Skip to main content

Historical trade integration: globalization and the distance puzzle in the long twentieth century


In times of ongoing globalization, the notion of geographic neutrality expects the impact of distance on trade to become ever more irrelevant. However, over the last three decades a wide range of studies has found an increase in the importance of distance during the second half of the twentieth century. This paper tries to reframe this discussion by characterizing the effect of distance over a broader historical point of view. To make maximal use of the available data, we use a state-space model to construct a bilateral index of historical trade integration. Our index doubles to quadruples yearly data availability before 1950, allowing us to expand the period of analysis to 1880–2011. This implies that the importance of distance as a determinant of the changing trade pattern can be analyzed for both globalization waves. In line with O’Rourke (Politics and trade: lessons from past globalisations. Technical Report, Bruegel, 2009) and Jacks et al. (J Int Econ 83(2):185–201, 2011), we find that the first wave was marked by a strong, continuing decrease in the effect of distance. Initially, the second globalization wave started out similarly, but from the 1960s onward the importance of distance starts increasing. Nevertheless, this change is dwarfed by the strong decrease preceding it.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. The hti index is made available at:

  2. In addition to freight rates and distance, Jacks et al. (2011) also control for tariffs, the gold standard, empire membership, railroad infrastructure, exchange rates, common language and shared borders.

  3. \(\sqrt{X_{ij} X_{ji} / \left( X_{ii} X_{jj} \right) }\), with \(X_{ij}\) the exports from \(i\) to \(j\) and \(X_{ii}\) the internal trade in country \(i\). Internal trade is usually approximated by subtracting exports from GDP, even though this can cause negative values for small open economies. Alternative solutions include using tariff data (Head and Mayer 2013).

  4. Since we will estimate this model using Bayesian techniques, it would be more correct to use the term highest posterior density intervals, but for readability’s sake, we will use confidence interval throughout this paper.

  5. We are grateful to Beatrice Dedinger ( for providing access to the unpublised RICardo data. It was converted from pounds to US dollars using the historical exchange rate from Williamson (2015).

  6. de la Escosura (2000) starts with current, exchange rate converted, GDPs and uses the shortcut method to compute the current, PPP converted, GDPs. Klasing and Milionis (2014) on the other hand start with Maddison’s GDPs in constant, PPP converted, 1990 US dollars and transform it using a GDP deflator in current US dollars. They subsequently transform this series into current, exchange rate converted, US dollars using a similar (but inverted) shortcut method.

  7. No of countries \(\times \) (no of countries \(-1\)) \(\times \) No of years (excl. World Wars) = \(225\times 224 \times (2011-1870+1-5-6).\)

  8. The Overseas Countries and Territories account for the remaining colonies after the year 2000.

  9. Initial tests found that the time dependency is the same for the vast majority of country couples: 94.4 % of the time \(T_{ij}\) is not significantly different at the 1 % level from \(T_{jl}\) with \(ij \ne jl\).

  10. The size of the dataset required the use of the resources of the Flemish Supercomputer Center, which was kindly provided by Ghent University, the Flemish Supercomputer Center (VSC), the Hercules Foundation and the Flemish Government—department EWI.

  11. \(hti^\star _{ij,t} = (hti_{ij,t} - \mu ) / \sigma .\)

    With \(\mu = \frac{\sum _{i=1}^{n}\sum _{j=1, j\ne i}^n \sum _{t=1}^T( hti_{ij,t} )}{n(n-1)T}\) and \(\sigma ^2 = \frac{\sum _{i=1}^{n}\sum _{j=1, j\ne i}^n \sum _{t=1}^T (hti_{ij,t} - \mu )^2}{n(n-1)T-1}.\)

  12. These and other yearly graphs are made available together with the indicator at

  13. Nafta also includes the preceding 1987 Canada-US Free Trade agreement.

  14. Multilateral resistance terms are country-specific barriers to trade that in this case are allowed to vary over time.

  15. Since the index is already normalized for the size of the sender country, the GDP of the sender country should actually be left out of the gravity model regressions. However, its inclusion did not significantly affect the results.

