare exogenous in VAR Brunei Darussalam 1.452 0.157 9.262 24 VEC 1,5 mshare exogenous in CE Bulgaria 0.788 0.143 5.523 28 FMOLS CE deterministic regressors = C, T, xgr, mshare Burkina Faso 0.813 0.037 22.102 43 VEC 1,5 C in CE xgr exogenous in CE; mshare exogenous in VAR Burundi 0.626 0.310 2.020 46 VEC 1,2 C,T in CE, VAR mgr as extra endogenous var Cabo Verde 0.799 0.085 9.372 36 VEC 1,2 Cambodia 0.496 0.191 2.595 25 GMM C, ar(1) as extra vars; insts=mshare, mgr Cameroon 0.411 0.094 4.355 43 VEC 1,5 C,T in CE; C in VAR xgr exogenous in VAR Canada 1.949 0.354 5.499 48 FMOLS CE deterministic regressor = C Central African Rep. 0.427 0.166 2.568 48 FMOLS CE deterministic regressor = C Chad 0.729 0.248 2.939 43 VEC 1,5 T in CE; C in VAR lyx exogenous in CE; xgr, mshare exogenous in VAR lyx exogenous in CE and VAR; mgr exogenous in Chile 0.895 0.157 5.698 45 VEC 1,3 C in CE, VAR VAR Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications xgr exogenous in CE and VAR; mshare exogenous China 0.529 0.032 16.650 45 VEC 1,3 C, T in CE; C in VAR in VAR Colombia 0.814 0.052 15.697 44 VEC 1,4 T in CE; C in VAR mshare exogenous in CE and VAR CE deterministic regressors= C, mgr, dummy after Comoros 0.745 0.075 9.961 38 FMOLS 1,2 2007; additional deterministic regressor= mshare Congo, Dem. Rep. 0.669 0.224 2.987 48 FMOLS CE deterministic regressors=C, T CE deterministic regressors=C, T,eshare; additional Congo, Rep. 0.568 0.194 2.922 48 FMOLS deterministic regressors= T^2, mshare Cook Islands 1.116 0.266 4.190 40 VEC 1,3 C in CE eshare exogenous in CE; mshare exogenous in VAR Costa Rica 0.869 0.036 23.996 46 VEC 1,2 C,T in CE, C in VAR mshare exogenous in CE and VAR CE deterministic regressors=C, T; additional Cote d'Ivoire 0.397 0.170 2.342 48 FMOLS deterministic regressors= T^2, mshare CE deterministic regressors=C, T, xgr; additional Croatia 0.866 0.382 2.267 23 FMOLS deterministic regressors= mshare, mgr Cuba 0.871 0.071 12.310 45 VEC 1,3 C in CE mshare exogenous in VAR Cyprus 1.699 0.160 10.642 40 VEC 1,3 C,T in CE; C in VAR mshare exogenous in VAR Czech Rep. 1.835 0.364 5.042 28 FMOLS CE deterministic regressors= C, dummy after 2002 dummy 2008 and after exogenous in CE; mshare Denmark 1.282 0.039 32.770 46 VEC 1,2 C in CE exogenous in VAR factor demand spec; xgr exogenous in CE; mshare Djibouti 0.621 0.135 4.608 39 VEC 1,5 C in CE exogenous in VAR Dominican Rep. 0.938 0.070 13.417 43 VEC 1,5 C, T in CE; C in VAR xgr exogenous in CE; mshare exogenous in VAR Ecuador 0.798 0.031 25.926 43 VEC 1,5 C in CE mshare exogenous in CE Egypt, Arab Rep. 0.868 0.022 40.085 44 VEC 1,4 C in CE, VAR mshare exogenous in VAR El Salvador 0.742 0.047 15.872 45 VEC 1,3 C in CE, VAR mshare exogenous in VAR Equatorial Guinea 0.828 0.058 14.211 34 VEC 1,4 C in CE xgr exogenous in CE; mshare exogenous in VAR Eritrea 0.817 0.111 7.337 25 VEC 1,3 C in CE mshare exogenous in CE; mgr exogenous in VAR Estonia 1.548 0.142 10.923 23 VEC 1,2 C in CE, VAR xgr exogenous in CE; mshare exogenous in VAR Eswatini 0.691 0.162 4.251 44 VEC 1,4 C, T in CE; C in VAR xgr exogenous in CE; mshare exogenous in VAR Ethiopia 0.831 0.022 37.644 26 VEC 1,2 T in CE; C in VAR mshare exogenous in VAR Fiji 0.659 0.157 4.207 46 VEC 1,2 C,T in CE; C in VAR eshare exogenous in VAR CE deterministic regressors= C, dummy for 2000 Finland 1.401 0.400 3.500 48 FMOLS and after; additional deterministic regressor= T France 2.232 0.378 5.911 46 VEC 1,2 mshare exogenous in CE mshare exogenous in CE and VAR; xgr exogenous French Polynesia 0.776 0.186 4.181 43 VEC 1,5 C in CE in VAR Gabon 0.478 0.115 4.165 45 VEC 1,3 C, T in CE; C in VAR CE deterministic regressors= C, lyx, dummy after Gambia, The 0.515 0.173 2.974 48 FMOLS 1990 and 2001; additional deterministic regressor= T CE deterministic regressor= C; additional Georgia 0.793 0.232 3.420 27 FMOLS deterministic regressors= T, mgr dummy 1990 and after exogenous in CE; xgr Germany 1.827 0.361 5.065 44 VEC 1,4 C, T in CE; C in VAR exogenous in VAR Ghana 0.856 0.117 7.310 44 VEC 1,4 C, T in CE; C in VAR mshare exogenous in CE; mgr exogenous in VAR Greece 1.632 0.436 3.745 46 VEC 1,2 dummy after 1990 as extra endogenous var; Greenland 1.765 0.873 2.023 42 VEC 1,6 C in CE mshare exogenous in VAR Grenada 0.486 0.116 4.175 39 VEC 1,2 C, T in CE, VAR factor demand spec; xgr as extra endogenous var 26 Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications Guatemala 0.852 0.297 2.869 45 VEC 1,3 T in CE; C in VAR xgr exogenous in CE and VAR CE deterministic regressors= C, mshare, dummy for Guinea 0.784 0.333 2.352 32 FMOLS 2000 and after; additional deterministic regressors= T, T^2, xgr, mgr Guinea-Bissau 0.752 0.083 9.051 46 VEC 1,2 C in CE, VAR Guyana 0.493 0.048 10.208 45 VEC 1,3 C, T in CE; C in VAR xgr exogenous in CE; mshare exogenous in VAR Haiti 0.873 0.016 53.691 26 VEC 1,4 C, T in CE, VAR mshare exogenous in VAR Honduras 0.690 0.059 11.648 46 VEC 1,2 C, T in CE, VAR mshare exogenous in CE factor demand spec; eshare exogenous in CE; xgr Hong Kong SAR, China 1.138 0.041 28.037 46 VEC 1,2 T in CE; C in VAR exogenous in VAR CE deterministic regressors= C, mshare; additional Hungary 1.388 0.140 9.930 27 FMOLS deterministic regressors= T, T^2, dummy after 2013, xgr xgr exogenous in CE and VAR; mshare exogenous Iceland 1.315 0.176 7.478 44 VEC 1,4 C in CE, VAR in VAR CE deterministic regressors: C, T, mshare; India 0.462 0.111 4.150 48 FMOLS additional deterministic regressor= mgr Indonesia 0.855 0.030 28.146 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Iran, Islamic Rep. 0.742 0.075 9.851 45 VEC 1,3 C, T in CE, VAR mshare exogenous in CE; xgr exogenous in VAR factor demand spec; xgr exogenous in CE; mshare Iraq 0.663 0.181 3.673 45 VEC 1,3 