metadata: authors: 0: author: Devarajan,Shantayanan
1: author: Go,Delfin Sia
2: author: Robinson,Sherman
count: World
docna: 0: docna: Trade Elasticities in Aggregate Models :
            Estimates for 191 Countries
docty: Policy Research Working Paper
abstracts: cdata!: Armington&#x2019;s insight that
            imports and domestically produced goods were imperfect
            substitutes has unleashed extensive estimates of the
            associated trade elasticity, primarily for developed
            countries. This notion of product differentiation, which
            extends symmetrically to exports and domestic goods, has
            underpinned trade-focused, computable general equilibrium
            models of developing countries, including the aggregate,
            compact version, the 1&#x2013;2&#x2013;3 model.
            Noting that estimates of trade elasticities for developing
            countries are few, this paper remedies the situation. Using
            the vector error correction model as the primary method and
            controlling for global trends and other factors, the
            analysis derives the long-run elasticity estimates for 191
            countries, ranging from China (population of 1.4 billion) to
            Tuvalu (11,200), including 45 of 48 Sub-Saharan African
            countries and understudied countries such as Benin, the
            Republic of Congo, Niger, Fiji, Haiti, Kiribati, and
            Tajikistan. Import and export elasticities of high-income
            countries average about 1.4, reflecting the greater
            diversity of their economies; developing
            countries&#x2019; elasticities average around 0.7 for
            imports and 0.6 for exports. Elasticities generally rise
            with per capita income. That the elasticity is greater than
            one for developed and less for developing countries implies
            asymmetric responses to shocks, which conforms to intuition
            and corroborates the analytical results from the
            1&#x2013;2&#x2013;3 model.
display_title: Trade Elasticities in Aggregate
            Models : Estimates for 191 Countries
listing_relative_url: /research/2023/06/34098318/trade-elasticities-aggregate-models-estimates-191-countries
topicv3: Trade m1326924 698,Computable General Equilibrium Modeling
            m1504533 3110
publishtoextweb_dt: 2023-06-20T00:00:00Z
dois: 10.1596/1813-9450-10490
author_keys: 0: author_key: 98278
1: author_key: 93791
2: author_key: 452473
model_extractions: 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 goods were imperfect substitutes has unleashed extensive estimates of the associated trade elasticity, primarily for developed countries. This notion of product differentiation, which extends symmetrically to exports and domestic goods, has underpinned trade-focused, computable general equilibrium models of developing countries, including the aggregate, compact version, the 1–2–3 model. Noting that estimates of trade elasticities for developing countries are few, this paper remedies the situation. Using the vector error correction model as the primary method and controlling for global trends and other factors, the analysis derives the long-run elasticity estimates for 191 countries, ranging from China (population of 1.4 billion) to Tuvalu (11,200), including 45 of 48 Sub-Saharan African countries and understudied countries such as Benin, the Republic of Congo, Niger, Fiji, Haiti, Kiribati, and Tajikistan. Import and export elasticities of high-income countries average about 1.4, reflecting the greater diversity of their economies; developing countries’ elasticities average around 0.7 for imports and 0.6 for exports. Elasticities generally rise with per capita income. That the elasticity is greater than one for developed and less for developing countries implies asymmetric responses to shocks, which conforms to intuition and corroborates the analytical results from 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 Robinson<sup>1</sup> 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,<sup>2</sup> including the aggregate, compact version, the 1-2-3 model, and additionally recent open-economy macroeconomic models.<sup>3</sup> 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.<sup>4</sup> 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 50<sup>th</sup> 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, Zof í o 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 shortrun 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).<sup>5</sup> 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 Gal í and Monacelli (2005) used unity. Among textbooks, V é gh (2013) examined the impact of devaluation under alternative elasticity of substitution between tradables and nontradables from 0.2 to 1.0;<sup>6</sup> Uribe and Schmitt-Groh é (2017) set the elasticity to unity but tested the economic impact of termsof-trade shocks of alternative values of 0.75 and 1.5.<sup>7</sup> 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 1-23 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.<sup>8</sup> 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.

where _X_ is the CES or CET composite Q<sup>S</sup> or X̅; A̅ is the shift parameter, α is the share parameter, and  is the exponent: In the CES case, X1 ≡M and X2 ≡D<sup>D</sup> ; and in the CET case, X1 ≡E and X2 ≡D<sup>S</sup> . The P's are the corresponding domestic prices of the inputs, P<sup>e</sup> , P<sup>m</sup> , and P<sup>d</sup> . The CES substitution elasticity σ and CET transformation elasticity Ω are given by σ= 1⁄(1 −ρ) ; −∞< ρ< 1 in the CES case and Ω = 1⁄(ρ−1); 1 < ρ< ∞ 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 D<sup>D</sup> = D<sup>S</sup> = D 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):

> 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
where δ and β are the corresponding share parameters in the CET export transformation and CES import aggregation functions.<sup>9</sup> The CET function describes the production transformation frontier between D and E for a fixed level of X̅ or real GDP (since there are no intermediate inputs). The assumption that X̅ is fixed is equivalent to assuming full employment of all primary factors. The composite good price P<sup>x</sup> corresponds to the GDP deflator. The composite price P<sup>q</sup> 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, P<sup>x</sup> and P<sup>e</sup> , the implicit price for the domestic good, P<sup>d</sup> , can be derived from the GDP identities: P<sup>x</sup> X̅ = P<sup>q</sup> Q+ P<sup>e</sup> E−P<sup>m</sup> M= P<sup>d</sup> D+ P<sup>e</sup> E where Q is 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: 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)<sup>10</sup> and the United Nations National Accounts database.<sup>11</sup> 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 <u>https://databank.worldbank.org/source/world-development-indicators.</u>

> 11 <u>https://unstats.un.org/unsd/snaama/Index.</u>

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.<sup>12</sup> 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 E, M, D, P<sup>e</sup> , P<sup>d</sup> ) 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 (ln[MDtt] , ln[DEtt ~~]~~ , ln[PPttmd ~~]~~ , ln[PPttde]), 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 ln[DEtt ~~]~~ and ln[PPttde] in one set and ln[MDtt] and ln[PPttmd ] 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.

> 12The WDI methods and ratios to impute missing observations are described in the link <u>here.</u>

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: The demand for Et as a ratio to the global domestic good Dtw is a function of their relative price PPtdwte ~~;~~ β<sup>w</sup> is Previous literature often uses just the aggregation of industrial countries for Dtw and Ptdw 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 Dtw and Ptdw, 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 σ<sup>w</sup> 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.

# _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
<!-- Start of picture text --> eM REPEL Wa fo ON<br>rp ar eaamaie<br><!-- End of picture text -->
Figure 2: Examples of Breakpoints – Germany and South Africa <!-- Start of picture text --> A. Germany – lpe<br>Dickey-Fuller t-statistics Dickey-Fuller autoregressive coefficients<br>0.5 1.04<br>0.0 1.00<br>-0.5<br>0.96<br>-1.0<br>0.92<br>-1.5<br>0.88<br>-2.0<br>0.84<br>-2.5 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>1975 1980 1985 1990 1995 2000 2005 2010 2015<br>B. South Africa – lye<br>Dickey-Fuller t-statistics Dickey-Fuller autoregressive coefficients<br>-0.5 .96<br>-1.0<br>.92<br>-1.5<br>-2.0 .88<br>-2.5<br>.84<br>-3.0<br>.80<br>-3.5<br>-4.0 .76<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Source: Authors' calculations.<br>Notes: lye = ln[DEtt ] ; lpe = ln[PPttde]. The graphs are from Eviews’ breakpoint unit root test for intercept break and innovation outlier.<br><!-- End of picture text --> Take two well-known cases, Germany and South Africa. Figure 2A indicates a break for ln ~~[~~ PPttde ~~]~~ in 1989 for Germany, coinciding with the fall of the Berlin Wall that led to the unification of East and West Germany in 1990.<sup>14</sup> Figure 2B shows that South Africa had a break for Et in 1991, the year trade ln[Dt ~~]~~ 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 ln[PPttde] but 1994 for ln[MDtt] and 1985 for ln[PPttmd ~~]~~ . In the case of South Africa, the breakpoint tests also indicate 1987 for ln[PPttde], 1990 for ln[MDtt], and 1989 for both intercept and trend of ln[PPttmd ]. 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 Dt and Ptd, we use Xt and Ptx, 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.<sup>15</sup> 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.<sup>16</sup> 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.<sup>17</sup> 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.<sup>18</sup> 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.

