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Distance Sensitivity of Export: A Firm-Product Level Approach

Abstract

Recent literature suggests that the extent to which exports of a product is influenced by distance depends on the product characteristics. Differentiated products with non-standardised attributes are typically claimed to be more distance-sensitive as transactions should involve interactions between buyers and sellers. But the empirical evidence still finds conflicting results. Previous studies have examined the effect of distance on export values across different product groups. This paper employs a gravity model on Swedish firm-product level export data to analyse the effect of distance on the export decisions as well as export values, respectively. The focus is on how the influence of distance varies across differentiated and non-differentiated products. For both export participation and intensity decisions, the results are not in line with the network/search view and suggest that homogeneous products are more sensitive to distance than differentiated products when controlling for annual shocks and industry heterogeneity. Moreover, I find evidence of a learning effect from past trade experience.

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Notes

  1. Take, for example, a case of price comparison for personal notebooks. You would need to gather information on many things, including the screen size, processor speed, RAM capacity, hard-drive capacity and reading technology, graphics card and memory, operating system version, and manufacturer.

  2. Alternatively, the possibility set will explode as we add more dimensions into the consideration. Consider a set of only 500 firms with 100 possible products shipping to 165 countries in a 10-year period. The total number of observations in the dataset to work with is 82.5 million.

  3. One common issue that arises from this approach is the frequent occurrence of zero observations. The problem of frequent zeros is typical in trade data including this one, in which the zeros account for 94.7 % of total observations. The problem arises because the estimation model is in a linearised form (by logarithmic transformation), which lead any zeros in the original dataset undefined. There are several alternative estimation methods that deal with data with frequent zeros, for example zero-Inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), pseudo poisson maximum likelihood (PPML) (Santos Silva and Tenreyro 2006) but such models are mainly appropriate for count data and an evidence of superiority over Heckman is still debatable (Martínez-Zarzoso 2013; Martin and Pham 2008).

  4. Both outcome and selection equations can be either jointly estimated with maximum likelihood or as a two-step approach, with maximum likelihood in the first stage and normal OLS in the second. I rely on the first approach to follow Verbeek (2008) who points out that the OLS standard errors from the two-step estimator are incorrect, whereas the maximum likelihood provides a consistent and asymptotically efficient estimator.

  5. Otherwise, using all exporters will lead to total observations of 98,375,860, which deem impossible to perform an analysis.

  6. I also considered including a dummy indicating EU membership states to take into account the reporting policy that excludes any firms with annual imports from or exports to EU members below 1 million euros from the database. But due to a high collinearity between the EU dummy and the regional trade agreement dummy, I decide to drop the EU dummy.

  7. NACE is abbreviated for Nomenclature des Activités Économiques dans la Communauté Européenne or Classification of Economic Activities in the European Union.

  8. The databases are part of Microdata On-line Access (MONA) service provided by Statistics Sweden. All analyses are executed via a remote access to the station. For information regarding the access to the database, please refer to Statistics Sweden at www.scb.se.

  9. For this division, there are conservative and liberal classifications, in which the former minimises the number of products belonging to either organised exchange or reference priced and the latter maximises this number. This does not affect my results because I mainly look at homogeneous products as a whole.

  10. The classification is available through Jon Haveman’s website at http://www.macalester.edu/research/economics/PAGE/HAVEMAN/Trade.Resources/TradeData.html#Rauch.

  11. The conversion is from SITC to Harmonized System (HS) and lastly to CN, similar to Persson (2013) but the classification in this paper is based on SITC rev. 2 while Persson’s study is based on SITC rev. 3.

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Acknowledgements

I am grateful for the grant from Torsten Söderbergs Stiftelse and the support from Departments of Economics at Lund University and Jönköping International Business School and Ratio Institute. I also thank my supervisors, Prof. Fredrik Sjöholm, Prof. Martin Andersson, Assoc. Prof. Patrik Tingvall, and the colleagues. Comments from the Uddevalla Symposium and European Regional Science Association conferences, and the two anonymous referees are helpful for shaping the structure of this paper.

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Correspondence to Viroj Jienwatcharamongkhol.

Appendix

Appendix

A.1 Variable description

Variable Description Source Exp. sign
Export dummy Dummy taking value of 1 if the firm has positive export of a product to destination country at current year, 0 otherwise Author-generated  
Export value Total amount exported in current Swedish krona Statistics Sweden  
Export per sales A ratio of firm’s export value and total sales Statistics Sweden  
GDP Gross Domestic Product of destination country in current USD (in log). CEPII +
GDP per capita GDP per capita of destination country (in log). CEPII +
Distance Weighted distance as measured in km. from Sweden, calculated using great circle distance between major cities as weight (in log). CEPII
Contiguity Dummy taking value of 1 if the destination country shares border with Sweden. CEPII +
Landlocked Dummy taking value of 1 if the destination country does not have coastal line. CEPII
English dummy Dummy taking value of 1 if one of the official languages in the destination country is English. CEPII +
Regional trade agreement Dummy taking value of 1 if the regional trade agreement is in effect. CEPII +
Value-added Firm’s value-added per employee in SEK (in log and lagged 1 year). Statistics Sweden +
Human capital Fraction of employees graduated at university level (lagged 1 year). Statistics Sweden + / −
Domestic Corporation Dummy taking value of 1 if the firm belongs to a domestic corporation group Statistics Sweden +
Multinationals Dummy taking value of 1 if the firm belongs to a multinational enterprise Statistics Sweden +
Import dummy Dummy taking value of 1 if the firm import from destination country a year before. Author-generated +
Export country experience Dummy taking value of 1 if the firm already exported to the country previously. Author-generated +

