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Determinants of services trade agreement membership

Review of World Economics volume 157, pages 21–64 (2021)Cite this article

Abstract

Existing literature has examined factors underlying the formation of goods trade agreements (GTA) and bilateral investment treaties but not the determinants of services trade agreement (STA) membership. This paper bridges the gap by studying the economic and political determinants of STA membership. Its main contribution lies in providing an economic explanation of unilateral services regulatory provisions, embodied in the World Bank’s Services Trade Restrictiveness Index (Borchert et al. in World Bank Econ Rev 28:162–188, 2014), and their interaction with services preferentialism. The authors find that unilateral services provisions are closely associated with economic determinants. They also find that countries’ participation in STAs is correlated with the similarity of their unilateral services trade restrictiveness, a finding not observed for “goods-only” trade agreements. While geographical and cultural determinants are found to be broadly similar for GTAs and STAs, association with economic size of partners, factor endowments and services cost shares in GDP comes through more strongly for goods-only agreements.

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Fig. 1

Source: Authors’ calculations based on STRI data in Borchert et al. (2014)

Fig. 2

Notes

  1. The advantage of assuming that \(f(\cdot )\) is linear about its arguments is convenient, since the parameters on \(x_{ij}\) may then be interpreted as marginal effects on the response probability. However, while such models are advertised by some (see Angrist and Pischke 2009), their fundamental drawbacks (of not guaranteeing predicted probabilities in the unit interval, of not generating unbiased standard errors and test statistics, etc.) are well known (see Lewbel and Yang 2012).

  2. Assuming normality of \(f(\cdot )\) and, hence, a symmetric density function about \(\Pi _{ij}^{*}\) as well as homoskedasticity about the unobservable determinants of \(\Pi _{ij}^{*}\). Probit models have been used for modelling the choices of preferential policy agreements in Baier and Bergstrand (2004), Egger et al. (2008), Baier and Bergstrand (2009), and Bergstrand and Egger (2013).

  3. Flexible nonlinear choice models which permit for some form of heteroskedasticity and for endogenous regressors had been introduced by Lewbel (2000).

  4. Such flexible, semi-nonparametric models have been introduced and employed in Gallant and Nychka (1987) and Gabler et al. (1993).

  5. A convenient way of dealing with endogenous regressors in nonlinear models is the control-function approach (see Terza et al. 2008; Wooldridge 2010). See Newey (1987) and Lewbel et al. (2012) for alternatives. In general, our instrumental-variable procedures—based on control functions or not—will involve first-stage regressions which model the minimum and the maximum services-trade-restrictiveness index (STRI) each in a pair of countries as two separate endogenous variables in a linear regression model.

  6. We treat STAs with the EU as agreements involving Member States of EU15, EU25, EU27 and EU28, as the case may be, depending on the year of accession.

  7. Our results remain qualitatively similar if we exclude the Australia-New Zealand STA and measure the time-varying variables in 1990.

  8. This stylized fact has a bearing on some of the findings we will see in Sect. 6, for instance with respect to examining the effect of differences in relative factor endowments between member countries and outsiders, on the probability to negotiate an STA.

  9. We would like to thank an anonymous referee for this suggestion.

  10. Baier and Bergstrand (2004) computed countries’ capital stocks by using the perpetual inventory method to compute capital-per-capita or capital-labor ratios directly. However, a problem with this procedure is that it requires sufficiently long time series on investments and investment deflators. For the country sample at hand, this would have led to an unjustifiable loss of observations. Moreover, real per-capita income ratios are highly correlated with capital-labor ratios (see Egger and Larch 2008; Bergstrand et al. 2016). The correlation coefficient between real real per-capita incomes and capital-labor ratios in the subsample of our data for which both variables exist is close to 0.9.

  11. Baier and Bergstrand (2004) also motivate the inclusion of distance of every pair of countries i and j from the Rest of the World, a variable they call remoteness. It turns out that, with \(STA_{ij}\) as the dependent variable—which is unity only for a small subset of preferential trade agreements—remotenes is highly collinear with the other regressors included in the model and does not add explanatory power. Therefore, we chose against including it in the specification.

  12. http://www.economics.harvard.edu/faculty/shleifer/files/qgov/_web.xls.

  13. GTAs are agreements that are notified under Article XXIV of the GATT. We construct an agreement indicator for this akin to \(STA_{ij}\) and use its weighted average for all country pairs except ij to construct a contagion variable for pair ij.

  14. Notice that we followed Baier and Bergstrand (2004) in including the simple as well as the squared term for the underlying variable (\(DGDPPC_{ij}\), \(SQDDGDPPC_{ij}\)). However, the coefficients on polynomial expressions cannot be interpreted with nonlinear models such as probit. Therefore, since reporting marginal effects throughout, we always report the marginal effect of \(DGDPPC_{ij}\) only. For instance, Baier and Bergstrand (2004) did not report marginal effects. Therefore, the results on STAs in this paper cannot be directly compared to the ones in Baier and Bergstrand (2004).