> 17 The terms – countries, territories, and economies – are used interchangeably. We include any that have national accounts data in the WDI and 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 Cura ç ao, 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**|**Arming**|**ton(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|
|Source: Authors' calculations.<br>Developingcountries||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 σ (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 σ and Ω of high-income countries tend to be higher than 1.0, about 1.4, while those of the lowerincome 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 σ and slightly less than 0.6 for Ω. The uppermiddle-income countries tend to have slightly higher values but still less than 1.0, around 0.7 for σ, 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.<sup>19</sup> The simple correlation of the variables is about 0.71 for both the σ and the Ω cases. The regression line in each plot confirms an approximately positive relationship.<sup>20</sup> 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 σ< 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 σ> 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 σ< 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.<sup>21</sup> 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
<u>Figure 3: Scatter plots of the elasticities against income per capita</u> <!-- Start of picture text --> A. Sigma (σ)<br>2.8<br>2.4<br>2.0<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>5 6 7 8 9 10 11 12 13<br>lny<br>B. Omega (Ω)<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>5 6 7 8 9 10 11 12 13<br>lny<br>Source: Authors' calculations.<br> Note: lny = natural log of GDP per capita, U.S. dollars, 2015.<br>Omega<br>Sigma<br><!-- End of picture text --> 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.<sup>22</sup> 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

||<br>**Arming**|<br>**ton (σ)**|<br>**CET**|<br>**(Ω)**|
|---|---|---|---|---|
||**Developing**<br>**countries**|**High-income**<br>**countries**|**Developing**<br>**countries**|**High-income**<br>**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 countryspecific 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. Not only do these estimates confirm

> 22 Argentina became high-income in 2017.

18
that most of the assumed parameters were broadly correct, but they provide the foundation for future, multicountry use of the 1-2-3 model, whose parsimony and data economy have been the hallmarks of its extensive application.

19
# **References**

- Ahmad, Saad, Christopher Montgomery, and Samantha Schreiber. 2020. "A Comparison of Armington Elasticity Estimates in the Trade Literature." Economics Working Paper 2020-04-A, US International Trade Commission, Washington, DC.

- Alston, Julian M., and Richard Green. 1990. "Elasticities in AIDS Models." _American Journal of Agricultural Economics_ . 72(2): 442-45.

- Alston, Julian M., Colin A. Carter, Richard Green, and Daniel Pick. 1990. "Whither Armington Trade Models." _American Journal of Agricultural Economics_ . 72(2): 455-67.

- Anderson, James E., and Eirc van Wincoop. 2004. “Trade Costs.” _Journal of Economic Literature_ 42 (3): 691-751.

- Arbache, Jorge, Delfin S. Go, and John Page (2008). "Is Africa's Economy at a Turning Point?" In Delfin S. Go and John Page, eds., _Africa at a Turning Point? Growth, Aid, and External Shocks._ Washington, DC: The World Bank.

- Armington, Paul. 1969. "A Theory of Demand for Products Distinguished by Place of Production." _IMF Staff Papers_ , Vol. 16, pp. 159-178.

- Arrow, Kenneth J., Hollis B. Chenery, Bagicha S. Minhas, and Robert M. Solow. 1961. "Capital-Labor Substitution and Economic Efficiency." _Review of Economics and Statistics,_ 43(3):225-50.

- Bajzik, Josef, Tomas Havranek, Zuzana Irsova, Jiri Schwarz (2020). "Estimating the Armington Elasticity: The Importance of Design Study and Publication Bias.” _Journal of International_ Economics 127 103383. See also _VOX, CEPR Policy Portal, September 23, 2020. https://voxeu.org/article/armington-elasticity-and-international-trade-models-fifty-years._

- Boehm, Christoph E., Andrei A. Levchenko, and Nitya Pandala-Nayar. 2023. “The Long and Shor (Run) of Trade Elasticities.” _American Economic Review_ 113 (4): 861-905.

- Bond, Marian. 1987. "An Econometric Study of Primary Commodity Exports from Developing Country Regions to the World. _IMF Staff Papers_ 34: 191-227.

- Branson, William H. 1972. "The Trade Effects of the 1971 Currency Realignments." _Brookings Papers on Economic Activity I:_ 15-58.

- Corsetti, Giancarlo, Luca Dedola, and Sylvian Leduc. 2008. "International Risk Sharing and the Transmission of Productivity Shocks." _Review of Economic Studies_ 75: 443-75.

- de Melo, Jaime, and Sherman Robinson. 1989. Product Differentiation and the Treatment of Foreign Trade in Computable General Equilibrium Models of Small Economies." _Journal of International Economics_ 27: 47-67.

Deardorff, Alan V. 2006. Glossary of International Economics. World Scientific Publishers.

Devarajan, Shantayanan and Sherman Robinson. 2013. “Contribution of Computable General Equilibrium Modeling to Policy Formulation in Developing Countries.” In P.B. Dixon and D.W.

20
Jorgenson, eds., _Handbook of Computable General Equilibrium Modeling._ North Holland, Eksevier V.B., pp. 227-301.

Devarajan, Shantayanan, and Delfin S. Go. 2012. "The Simplest General-Equilibrium Model of a Small, Open Economy." Chapter in a book in honor of Ernȍ Zalai, 2013 _._

- _______. 1998. "The Simplest Dynamic General Equilibrium Model of an Open Economy." _Journal of Policy Modeling_ 29(6): 677-714.

- _______. 2003. "The 123PRSP Model." In François Bourguignon and Luiz A. Pereira da Silva, _The Impact of Economic Policies on Poverty and Income Distribution._ Washington, DC: The World Bank and Oxford University Press.

- Devarajan, Shantayanan, Delfin S. Go, and Hongyi Li. 1999. "Quantifying the Fiscal Effect of Trade Reform: A General Equilibrium Model Estimated for 60 Countries." Policy Research Working Paper 2162. World Bank, Development Research Group, Washington, DC.

- Devarajan, Shantayanan, Delfin S. Go, Jeffrey D. Lewis, Sherman Robinson, and Pekka Sinko. 1997. "Simple General Equilibrium Modeling." In Joseph F. Francois and Kenneth A. Reinert, eds., Applied Methods for Trade Policy Analysis: A Handbook. Cambridge: Cambridge University Press.

- Devarajan, Shantayanan, Delfin S. Go, John Page, Sherman Robinson, and Karen Thierfelder. 2008. "The Macroeconomic Dynamics of Scaling Up Foreign Aid." In Delfin S. Go and John Page, eds. _Africa at a Turning Point? Growth, Aid, and External Shocks._ Washington, DC: The World Bank.