C, T in CE; C in VAR exogenous in VAR factor demand spec; eshare exogenous in CE; xgr Ireland 1.674 0.260 6.430 45 VEC 1,3 T in CE; C in VAR exogenous in VAR Israel 1.150 0.086 13.393 45 VEC 1,3 C, T in CE; C in VAR mshare exogenous in CE; xgr exogenous in VAR Italy 2.355 0.570 4.129 45 VEC 1,3 Jamaica 0.781 0.055 14.213 46 VEC 1,2 C in CE mshare exogenous in CE; xgr exogenous in VAR Japan 1.794 0.097 18.469 42 VEC 1,6 C, T in CE, VAR eshare exogenous in CE; mshare exogenous in VAR xgr exogenous in CE and VAR; mshare exogenous Jordan 0.691 0.063 10.898 38 VEC 1,4 C in CE, VAR in VAR Kazakhstan 0.635 0.062 10.217 26 VEC 1,2 C in CE, VAR mshare exogenous in VAR CE deterministic regressors= C, xgr; additional Kenya 0.738 0.083 8.912 48 FMOLS deterministic regressor= mshare Kiribati 0.650 0.092 7.036 36 VEC 1,3 C in CE, VAR xgr exogenous in VAR Korea, Rep. 1.465 0.254 5.776 48 FMOLS C as CE determinisitic regressor Kosovo 0.690 0.194 3.558 26 VEC 1,2 xgr exogenous in CE; mshare exogenous in VAR xgr exogenous in CE and VAR; eshare exogenous in Kuwait 1.578 0.381 4.140 46 VEC 1,2 C, T in CE; C in VAR VAR Kyrgyz, Rep. 0.760 0.105 7.207 26 VEC 1,2 C, T in CE, VAR mshare exogenous in CE and VAR xgr exogenous in CE and VAR; mshare exogenous Lao PDR 0.451 0.202 2.233 30 VEC 1,3 C,T in CE, VAR in VAR CE deterministic regressors= C, mgr, mshare; Latvia 0.875 0.875 7.223 28 FMOLS additional deterministic regressors= T, T^2 Lebanon 0.550 0.072 7.603 26 VEC 1,2 C,T in CE, VAR xgr as extra endogenous var Lesotho 0.855 0.246 3.478 44 VEC 1,4 C, T in CE, VAR mshare exogenous in CE; mgr exogenous in VAR xgr as extra endogenous var; mshare exogenous in Liberia 0.798 0.147 5.415 46 VEC 1,2 C in CE, VAR VAR factor demand spec; dummy after 2010 added to Libya 0.936 0.154 6.056 48 FMOLS endogenous vars; CE deterministic regressors: C, mshare Liechtenstein 1.433 0.342 4.192 45 VEC 1,3 C in CE, VAR xgr as additional endogenous var 27 Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications dummy 2010 and after exogenous in CE, VAR; Lithuania 1.221 0.055 22.165 26 VEC 1,2 C in CE mshare exogenous in VAR factor demand spec; CE deterministic regressors= Luxembourg 1.413 0.354 3.990 48 FMOLS C, T, xgr; additional deterministic regressor= T^2 Macao SAR, China 1.587 0.209 7.602 34 VEC 1,2 C in CE factor demand spec; mshare exogenous in VAR C deterministic regressors= C, lyx; additional Madagascar 0.789 0.177 4.456 48 FMOLS deterministic regressors= T, T^2 Malawi 0.588 0.238 2.467 44 VEC 1,4 C in CE, VAR xgr as extra endogenous var factor demand spec; xgr as extra endogenous var; Malaysia 0.794 0.238 3.333 45 VEC 1,3 C in CE, VAR eshare exogenous in VAR Maldives 0.944 0.207 4.552 46 VEC 1,2 factor demand spec; mgr as extra endogenous var xgr as extra endogenous var; mshare exogenous in Mali 0.883 0.127 6.954 45 VEC 1,3 C, T in CE; C in VAR VAR factor demand spec; mshare exogenous in CE; Malta 1.026 0.031 32.782 46 VEC 1,2 C, T in CE; C in VAR eshare exogenous in VAR Marshall Islands 0.895 0.043 20.596 42 VEC 1,6 mshare exogenous in CE; eshare exogenous in VAR CE deterministic regressors= C, T, mshare; Mauritania 0.749 0.145 5.163 48 FMOLS additional deterministic regressor= T^2 CE deterministic regressors= dummy for 1985- Mauritius 0.950 0.327 2.903 42 FMOLS 2014; additional deterministic regressor= mshare Mexico 0.885 0.065 13.569 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Moldova 0.516 0.065 7.926 21 VEC 1,2 C, T in CE; C in VAR mshare exogenous in CE; xgr exogenous in VAR Mongolia 0.612 0.044 13.862 35 VEC 1,2 C, T in CE, VAR mshare exogenous in CE; xgr exogenous in VAR CE deterministic regressors= C, T, mshare; Montenegro 0.953 0.076 12.613 27 FMOLS additional deterministic regressor= T^2, xgr Morocco 0.889 0.046 19.313 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Mozambique 0.675 0.035 19.214 34 VEC 1,3 C, T in CE; C in VAR mshare exogenous in VAR Myanmar 0.788 0.068 11.626 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Namibia 0.660 0.057 11.626 36 VEC 1,2 C in CE, VAR mshare exogenous in CE; xgr exogenous in VAR Nauru 1.082 0.346 3.128 43 VEC 1,5 C in CE mshare exogenous in VAR mshare exogenous in CE and VAR; dummy 2001 Netherlands 1.326 0.097 13.614 43 VEC 1,5 C, T in CE; C in VAR and after exogenous in CE New Zealand 1.716 0.366 4.685 45 VEC 1,3 C, T in CE; C in VAR xgr exogenous in CE xgr as extra endogenous var; mshare exogenous in Nicaragua 0.844 0.043 19.682 45 VEC 1,3 C in CE, VAR VAR CE deterministic regressors= C, xgr; additional Niger 0.579 0.158 3.664 48 FMOLS deterministic regressor= T Nigeria 0.768 0.061 12.664 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in CE; xgr exogenous in VAR North Macedonia 0.704 0.223 3.160 28 FMOLS CE deterministic regressor= lyx dummy 1980 and after exogenous in CE and VAR; Norway 1.724 0.303 5.690 46 VEC 1,2 xgr exogenous in VAR dummy for 1982-2005 exogenous in CE and VAR; Oman 0.938 0.087 10.809 46 VEC 1,2 C in CE mshare exogenous in CE; xgr exogenous in VAR Pakistan 0.779 0.032 24.206 45 VEC 1,3 C, T in CE; C in VAR mshare exogenous in VAR Panama 0.737 0.037 19.742 45 VEC 1,3 C in CE mshare exogenous in VAR CE deterministic regressors= C, T, mshare; Papua New Guinea 0.600 0.292 2.050 48 FMOLS additional deterministic regressors= T^2, lyx Paraguay 0.811 0.100 8.094 45 VEC 1,3 C, T in CE; C in VAR mgr, mshare exogenous in VAR Peru 0.776 0.056 13.762 43 VEC 1,5 C, T in CE; C in VAR mshare exogenous in CE; xgr exogenous in VAR 28 Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications CE deterministic regressors= C, T, eshare, dummy Philippines 0.654 0.319 2.046 48 FMOLS 1988 and after; additional deterministic regressors= T^2, xgr Poland 0.974 0.057 17.135 26 