Abstract
Globalization can be characterized as the rapid increase in international trade spurred by advances in technology that have decreased the cost of trade. As costs have declined, so too, it would seem, should the estimated distance coefficient in the gravity model of bilateral trade. But a standard empirical result is that these estimated coefficients have been broadly stable, a result that might be called the “missing globalization puzzle.” In contrast to results from the literature, we find evidence of globalization reflected in the estimated coefficients on distance in both cross-section and panel data. Our estimation procedures fully incorporate the information contained in observations where bilateral trade is zero and hence do not suffer from the potential estimation bias when observations where bilateral trade is zero are arbitrarily excluded from the sample.
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Notes
We omit for simplicity the constant of proportionality in Equation (1); it is incorporated in the barriers to trade function below.
Another candidate is a dummy variable for members of currency unions. Exploratory regressions indicate that exclusion of a currency union dummy has little effect on our estimated distance coefficients.
If transport costs are eliminated, the gravity model reduces to an equation relating trade to economic mass alone, called the frictionless gravity model by Deardorff (1998, see Equation (21)); see also Anderson and van Wincoop (2003, Equation (13)).
Eichengreen and Irwin (1998) is one of the few studies reporting a decline in the estimated distance coefficient, from about −0.85 for 1949 to about −0.75 for 1964, perhaps reflecting the relatively long sample. Boisso and Ferrantino (1997) report distance coefficients that rise until the early to mid-1970s and fall thereafter.
For simplicity, we use the same symbols to represent coefficients on the same variables in both the nonlinear and the log-linear specifications.
A theoretical reason to prefer the nonlinear specification is that it implies that trade will go to zero as the size of either country goes to zero, which, as noted by Deardorff (1998, p. 9), must be correct; log-linear specifications do not have this property. In general, neither the nonlinear nor the log-linear specifications can predict zero trade (except in trivial cases). Helpman, Melitz, and Rubinstein (2007) present a theoretical and empirical model that does predict zero trade for some country pairs.
Other estimation methods that incorporate information in the zero observations include Tobit, pseudo-maximum likelihood, and the two-stage procedure proposed by Helpman, Melitz, and Rubinstein (2007), which incorporates a Tobit-like probit estimate in the first stage; we apply each of these methods below in our panel estimates. Estimation methods designed to deal with unobserved or missing variables, such as proposed by Heckman (1979), seem inappropriate given that the dependent variable is neither missing nor unobserved.
The compilation methodology for trade statistics is discussed in IMF, Direction of Trade Statistics. Zero observations in Direction of Trade Statistics either represent bilateral trade reported by national authorities as explicitly zero or represent unreported bilateral trade (in some cases unreported trade in earlier periods is subsequently explicitly identified by national authorities as zero trade, suggesting that in these cases the missing trade is in fact zero trade). The compilation methodology is designed to identify obviously missing bilateral trade flows through partner information, estimation, or extrapolation. Moreover, a validation check compares the sum of bilateral trade with the independently reported aggregate trade levels reported in IMF, International Financial Statistics. In most cases, this check reveals virtually no differences or only minimal differences of 1–2 percent, suggesting that no significant amount of bilateral trade reported by the authorities is omitted. These procedures imply that observations reported as zero in Direction of Trade Statistics are either truly zero or extremely small.
Greene (1981) shows that when the variables are distributed normally, the size of the bias is inversely proportional to the share of the sample included in the regression; that is, the greater the share of zero observations excluded, the greater the bias.
Even if heteroscedasticity is unaddressed, the parameter estimates remain consistent, although the standard error estimates are biased.
For the nonlinear Anderson and van Wincoop (2003) specification, in addition to the standard exclusion of a dummy variable (fixed effect) for one country in a regression with a constant, we excluded the fixed effects for the United States and China, which are highly correlated with income and population, respectively (with coefficients of correlation of about 0.8–0.9), to avoid multicollinearity. This was not necessary in the panel regressions reported below, which include fixed effects for all countries.