- Devarajan, Shantayanan, Delfin S. Go, Sethaput Suthiwart-Narueput, and John Voss. 1997b. "Direct and Indirect Fiscal Effects of the Euro-Mediterranean Free Trade Agreements." Paper presented at the Mediterranean Development Forum, Knowledge and Skills for Development in the Information Age, Marrakech, Morocco, May 12-17, 1997.

- Devarajan, Shantayanan, Jeffrey D. Lewis, and Sherman Robinson. 1990. "Policy Lessons from Tradefocused, Two-sector Models." _Journal of Policy Modeling_ 12(4): 625-57.

- _______. 1993. "External Shocks, Purchasing Power Parity, and the Equilibrium Real Exchange Rate." World Bank Economic Review 7(1): 45-63.

- Devarajan, Shantayanan, Yazid  Dissou, Delfin S. Go, and Sherman Robinson. 2017. "Budget Rules and Resource Booms and Busts – A Dynamic Stochastic General Equilibrium Analysis," _World Bank Economic Review_ (2017) 31(1):71-96. https://doi.org/10.1093/wber/lhv059.

- Devarajan, Shantayanan. 1997. "Real Exchange Rate Misalignment in the CFA Zone." _Journal of African Economies_ 6(10): 35-53.

- _______. 1999. "Estimates of Real Exchange Rate Misalignment with a Simple General-Equilibrium Model." In Lawrence E. Hinkle and Peter J. Montiel _, Exchange Rate Misalignment: Concepts and Measurement for Developing Countries_ . Washington, DC: World Bank and Oxford University Press.

- Faini, Ricardo, Lant Pritchett, and Fernando Clavijo. 1992. "Import Demand in Developing Countries." _<mark>International Trade Modelling</mark>_ <mark>, 279-97.</mark>

21
- Faini, Ricardo. 1994. "Export Supply, Capacity and Relative Prices." _Journal of Development Economics_ 45: 81-100.

- Feenstra, Robert C., Philip Luck, Maurice Obstfeld, and Katheryn N. Russ. 2018. "In Search of the Armington Elasticity." _The Review of Economics and Statistics_ 100(1): 135–150.

- Fleming, Marcus J. 1962. “Domestic Financial Policies Under Fixed and Floating Exchange Rates.” _IMF Staff Papers_ 9 (3): 369-79.

- Gal í , Jordi, and Tommaso Montacelli (2005). "Monetary Policy and Exchange Rate Volatility in a Small Open Economy." _Review of Economic Studies_ 72(3): 707-34.

- Gali, Jordi. 2015. _Monetary Policy, Inflation, and the Business Cycle – An Introduction to the New Keynesian Framework and Its Applications,_ second edition. Princeton University Press, Princeton, New Jersey.

- Gallaway, Michael P., Christine A. McDaniel, and Sandra A. Rivera. 2003. "Short-Run and Long-Run Industry-Level Estimates of US Armington Elasticities." _The North American Journal of Economics and Finance_ 14: 49-63.

- Gust, Christopher, Sylvain Leduc, and Nathan Sheets. 2009. "The Adjustment of Global External Balances: Does Partial Exchange-Rate Pass-Through to Trade Prices Matter?" _Journal of International Economics_ 79: 173-85.

- Hanson, Kenneth, Sherman Robinson, and Stephen Tokarick. 1993. "U.S. Adjustment in the 1990's: A CGE Analysis of Alternative Trade Strategies." _International Economic Journal_ 7: 27-49.

- Head, Keith, and Thierry Mayer. 2014. “Gravity Equations: Workhorse, Toolkit, and Cookbook.” In Elhanan Helpman, Kenneth Rogoff, and Gita Gopinath, eds., _Handbook of International Economics_ , Vol. 4, 131-95. Amsterdam: Elsevier.

- Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. 2007. "How Confident Can We Be of CGE-based Assessments of Free Trade Agreements?" _Economic Modelling_ 24: 611-635.

- Hillberry, Russell and David Hummels. 2013. "Trade Elasticity Parameters for a Computable General Equilibrium Model." In Peter B. Dixon and Dale W. Jorgenson, eds. _Handbook of Computable General Equilibrium Modeling, Vol. 1B._ Oxford: North-Holland, Elsevier, pp. 1213-70.

- Ho, Mun S., and Dale W. Jorgenson. 1998. "Modeling Trade Policies and U.S. Growth: Some Methodological Issues." In N. Benjamin, ed., _The Economic Implications of Liberalizing APEC Tariff and Nontariff Barriers to Trade._ Washington, D.C: U.S. International Trade Commission (Publication 3101).

- Justiniano, Alejandro, and Bruce Preston. 2010. "Can Structural Small Open Economy Models Account for the Influence of Foreign Disturbances?" _Journal of International Economics_ 81: 61-74.

- Khan, Moshin S. 1974. "Import and Export Demand in Developing Countries. _IMF Staff Papers_ 21: 67893.

- Kohli, Ulrich R. 1978. "A Gross National Product Function and Derived Demand for Imports and Supply of Exports. _Canadian Journal of Economics_ 11(2): 167-82.

22
Marquez, Jaime, and Caryl McNeilly. 1988. Income and Price Elasticities for Exports of Developing Countries. _Review of Economics and Statistics_ 70: 306-14.

- Marquez, Jaime. 1994. "The Econometrics of Elasticities or the Elasticity of Econometrics: an Empirical Analysis of the Behavior of U.S. Imports." _Review of Economics and Statistics_ 76(3): 471-81.

- McDaniel, Christine A., and Edward J. Blistreri (2003). "A Review of Armington Trade Substitution Elasticities." _Économie international_ 94-95: 301-14.

- McDonald, Scott, Karen Thierfelder, and Terry Walmsley. 2012. "R23 Model: A SAM Based Global Macro CGE Model." Mimeo. The World Bank DEC Prospects Group.

- Mundell, Robert A. 1963. “Capital Mobility and Stabilization Policy Under Fixed and Flexible Exchange Rates.” _Canadian Journal of Economic and Political Science_ 29 (4): 475-85.

- Ostry, Jonathan D., and Andrew K. Rose. 1992. "An Empirical Evaluation of the Macroeconomic Effects of Tariffs." _Journal of International Money and Finance_ 11:63-79.

- Panagariya, Arvind, Shekhar Shah, and Deepak Mishra. 1996. "Demand Elasticities in International Trade - Are They Really Low?" World Bank Policy Research Working Paper 1712, Washington D.C.

Petri, Peter A. 1984. _Modeling Japanese-American Trade._ Cambridge: Cambridge University Press.

- Reinert, Kenneth, and David Roland-Holst. 1992. "Armington Elasticities for United States Manufacturing Sectors." _Journal of Policy Modeling_ 14: 631-639.

- Reinhart, Carmen M. (1995). "Devaluation, Relative Prices, and International Trade: Evidence from Developing Countries." _IMF Staff Papers_ 42 (2): 290-312.

- Rittenberg, Libby. 1986. "Export Growth Performance of Less-developed Countries." _Journal of Development Economics_ 24: 167-77.

- Rose, Andrew K. 1990. "Exchange Rates and the Trade Balance: Some Evidence from Developing Countries." _Economic Letters_ 34: 271-75.

- Rose, Andrew K. 1991. "The Role of Exchange Rates in Popular Models of International Trade: Does the Marshall-Lerner Condition Hold? _Journal of International Economics_ 30:1-16.

- Rutherford, Thomas. 2003. "Lecture Notes on Constant Elasticity Functions." Mimeo, University of Colorado.

- Salter, Wilfred E. G. 1959. "Internal and External Balance: The Role of Price and Expenditure Effects." _Economic Record_ , Vol 35, pp. 226-238.