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Portugal 1.386 0.150 9.219 46 VEC 1,2 xgr, mshare exogenous in VAR Puerto Rico 1.503 0.224 6.715 45 VEC 1,3 C in CE, VAR mshare exogenous in VAR Qatar 2.177 0.291 7.473 45 VEC 1,3 factor demand spec; mgr exogenous in VAR Romania 0.736 0.063 11.738 26 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Russian Federation 0.796 0.028 28.648 26 VEC 1,2 C in CE, VAR CE deterministic regressors= C, T, mshare; Rwanda 0.657 0.086 7.675 48 FMOLS additional deterministic regressors= T^2, xgr CE deterministic regressors= C, dummy 2002 and Samoa 0.348 0.044 7.922 48 FMOLS after, mshare factor demand spec; xgr exogenous in CE; eshare San Marino 1.215 0.025 48.905 44 VEC 1,4 C, T in CE; C in VAR exogenous in CE dummy for 1970-1986 and after 2000 exogenous Sao Tome and Principe 0.852 0.184 4.633 46 VEC 1,2 in CE and VAR. Dummy after 1973 exogenous in CE and VAR; Saudi Arabia 1.480 0.135 10.943 46 VEC 1,2 C in CE mshare exogenous in VAR mshare exogenous in CE and VAR; dummy after Senegal 0.773 0.043 18.094 44 VEC 1,4 C in CE 1981 exogenous in VAR CE deterministic regressors= C, mshare; additional Serbia 0.881 0.198 4.457 23 FMOLS deterministic regressors= T, lyx factor demand spec; C, ar(1), dummy 2000 and Seychelles 1.071 0.365 2.936 42 GMM after as extra vars; insts= T, mgr, eshare Sierra Leone 0.430 0.171 2.522 48 GMM C, ar(1), lyx as extra vars; insts= T, T^2 Singapore 2.048 0.469 4.369 45 VEC 1,3 C, T in CE; C in VAR factor demand spec; xgr exogenous in CE mgr as extra endogenous var; mshare exogenous Slovak Republic 0.840 0.259 3.246 25 VEC 1,3 C in CE in VAR Slovenia 0.804 0.172 4.684 24 VEC 1,4 C, T in CE; C in VAR mshare exogenous in VAR xgr exogenous in CE; dummy 1993 and after Solomon Islands 0.681 0.131 5.220 35 VEC 1,3 exogenous in VAR dummy 1991 and after exogenous in CE and VAR; South Africa 0.968 0.032 29.823 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Spain 1.979 0.098 20.138 45 VEC 1,3 C in CE, VAR xgr exogenous in VAR dummy for 1980-2004 exogenous in CE and VAR; Sri Lanka 0.603 0.179 3.367 46 VEC 1,2 C in CE xgr exogenous in VAR St. Kitts and Nevis 1.120 0.274 4.081 45 VEC 1,3 C in CE, VAR mgr, mshare exogenous in VAR St. Lucia 0.516 0.126 4.090 46 VEC 1,2 mgr exogenous in VAR St. Vincent and the 0.342 0.134 2.555 48 GMM ar(1), C, T as extra vars; inst=mshare Grenadines Sweden 1.565 0.385 4.061 44 VEC 1,4 C, T in CE, VAR mgr exogenous in VAR Switzerland 1.105 0.030 37.201 46 VEC 1,2 C in CE mgr, mshare exogenous in VAR CE deterministic regressors= C, mgr, lyx; additional Syrian Arab Republic 0.864 0.151 5.737 48 FMOLS deterministic regressor= xgr Tajikistan 0.320 0.151 2.125 28 FMOLS factor demand spec; CE deterministic regressor= C CE deterministic regressor= C; additional Tanzania 0.468 0.151 3.102 28 FMOLS deterministic regressor= T Thailand 0.741 0.089 8.302 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Timor-Leste 0.452 0.205 2.203 25 VEC 1,3 C, T in CE, VAR mshare exogenous in VAR Togo 0.620 0.061 10.096 45 VEC 1,3 C, T in CE; C in VAR mshare exogenous in VAR 29 Appendix Table A1: Estimates of σ, the Armington CES Elasticities Country Estimate and test Model NOB Lags Constant and Standard t- after intervals trend structure in σ Error statistic adj Method in VEC VEC Additional specifications CE deterministic regressors = C, dummy 1985 and Tonga 0.920 0.198 4.636 43 FMOLS after Trinidad and Tobago 0.937 0.168 5.580 46 VEC 1,2 C, T in CE, VAR xgr exogenous in CE; xgr exogenous in VAR Tunisia 0.595 0.048 12.508 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in VAR Türkiye 0.915 0.098 9.370 46 VEC 1,2 C, T in CE, VAR mshare exogenous in VAR Turkmenistan 0.471 0.088 5.323 25 VEC 1,3 C, T in CE, VAR mgr, mshare exogenous in VAR Tuvalu 0.745 0.071 10.553 46 VEC 1,2 C, T in CE; C in VAR xgr, mshare exogenous in VAR xgr exogenous in CE; dummy 1998 and after Uganda 0.669 0.125 5.358 44 VEC 1,4 C, T in CE; C in VAR exogenous in VAR Ukraine 0.685 0.243 2.815 26 VEC 1,2 C, T in CE, VAR factor demand spec; mgr exogenous in VAR factor demand spec; dummy 1993 and after United Arab Emirates 1.822 0.335 5.443 46 VEC 1,2 C, T in CE, VAR exogenous in CE and VAR; xgr exogenous in CE; eshare exogenous in VAR United Kingdom 1.897 0.330 5.741 45 VEC 1,3 C, T in CE, VAR mgr exogenous in VAR United States 2.141 0.210 10.181 45 VEC 1,3 C in VAR mgr exogenous in VAR Uruguay 0.989 0.023 42.842 45 VEC 1,3 C in CE, VAR mshare exogenous in VAR Uzbekistan 0.746 0.050 14.892 25 VEC 1,3 xgr exogenous in CE; mshare exogenous in VAR C, T in CE; C in VAR CE deterministic regressors= C, T, mshare; Vanuatu 0.837 0.141 5.926 38 FMOLS additional deterministic regressors= T^2, eshare Venezuela 0.633 0.106 5.981 44 VEC 1,4 C in CE factor demand spec; lyx exogenous in CE factor demand spec; CE deterministic regressors= Vietnam 0.622 0.219 2.840 32 FMOLS C, dummy 1990 and after, T, eshare; additional deterministic regressors= T^2, xgr West Bank and Gaza 0.747 0.056 13.375 22 VEC 1,2 C, T in CE; C in VAR xgr exogenous in CE; mshare exogenous in VAR Yemen 0.748 0.289 2.590 24 VEC 1,4 C in CE factor demand spec; xgr exogenous in VAR CE deterministic regressors= C, dummy after 1989, Zambia 0.910 0.063 14.445 48 FMOLS additional deterministic regressors= T, mshare CE deterministic regressors= C, T, mshare, dummy Zimbabwe 0.660 0.122 5.409 48 FMOLS after 1997; additional deterministic regressors= T^2, dummy after 2007 Source: Authors' calculations Notes: C = constant CE = cointegration equation dummy = dummy variable egr = growth rate of real exports eshare = exports/GDP FMOLS = fully modified OLS for cointegration regression global demand and price ratios = variables in equation 9 GMM = generalized method