Using land area instead of population makes almost no difference to either the cross-section or the panel results discussed below. We report results with population rather than land area to facilitate comparisons with other empirical studies.
This also implies that the nonrandom screening of the data implicit in the exclusion of the zero observations does not result in biased parameter estimates in the nonlinear specification.
See also the discussion in Anderson and van Wincoop (2004, pp. 729–31). Grossman's Cobb-Douglas assumption and the implied elasticity of substitution between home and foreign goods of 1 is problematic because it would suggest a distance elasticity of zero.
The relevant elasticity of substitution for this calculation is that between any pair of goods, whether domestically produced or imported. To our knowledge, estimates of this elasticity are not available, but it can be thought of as an average (with unknown weights) of the elasticity of substitution between domestic and imported goods and the elasticity of substitution among imports from different countries. Obstfeld and Rogoff (2000) suggest a consensus estimate of the elasticity of substitution between domestic and imported goods of 5–6; Saito (2004) estimates the elasticity of substitution among imports from Organization for Economic Cooperation and Development countries to be about 0.9.
Ideally, panel estimations that are consistent with the Anderson and van Wincoop (2003) approach would include time-varying fixed effects. Except for the pseudo-maximum likelihood estimates reported in Table 5, this is computationally not feasible because including time-varying fixed effects would add an additional 1,500 coefficients to be estimated, whereas Stata does not allow more than 100 regressors in nonlinear estimations.
There are other instances, however, where nonlinear and log-linear gravity models may give similar results. Coe and Hoffmaister (1999), for example, find that Africa slightly overtrades, based on a nonlinear specification of the gravity model, as does IMF (2002), based on the conventional log-linear specification. See also Subramanian and Tamirisa (2003).
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Additional information
*David T. Coe is Senior Advisor in the Asia and Pacific Department at the IMF, Arvind Subramanian is Assistant Director in the IMF Research Department, and Natalia T. Tamirisa is a senior economist in the IMF European Department. We thank, without implication, Alan Deardorff, Elhanan Helpman, Alexander Hoffmaister, Paul Masson, John McDermott, Sam Ouliaris, Adrian Pagan, Jacques Polak, Antonio Spilimbergo, Shang-Jin Wei, and participants at seminars at the IMF and the Korea Institute for International Economics for useful comments and suggestions. We are particularly grateful to Wipada Soonthornsima and Nalini Umashankar of the IMF Statistics Department for helpful discussions on the compilation methodology for bilateral trade statistics, and to Rikhil Bhavnani for extensive collaboration and assistance on an earlier version of this paper.
Appendix
Appendix
Countries
Algeria
Argentina
Australia
Austria
Bangladesh
Bolivia
Brazil
Cameroon
Canada
Chile
China
Colombia
Congo, Democratic Republic of Congo, Republic of Costa Rica
Côte d’Ivoire
Denmark
Egypt
Ethiopia
Finland
France
Germany
Ghana
Greece
Guatemala
Guyana
Hong Kong SAR
Iceland
India
Indonesia
Iran, I.R. of Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kenya
Korea
Madagascar
Malawi
Malaysia
Mauritius
Mexico
Morocco
Netherlands
New Zealand
Nigeria
Norway
Pakistan
Paraguay
Peru
Philippines
Portugal
Saudi Arabia
Senegal
Singapore
Spain
Sri Lanka
Sweden
Switzerland
Taiwan Province of China
Tanzania
Thailand
Tunisia
Turkey
Uganda
United Kingdom
United States
Uruguay
Venezuela
Zambia
Zimbabwe
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Coe, D., Subramanian, A. & Tamirisa, N. The Missing Globalization Puzzle: Evidence of the Declining Importance of Distance. IMF Econ Rev 54, 34–58 (2007). https://doi.org/10.1057/palgrave.imfsp.9450003
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DOI: https://doi.org/10.1057/palgrave.imfsp.9450003