- Schurenberg-Frosch, H (2015), “We Could Not Care Less about Armington Elasticities–But Should We? A Meta-Sensitivity Analysis of the Influence of Armington Elasticity Misspecification on Simulation Results”, Ruhr Economic Papers 594/2015, Ruhr Universitat Bochum, Germany.

- Senhadji, Abdelhak S., and Claudio E. Montenegro. 1999. "Time Series Analysis of Export Demand Equations: A Cross-Country Analysis." _IMF Staff Papers_ 46 (3): 259-73.

23
- Shiells, Clinton R., David W. Roland-Holst, and Kenneth A. Reinert. 1993. "Modeling a North American Free Trade Area: Estimation of Flexible Functional Forms." _Weltwirtschaftliches Archiv_ . 129:1, 55-77.

- Swan, Trevor. 1960. "Economic Control in a Dependent Economy." _Economic Record_ , Vol. 36, pp. 51-66.

- Uribe, Martin, and Stephanie Schmitt-Groh é . 2017. _Open Economy Macroeconomics._ Princeton University Press, Princeton, New Jersey.

- V é gh, Carlos A. 2013. _Open Economy Macroeconomics in Developing Countries._ The MIT Press, Cambridge, Massachusetts.

- Whalley, John. 1985. _Trade Liberalization among Major World Trading Areas._ MIT Press, Cambridge, MA.

- Zof í o, Jos é L., Jorge D í as-Lanchas, Damiaan Persyn, and Javier Barbero. 2020. "Estimating Foreign and National Trade Elasticities in the EU Internal Market Using Generalized Transport Costs." 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**|
|---|---|---|---|---|---|---|---|---|
||**σ**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**after**<br>**adj**|**Method**|**Lags**<br>**intervals**<br>**in VEC**|**Constant and**<br>**trend structure in**<br>**VEC**|**Additional specifications**|
|Albania|0.551|0.235|2.342|28|FMOLS|||CE deterministic regressors= C, T, xgr; additional<br>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|||<br>C, T, eshare, xgr; additional deterministic<br>regressors = T^2|
|Argentina|0.993|0.055|18.109|43|VEC|1,5|C, T in CE,VAR|<br>mshare exogenous in CE; a dummy for 1998-2002<br>(depression)|
|Armenia|0.709|0.094|7.532|26|VEC|1,2|C in CE,VAR|<br>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|
|Bangladesh|0.732|0.097|7.560|43|VEC|1,5||mgr, xgr exogenous in CE; mshare, xgr exogenous<br>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||
|Belize|0.544|0.171|3.174|48|FMOLS|||CE deterministic regressors= C, T, xgr; additional<br>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|
|Bolivia|0.668|0.116|5.756|48|FMOLS|||CE deterministic regressors= C, T, mshare, xgr;<br>additional deterministic regressors: T^2|
|Bosnia and<br>Herzegovina|0.339|0.032|10.671|23|VEC|1,3|C, T in VAR|xgr as extra endogenous var in CE<br>|
|Botswana|0.905|0.353|2.563|48|FMOLS|||factor demand spec; CE deterministic regressors=<br>C, T, lyx; additional deterministic regressors: T^2,<br>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|
|Chile|0.895|0.157|5.698|45|VEC|1,3|C in CE, VAR|lyx exogenous in CE and VAR; mgr exogenous in<br>VAR|
**Appendix Table A1: Estimates of  σ, the Armington CES Elasticities**