of moments inst(s) = instrument variable(s) lyx = log of output (real GDP) index mgr = growth rate of real imports mshare = imports/GDP NOB = number of observations after adjustments T = linear trend T^2 = quadratic trend reduced eq of export system = equation 9 VEC = vector error correction VAR = error correction part in VEC or vector autoregression xgr = growth of output (real GDP) 30 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications Albania 0.604 0.078 7.754 28 VEC 1,2 C, T in CE, VAR reduced eq of export system Algeria 0.959 0.382 2.510 45 VEC 1,2 xgr as extra variable; eshare exogenous in VAR reduced eq of export system; dummy after 2000 Angola 0.609 0.161 3.774 36 VEC 1,2 C in CE, VAR exogenous in CE factor demand (re-export) spec.; reduced eq of export Antigua and Barbuda 1.666 0.801 2.081 41 FMOLS system; CE deterministic regressors= C, T, xgr; additional deterministic regressors=mshare, T^2 global demand and price ratios, dummy for 1998-2002 Argentina 0.680 0.146 4.666 42 VEC 1,6 C, T in CE; C in VAR (depression) exogenous in CE, VAR Armenia 0.766 0.378 2.025 25 VEC 1,3 C, T in CE, VAR Aruba 2.263 0.477 4.747 24 VEC 1,4 C, T in CE, VAR reduced eq of export system; eshare exogenous in CE; Australia 1.331 0.389 3.427 43 VEC 1,5 egr exogenous in VAR Austria 2.180 0.485 4.497 44 VEC 1,4 C, T in CE,VAR reduced eq of export system; xgr exogenous in VAR Azerbaijan 0.362 0.156 2.316 26 GMM inst= eshare Bahamas, The 0.906 0.153 5.914 25 VEC 1,4 C, T in CE; C in VAR reduced eq of export system factor demand (re-export) spec; dummy 2003 and after Bahrain 1.428 0.517 2.764 46 VEC 1,2 exogenous in CE and VAR; xgr exogenous in VAR ar(1), xgr, global demand ratio as extra vars; insts= Bangladesh 0.651 0.316 2.057 48 GMM eshare, T, T^2 reduced eq of export system; dummy 1990 and after in Barbados 0.785 0.241 3.265 43 VEC 1,5 C, T in CE; C in VAR CE, VAR Belarus 0.308 0.116 2.643 27 GMM ar(1), ma(1); insts=global demand and price ratios Belgium 2.868 0.875 3.279 45 VEC 1,3 C, T in CE; C in VAR reduced eq of export system; eshare exogenous in VAR dummy after 1999 exogenous in CE; xgr, global demand Belize 0.658 0.212 3.104 45 VEC 1,3 C in CE and price ratios exogenous in VAR Benin 0.881 0.248 3.555 45 VEC 1,3 C in CE reduced eq of export system; egr exogenous in VAR CE deterministic regressors= C, egr, log(M); additional Bermuda 0.955 0.412 2.320 48 FMOLS deterministic regressor= eshare Bhutan 0.722 0.266 2.718 35 VEC 1,3 C in CE reduced eq of export system CE deterministic regressors= C, T, xgr, global price ratio; Bolivia 0.344 0.152 2.261 48 FMOLS additional deterministic regressors= T^2, eshare Bosnia and 0.496 0.089 5.558 23 VEC 1,3 C, T in VAR reduced eq of export system; egr exogenous in VAR Herzegovina factor demand (re-export) spec; reduced eq of export Botswana 0.915 0.351 2.605 44 VEC 1,4 C, T in CE; C in VAR system; lym exogenous in VAR Brazil 0.531 0.104 5.088 48 GMM ar(1); inst=eshare dummy after 2007 exogenous in CE; xgr exogenous in Brunei Darussalam 1.146 0.419 2.733 25 VEC 1,4 C, T in CE; C in VAR VAR Bulgaria 0.760 0.313 2.431 26 VEC 1,2 C, T in CE, VAR reduced eq of export system; xgr exogenous in VAR reduced eq of export system; xgr exogenous in CE; egr Burkina Faso 0.586 0.272 2.157 46 VEC 1,2 C, T in CE; C in VAR exogenous in VAR Burundi 0.400 0.197 2.034 46 VEC 1,2 eshare and global price ratio exogenous in VAR Cabo Verde 0.856 0.063 13.632 34 VEC 1,4 C,T in CE ; C in VAR global demand and price ratios exgenous in VAR Cambodia 0.392 0.015 26.372 20 VEC 1,5 C,T in CE, VAR Cameroon 0.556 0.276 2.015 48 GMM C, ar(1) as extra vars; insts=eshare, global demand ratio Canada 1.124 0.399 2.815 46 VEC 1,2 C, T in CE; C in VAR global demand and price ratios exogenous in VAR 31 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications Central African Rep. 0.353 0.170 2.080 49 LIML C as extra var; insts=eshare, T Chad 0.696 0.154 4.525 46 VEC 1,2 C,T in CE,VAR xgr exogenous in CE,VAR Chile 0.837 0.306 2.734 46 VEC 1,2 C in CE, VAR reduced eq of export system China 0.554 0.248 2.239 45 VEC 1,3 reduced eq of export system; eshare exogenous in CE egr, ar(1) as extra vars; insts= eshare, global demand Colombia 0.502 0.132 3.806 48 GMM ratio deterministic regressors= xgr, M/D ratio; additional Comoros 0.698 0.110 6.349 38 FMOLS deterministic variable=dummy after 2007 Congo, Dem. Rep. 0.503 0.245 56.713 49 GMM C, T as extra vars; insts=eshare, global price ratio deterministic regressors= C, global demand and price Congo, Rep. 0.334 0.074 4.487 48 FMOLS ratios; additional deterministic regressors= T, eshare ar(1), dummy after 1999 as extra vars; insts= eshare, Cook Islands 1.036 0.474 2.185 46 GMM global demand and price ratios reduced eq of export system; CE deterministic regressor= Costa Rica 0.416 0.132 3.158 48 FMOLS C; additional deterministic regressors= T, T^2 Cote d'Ivoire 0.426 0.127 3.353 46 VEC 1,2 C in CE global demand and price ratios exogenous in VAR xgr as extra endogenous var; global demand ratio Croatia 0.929 0.261 3.559 21 VEC 1,2 C, T in CE, VAR exogenous in VAR Cuba 0.892 0.401 2.226 45 VEC 1,3 eshare exogenous in VAR Cyprus 1.327 0.538 2.465 43 GMM C, ar(1) as extra vars; inst= eshare Czech Rep. 1.351 0.443 3.052 28 GMM C, ar(1) as extra vars; inst= eshare global demand and price ratios exogenous in CE, VAR; lyx Denmark 1.563 0.353 4.431 44 VEC 1,4 C, T in CE; C in VAR exogenous in VAR ar(1), ma(1), egr, lyx as extra vars; insts= eshare, T^2, Djibouti 0.699 0.368 1.900* 47 GMM dummy 2000 and after xgr exogenous in CE; eshare exogenous in VAR; dummy Dominican Rep. 0.915 0.302 3.034 44 VEC 1,4 after 1988 exogenous in CE,VAR C, T, xgr, ar(1) as extra vars; insts=eshare and global Ecuador 0.521 