  16. The number of dyads covered increase more than sixfold between 1870 and 1880.


  • Alcalá F, Ciccone A (2004) Trade and productivity. Q J Econ 119(2):613–646

    Article  Google Scholar 

  • Arribas I, Pérez F, Tortosa-Ausina E (2011) A new interpretation of the distance puzzle based on geographic neutrality. Econ Geogr 87(3):335–362

    Article  Google Scholar 

  • Baldwin R, Taglioni D (2006) Gravity for dummies and dummies for gravity equations. Technical Report w12516, National Bureau for Economic Research

  • Barbieri K, Keshk O (2012) Correlates of war project trade data set codebook, verion 3.0.

  • Barbieri K, Keshk O, Pollins B (2009) Trading data: evaluating our assumptions and coding rules. Confl Manag Peace Sci 26(4):471–491

    Article  Google Scholar 

  • Berthelon M, Freund C (2008) On the conservation of distance in international trade. J Int Econ 75(2):310–320

    Article  Google Scholar 

  • Bleaney M, Neaves AS (2013) Declining distance effects in international trade: some country-level evidence. World Econ 36(8):1029–1040

    Article  Google Scholar 

  • Bolt J, van Zanden J (2013) The first update of the maddison project; re-estimating growth before 1820. Maddison Project Working Paper 4.

  • Bosquet C, Boulhol H (2013) What is really puzzling about the “distance puzzle”. Rev World Econ 151(1):1–21

    Article  Google Scholar 

  • Boulhol H, De Serres A (2010) Have developed countries escaped the curse of distance? J Econ Geogr 10(1):113–139

    Article  Google Scholar 

  • Brun JF, Carrère C, Guillaumont P, De Melo J (2005) Has distance died? evidence from a panel gravity model. World Bank Econ Rev 19(1):99–120

    Article  Google Scholar 

  • Buch CM, Kleinert J, Toubal F (2004) The distance puzzle: on the interpretation of the distance coefficient in gravity equations. Econ Lett 83(3):293–298

    Article  Google Scholar 

  • Carter CK, Kohn R (1994) On gibbs sampling for state space models. Biometrika 81(3):541–553

    Article  Google Scholar 

  • Coe DT, Subramanian A, Tamirisa NT (2007) The missing globalization puzzle: evidence of the declining importance of distance. IMF staff papers, pp 34–58

  • Crafts N (2004) Globalisation and economic growth: a historical perspective. World Econ 27(1):45–58

    Article  Google Scholar 

  • Dilip K (2003) The economic dimensions of globalization. Palgrave Macmillan, Hampshire

    Google Scholar 

  • Disdier AC, Head K (2008) The puzzling persistence of the distance effect on bilateral trade. Rev Econ Stat 90(1):37–48

    Article  Google Scholar 

  • Durbin J, Koopman S (2012) Time series analysis by state space methods, 2nd edn. Oxford University Press, Oxford

    Book  Google Scholar 

  • de la Escosura LP (2000) International comparisons of real product, 1820–1990: an alternative data set. Explor Econ Hist 37:1–41

    Article  Google Scholar 

  • Estevadeordal A, Frantz B, Taylor AM (2002) The rise and fall of world trade, 1870–1939. Technical Report w9318, National Bureau of Economic Research

  • Fagiolo G, Reyes J, Schiavo S (2008) On the topological properties of the world trade web: a weighted network analysis. Phys A Stat Mech Appl 387(15):3868–3873

    Article  Google Scholar 

  • Feenstra RC, Inklaar R, Timmer MP (2013) The next generation of the penn world table.