|**Country**||**Estimate**|**and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**σ**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**after**<br>**adj**|**Method**|**Lags**<br>**intervals**<br>**in VEC**|**Constant and**<br>**trend structure in**<br>**VEC**|**Additional specifications**<br>|
|China|0.529|0.032|16.650|45|VEC|1,3|C, T in CE; C in VAR|xgr exogenous in CE and VAR; mshare exogenous<br>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|
|Comoros|0.745|0.075|9.961|38|FMOLS|1,2||CE deterministic regressors= C, mgr, dummy after<br>2007; additional deterministic regressor= mshare|
|Congo, Dem. Rep.|0.669|0.224|2.987|48|FMOLS|||<br>CE deterministic regressors=C, T|
|Congo, Rep.|0.568|0.194|2.922|48|FMOLS|||CE deterministic regressors=C, T,eshare; additional<br>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|
|Cote d'Ivoire|0.397|0.170|2.342|48|FMOLS|||CE deterministic regressors=C, T; additional<br>deterministic regressors= T^2, mshare|
|Croatia|0.866|0.382|2.267|23|FMOLS|||CE deterministic regressors=C, T, xgr; additional<br>deterministic regressors= mshare, mgr|
|Cuba|0.871|0.071|12.310|45|VEC|1,3|C in CE|<br>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|
|Denmark|1.282|0.039|32.770|46|VEC|1,2|C in CE|dummy 2008 and after exogenous in CE; mshare<br>exogenous in VAR|
|Djibouti|0.621|0.135|4.608|39|VEC|1,5|C in CE|factor demand spec; xgr exogenous in CE; mshare<br>exogenous in VAR|
|Dominican Rep.|0.938|0.070|13.417|43|VEC|1,5|C, T in CE; C in VAR|<br>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|
|Finland|1.401|0.400|3.500|48|FMOLS|||CE deterministic regressors= C, dummy for 2000<br>and after; additional deterministic regressor= T|
|France|2.232|0.378|5.911|46|VEC|1,2||<br>mshare exogenous in CE|
|French Polynesia|0.776|0.186|4.181|43|VEC|1,5|C in CE|mshare exogenous in CE and VAR; xgr exogenous<br>in VAR|
|Gabon|0.478|0.115|4.165|45|VEC|1,3|C, T in CE; C in VAR||
|||||||||CE dtiiti = C l d ft|
|Gambia, The|0.515|0.173|2.974|48|FMOLS|||eermnsc regressors , yx, ummy aer<br>1990 and 2001; additional deterministic regressor=<br>T|
|Georgia|0.793|0.232|3.420|27|FMOLS|||<br>CE deterministic regressor= C; additional<br>deterministic regressors= T, mgr|
|Germany|1.827|0.361|5.065|44|VEC|1,4|C, T in CE; C in VAR|<br>dummy 1990 and after exogenous in CE; xgr<br>exogenous in VAR|
|Ghana|0.856|0.117|7.310|44|VEC|1,4|C, T in CE; C in VAR|<br>mshare exogenous in CE; mgr exogenous in VAR|
|Greece|1.632|0.436|3.745|46|VEC|1,2|||
|Greenland|1.765|0.873|2.023|42|VEC|1,6|C in CE|dummy after 1990 as extra endogenous var;<br>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 a**|**nd test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**σ**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**after**<br>**adj**|**Method**|**Lags**<br>**intervals**<br>**in VEC**|**Constant and**<br>**trend structure in**<br>**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|||, ,<br>2000 and after; additional deterministic<br>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|
|Hong Kong SAR, China|1.138|0.041|28.037|46|VEC|1,2|T in CE; C in VAR|factor demand spec; eshare exogenous in CE; xgr<br>exogenous in VAR|
|||||||||CE deterministic reressors= C mshare additional|
|Hungary|1.388|0.140|9.930|27|FMOLS|||g , ;<br>deterministic regressors= T, T^2, dummy after<br>2013, xgr|
|Iceland|1.315|0.176|7.478|44|VEC|1,4|C in CE, VAR|xgr exogenous in CE and VAR; mshare exogenous<br>in VAR|
|India|0.462|0.111|4.150|48|FMOLS|||CE deterministic regressors: C, T, mshare;<br>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|
|Iraq|0.663|0.181|3.673|45|VEC|1,3|C, T in CE; C in VAR|factor demand spec; xgr exogenous in CE; mshare<br>exogenous in VAR|
|Ireland|1.674|0.260|6.430|45|VEC|1,3|T in CE; C in VAR|factor demand spec; eshare exogenous in CE; xgr<br>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|
|Jordan|0.691|0.063|10.898|38|VEC|1,4|C in CE, VAR|xgr exogenous in CE and VAR; mshare exogenous<br>in VAR|
|Kazakhstan|0.635|0.062|10.217|26|VEC|1,2|C in CE, VAR|mshare exogenous in VAR|
|Kenya|0.738|0.083|8.912|48|FMOLS|||CE deterministic regressors= C, xgr; additional<br>deterministic regressor= mshare|
|Kiribati|0.650|0.092|7.036|36|VEC|1,3|C in CE, VAR|<br>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|
|Kuwait|1.578|0.381|4.140|46|VEC|1,2|C, T in CE; C in VAR|xgr exogenous in CE and VAR; eshare exogenous in<br>VAR|
|Kyrgyz, Rep.|0.760|0.105|7.207|26|VEC|1,2|C, T in CE, VAR|mshare exogenous in CE and VAR|
|Lao PDR|0.451|0.202|2.233|30|VEC|1,3|C,T in CE, VAR|xgr exogenous in CE and VAR; mshare exogenous<br>in VAR|
|Latvia|0.875|0.875|7.223|28|FMOLS|||CE deterministic regressors= C, mgr, mshare;<br>additional deterministic regressors= T, T^2|
|Lebanon|0.550|0.072|7.603|26|VEC|1,2|C,T in CE, VAR|<br>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|
|Liberia|0.798|0.147|5.415|46|VEC|1,2|C in CE, VAR|xgr as extra endogenous var; mshare exogenous in<br>VAR<br>factor demand spec; dumm after 2010 added to|
|Libya|0.936|0.154|6.056|48|FMOLS|||y<br>endogenous vars; CE deterministic regressors: C,<br>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 strctre in**||
||**σ**|<br>**Error**|**statistic**|<br>**adj**|**Method**|<br>**in VEC**|**uu**<br>**VEC**|**Additional specifications**|
|Lithuania|1.221|0.055|22.165|26|VEC|1,2|C in CE|dummy 2010 and after exogenous in CE, VAR;<br>mshare exogenous in VAR|
|Luxembourg|1.413|0.354|3.990|48|FMOLS|||factor demand spec; CE deterministic regressors=<br>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|
|Madagascar|0.789|0.177|4.456|48|FMOLS|||C deterministic regressors= C, lyx; additional<br>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|
|Malaysia|0.794|0.238|3.333|45|VEC|1,3|C in CE, VAR|factor demand spec; xgr as extra endogenous var;<br>eshare exogenous in VAR|
|Maldives|0.944|0.207|4.552|46|VEC|1,2||<br>factor demand spec; mgr as extra endogenous var|
|Mali|0.883|0.127|6.954|45|VEC|1,3|C, T in CE; C in VAR|xgr as extra endogenous var; mshare exogenous in<br>VAR|
|Malta|1.026|0.031|32.782|46|VEC|1,2|C, T in CE; C in VAR|factor demand spec; mshare exogenous in CE;<br>eshare exogenous in VAR|
|Marshall Islands|0.895|0.043|20.596|42|VEC|1,6||<br>mshare exogenous in CE; eshare exogenous in VAR|
|Mauritania|0.749|0.145|5.163|48|FMOLS|||CE deterministic regressors= C, T, mshare;<br>additional deterministic regressor= T^2|
|Mauritius|0.950|0.327|2.903|42|FMOLS|||<br>CE deterministic regressors= dummy for 1985-<br>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|
|Montenegro|0.953|0.076|12.613|27|FMOLS|||CE deterministic regressors= C, T, mshare;<br>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|
|Netherlands|1.326|0.097|13.614|43|VEC|1,5|C, T in CE; C in VAR|mshare exogenous in CE and VAR; dummy 2001<br>and after exogenous in CE|
|New Zealand|1.716|0.366|4.685|45|VEC|1,3|C, T in CE; C in VAR|<br>xgr exogenous in CE|
|Nicaragua|0.844|0.043|19.682|45|VEC|1,3|C in CE, VAR|xgr as extra endogenous var; mshare exogenous in<br>VAR|
|Niger|0.579|0.158|3.664|48|FMOLS|||CE deterministic regressors= C, xgr; additional<br>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|
|Norway|1.724|0.303|5.690|46|VEC|1,2||dummy 1980 and after exogenous in CE and VAR;<br>xgr exogenous in VAR|
|Oman|0.938|0.087|10.809|46|VEC|1,2|C in CE|<br>dummy for 1982-2005 exogenous in CE and VAR;<br>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|<br>mshare exogenous in VAR|
|Panama|0.737|0.037|19.742|45|VEC|1,3|C in CE|mshare exogenous in VAR|