  15. Note, however, that a more precise empirical evaluation of Hypothesis (c) would require looking at the marginal effect of differential economic size on the probability of STA membership at various levels of regulatory costs in the large economy. Similarly, a more precise empirical evaluation of Hypothesis (e) would require looking at the marginal effects of services-intensiveness of the economies at various (conditional) levels of the explanatory variables. However, notice that the cumulative probability of STA membership in any customary nonlinear probability model follows an S-shaped pattern in terms of the linear index. On average for the data analyzed, much fewer than 50% of the country pairs actually are members of an STA. This means that the data are situated in the left branch of the S-shaped probability function. Thus, everything evaluated on average except for one explanatory variable in the data does not move a country pair beyond the 50-percent probability threshold. Hence, if we marginally raised \(DGDP_{ij}\) for a country pair with such a ratio in the 25-th or the 75-th percentile of the distribution, we would get a higher or lower, respectively, marginal effect than on average. Conversely, if we marginally raised \(AVGSRATIO_{ij}\) for a country pair with such a ratio in the 25-th or the 75-th percentile of the distribution, we would get a lower or higher, respectively, marginal effect (though not statistically significantly so) than on average. This empirically supports Hypothesis (c) but provides statistically insignificant evidence for Hypothesis (e).

  16. Notice that goods-trade remoteness is negatively associated with \(WGTA_{ij}\), and the latter is positively associated with STA membership between i and j, which supports the claim.

  17. This was the earliest year data on all these ten countries existed.

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Authors and Affiliations

  1. Department of Management, Technology, and Economics, ETH Zurich, Leonhardstr. 21, 8092, Zurich, Switzerland

    Peter Egger

  2. Indian Council for Research on International Economic Relations, New Delhi, India

    Anirudh Shingal

  3. European University Institute, Fiesole, Italy

    Anirudh Shingal

  4. World Trade Institute, Bern, Switzerland

    Anirudh Shingal

  5. Centre for the Analysis of Regional Integration at Sussex, Falmer, Brighton, United Kingdom

    Anirudh Shingal

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  1. Peter Egger
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Correspondence to Anirudh Shingal.

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The project leading to this paper has received funding from the Swiss NCCR in Trade Regulation. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Appendices

Appendix A1: Polity IV variables used as instruments for the STRI variables

XRREG captures the extent to which a political system institutionalizes procedures for transferring executive power. The country-time-specific variable may take on three values, \(XRREG\in \{1,2,3\}\) which refer to unregulated (coded 1), designational/transitional (coded 2), and regulated (coded 3) systems. XRCOMP captures the extent that prevailing modes of advancement give subordinates equal opportunities to become superordinates (see Gurr 1974). For instance, the selection of chief executives through popular elections matching two or more viable parties or candidates is regarded as competitive. If power transfers are dubbed unregulated (coded 1) in XRREG or involve a transition to or from and unregulated system, XRCOMP is coded 0. Otherwise, XRCOMP may take on three integer values, depending on whether superordinates in a political system are chosen by selection (coded 1), election (coded 3), or a dual/transitional system (coded 2). XROPEN captures whether the recruitment of the chief executive is open to the extent that all politically active population principally has an opportunity to attain the position through a regularized process. If power transfers are dubbed unregulated (coded 1) in XRREG or involve a transition to or from an unregulated system, XROPEN is coded 0. Otherwise, XROPEN may take on four integer values, to measure whether the recruitment of the chief executive is closed (coded 1), done through dual-executive designation (coded 2), through dual-executive election (coded 3), or it is open (coded 4) (Table 6).

Table 6 Correlation coefficients
Full size table

PARREG measures the extent to which political participation is regulated through binding rules on when, whether, and how political preferences are expressed. One-party states and Western democracies both regulate participation but they do so differently, the former by channelling participation through a single party structure, with sharp limits on diversity of opinion; the latter by allowing relatively stable and enduring groups to compete non-violently for political influence. The polar opposite is unregulated participation, in which there are no enduring national political organizations and no effective regime controls on political activity. In such situations political competition is fluid and often characterized by recurring coercion among shifting coalitions of partisan groups. A five-category scale is used to code PARREG as: unregulated (coded 1); multiple identity (coded 2); sectarian (coded 3); restricted (coded 4); or regulated (coded 5). PARCOMP measures the extent to which alternative preferences for policy and leadership can be pursued in a country’s political arena. Political competition implies a significant degree of civil interaction, so polities which are dubbed unregulated (coded 1) in PARREG are not coded in PARCOMP. Polities in transition between unregulated and any of the regulated forms of PARREG are also not coded in PARCOMP. Otherwise, PARCOMP is coded on a five-category scale as: repressed (coded 1); suppressed (coded 2); factional (coded 3); transitional (coded 4); and competitive (coded 5).