  • Findlay R, O’Rourke KH (2007) Power and plenty: trade, war, and the world economy in the second millennium, vol 51. Princeton University Press, Princeton

    Google Scholar 

  • Frances C (1997) The death of distance. Harvard Business School Press, Boston

    Google Scholar 

  • Guimarães P, Portugal P (2009) A simple feasable alternative procedure to estimate models with high-dimensional fixed effects. IZA Discussion paper 3935

  • Head K, Mayer T (2013) Gravity equations: workhorse, toolkit, and cookbook. Center for Economic Policy Research 9322

  • Head K, Ries J (2001) The erosion of colonial trade linkages after independence. Am Econ Rev 91(4):858–876

    Article  Google Scholar 

  • Irwin DA, O’Rourke KH (2011) Coping with shocks and shifts: The multilateral trading system in historical perspective. Technical Report w17598, National Bureau of Economic Research

  • Jacks DS (2009) On the death of distance and borders: evidence from the nineteenth century. Econ Lett 105(3):230–233

    Article  Google Scholar 

  • Jacks DS, Meissner CM, Novy D (2010) Trade costs in the first wave of globalization. Explor Econ Hist 47(2):127–141

    Article  Google Scholar 

  • Jacks DS, Meissner CM, Novy D (2011) Trade booms, trade busts, and trade costs. J Int Econ 83(2):185–201

    Article  Google Scholar 

  • Kim CJ, Nelson CR (1999) State-space models with regime switching: classical and Gibbs-sampling approaches with applications. MIT Press, Cambridge

    Google Scholar 

  • Klasing MJ, Milionis P (2014) Quantifying the evolution of world trade, 1870–1949. J Int Econ 92(1):185–197

    Article  Google Scholar 

  • Lampe M, Sharp P (2013) Tariffs and income: a time series analysis for 24 countries. Cliometrica 7(3):207–235

    Article  Google Scholar 

  • Lampe M, Sharp P (2015) Cliometric approaches to international trade. In: Diebolt C, Haupert M (eds) Handbook of cliometrics. Springer, Berlin

    Google Scholar 

  • Larch M, Norbäck PJ, Sirries S, Urban D (2013) Heterogeneous firms, globalization and the distance puzzle. Technical Report, IFN Working Paper

  • Leamer EE, Levinsohn J (1995) International trade theory: the evidence. Handb Int Econ 3:1339–1394

    Article  Google Scholar 

  • Lin F, Sim N (2012) Death of distance and the distance puzzle. Econ Lett 116(2):225–228

    Article  Google Scholar 

  • Mongelli FP, Dorrucci E, Agur I (2005) What does european institutional integration tell us about trade integration. European Central Bank Occasional Paper Series 40

  • Morgenstern O (1962) On the accuracy of economic observations. Princeton University Press, Princeton

    Google Scholar 

  • Newman M (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  Google Scholar 

  • O’Rourke K (2009) Politics and trade: lessons from past globalisations. Technical Report, Bruegel

  • O’Rourke KH, Williamson JG (2004) Once more: When did globalisation begin? Eur Rev Econ Hist 8(1):109–117

    Article  Google Scholar 

  • Rayp G, Standaert S (2015) Measuring actual integration: An outline of a bayesian state-space approach. In: Lombaerde PD, Saucedo E (eds) Indicator-based monitoring of regional economic integration, UNU series on regionalism. Springer, Dordrecht

  • Sarkees MR, Wayman F (2010) Resort to war: 1816–2007. CQ Press, Washington, DC

    Google Scholar 

  • Schiff M, Carrere C (2003) On the geography of trade: distance is alive and well. Available at SSRN 441467

  • Siliverstovs B, Schumacher D (2009) Disaggregated trade flows and the missing “globalization puzzle”. Econ Int 115(3):141–164

    Google Scholar 

  • Silva JS, Tenreyro S (2006) The log of gravity. Rev Econ Stat 88(4):641–658

    Article  Google Scholar 

  • Singer JD, Bremer S, Stuckey J (1972) Capability distribution, uncertainty, and major power war, 1820–1965. In: Russet B (ed) Peace, war, and numbers. Sage, Beverly Hills, pp 19–48

    Google Scholar 

  • Standaert S (2014) Divining the level of corruption: a bayesian state-space approach. J Comp Econ. doi:10.1016/j.jce.2014.05.007

  • Williamson SH (2015) What was the U.S. GDP then? Technical Report, Measuring Worth

Download references


We would like to thank Guillaume Daudin, Luca De Benedictis, Kevin O’Rourke and Eric Vanhaute for their feedback and suggestions, as well as the Flemish Supercomputer Center for allowing us access to its infrastructure. Funding for this research was provided by the Research Foundation - Flanders and the Belgian National Bank.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Samuel Standaert.