A.2 Descriptive statistics

Variables Obs. Mean Std. Dev. Min Max
Export dummy 395,900 0.05 0.22 0 1
Export value 20,814 675,424.80 2,479,948 1 125,036,227
Export per sales 395,900 0.0001 0.002 0 0.80
GDP 395,361 910,098.90 2,149,115 367.2 13,201,819
GDP per capita 395,202 24,864.0 17,153.82 84.56 89,563.63
Distance 395,900 2,531.94 3,196.99 450.08 17,389.62
Contiguity dummy 395,900 0.25 0.43 0 1
Landlocked dummy 395,900 0.09 0.28 0 1
English dummy 395,900 0.15 0.36 0 1
RTA dummy 395,900 0.73 0.45 0 1
Value-Added 395,900 30,120.62 151,299.30 3 5,593,307
Human capital 395,900 0.06 0.11 0 1
Domestic Corp. 395,900 0.31 0.46 0 1
Multinational Corp. 395,900 0.32 0.47 0 1
Import dummy 395,900 0.63 0.48 0 1
Country experience 395,900 0.56 0.50 0 1
  1. The mean value for dummy variables indicates the percentage of 1’s.

A.3 Participation of Swedish exports

SNI Industry Total producers Exporters* (%) Exported** (%) Average products Average destinations
15 Food products; beverages and tobacco 1296 18.9 17.57 11.08 6.97
16 Tobacco products 3 33.33 3.58 23.82 29.82
17 Textiles and textile products 380 41.84 18.58 12.16 8.10
18 Wearing apparel; dressing and dyeing of fur 102 51.96 26.6 32.83 7.57
19 Leather; luggage, handbags, and footwear 65 58.46 19.98 8.23 5.71
20 Wood and wood products except furniture 1540 31.75 25.32 4.94 5.23
21 Pulp, paper and paper products 218 78.44 31.96 11.55 17.86
22 Publishing, printing and reproduction of recorded media 1958 18.74 5.03 4.75 4.36
23 Coke, refined petroleum products and nuclear fuel 16 56.25 49.21 14.70 11.29
24 Chemicals, chemical products and man-made fibres 308 75 32.24 20.74 16.37
25 Rubber and plastic products 718 58.91 23.15 9.17 9.05
26 Other non-metallic mineral products 401 39.9 18.14 10.07 8.34
27 Basic metals 226 64.6 35.07 15.98 15.92
28 Fabricated metal products except machinery 4272 27.88 16.04 6.70 5.98
29 Machinery and equipment n.e.c. 2069 48.53 29.55 13.79 13.41
30 Office machinery and computers 90 36.67 34.4 13.31 14.15
31 Electrical machinery and apparatus n.e.c. 527 49.91 21.03 12.62 10.27
32 Radio, television and communication equipment and apparatus 192 45.83 31.05 15.47 10.29
33 Medical, precision and optical instruments, watches and clocks 747 37.88 36.77 15.53 16.97
34 Motor vehicles, trailers and semi-trailers 366 59.56 26.15 20.76 9.42
35 Other transport equipment 353 36.26 28.54 15.17 7.35
36 Furniture; manufacturing n.e.c. 859 45.52 18.27 8.52 6.88
  Average 759 46.19 24.92   
  1. * Exporters’ share of total number of producers.
  2. ** Average share of exports per total firm’s sales.