0.521 1.990* 48 GMM demand ratio egr, ar(1) as extra vars; insts= eshare, T, global demand Egypt, Arab Rep. 0.703 0.241 2.916 48 GMM ratio El Salvador 0.726 0.327 2.220 48 GMM exgr, ar(1) as extra vars; insts= eshare, mshare C as CE deterministic regressor; T as additional Equatorial Guinea 0.493 0.167 2.951 36 FMOLS deterministic regressor Eritrea 0.550 0.175 3.143 26 VEC 1,2 C in CE reduced eq of export system; xgr exogenous in CE Estonia 1.706 0.511 3.337 23 VEC 1,2 C, T in CE; C in VAR reduced eq of export system xgr exogeous in CE; global demand and price ratios Eswatini 0.516 0.092 5.627 44 VEC 1,4 C, T in CE; C in VAR exogenous in VAR Ethiopia 0.390 0.167 2.332 24 VEC 1,4 C, T in CE; C in VAR global demand and price ratios exogenous in VAR Fiji 0.389 0.149 2.621 45 VEC 1,3 C,T in CE; C in VAR global demand and price ratios exogenous in VAR C, dummy for 2000 and after, ar(1) as extra vars; inst= Finland 1.635 0.270 6.054 48 GMM eshare France 2.488 1.035 2.403 46 VEC 1,2 lyx exogenous in CE; eshare exogenous in VAR French Polynesia 0.899 1.405 0.640* 47 GMM ar(1), ma(1). T, xgr as extra vars; insts= eshare, mshare Gabon 0.275 0.118 2.325 48 GMM ar(1) as extra var; inst= eshare Gambia, The 0.261 0.055 4.753 46 VEC 1,2 C in CE, VAR global demand and price ratios exogenous in VAR Georgia 0.448 0.085 5.252 26 GMM C, egr, ar(1), ma(1) as extra vars; insts= eshare, mshare 32 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications reduced eq of export system; dummy 1990 and after Germany 1.821 0.258 7.064 42 VEC 1,6 C, T in CE, VAR exogenous in CE and VAR egr exogenous in CE; global demand and price ratios Ghana 0.587 0.289 2.032 45 VEC 1,3 C , T in CE; C in VAR exogenous in VAR Greece 1.583 0.532 2.973 48 GMM T, ar(1), xgr as extra vars; inst= eshare deterministic regressors= C, T, dummy for 1970-1980, Greenland 1.300 0.612 2.125 48 FMOLS global demand and price ratios; additional deterministic regressors= T^2, lyx, eshare factor demand (re-export) spec; xgr, ar(1), ma(1) as extra Grenada 0.397 0.196 2.026 40 TSLS vars; inst= eshare Guatemala 0.326 0.087 3.733 44 VEC 1,4 C in CE, VAR dummy after 1985 exogenous in VAR CE deterministic regressors= C, T, global demand and Guinea 0.808 0.321 2.518 32 FMOLS price ratios; additional deterministic regressors= T^2, eshare Guinea-Bissau 0.834 0.397 2.103 48 GMM T, ar(1), egr as extra vars; insts= eshare, lyx Guyana 0.474 0.055 8.540 44 VEC 1,4 C, T in CE, VAR reduced eq of export system Haiti 0.620 0.307 2.019 30 GMM C, xgr, ar(1), lyx as extra vars; insts= T, eshare dummy for 1992 and after exogenous in CE and VAR; Honduras 0.658 0.179 3.670 45 VEC 1,3 C, T in CE; C in VAR global demand and price ratios exogenous in VAR Hong Kong SAR, factor demand (re-export) spec; reduced eq of export 1.239 0.352 3.518 46 VEC 1,2 C in CE, VAR China system; xgr exogenous in VAR dummy after 2013 exogenous in CE; mshare exogenous Hungary 1.706 0.217 7.847 25 VEC 1,2 C in CE in VAR global demand ratio exogenous in CE; global price ratio Iceland 1.787 0.580 3.081 45 VEC 1,3 C in CE exogenous in VAR reduced eq of export system; egr exogenous in CE; India 0.647 0.275 2.358 46 VEC 1,2 C, T in CE; C in VAR eshare exogenous in VAR Indonesia 0.889 0.359 2.478 46 VEC 1,2 xgr as extra endogenous var; eshare exogenous in VAR Iran, Islamic Rep. 0.746 0.293 2.544 45 VEC 1,3 C, T in CE, VAR egr exogenous in CE and VAR; xgr exogenous in VAR factor demand (re-export) spec; eshare exogenous in CE, Iraq 0.853 0.152 5.604 46 VEC 1,2 C, T in CE, VAR VAR; egr exogenous in VAR factor demand (re-export) spec; reduced eq of export Ireland 1.541 0.255 6.045 46 VEC 1,2 C in CE system; mshare exogenous in CE, VAR Israel 1.259 0.468 2.688 46 VEC 1,2 C in CE reduced eq of export system Italy 1.421 0.476 2.987 48 GMM xgr, ar(1) as extra vars; inst= eshare Jamaica 0.779 0.267 2.920 48 GMM egr, ar(1) as extra vars; inst= eshare Japan 2.010 0.087 23.194 45 VEC 1,3 C, T in CE; C in VAR reduced eq of export system; eshare exogenous in VAR Jordan 0.853 0.251 3.397 40 VEC 1,2 C, T in CE, VAR reduced eq of export system; eshare exogenous in VAR Kazakhstan 0.609 0.090 6.737 26 VEC 1,2 C, T in CE; C in VAR reduced eq of export system; egr as extra endogenous Kenya 0.577 0.208 2.769 46 VEC 1,2 C,T in CE, VAR var Kiribati 0.252 0.113 2.228 37 VEC 1,2 C in CE xgr as additional endogenous var reduced eq of export system; xgr as extra endogenous Korea, Rep. 1.285 0.265 4.840 46 VEC 1,2 C in CE, VAR var in CE xgr as extra endogenous var in CE; eshare exogenous in Kosovo 0.738 0.306 2.407 26 VEC 1,2 VAR Kuwait 1.625 0.524 3.103 46 VEC 1,2 xgr exogenous in CE and VAR 33 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications reduced eq of export system; xgr exogenous in CE; egr Kyrgyz, Rep. 0.687 0.257 2.668 24 VEC 1,4 C in CE exogenous in VAR Lao PDR 0.372 0.122 3.051 31 GMM ar(1), ma(1) as extra vars; insts= xgr, eshare CE deterministic regressors= C, T, xgr; additional Latvia 0.736 0.254 2.896 28 FMOLS deterministic regressors= T^2, eshare CE deterministic regressors= C, xgr; additional Lebanon 0.892 0.396 2.254 28 FMOLS deterministic regressors= T, T^2, eshare Lesotho 0.747 0.044 16.816 46 VEC 1,2 C in C reduced eq of export system CE deterministic regressors=C, egr; additional Liberia 0.585 0.157 3.730 48 FMOLS deterministic regressors= eshare factor demand (re-export) spec; xgr as additional Libya 0.740 0.287 2.575 45 VEC 1,3 C in CE, VAR endogenous var; global price ratio exogenous in VAR Liechtenstein 1.815 0.491 3.700 48 GMM C, T, ar(1) as additional vars; inst= eshare deterministic regressors: C, T, T^2, mgr, lyx, dummy after Lithuania 1.032 0.294 3.507 28 FMOLS 2009; additional deterministic regressors= eshare factor demand (re-export) spec; reduced eq of export Luxembourg 2.319 0.599 3.870 45 VEC 1,3 C in CE, VAR system; eshare exogenous in VAR factor demand (re-export) spec; deterministic Macao SAR, China 1.156 0.141 8.180 36 FMOLS regressors= C, T, xgr; additional deterministic regressors= T^2, eshare, global demand and price ratios xgr, ar(1) as added vars; insts=eshare, global demand Madagascar 0.485 0.230 2.110 48 GMM ratio egr, ar(1) as extra vars; insts= eshare, global demand Malawi 0.303 0.112 2.707 48 GMM ratio factor demand (re-export) spec; C, xgr, T, T^2 as extra Malaysia 0.736 0.302 2.437 48 GMM vars; insts= mshare, global demand ratio factor demand (re-export) spec; C, T, T^2, egr, ar(1) as Maldives 0.638 0.304 2.102 48 GMM extra vars; insts= eshare, mshare, xgr Mali 0.931 0.422 2.204 48 GMM T, ar(1) as extra vars; insts= eshare, xgr factor demand (re-export) spec; xgr as extra endogenous Malta 1.074 0.328 3.275 45 VEC 1,3 C in CE, VAR var; dummy 2000 and after exogenous in CE and VAR CE deterministic regressors= C, T, egr, global demand and Marshall Islands 0.402 0.112 3.587 48 FMOLS price ratios; additional deterministic regressors= T^2, eshare Mauritania 0.568 0.278 2.042 47 GMM egr, T, ar(1), ma(1) as extra vars; inst= eshare dummy 1990 and after exogenous in CE and VAR; global Mauritius 0.908 0.390 2.329 37 VEC 1,5 C, T in CE, VAR demand and price ratios exogenous in VAR Mexico 0.665 0.116 5.748 48 GMM egr, ar(1) as extra vars; inst= eshare xgr exogenous in CE, VAR; global demand and price ratios Moldova 0.760 0.117 6.508 20 VEC 1,3 C, T in CE, VAR exogenous in VAR Mongolia 0.698 0.081 8.651 34 VEC 1,3 C, T in CE, VAR reduced eq of export system; egr exogenous in VAR Montenegro 0.732 0.666 1.099* 27 GMM ar(1) and global demand ratio as extra vars; inst= eshare Morocco 0.520 0.259 2.004 45 VEC 1,3 C, T in CE; C in VAR global demand ratio exogenous in VAR reduced eq of export system; CE deterministic regressor= Mozambique 0.294 0.100 2.950 37 FMOLS C; additional deterministic regressors: T, mshare Myanmar 0.221 0.071 3.099 48 GMM C, T ar(1) as extra vars; insts= T^2, eshare Namibia 0.595 0.151 3.944 36 VEC 1,2 C in CE global demand and price ratios exogenous in VAR factor demand (re-export) spec; reduced eq of export Nauru 1.181 0.477 2.474 47 FMOLS system; CE deterministic regressors= C, T, xgr, dummy 2005 and after; additional deterministic regressors= T^2 reduced eq of export system; dummy 2001 and after Netherlands 2.092 0.302 6.926 42 VEC 1,6 C,T in CE; C in VAR exogenous in CE; eshare exogenous in VAR 34 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications reduced eq of export system; egr as extra endogenous New Zealand 2.536 0.634 3.997 44 VEC 1,4 C in CE var Nicaragua 0.547 0.232 2.356 48 GMM xgr, ar(1) as extra vars; inst= eshare Niger 0.551 0.260 2.120 46 VEC 1,2 C, T in CE, VAR egr exogenous in VAR dummy between 1981 and 1987 exogenous in CE and Nigeria 0.569 0.136 4.175 45 VEC 1,3 C, T in CE; C in VAR VAR; xgr exogenous in VAR North Macedonia 0.498 0.083 5.982 26 VEC 1,2 C, T in CE, VAR reduced eq of export system; mshare exogenous in VAR dummy for 1990-2005 exogenous in CE and VAR; egr Norway 2.507 0.627 3.997 46 VEC 1,2 exogenous in VAR dummy 1988 and after exogenous in CE; egr, eshare Oman 0.896 0.268 3.341 44 VEC 1,4 exogenous in VAR Pakistan 0.633 0.185 3.422 42 VEC 1,6 C in CE, VAR global demand ratio exogenous in VAR Panama 0.536 0.173 3.101 46 VEC 1,2 C, T in CE; C in VAR global demand and price ratios exogenous in VAR Papua New Guinea 0.289 0.121 2.394 46 VEC 1,2 C in CE global demand anr price ratios exogenous in VAR Paraguay 0.447 0.123 3.630 45 VEC 1,3 C in CE, VAR global demand anr price ratios exogenous in VAR Peru 0.520 0.243 2.140 46 VEC 1,2 C, T in CE; C in VAR mshare exogenous in CE; egr exogenous in VAR Philippines 0.783 0.158 4.964 46 VEC 1,2 C in CE, VAR egr, global demand and price ratios exogenous in VAR Poland 0.986 0.118 8.358 26 VEC 1,2 C, T in CE, VAR reduced eq of export system Portugal 1.286 0.302 4.257 46 VEC 1,2 C, T in CE; C in VAR xgr exogenous in CE; dummy after 2016 exogenous in Puerto Rico 1.547 0.562 2.754 44 VEC 1,4 C in CE, VAR VAR factor demand (re-export) spec; dummy 1998 and after Qatar 1.771 0.324 5.468 46 VEC 1,2 C, T in CE; C in VAR exogenous in CE; global demand ratio exogenous in VAR Romania 0.943 0.239 3.953 24 VEC 1,4 C in CE, VAR global demand ratio exogenous in VAR Russian Federation 0.718 0.148 4.857 26 VEC 1,2 C, T in CE; C in VAR C, egr, ar(1) as extra vars; insts= eshare, global demand Rwanda 0.615 0.274 2.243 48 GMM and price ratios dummy for 1985-1997 exogenous in CE and VAR; xgr Samoa 0.222 0.067 3.335 46 VEC 1,2 exogenous in CE factor demand (re-export) spec; reduced eq of export San Marino 1.280 0.217 5.894 44 VEC 1,4 C, T in CE, VAR system; eshare exogenous in VAR Sao Tome and xgr exogenous in CE; global demand and price ratios 0.799 0.139 5.756 46 VEC 1,2 C, T in CE; C in VAR Principe exogenous in VAR CE deterministic regressors= C, dummy after 1973, xgr, Saudi Arabia 1.128 0.170 6.620 48 FMOLS global demand and price ratios; additional deterministic regressor= eshare CE deterministic regressors= C, T, global demand ratio; Senegal 0.421 0.158 2.654 48 FMOLS additional deterministic regressors= egr, eshare C, ar(1), global demand ratio as extra vars; insts= T^2, Serbia 0.646 0.306 2.113 23 GMM eshare factor demand (re-export) spec; global demand and price Seychelles 1.240 0.076 16.372 39 VEC 1,3 C,T in CE; C in VAR ratios exogenous in VAR reduced eq of export system; egr, eshare exogenous in Sierra Leone 0.571 0.125 4.579 45 VEC 1,3 C, T in CE, VAR VAR factor demand (re-export) spec; C, xgr, T, T^2, ar(1) as Singapore 1.070 0.227 4.713 48 GMM extra