Appendix 1: Data sources and transformations

See Table 2.

Table 2 Data sources and transformations

Appendix 2: Estimating the state-space model

To estimate the state-space model, we need to solve for the structural parameters of the state-space model (\(C\), \(Z\), \(T_t\), \(H\) and \(Q\)) as well as the level of trade integration (\(hti\)). While it is possible to maximize the combined distribution numerically for small datasets, using a Gibbs sampler simplifies the estimation procedure considerably by splitting up the process into conditional probabilities.

For example, say we have to draw from the joint probability of two variables \(p(A,B)\), when only the conditional probability of \(p(A|B) \) and \(p(B|A)\) are known. Starting from a (random) value \(b_0\), the Gibbs sampler will draw a first value of A conditional on \(B^{(0)}\): \(A^{(1)} \sim p(A|B^{(0)})\). Conditional on this last draw, a value of B is drawn (\(B^{(1)} \sim p(B|A^{(1)})\)) which is in turn used to draw a new value for A (\(A^{(2)} \sim p(A|B^{(1)})\). This process is repeated thousands of times, until the draws from the conditional distributions have converged to those of the combined distribution \(p(A,B)\). After discarding the unconverged draws (the burn-in), the remaining draws of A and B can be used to reconstitute their respective (unconditional) distributions.

Because we are using a Bayesian analysis framework, we have to be explicit about the prior distribution of the parameters. In other words, we have to state what we know about their distribution before looking at the data. Because there is no prior information, we imposed flat priors on \(Z\), \(C\) and \(log(H)\), meaning that all values in the real space (or real positive space for the variance H) are equally probable.

In the case of the state-space model, the Gibbs sampler consists of two main blocks (Kim and Nelson 1999):

  1. 1.

    If the level of trade integration (\(hti\)) was known, the parameters of the measurement and state equations (Eqs. 1 and 2) could be obtained using simple linear regressions. To ensure the model is identified, the variance of the error term of the state equation (\(Q\)) is typically set to 1. Taking, for example, the situation, where there is only one dyad to simplify notation: \(hti = (hti_{1},\ldots , hti_{n})'\)

    $$ p(T|hti)\propto .5 * 1\!\!1_{|T|\le 1} * N(b_T, v_T) $$
    $$ p(Z^k,C^k|hti,y,H)\propto N(b^k_{Z,C}, v^k_{Z,C}) $$
    $$ p(H_{(k,k)}|hti,y)\propto iWish[e^{k\prime } e^k ; \; n ] $$

    with \(v_T = (T_{t-1}'T_{t-1})^{-1}\); \(b_T = v_T * T_{t-1}'T_t\); \(v^k_{Z,C} = (hti'hti)^{-1}*H_{(k,k)}\); \(b^k_{Z,C} = (hti'hti)^{-1} * hti'y^k\); \(e^k = y^k - C^k - Z^k * hti\); and \(iWish\) the inverse Wishart distribution.

  2. 2.

    Conditional on the parameters of the state and measurement equations, the distribution of \(hti\) can be computed and drawn using the Carter and Kohn (1994) simulation smoother.

    • The Kalman filter: computes the distribution of \(hti\) conditional on the information in all previous years. Starting from a wild guess, \(p(hti_0) = N(0,\infty )\), the following equations are iteratively solved for \(t = 1\) to \(t = n\):

      $$\begin{aligned} a_{t|t}&= E(hti_t | y_1, \ldots , y_t) \nonumber \\&= T*a_{t-1|t-1} + \kappa (y_t - C - Z T a_{t-1|t-1}) \end{aligned}$$
      $$\begin{aligned} p_{t|t}&= V(hti_t | y_1, \ldots , y_t) \nonumber \\&= p_{t|t-1} + \kappa Z p_{t-1|t-1} \end{aligned}$$

      with \(\kappa = p_{t|t-1} Z'(Z p_{t|t-1} Z' + H)^{-1}\); and \(p_{t|t-1} = Tp_{t-1|t-1}T'+Q \).