|Papua New Guinea|0.600|0.292|2.050|48|FMOLS|||CE deterministic regressors= C, T, mshare;<br>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 a**|**nd test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**σ**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**after**<br>**adj**|**Method**|**Lags**<br>**intervals**<br>**in VEC**|**Constant and**<br>**trend structure in**<br>**VEC**|**Additional specifications**|
|Philippines|0.654|0.319|2.046|48|FMOLS|||CE deterministic regressors= C, T, eshare, dummy<br>1988 and after; additional deterministic<br>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||
|Rwanda|0.657|0.086|7.675|48|FMOLS|||CE deterministic regressors= C, T, mshare;<br>additional deterministic regressors= T^2, xgr|
|Samoa|0.348|0.044|7.922|48|FMOLS|||CE deterministic regressors= C, dummy 2002 and<br>after, mshare|
|San Marino|1.215|0.025|48.905|44|VEC|1,4|C, T in CE; C in VAR|factor demand spec; xgr exogenous in CE; eshare<br>exogenous in CE|
|Sao Tome and Principe|0.852|0.184|4.633|46|VEC|1,2||dummy for 1970-1986 and after 2000 exogenous<br>in CE and VAR.|
|Saudi Arabia|1.480|0.135|10.943|46|VEC|1,2|C in CE|Dummy after 1973 exogenous in CE and VAR;<br>mshare exogenous in VAR|
|Senegal|0.773|0.043|18.094|44|VEC|1,4|C in CE|<br>mshare exogenous in CE and VAR; dummy after<br>1981 exogenous in VAR|
|Serbia|0.881|0.198|4.457|23|FMOLS|||CE deterministic regressors= C, mshare; additional<br>deterministic regressors= T, lyx|
|Seychelles|1.071|0.365|2.936|42|GMM|||factor demand spec; C, ar(1), dummy 2000 and<br>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|
|Slovak Republic|0.840|0.259|3.246|25|VEC|1,3|C in CE|mgr as extra endogenous var; mshare exogenous<br>in VAR|
|Slovenia|0.804|0.172|4.684|24|VEC|1,4|C, T in CE; C in VAR|mshare exogenous in VAR|
|Solomon Islands|0.681|0.131|5.220|35|VEC|1,3||xgr exogenous in CE; dummy 1993 and after<br>exogenous in VAR|
|South Africa|0.968|0.032|29.823|46|VEC|1,2|C, T in CE; C in VAR|dummy 1991 and after exogenous in CE and VAR;<br>mshare exogenous in VAR|
|Spain|1.979|0.098|20.138|45|VEC|1,3|C in CE, VAR|xgr exogenous in VAR|
|Sri Lanka|0.603|0.179|3.367|46|VEC|1,2|C in CE|dummy for 1980-2004 exogenous in CE and VAR;<br>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<br>Grenadines|0.342|0.134|2.555|48|GMM|||ar(1), C, T as extra vars; inst=mshare|
|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|
|Syrian Arab Republic|0.864|0.151|5.737|48|FMOLS|||CE deterministic regressors= C, mgr, lyx; additional<br>deterministic regressor= xgr|
|Tajikistan|0.320|0.151|2.125|28|FMOLS|||<br>factor demand spec; CE deterministic regressor= C|
|Tanzania|0.468|0.151|3.102|28|FMOLS|||CE deterministic regressor= C; additional<br>deterministic regressor= T|
|Thailand|0.741|0.089|8.302|46|VEC|1,2|C, T in CE; C in VAR|<br>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**|
|---|---|---|---|---|---|---|---|---|
||**σ**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**after**<br>**adj**|**Method**|**Lags**<br>**intervals**<br>**in VEC**|**Constant and**<br>**trend structure in**<br>**VEC**|**Additional specifications**|
|Tonga|0.920|0.198|4.636|43|FMOLS|||CE deterministic regressors = C, dummy 1985 and<br>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|
|Uganda|0.669|0.125|5.358|44|VEC|1,4|C, T in CE; C in VAR|xgr exogenous in CE; dummy 1998 and after<br>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|
|United Arab Emirates|1.822|0.335|5.443|46|VEC|1,2|C, T in CE, VAR|factor demand spec; dummy 1993 and after<br>exogenous in CE and VAR; xgr exogenous in CE;<br>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|C, T in CE; C in VAR|xgr exogenous in CE; mshare exogenous in VAR|
|Vanuatu|0.837|0.141|5.926|38|FMOLS|||CE deterministic regressors= C, T, mshare;<br>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<br>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|
|Zambia|0.910|0.063|14.445|48|FMOLS|||CE deterministic regressors= C, dummy after 1989,<br>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=<br>T^2,dummyafter 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**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**afte**<br>**r adj**|**Method**|**Lag**<br>**interval**<br>**in VEC**|**C and trend**<br>**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|
|Angola|0.609|0.161|3.774|36|VEC|1,2|C in CE, VAR|reduced eq of export system; dummy after 2000<br>exogenous in CE<br>|
|Antigua and Barbuda|1.666|0.801|2.081|41|FMOLS|||factor demand (re-export) spec.; reduced eq of export<br>system; CE deterministic regressors= C, T, xgr; additional<br>deterministic regressors=mshare, T^2|
|Argentina|0.680|0.146|4.666|42|VEC|1,6|C, T in CE; C in VAR|global demand and price ratios, dummy for 1998-2002<br>(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||
|Australia|1.331|0.389|3.427|43|VEC|1,5||reduced eq of export system; eshare exogenous in CE;<br>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|
|Bahrain|1.428|0.517|2.764|46|VEC|1,2||factor demand (re-export) spec; dummy 2003 and after<br>exogenous in CE and VAR; xgr exogenous in VAR|
|Bangladesh|0.651|0.316|2.057|48|GMM|||ar(1), xgr, global demand ratio as extra vars; insts=<br>eshare, T, T^2|
|Barbados|0.785|0.241|3.265|43|VEC|1,5|C, T in CE; C in VAR|reduced eq of export system; dummy 1990 and after in<br>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|
|Belize|0.658|0.212|3.104|45|VEC|1,3|C in CE|dummy after 1999 exogenous in CE; xgr, global demand<br>and price ratios exogenous in VAR|
|Benin|0.881|0.248|3.555|45|VEC|1,3|C in CE|<br>reduced eq of export system; egr exogenous in VAR|
|Bermuda|0.955|0.412|2.320|48|FMOLS|||CE deterministic regressors= C, egr, log(M); additional<br>deterministic regressor= eshare|
|Bhutan|0.722|0.266|2.718|35|VEC|1,3|C in CE|<br>reduced eq of export system|
|Bolivia|0.344|0.152|2.261|48|FMOLS|||CE deterministic regressors= C, T, xgr, global price ratio;<br>additional deterministic regressors= T^2, eshare|
|Bosnia and<br>Herzegovina|0.496|0.089|5.558|23|VEC|1,3|C, T in VAR|reduced eq of export system; egr exogenous in VAR|
|Botswana|0.915|0.351|2.605|44|VEC|1,4|C, T in CE; C in VAR|factor demand (re-export) spec; reduced eq of export<br>system; lym exogenous in VAR|
|Brazil|0.531|0.104|5.088|48|GMM|||ar(1); inst=eshare|
|Brunei Darussalam|1.146|0.419|2.733|25|VEC|1,4|C, T in CE; C in VAR|dummy after 2007 exogenous in CE; xgr exogenous in<br>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|
|Burkina Faso|0.586|0.272|2.157|46|VEC|1,2|C, T in CE; C in VAR|reduced eq of export system; xgr exogenous in CE; egr<br>exogenous in VAR|
|Burundi|0.400|0.197|2.034|46|VEC|1,2||<br>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**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
|||||**NOB**||**Lag**|||
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**afte**<br>**r adj**|**Method**|<br>**interval**<br>**in VEC**|**C and trend**<br>**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|
|Colombia|0.502|0.132|3.806|48|GMM|||egr, ar(1) as extra vars; insts= eshare, global demand<br>ratio|
|Comoros|0.698|0.110|6.349|38|FMOLS|||deterministic regressors= xgr, M/D ratio; additional<br>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|
|Congo, Rep.|0.334|0.074|4.487|48|FMOLS|||deterministic regressors= C, global demand and price<br>ratios; additional deterministic regressors= T, eshare|
|Cook Islands|1.036|0.474|2.185|46|GMM|||ar(1), dummy after 1999 as extra vars; insts= eshare,<br>global demand and price ratios|