vars; insts= mshare, global demand and price ratios ar(1), dummy 2010 and after, T, T^2 as extra vars; insts= Slovak Republic 0.943 0.319 2.952 28 GMM eshare, lyx CE deterministic regressors= C, T, global demand and Slovenia 0.738 0.223 3.310 28 FMOLS price ratios 35 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications Solomon Islands 0.482 0.173 2.790 38 GMM C, ar(1) as extra vars; inst= eshare dummy 1991 and after exogenous in CE and VAR; eshare South Africa 0.858 0.190 4.521 46 VEC 1,2 exogenous in CE; xgr exogenous in VAR ar(1) as extra var; inst= egr, global demand and price Spain 1.800 0.617 2.917 48 GMM ratios Sri Lanka 0.535 0.213 2.515 46 VEC 1,2 C, T in CE, VAR xgr, global demand and price ratios exogenous in VAR St. Kitts and Nevis 1.225 0.350 3.502 46 VEC 1,2 C in CE egr, global demand and price ratios exogenous in VAR dummy 1980 and after exogenous in CE and VAR; xgr St. Lucia 0.334 0.162 2.065 42 VEC 1,6 C in CE, VAR exognous in VAR St. Vincent and the ar(1), C,egr, global demand and price ratios as extra vars; 0.370 0.175 2.114 48 GMM Grenadines insts= T, T^2, eshare Sweden 1.145 0.371 3.087 48 GMM C, T, ar(1) as extra vars; inst= eshare Switzerland 1.709 0.355 4.809 48 GMM C, ar(1) as extra vars; inst= eshare Syrian Arab Republic 0.842 0.194 4.329 48 GMM ar(1), egr, lyx, T^2 as extra vars; insts= mgr, T, eshare Tajikistan 0.355 0.122 2.914 26 VEC 1,2 C, T in CE; C in VAR factor demand spec; reduced eq of export system deterministic regressors= C, egr, dummy 1998 and after; Tanzania 0.766 0.365 2.097 28 FMOLS additional deterministic regressor= T, eshare egr, ar(1), ma(1) as extra vars; insts= eshare, global Thailand 0.490 0.214 2.292 47 GMM demand and price ratios reduced eq of export system; dummy 2000 and after Timor-Leste 0.418 0.132 3.168 25 VEC 1,3 C, T in CE, VAR exogenous in CE; xgr exogenous in VAR CE deterministic regressors= C, global demand and price Togo 0.542 0.257 2.104 46 FMOLS ratios; additional deterministic regressors= T, egr, eshare Tonga 0.823 0.327 2.519 40 VEC 1,3 C, T in CE, VAR global demand and price ratios exogenous in VAR Trinidad and Tobago 0.790 0.384 2.055 48 GMM ar(1) and dummy after 1986 as extra vars; inst= eshare Tunisia 0.520 0.168 3.094 48 GMM C, ar(1) as extra vars; insts= T, eshare Türkiye 0.703 0.266 2.641 48 GMM xgr, ar(1) as extra vars; inst= eshare Turkmenistan 0.472 0.082 5.743 26 VEC 1,2 C, T in CE; C in VAR xge exogenous in CE dummy 1995 and after exogenous in CE and VAR; eshare Tuvalu 0.719 0.234 3.070 45 VEC 1,3 exogenous in VAR Uganda 0.782 0.333 2.347 46 VEC 1,2 C in CE reduce eq of export system factor demand (re-export) spec; reduced eq of export Ukraine 0.717 0.175 4.094 27 VEC 1,1 C in CE system; egr exogenous in CE; eshare exogenous in VAR factor demand (re-export) spec; reduced eq of export United Arab Emirates 1.580 0.485 3.255 45 VEC 1,3 C in CE system; eshare exogenous in CE; dummy 1986 and after exogenous in VAR reduce eq of export system; lyx exogenous in CE; eshare United Kingdom 2.026 0.381 5.322 46 VEC 1,2 C in CE exogenous in VAR United States 2.248 0.658 3.415 45 VEC 1,3 C, T in CE; C in VAR mgr as extra endognous var; lyx exogenous in CE Uruguay 1.418 0.307 4.616 40 VEC 1,8 lyx exogenous in CE; eshare exogenous in VAR egr exogenous in CE; dummy 2008 and after exogenous Uzbekistan 0.755 0.292 2.589 24 VEC 1,4 in VAR Vanuatu 0.477 0.214 2.230 38 GMM egr, ar(1) as extra vars; insts= T, eshare, global price ratio factor demand (re-export) spec; xgr, ar(1), dummy 2008 Venezuela 0.474 0.095 4.986 48 GMM and after as extra vars; insts= T, eshare factor demand (re-export) spec; reduced eq of export Vietnam 0.849 0.076 11.181 30 VEC 1,2 C in CE system West Bank and Gaza 0.527 0.188 2.806 21 VEC 1,3 C, T in CE; C in VAR reduced eq of export system 36 Appendix Table A2: Estimates of Ω, the CET Elasticities Country Estimate and test Model NOB Lag Standard t- afte interval C and trend Ω Error statistic r adj Method in VEC structure in VEC Additional specifications factor demand (re-export) spec; dummy before 1994 and Yemen 0.669 0.243 2.756 25 VEC 1,3 dummy after 2005 exogenous in CE and VAR; xgr exogenous in VAR C, T, ar(1), ma(1) as extra vars; insts=xgr, eshare, global Zambia 0.232 0.063 3.709 47 GMM demand ratio Zimbabwe 0.462 0.163 2.831 46 VEC 1,2 C, T in CE; C in VAR global demand and price ratios exogenous in VAR Source: Authors' calculations Notes: * = low t-test, insignificant at prob=0.05. C = constant CE = cointegration equation dummy = dummy variable egr = growth rate of real exports eshare = exports/GDP FMOLS = fully modified OLS for cointegration regression global demand and price ratios = variables in equation 9 GMM = generalized method of moments inst(s) = instrument variable(s) LIML = limited information maximum likelihood lyx = log of output (real GDP) index mgr = growth rate of real imports mshare = imports/GDP NOB = number of observations after adjustments T = linear trend T^2 = quadratic trend reduced eq of export system = equation 9 VEC = vector error correction VAR = error correction part in VEC or vector autoregression xgr = growth of output (real GDP) 37 Appendix Table A3: Data Country Source and coverage WDI Country Period Covered Country Name Code Source (before estimation adj.) Albania ALB WDI & UN 1991-2018 Algeria DZA WDI 1970-2018 Angola AGO WDI & UN 1980-2018 Antigua and Barbuda ATG WDI & UN 1977-2018 Argentina ARG WDI 1970-2018 Armenia ARM WDI & UN 1990-2018 Aruba ABW WDI & UN 1995-2018 Australia AUS WDI 1970-2018 Austria AUT WDI 1970-2018 Azerbaijan AZE WDI 1992-2018 Bahamas, The BHS WDI 1989-2018 Bahrain BHR WDI & UN 1970-2018 Bangladesh BGD WDI 1970-2018 Barbados BRB WDI & UN 1970-2018 Belarus BLR WDI 1990-2018 