    • Simulation smoother: Draws from the distribution of \(hti\) conditional on all information in the data and the previous draws. Starting from the last iteration of the Kalman filter, draw \(\hat{hti}_n\) from \(N(a_{n|n}; \;p_{n|n})\) and iterate backwards from \(t=n-1\) to \(t=1\):

      $$\begin{aligned} a_{t|n}&= E(hti_t | y_1, \ldots y_n) \nonumber \\&= a_{t|t} + \varsigma (\hat{hti}_{t+1} - Ta_{t|t}) \end{aligned}$$
      $$\begin{aligned} p_{t|n}&=V(hti_t | y_1, \ldots y_n) \nonumber \\&=p_{t|t} + \varsigma (p_{t+1|n} - Tp_{t|t}T'-Q)\varsigma ' \end{aligned}$$

      with \(\varsigma = p_{t|t}T'p_{t+1|t}^{-1}\); and \(\hat{hti}_{t+1}\) a random draw from \(N(a_{t+1|n}; \; p_{t+1|n})\).

Appendix 3: The historical trade network

In order to combine the historical trade integration indices into a network, the index values corresponding to countries that are integrated need to be separated from those corresponding to countries that are not. A natural way of making this distinction is to contrast countries that trade with each other (\(X_{ij,t} > 0\)) to those that do not (\(X_{ij,t} = 0\)). The problem is that this approach is skewed by a large number of very small nonzero trade flows.

Rather than choosing an arbitrary cut-off value, the hti allows us to use significant differences to determine which countries are linked. To start, we used the estimates of the structural parameters of the state-space model to generate index values for a fictional dyad where trade was zero for the entire period. Labeling these observations as \(hti_{0,t}\), we defined significant levels of trade in the following way: An edge \(e\) from country \(i\) to country \(j\) exists if, and only if, its level of trade in year \(t\) is significantly higher than that of \(hti_{0,t}\): \(e_{ij,t} = 1 \iff \, hti_{0,t} < hti_{ij,t}\) in at least 99 % of all iterations of the (converged) Gibbs sampler. Using the \(hti_{0,t}\) definition, 115,911 edges were identified (6.3 % of observations).

Panel a of Fig. 7 shows the overall network density (the fraction of dyads that are connected) gradually decreasing throughout the first globalization wave. In contrast, the trade network becomes increasingly connected during the second globalization wave. As can be seen in panel b, the number of trade links (edges) more or less continuously grows over the entire time-period and is initially offset by the rapid rise in the number of countries. This is especially noticeable when the Soviet Block breaks up in the 1990s, causing a rapid downward shift in the network density.

Fig. 7
figure 7

Network density (a) and the number of nodes and edges (b) over time. a Density. b Number of nodes and edges

Similar to the distance regressions, the density was also computed when the number of countries was kept constant using the 1880 and 1950 subsets. This reveals that the decrease in density during the first globalization wave was driven by the addition of new countries. When this is kept constant, the network density almost doubles during the first wave. In addition, it reinforces the effects of the 1930 and 2008 economic crises, both causing a substantial drop in the density. To ensure that these results were not driven by the inclusion of the colonial trade data, the density was also computed using only the official countries according to the COW state system dataset. However, this did not significantly alter the conclusion (available upon request). In other words, once the density is corrected for the increasing number of countries, it conforms to the globalization pattern found in the literature.