|Costa Rica|0.416|0.132|3.158|48|FMOLS|||reduced eq of export system; CE deterministic regressor=<br>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|
|Croatia|0.929|0.261|3.559|21|VEC|1,2|C, T in CE, VAR|xgr as extra endogenous var; global demand ratio<br>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|
|Denmark|1.563|0.353|4.431|44|VEC|1,4|C, T in CE; C in VAR|global demand and price ratios exogenous in CE, VAR; lyx<br>exogenous in VAR|
|Djibouti|0.699|0.368|1.900*|47|GMM|||<br>ar(1), ma(1), egr, lyx as extra vars; insts= eshare, T^2,<br>dummy 2000 and after|
|Dominican Rep.|0.915|0.302|3.034|44|VEC|1,4||xgr exogenous in CE; eshare exogenous in VAR; dummy<br>after 1988 exogenous in CE,VAR|
|Ecuador|0.521|0.521|1.990*|48|GMM|||C, T, xgr, ar(1) as extra vars; insts=eshare and global<br>demand ratio|
|Egypt, Arab Rep.|0.703|0.241|2.916|48|GMM|||egr, ar(1) as extra vars; insts= eshare, T, global demand<br>ratio|
|El Salvador|0.726|0.327|2.220|48|GMM|||exgr, ar(1) as extra vars; insts= eshare, mshare|
|Equatorial Guinea|0.493|0.167|2.951|36|FMOLS|||C as CE deterministic regressor; T as additional<br>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|
|Eswatini|0.516|0.092|5.627|44|VEC|1,4|C, T in CE; C in VAR|xgr exogeous in CE; global demand and price ratios<br>exogenous in VAR|
|Ethiopia|0.390|0.167|2.332|24|VEC|1,4|C, T in CE; C in VAR|<br>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|
|Finland|1.635|0.270|6.054|48|GMM|||C, dummy for 2000 and after, ar(1) as extra vars; inst=<br>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**|
|---|---|---|---|---|---|---|---|---|
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**afte**<br>**r adj**|**Method**|**Lag**<br>**interval**<br>**in VEC**|**C and trend**<br>**structure in VEC**|**Additional specifications**|
|Germany|1.821|0.258|7.064|42|VEC|1,6|C, T in CE, VAR|reduced eq of export system; dummy 1990 and after<br>exogenous in CE and VAR|
|Ghana|0.587|0.289|2.032|45|VEC|1,3|C , T in CE; C in VAR|egr exogenous in CE; global demand and price ratios<br>exogenous in VAR|
|Greece|1.583|0.532|2.973|48|GMM|||T, ar(1), xgr as extra vars; inst= eshare|
|Greenland|1.300|0.612|2.125|48|FMOLS|||deterministic regressors= C, T, dummy for 1970-1980,<br>global demand and price ratios; additional deterministic<br>regressors= T^2, lyx, eshare|
|Grenada|0.397|0.196|2.026|40|TSLS|||factor demand (re-export) spec; xgr, ar(1), ma(1) as extra<br>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|||, ,<br>price ratios; additional deterministic regressors= T^2,<br>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|
|Honduras|0.658|0.179|3.670|45|VEC|1,3|C, T in CE; C in VAR|dummy for 1992 and after exogenous in CE and VAR;<br>global demand and price ratios exogenous in VAR|
|Hong Kong SAR,<br>China|1.239|0.352|3.518|46|VEC|1,2|C in CE, VAR|factor demand (re-export) spec; reduced eq of export<br>system; xgr exogenous in VAR|
|Hungary|1.706|0.217|7.847|25|VEC|1,2|C in CE|dummy after 2013 exogenous in CE; mshare exogenous<br>in VAR|
|Iceland|1.787|0.580|3.081|45|VEC|1,3|C in CE|global demand ratio exogenous in CE; global price ratio<br>exogenous in VAR|
|India|0.647|0.275|2.358|46|VEC|1,2|C, T in CE; C in VAR|reduced eq of export system; egr exogenous in CE;<br>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|
|Iraq|0.853|0.152|5.604|46|VEC|1,2|C, T in CE, VAR|factor demand (re-export) spec; eshare exogenous in CE,<br>VAR; egr exogenous in VAR|
|Ireland|1.541|0.255|6.045|46|VEC|1,2|C in CE|factor demand (re-export) spec; reduced eq of export<br>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||
|Kenya|0.577|0.208|2.769|46|VEC|1,2|C,T in CE, VAR|reduced eq of export system; egr as extra endogenous<br>var|
|Kiribati|0.252|0.113|2.228|37|VEC|1,2|C in CE|xgr as additional endogenous var|
|Korea, Rep.|1.285|0.265|4.840|46|VEC|1,2|C in CE, VAR|reduced eq of export system; xgr as extra endogenous<br>var in CE|
|Kosovo|0.738|0.306|2.407|26|VEC|1,2||xgr as extra endogenous var in CE; eshare exogenous in<br>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**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
|||**Stdd**|**t**|**NOB**<br>**ft**||**Lag**<br>**itl**|**C d td**||
||**Ω**|**anar**<br>**Error**|**-**<br>**statistic**|**ae**<br>**r adj**|**Method**|**nerva**<br>**in VEC**|**an ren**<br>**structure in VEC**|**Additional specifications**<br>|
|Kyrgyz, Rep.|0.687|0.257|2.668|24|VEC|1,4|C in CE|reduced eq of export system; xgr exogenous in CE; egr<br>exogenous in VAR|
|Lao PDR|0.372|0.122|3.051|31|GMM|||ar(1), ma(1) as extra vars; insts= xgr, eshare|
|Latvia|0.736|0.254|2.896|28|FMOLS|||CE deterministic regressors= C, T, xgr; additional<br>deterministic regressors= T^2, eshare|
|Lebanon|0.892|0.396|2.254|28|FMOLS|||CE deterministic regressors= C, xgr; additional<br>deterministic regressors= T, T^2, eshare|
|Lesotho|0.747|0.044|16.816|46|VEC|1,2|C in C|<br>reduced eq of export system|
|Liberia|0.585|0.157|3.730|48|FMOLS|||CE deterministic regressors=C, egr; additional<br>deterministic regressors= eshare|
|Libya|0.740|0.287|2.575|45|VEC|1,3|C in CE, VAR|factor demand (re-export) spec; xgr as additional<br>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|
|Lithuania|1.032|0.294|3.507|28|FMOLS|||deterministic regressors: C, T, T^2, mgr, lyx, dummy after<br>2009; additional deterministic regressors= eshare|
|Luxembourg|2.319|0.599|3.870|45|VEC|1,3|C in CE, VAR|factor demand (re-export) spec; reduced eq of export<br>system; eshare exogenous in VAR<br>|
|Macao SAR, China|1.156|0.141|8.180|36|FMOLS|||factor demand (re-export) spec; deterministic<br>regressors= C, T, xgr; additional deterministic regressors=<br>T^2, eshare, global demand and price ratios|
|Madagascar|0.485|0.230|2.110|48|GMM|||xgr, ar(1) as added vars; insts=eshare, global demand<br>ratio|
|Malawi|0.303|0.112|2.707|48|GMM|||egr, ar(1) as extra vars; insts= eshare, global demand<br>ratio|
|Malaysia|0.736|0.302|2.437|48|GMM|||factor demand (re-export) spec; C, xgr, T, T^2 as extra<br>vars; insts= mshare, global demand ratio|
|Maldives|0.638|0.304|2.102|48|GMM|||factor demand (re-export) spec; C, T, T^2, egr, ar(1) as<br>extra vars; insts= eshare, mshare, xgr|
|Mali|0.931|0.422|2.204|48|GMM|||T, ar(1) as extra vars; insts= eshare, xgr|
|Malta|1.074|0.328|3.275|45|VEC|1,3|C in CE, VAR|factor demand (re-export) spec; xgr as extra endogenous<br>var; dummy 2000 and after exogenous in CE and VAR|
|Marshall Islands|0.402|0.112|3.587|48|FMOLS|||CE deterministic regressors= C, T, egr, global demand and<br>price ratios; additional deterministic regressors= T^2,<br>eshare|
|Mauritania|0.568|0.278|2.042|47|GMM|||egr, T, ar(1), ma(1) as extra vars; inst= eshare|
|Mauritius|0.908|0.390|2.329|37|VEC|1,5|C, T in CE, VAR|dummy 1990 and after exogenous in CE and VAR; global<br>demand and price ratios exogenous in VAR|
|Mexico|0.665|0.116|5.748|48|GMM|||<br>egr, ar(1) as extra vars; inst= eshare|
|Moldova|0.760|0.117|6.508|20|VEC|1,3|C, T in CE, VAR|xgr exogenous in CE, VAR; global demand and price ratios<br>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|
|Mozambique|0.294|0.100|2.950|37|FMOLS|||reduced eq of export system; CE deterministic regressor=<br>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|||<br>system; CE deterministic regressors= C, T, xgr, dummy<br>2005 and after; additional deterministic regressors= T^2|
|Netherlands|2.092|0.302|6.926|42|VEC|1,6|C,T in CE; C in VAR|reduced eq of export system; dummy 2001 and after<br>exogenous in CE; eshare exogenous in VAR|