Belgium BEL WDI 1970-2018 Belize BLZ WDI 1980-2018 Benin BEN WDI 1970-2018 Bermuda BMU UN 1970-2018 Bhutan BTN WDI & UN 1980-2018 Bolivia BOL WDI 1970-2018 Bosnia and Herzegovina BIH WDI & UN 1992-2018 Botswana BWA WDI & UN 1970-2018 Brazil BRA WDI 1970-2018 Brunei Darussalam BRN WDI 1989-2018 Bulgaria BGR WDI 1990-2018 Burkina Faso BFA WDI 1970-2018 Burundi BDI WDI & UN 1970-2018 Cabo Verde CPV WDI & UN 1980-2018 Cambodia KHM WDI 1993-2018 Cameroon CMR WDI 1970-2018 Canada CAN WDI & UN 1970-2018 Central African Rep. CAF WDI & UN 1970-2018 Chad TCD WDI & UN 1970-2018 Chile CHL WDI 1970-2018 China CHN WDI & UN 1970-2018 Colombia COL WDI 1970-2018 Comoros COM WDI 1980-2018 38 Appendix Table A3: Data Country Source and coverage Congo, Dem. Rep. COD WDI & UN 1970-2018 Congo, Rep. COG WDI & UN 1970-2018 Cook Islands UN 1970-2018 Costa Rica CRI WDI 1970-2018 Cote d'Ivoire CIV WDI & UN 1970-2018 Croatia HRV WDI 1995-2018 Cuba CUB WDI 1970-2018 Cyprus CYP WDI 1975-2018 Czech Rep. CZE WDI 1990-2018 Denmark DNK WDI 1970-2018 Djibouti DJI UN 1980-2018 Dominican Rep. DOM WDI 1970-2018 Ecuador ECU WDI 1970-2018 Egypt, Arab Rep. EGY WDI 1970-2018 El Salvador SLV WDI 1970-2018 Equatorial Guinea GNQ WDI & UN 1980-2018 Eritrea ERI UN 1990-2018 Estonia EST WDI & UN 1993-2018 Eswatini SWZ WDI 1970-2018 Ethiopia ETH WDI & UN 1990-2018 Fiji FJI WDI & UN 1970-2018 Finland FIN WDI 1970-2018 France FRA WDI 1970-2018 French Polynesia PYF UN 1970-2018 Gabon GAB WDI 1970-2018 Gambia, The GMB WDI & UN 1970-2018 Georgia GEO WDI & UN 1990-2018 Germany DEU WDI 1970-2018 Ghana GHA WDI & UN 1970-2018 Greece GRC WDI 1970-2018 Greenland GRL WDI & UN 1970-2018 Grenada GRD WDI & UN 1977-2018 Guatemala GTM WDI 1970-2018 Guinea GIN WDI & UN 1986-2018 Guinea-Bissau GNB WDI & UN 1970-2018 Guyana GUY WDI & UN 1970-2018 Haiti HTI WDI 1988-2018 Honduras HDN WDI 1970-2018 Hong Kong SAR, China HKG WDI 1970-2018 Hungary HUN WDI 1991-2018 Iceland ISL WDI & UN 1970-2018 India IND WDI 1970-2018 39 Appendix Table A3: Data Country Source and coverage Indonesia IDN WDI 1970-2018 Iran, Islamic Rep. IRN WDI & UN 1970-2018 Iraq IRQ WDI & UN 1970-2018 Ireland IRL WDI 1970-2018 Israel ISR WDI & UN 1970-2018 Italy ITA WDI 1970-2018 Jamaica JAM WDI & UN 1970-2018 Japan JPN WDI 1970-2018 Jordan JOR WDI 1976-2018 Kazakhstan KAZ WDI & UN 1990-2018 Kenya KEN WDI 1970-2018 Kiribati KIR WDI & UN 1979-2018 Korea, Rep. KOR WDI 1970-2018 Kosovo XKX WDI & UN 1990-2018 Kuwait KWT WDI & UN 1970-2018 Kyrgyz, Rep. KGZ WDI & UN 1990-2018 Lao PDR LAO WDI & UN 1985-2018 Latvia LVA WDI & UN 1990-2018 Lebanon LBN WDI 1990-2018 Lesotho LSO WDI 1970-2018 Liberia LBR WDI & UN 1970-2018 Libya LBY WDI & UN 1970-2018 Liechtenstein LIE UN 1970-2018 Lithuania LTU WDI & UN 1990-2018 Luxembourg LUX WDI 1970-2018 Macao SAR, China MAC WDI 1982-2018 Madagascar MDG WDI 1970-2018 Malawi MWI UN 1970-2018 Malaysia MYS WDI 1970-2018 Maldives MDV UN 1970-2018 Mali MLI WDI 1970-2018 Malta MLT WDI & UN 1970-2018 Marshall Islands MHL WDI & UN 1970-2018 Mauritania MRT WDI 1970-2018 Mauritius MUS WDI 1976-2018 Mexico MEX WDI 1970-2018 Moldova MDA WDI 1995-2018 Mongolia MNG WDI & UN 1981-2018 Montenegro MNE WDI & UN 1991-2018 Morocco MAR WDI 1970-2018 Mozambique MOZ WDI & UN 1981-2018 Myanmar MMR WDI & UN 1970-2018 40 Appendix Table A3: Data Country Source and coverage Namibia NAM WDI 1980-2018 Nauru NRU WDI & UN 1970-2018 Netherlands NLD WDI 1970-2018 New Zealand NZL WDI & UN 1970-2018 Nicaragua NIC WDI 1970-2018 Niger NER WDI & UN 1970-2018 Nigeria NGA WDI & UN 1970-2018 North Macedonia MKD WDI 1990-2018 Norway NOR WDI 1970-2018 Oman OMN WDI & UN 1970-2018 Pakistan PAK WDI 1970-2018 Panama PAN WDI 1970-2018 Papua New Guinea PNG UN 1970-2018 Paraguay PRY WDI 1970-2018 Peru PER WDI 1970-2018 Philippines PHL WDI & UN 1970-2018 Poland POL WDI & UN 1990-2018 Portugal PRT WDI 1970-2018 Puerto Rico PRI WDI & UN 1970-2018 Qatar QAT WDI & UN 1970-2018 Romania ROU WDI 1990-2018 Russian Federation RUS WDI 1990-2018 Rwanda RWA WDI 1970-2018 Samoa WSM WDI & UN 1970-2018 San Marino SMR WDI & UN 1970-2018 Sao Tome and Principe STP UN 1970-2018 Saudi Arabia SAU WDI & UN 1970-2018 Senegal SEN WDI 1970-2018 Serbia SRB WDI 1995-2018 Seychelles SYC WDI 1976-2018 Sierra Leone SLE WDI 1970-2018 Singapore SGP WDI 1970-2018 Slovak Republic SVK WDI & UN 1990-2018 Slovenia SVN WDI & UN 1990-2018 Solomon Islands SLB WDI 1980-2018 South Africa ZAF WDI 1970-2018 Spain ESP WDI 1970-2018 Sri Lanka LKA WDI 1970-2018 St. Kitts and Nevis KNA UN 1970-2018 St. Lucia LCA UN 1970-2018 St. Vincent and the Grenadines VCT UN 1970-2018 Sweden SWE WDI 1970-2018 41 Appendix Table A3: Data Country Source and coverage Switzerland CHE WDI & UN 1970-2018 Syrian Arab Republic SYR WDI 1970-2018 Tajikistan TJK UN 1990-2018 Tanzania TZA WDI 1990-2018 Thailand THA WDI 1970-2018 Timor-Leste TLS WDI & UN 1990-2018 Togo TGO WDI 1970-2018 Tonga TON WDI & UN 1975-2018 Trinidad and Tobago TTO UN 1970-2018 Tunisia TUN WDI 1970-2018 Türkiye TUR WDI & UN 1970-2018 Turkmenistan TKM WDI & UN 1990-2018 Tuvalu TUV UN 1970-2018 Uganda UGA WDI & UN 1970-2018 Ukraine UKR WDI 1990-2018 United Arab Emirates ARE WDI & UN 1970-2018 United Kingdom GBR WDI 1970-2018 United States USA WDI 1970-2018 Uruguay URY WDI 1970-2018 Uzbekistan UZB WDI & UN 1990-2018 Vanuatu VUT WDI & UN 1980-2018 Venezuela VEN UN 1970-2018 Vietnam VNM WDI & UN 1986-2018 West Bank and Gaza PSE WDI 1994-2018 Yemen YEM WDI & UN 1990-2018 Zambia ZMB WDI & UN 1970-2018 Zimbabwe ZWE WDI & UN 1970-2018 Data sources: World Bank WDI: https://databank.worldbank.org/source/world-development-indicators United Nations national accounts: https://unstats.un.org/unsd/snaama/Index Notes: UN = United Nations WDI = World Development Indicators 42