Appendix A2: Political determinants of STA membership

POLITY reflects a combined polity score, where the autocracy (AUTOC) score for a country is subtracted from the democracy (DEMOC) score such that the resulting unified polity scale ranges from \(+10\) (strongly democratic) to \(-10\) (strongly autocratic). Instances of standardized authority scores taking on values of \(-66\), \(-77\), and \(-88\) in the coding are converted to scores within the range of \(-10\) to \(+10\). REGDUR measures the number of years since the most recent regime change (defined by a three-point change in the POLITY score over a period of three years or less) or the end of a transition period defined by the lack of stable political institutions (denoted by a standardized authority score). In calculating REGDUR, the first year during which a new (post-change) polity is established is coded as the baseline year zero, and each subsequent year adds one to the value of the REGDUR variable consecutively until a new regime change or transition period occurs. Values are entered for all years beginning with the first regime change since 1800 or the date of independence if that event occurred after 1800. The range for the variable in 1980 is between 0 and 171. The state fragility index underlying SFI ranges from no state fragility (coded 0) extreme state fragility (coded 25). The data cover all independent countries in the world in which the total country population is greater than 500,000 in 2013 (167 countries). The fragility matrix scores each country on effectiveness and legitimacy along four performance dimensions: security; politics; economics; and social matters. Each of the indicators is rated on a four-point fragility scale: no fragility (coded 0); low fragility (coded 1); medium fragility (coded 2); and high fragility (coded 3) with the exception of the economic effectiveness indicator, which is rated on a five-point fragility scale (where extreme fragility is coded 4). The state fragility index combines scores on these eight indicators and ranges from 0 (no fragility) to 25 (extreme fragility). A country’s fragility is closely associated with its state capacity to manage conflict; make and implement public policy; and deliver essential services and its systemic resilience in maintaining system coherence, cohesion, and quality of life; responding effectively to challenges and crises, and sustaining progressive development (Table 7).

Table 7 Determinants of goods trade agreement (GTA) membership assuming exogenous unilateral STRI (All coefficients and standard errors pertain to marginal effects evaluated at sample means of variables)
Full size table

Appendix A3: Sample composition

1.1 Size of the dataset

The STRI data are available for 103 countries, so that there are 5253 \([=(103\times 102)/2]\) possible dyads (treating pair ij and pair ji as the same dyad). By August 2014, 542 of these dyads were members of an STA.

1.2 List of included countries

Albania, Argentina, Armenia, Australia, Austria, Burundi, Belgium, Bangladesh, Bulgaria, Bahrain, Belarus, Bolivia, Brazil, Botswana, Canada, Chile, China, Cote d’Ivoire, Cameroon, Congo (Democratic Republic), Colombia, Costa Rica, Czech Republic, Germany, Denmark, Dominican Republic, Algeria, Ecuador, Egypt, Spain, Ethiopia, Finland, France, Great Britain, Georgia, Ghana, Greece, Guatemala, Honduras, Hungary, Indonesia, India, Ireland, Iran, Italy, Jordan, Japan, Kazakhstan, Kenya, Kyrgyz Republic, Cambodia, South Korea, Kuwait, Lebanon, Sri Lanka, Lesotho, Lithuania, Morocco, Madagascar, Mexico, Mali, Mongolia, Mozambique, Mauritius, Malawi, Malaysia, Namibia, Nigeria, Nicaragua, the Netherlands, Nepal, New Zealand, Oman, Pakistan, Panama, Peru, the Philippines, Poland, Portugal, Paraguay, Qatar, Romania, Russian Federation, Rwanda, Saudi Arabia, Senegal, Sweden, Thailand, Trinidad & Tobago, Tunisia, Turkey, Tanzania, Uganda, Ukraine, Uruguay, USA, Uzbekistan, Venezuela, Vietnam, Yemen, South Africa, Zambia, Zimbabwe.

1.3 Non-existence of some countries in 1980

Note that ten countries in our sample did not exist in the year 1980: Czech Republic and nine former former Soviet Union republics (Armenia, Belarus, Georgia, Kazakhstan, the Kyrgyz Republic, Lithuania, Russia, Ukraine, and Uzbekistan). GDP and population for these countries were constructed for the year 1980 and 1980–1982, respectively. This was done by multiplying historical GDP (corrected for inflation) and population for Czechoslovakia and the USSR by the shares of the Czech Republic and each of the nine former USSR republics, respectively, in the year 1994.Footnote 17

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Egger, P., Shingal, A. Determinants of services trade agreement membership. Rev World Econ 157, 21–64 (2021). https://doi.org/10.1007/s10290-020-00394-y

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