34
**Appendix Table A2: Estimates of Ω, the CET Elasticities**

|**Country**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**afte**<br>**r adj**|**Method**|**Lag**<br>**interval**<br>**in VEC**|**C and trend**<br>**structure in VEC**|**Additional specifications**|
|New Zealand|2.536|0.634|3.997|44|VEC|1,4|C in CE|reduced eq of export system; egr as extra endogenous<br>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|
|Nigeria|0.569|0.136|4.175|45|VEC|1,3|C, T in CE; C in VAR|dummy between 1981 and 1987 exogenous in CE and<br>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|
|Norway|2.507|0.627|3.997|46|VEC|1,2||dummy for 1990-2005 exogenous in CE and VAR; egr<br>exogenous in VAR|
|Oman|0.896|0.268|3.341|44|VEC|1,4||dummy 1988 and after exogenous in CE; egr, eshare<br>exogenous in VAR|
|Pakistan|0.633|0.185|3.422|42|VEC|1,6|C in CE, VAR|<br>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||
|Puerto Rico|1.547|0.562|2.754|44|VEC|1,4|C in CE, VAR|xgr exogenous in CE; dummy after 2016 exogenous in<br>VAR|
|Qatar|1.771|0.324|5.468|46|VEC|1,2|C, T in CE; C in VAR|factor demand (re-export) spec; dummy 1998 and after<br>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||
|Rwanda|0.615|0.274|2.243|48|GMM|||C, egr, ar(1) as extra vars; insts= eshare, global demand<br>and price ratios|
|Samoa|0.222|0.067|3.335|46|VEC|1,2||dummy for 1985-1997 exogenous in CE and VAR; xgr<br>exogenous in CE|
|San Marino|1.280|0.217|5.894|44|VEC|1,4|C, T in CE, VAR|factor demand (re-export) spec; reduced eq of export<br>system; eshare exogenous in VAR|
|Sao Tome and<br>Principe|0.799|0.139|5.756|46|VEC|1,2|C, T in CE; C in VAR|<br>xgr exogenous in CE; global demand and price ratios<br>exogenous in VAR|
|Saudi Arabia|1.128|0.170|6.620|48|FMOLS|||CE deterministic regressors= C, dummy after 1973, xgr,<br>global demand and price ratios; additional deterministic<br>regressor= eshare|
|Senegal|0.421|0.158|2.654|48|FMOLS|||<br>CE deterministic regressors= C, T, global demand ratio;<br>additional deterministic regressors= egr, eshare|
|Serbia|0.646|0.306|2.113|23|GMM|||<br>C, ar(1), global demand ratio as extra vars; insts= T^2,<br>eshare|
|Seychelles|1.240|0.076|16.372|39|VEC|1,3|C,T in CE; C in VAR|factor demand (re-export) spec; global demand and price<br>ratios exogenous in VAR|
|Sierra Leone|0.571|0.125|4.579|45|VEC|1,3|C, T in CE, VAR|<br>reduced eq of export system; egr, eshare exogenous in<br>VAR|
|Singapore|1.070|0.227|4.713|48|GMM|||factor demand (re-export) spec; C, xgr, T, T^2, ar(1) as<br>extra vars; insts= mshare, global demand and price ratios|
|Slovak Republic|0.943|0.319|2.952|28|GMM|||ar(1), dummy 2010 and after, T, T^2 as extra vars; insts=<br>eshare, lyx|
|Slovenia|0.738|0.223|3.310|28|FMOLS|||CE deterministic regressors= C, T, global demand and<br>price ratios|

35
**Appendix Table A2: Estimates of Ω, the CET Elasticities**

|**Country**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
|||||**NOB**<br>||**Lag**<br>|||
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**afte**<br>**r adj**|**Method**|**interval**<br>**in VEC**|**C and trend**<br>**structure in VEC**|**Additional specifications**|
|Solomon Islands|0.482|0.173|2.790|38|GMM|||C, ar(1) as extra vars; inst= eshare|
|South Africa|0.858|0.190|4.521|46|VEC|1,2||dummy 1991 and after exogenous in CE and VAR; eshare<br>exogenous in CE; xgr exogenous in VAR|
|Spain|1.800|0.617|2.917|48|GMM|||ar(1) as extra var; inst= egr, global demand and price<br>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|
|St. Lucia|0.334|0.162|2.065|42|VEC|1,6|C in CE, VAR|dummy 1980 and after exogenous in CE and VAR; xgr<br>exognous in VAR|
|St. Vincent and the<br>Grenadines|0.370|0.175|2.114|48|GMM|||<br>ar(1), C,egr, global demand and price ratios as extra vars;<br>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|
|Tanzania|0.766|0.365|2.097|28|FMOLS|||deterministic regressors= C, egr, dummy 1998 and after;<br>additional deterministic regressor= T, eshare|
|Thailand|0.490|0.214|2.292|47|GMM|||egr, ar(1), ma(1) as extra vars; insts= eshare, global<br>demand and price ratios|
|Timor-Leste|0.418|0.132|3.168|25|VEC|1,3|C, T in CE, VAR|reduced eq of export system; dummy 2000 and after<br>exogenous in CE; xgr exogenous in VAR|
|Togo|0.542|0.257|2.104|46|FMOLS|||CE deterministic regressors= C, global demand and price<br>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|
|Tuvalu|0.719|0.234|3.070|45|VEC|1,3||dummy 1995 and after exogenous in CE and VAR; eshare<br>exogenous in VAR|
|Uganda|0.782|0.333|2.347|46|VEC|1,2|C in CE|reduce eq of export system|
|Ukraine|0.717|0.175|4.094|27|VEC|1,1|C in CE|factor demand (re-export) spec; reduced eq of export<br>system; egr exogenous in CE; eshare exogenous in VAR<br>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|<br>system; eshare exogenous in CE; dummy 1986 and after<br>exogenous in VAR|
|United Kingdom|2.026|0.381|5.322|46|VEC|1,2|C in CE|reduce eq of export system; lyx exogenous in CE; eshare<br>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|
|Uzbekistan|0.755|0.292|2.589|24|VEC|1,4||egr exogenous in CE; dummy 2008 and after exogenous<br>in VAR|
|Vanuatu|0.477|0.214|2.230|38|GMM|||egr, ar(1) as extra vars; insts= T, eshare, global price ratio|
|Venezuela|0.474|0.095|4.986|48|GMM|||factor demand (re-export) spec; xgr, ar(1), dummy 2008<br>and after as extra vars; insts= T, eshare|
|Vietnam|0.849|0.076|11.181|30|VEC|1,2|C in CE|factor demand (re-export) spec; reduced eq of export<br>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**||**Estimat**|**e and test**|||||**Model**|
|---|---|---|---|---|---|---|---|---|
||**Ω**|**Standard**<br>**Error**|**t-**<br>**statistic**|**NOB**<br>**afte**<br>**r adj**|**Method**|**Lag**<br>**interval**<br>**in VEC**|**C and trend**<br>**structure in VEC**|**Additional specifications**|
|Yemen|0.669|0.243|2.756|25|VEC|1,3||factor demand (re-export) spec; dummy before 1994 and<br>dummy after 2005 exogenous in CE and VAR; xgr<br>exogenous in VAR|
|Zambia|0.232|0.063|3.709|47|GMM|||C, T, ar(1), ma(1) as extra vars; insts=xgr, eshare, global<br>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**||**Sou**|**rce and coverage **|
|---|---|---|---|
|**Country Name**|**WDI Country**<br>**Code**|**Source**|**Period Covered**<br>**(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|
|<br>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|

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**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<br>**Data sources:**<br>World Bank WDI:https://databank.worldbank.org/sourc|ZWE<br>e/world-development-in|WDI & UN<br>dicators|1970-2018|

**Data sources:** World Bank WDI: <u>https://databank.worldbank.org/source/world-development-indicators</u> United Nations national accounts: <u>https://unstats.un.org/unsd/snaama/Index</u>

**Notes:** UN = United Nations WDI = World Development Indicators

42