A.4 Country list

ISO2 Country name Distance* ISO2 Country name Distance*
AE United Arab Emirates 4,859.49 DK Denmark 450.08
AF Afghanistan 4,644.21 DO Dominican Republic 8,006.54
AL Albania 1,995.41 DZ Algeria 2,709.28
AM Armenia 2,899.19 EC Ecuador 10,457.59
AN Netherland Antilles 8,441.07 EE Estonia 595.36
AO Angola 7,644.17 EG Egypt 3,412.79
AR Argentina 12,404.68 ER Eritrea 5,250.37
AT Austria 1,228.47 ES Spain 2,486.55
AU Australia 15,385.40 ET Ethiopia 5,847.94
AW Aruba 8,587.53 FI Finland 604.91
BA Bosnia & Herzegovina 1,644.60 FJ Fiji 15,252.19
BB Barbados 7,930.83 FO Faroe Islands 1,303.04
BD Bangladesh 6,912.31 FR France 1,616.32
BE Belgium 1,151.50 GA Gabon 6,577.58
BF Burkina Faso 5,408.34 GB United Kingdom 1,292.80
BG Bulgaria 1,912.32 GE Georgia 2,708.50
BH Bahrain 4,526.21 GH Ghana 6,005.78
BI Burundi 7,027.18 GI Gibraltar 2,956.84
BJ Benin 5,803.46 GL Greenland 3,368.65
BM Bermuda 6,456.30 GM Gambia 5,712.82
BN Brunei Darussalam 10,069.25 GN Guinea 5,966.61
BO Bolivia 11,201.18 GR Greece 2,353.03
BR Brazil 10,185.49 GT Guatemala 9,539.39
BS Bahamas 7,808.63 HK Hong Kong 8,368.68
BW Botswana 9,199.48 HN Honduras 9,338.07
BY Belarus 986.48 HR Croatia 1,519.27
CA Canada 6,347.80 HT Haiti 8,142.33
CG Congo 7,007.02 HU Hungary 1,315.38
CH Switzerland 1,422.90 ID Indonesia 10,632.05
CI Cte d’Ivoire 6,129.18 IE Ireland 1,549.43
CL Chile 12,956.19 IL Israel 3,315.60
CM Cameroon 5,907.75 IN India 6,308.11
CN China 7,276.97 IQ Iraq 3,552.56
CO Colombia 9,491.13 IR Iran 3,765.08
CR Costa Rica 9,629.91 IS Iceland 2,047.33
CU Cuba 8,246.69 IT Italy 1,833.43
CV Cape Verde 5,794.42 JM Jamaica 8,463.56
CY Cyprus 2,955.68 JO Jordan 3,358.22
CZ Czech Republic 1,009.36 JP Japan 8,226.76
DE Germany 929.32 KE Kenya 6,957.80
KH Cambodia 8,820.19 PL Poland 848.39
KP North Korea 7,371.20 PT Portugal 2,821.62
KR South Korea 7,682.77 PY Paraguay 11,477.31
KW Kuwait 4,107.62 QA Qatar 4,653.14
KY Cayman Islands 8,589.82 RW Rwanda 6,884.48
KZ Kazakstan 3,774.62 SA Saudi Arabia 4,479.74
LB Lebanon 3,148.39 SD Sudan 5,100.44
LC Saint Lucia 7,928.13 SG Singapore 9,782.64
LK Sri Lanka 7,849.86 SI Slovenia 1,420.52
LT Lithuania 676.56 SK Slovakia 1,176.30
LU Luxembourg 1,207.73 SL Sierra Leone 6,101.36
LV Latvia 591.22 SM San Marino 1,678.00
LY Libya 2,993.48 SN Senegal 5,613.46
MA Morocco 3,274.22 SO Somalia 6,638.56
MD Moldova, Rep.of 1,580.09 SR Suriname 8,366.51
MG Madagascar 9,152.54 SV El Salvador 9,548.48
MH Marshall Islands 12,283.25 SY Syrian Arab Republic 3,084.28
MK Macedonia 1,950.69 TC Turks & Caicos Is. 7,815.33
MO Macau (Aomen) 8,201.04 TG Togo 5,878.81
MT Malta 2,558.88 TH Thailand 8,415.42
MU Mauritius 9,593.82 TJ Tajikistan 4,346.91
MV Maldives 7,861.62 TK Tokelau 14,475.37
MW Malawi 8,326.36 TN Tunisia 2,582.25
MX Mexico 9,357.39 TO Tonga 15,710.15
MY Malaysia 9,568.98 TR Turkey 2,453.42
MZ Mozambique 9,058.94 TT Trinidad & Tobago 8,286.25
NA Namibia 8,993.66 TW Taiwan 8,551.70
NC New Caledonia 15,294.21 TZ Tanzania 7,468.98
NE Niger 5,062.04 UA Ukraine 1,616.60
NG Nigeria 5,721.76 UG Uganda 6,634.94
NI Nicaragua 9,522.18 US U.S.A. 7,440.51
NL Netherlands 1,009.40 UY Uruguay 12,286.37
NO Norway 502.69 UZ Uzbekistan 4,141.06
NP Nepal 6,223.75 VC St Vincent 8,018.46
NZ New Zealand 17,389.62 VE Venezuela 8,692.38
OM Oman 5,162.00 VG British Virgin Is. 7,718.33
PA Panama 9,511.23 VN Viet Nam 8,727.68
PE Peru 11,219.56 YE Yemen 5,474.30
PF French Polynesia 15,277.91 YU Serbia & Montenegro 1,686.69
PH Philippines 9,639.51 ZA South Africa 9,838.57
PK Pakistan 5,294.92 ZM Zambia 8,207.19
RO Romania 1,640.88 ZW Zimbabwe 8,722.59
RU Russian Federation 2,081.84   Total countries 165
  1. * Great-circle distance measured as km. from Sweden with major cities’ population as weight.
Table 6 Summary of signs and magnitude comparison for the estimated coefficients
Table 7 Distance coefficients from all model specifications

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Jienwatcharamongkhol, V. Distance Sensitivity of Export: A Firm-Product Level Approach. J Ind Compet Trade 14, 531–554 (2014). https://doi.org/10.1007/s10842-013-0169-6

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Keywords

  • Distance sensitivity
  • Export decisions
  • Gravity model
  • Firm-product level
  • Micro-data

JEL Classification

  • F12
  • F14
  • F41