Appendix 4: Estimating models with high-dimensional fixed effects

Following Guimarães and Portugal (2009), the number of fixed effects can be reduced by half by first demeaning both dependent and explanatory variables in the sender-year dimension, leaving only the sender-target dummies. Using conditional probabilities, the fixed effects (\(c_i\)) can be separated from the explanatory variables (\(X_{i,t}\)), which significantly reduces the size of the matrix that needs to be inverted.

$$ y_{i,t} = c_i + X_{i,t} \beta + \epsilon _{i,t} \quad \hbox {with } \epsilon _{i,t} \sim N(0,\sigma ^2) $$

Equation 15 can be estimated using a three-step Gibbs sampling procedure. For example, when using flat (uninformative) priors, the conditional probabilities are:

  1. 1.

    \(\beta | c_i, \sigma ^2 \sim N(e_\beta ,v_\beta )\)

    \(e_\beta = (X'X)^{-1}(X'(y-c))\) with \(\{X\}_{i,t} = X_{i,t}\) and \(\{y-c\}_{i,t} = y_{i,t}-c_i\)

    \(v_\beta = \sigma ^2 (X'X)^{-1}\)

  2. 2.

    \(c_i | beta, \sigma ^2 \sim N(\bar{c_i},\sigma ^2/n)\)

    \(\bar{c_i} = \sum ^n_t(y_{i,t} - X_{i,t}\beta )/n\) with \(n\) the number of observations of country \(i\)

  3. 3.

    \(\sigma ^2 | beta, c_i \sim \hbox {iWishart}(e'e,N)\)

    \(e = y_{i,t} - c_i - X_{i,t}\beta \)

Appendix 5: Country subsets

Group 1: included in 1880< and 1950<
Algeria Egypt Italy Romania
Argentina El Salvador Jamaica Russia
Ascension Falkland Isl. Japan Senegal
Australia Fiji Liberia Sierra Leone
Austria Finland Luxembourg Singapore
Barbados France Macau South Africa
Belgium French Guiana Madagascar Spain
Belize Germany Maldives Sri Lanka
Bermuda Ghana Malta St. Pierre and Miquelon
Bolivia Gibraltar Mauritius Suriname
Brazil Greece Mexico Sweden
Bulgaria Guadeloupe Morocco Switzerland
Canada Guatemala Mozambique Thailand
Chile Guyana Netherlands Trinidad and Tobago
China Haiti New Zealand Tunisia
Colombia Honduras Nicaragua Turkey
Costa Rica Hong Kong Norway UK
Cuba Iceland Paraguay USA
Denmark India Peru Uruguay
Dominican Rep. Indonesia Philippines Venezuela
Dutch Antilles Iran Portugal Yugoslavia
Group 2: included in 1950<
Afghanistan Djibouti Lebanon Saint Lucia
Albania Dominica Lesotho Saint Vincent
American Samoa Equatorial Guinea Libya Samoa
Angola Eritrea Lithuania Sao Tome and Principe
Antigua and Barbuda Estonia Malawi Saudi Arabia
Bahamas Ethiopia Malaysia Seychelles
Bahrain Faroe Islands Mali Solomon Islands
Bangladesh French Polynesia Mauritania Somalia
Benin Gabon Mongolia South Korea
Bosnia Gambia N. Mariana Isl. St. Kitts and Nevis
Botswana Greenland Namibia Sudan
Brunei Grenada Nauru Swaziland
Burkina Faso Guam Nepal Syria
Burma Guinea New Caledonia Tanzania
Burundi Guinea-Bissau Niger Togo
Cambodia Hungary Nigeria Tonga
Cameroon Iraq North Korea Tuvalu
Cape Verde Ireland Oman UAE
Central African Rep. Israel Pakistan Uganda
Chad Jordan Palestine Vanuatu
Comoros Kenya Panama Vietnam
Congo, Rep. Kiribati Papua New Guinea Wallis and Futuna
Congo, Dem. Rep. Kuwait Poland Yemen
Cote d’Ivoire Laos Qatar Zambia
Cyprus Latvia Rwanda Zimbabwe

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Standaert, S., Ronsse, S. & Vandermarliere, B. Historical trade integration: globalization and the distance puzzle in the long twentieth century. Cliometrica 10, 225–250 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Trade integration
  • Globalization
  • Distance puzzle
  • State-space model

JEL Classification

  • F15
  • C4
  • F14