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Unfortunately, the data on capital per worker is not available for the CEE countries and most of their trading partners. Therefore, in our study we approximate capital-labour ratios in trading partners with their per capita GDP. 16 Data on GDP per capita also comes from the WDI CD-ROM and is expressed in constant 2000 US dollars and evaluated in PPP terms to enable its cross-country comparability. The remaining control variables include proxies for transportation and transaction costs. Distance between trading partners is measured as simple geographic “as the crow flies” distance between their capital cities and is expressed in kilometres. Distance data comes from the CEPII database available online at www.cepii.fr. In addition to the simple geographic proximity of trading partners we also include a dummy variable for the existence of a common border that takes the value 1 when countries share a common border, zero otherwise. We include two dummy variables for a common language of trading partners that take on value 1 when the same language used in both countries is spoken by at least 9 percent of the population, zero otherwise. Finally, to control for historical ties we include two dummy variables indicating whether trading countries were in a colonial relationship or had a common colonizer. In the context of Central and Eastern Europe a colonial relationship mostly means whether trading countries were the part of the Austro-Hungarian Empire that in the early 20 th century embraced a larger part of Central Europe. In a similar manner the common colonizer variable applies to the Eastern European countries that were the part of the Soviet Union until the early 1990s. Empirical results In this section we present two sets of empirical results based on estimating equation (1) that include the averaged estimates for the whole CEE sample as well as the estimates obtained separately for particular CEE countries. 16 The previous empirical research, for example studies by Hummels and Levinsohn (1995) and Evenett and Keller (2002), shows that capital per worker and GDP per capita are highly correlated. 35
Estimates for the whole CEE sample The estimation results obtained for bilateral imports of the CEE countries using different estimation methods are presented in Table 4. The baseline estimates obtained via the traditional OLS estimation method on the pooled dataset for all CEE countries used in our study with individual time effects for particular years of our sample are reported in column (1). 17 The estimation results indicate that both agreements with Western European countries: the EU Association and the EFTA agreements, as well as sub-regional intra-CEE agreements: BAFTA, CEFTA and bilateral agreements concluded between the members of BAFTA and CEFTA were effective in stimulating imports of the Central and East European countries. The evidence for other bilateral agreements is not clear cut. While the agreements concluded with the Mediterranean countries: Israel and Turkey appear to have increased bilateral imports of the CEE countries the estimates for other agreements are either not statistically significant or display negative signs. The robustness of the OLS estimates is subsequently investigated in columns (2)-(5) that display the estimation results obtained using various estimation techniques that allow us to exploit the panel properties of our dataset. In column (2) in addition to time specific effects we also control for individual country-pair random effects, in column (3) for individual country-pair fixed effects, respectively. However, it has been recently argued that these estimation methods might not be fully correct as specific fixed effects for both trading countries must be taken into account. The estimation results obtained using the fixed effects for both exporting and importing countries are presented in column (4). Finally, using this estimation method in column (5) we control for potential endogeneity of trade agreements using their lagged values. Table 4. The estimates for bilateral imports of the CEE countries OLS RE FE 2FE 2FE_IV GDP Partner 1.062 1.068 -1.396 -2.037 -1.956 [121.64]*** [47.87]*** [5.63]*** [6.28]*** [5.34]*** GDP Reporter 0.91 0.909 -6.826 -5.422 -4.241 [58.02]*** [21.93]*** [9.54]*** [6.02]*** [4.11]*** GDP pcap Partner 0.369 0.473 2.833 3.318 3.417 [19.54]*** [12.03]*** [11.60]*** [10.75]*** [10.16]*** 17 The F-tests for time specific effects confirm the appropriateness of including time dummies for particular years of our sample in all estimated regressions for the whole CEE sample. 36
Page 1 and 2: In collaboration with: F E M I S E
Page 3 and 4: Table of contents INTRODUCTION AND
Page 5 and 6: Introduction and summary In the pro
Page 7 and 8: that of the NMS, we would observe a
Page 9 and 10: accession to the EU. Instead, a num
Page 11 and 12: trade area (FTA). The Agadir Agreem
Page 13 and 14: liberalization has taken five years
Page 15 and 16: signed Europe Agreements, EFTA and
Page 17 and 18: used as industry organization varia
Page 19 and 20: The main goal of the project was to
Page 21 and 22: Chapter 1: Assessing trade liberali
Page 23 and 24: Agricultural Policy (CAP). Further
Page 25 and 26: Agreements concluded with the MPCs.
Page 27 and 28: n lnTijt = ∑ k= 1 β RTAijt + α1
Page 29 and 30: Preferential trade liberalization i
Page 31 and 32: the EU enlargement, the EFTA lost t
Page 33 and 34: The most important of these was the
Page 35: The Definitions of the Variables an
Page 39 and 40: agreements is, however, mixed. Whil
Page 41 and 42: The OLS estimates suggest that both
Page 43 and 44: Czech Republic Estonia Hungary Lith
Page 45 and 46: Czech Slovak Republic Estonia Hunga
Page 47 and 48: products. The provisions of the Eur
Page 49 and 50: foreseen to take effect in 2005. Ho
Page 51 and 52: Similar to the AMU, the ACC was for
Page 53 and 54: Unfortunately, the data on capital
Page 55 and 56: Table 2. The estimates for bilatera
Page 57 and 58: obust, while the dummy variable for
Page 59 and 60: Yi (partner) 0.944 1.009 2.267 1.96
Page 65 and 66: Agreements on the third countries a
Page 67 and 68: Chapter 2: Grossman-Helpman Model I
Page 69 and 70: N function (U ), given by: + ( ) u
Page 71 and 72: ti 1+ ti xi Ii −α i mi = ⋅ ,
Page 73 and 74: Probably the most extensive list of
Page 75 and 76: eginning of 1990. But the tariff st
Page 77 and 78: The non-preferential (MFN, conventi
Page 79 and 80: have important impact on political
Page 81 and 82: xi where ti is the tariff, ei is th
Page 83 and 84: We can recover the structural param
Page 85 and 86: Appendix Table 2 Estimation results
Page 87 and 88:
Table 4 Estimation results using mo
Page 89 and 90:
Testing Grossman-Helpman model for
Page 91 and 92:
practice most importers do not pay
Page 93 and 94:
countries were traditionally subjec
Page 95 and 96:
point to notify the US government o
Page 97 and 98:
and between 157% and 247% in 2004.
Page 99 and 100:
policy reforms. On the other hand i
Page 101 and 102:
Table 1 Estimation results for Isra
Page 103 and 104:
1995 0.00202 [2.04]** -0.00199 [2.0
Page 105 and 106:
extended to disaggregation of burea
Page 107 and 108:
The Importance of Corruption A subs
Page 109 and 110:
transition countries. Sekkat and Ve
Page 111 and 112:
society. In addition, they show tha
Page 113 and 114:
Johnson, Kaufmann, McMillan, and Wo
Page 115 and 116:
corruption. For example, upon grant
Page 117 and 118:
and criminal prosecution. On the ot
Page 119 and 120:
estimation are likely to be influen
Page 121 and 122:
levels have been used to derive fur
Page 123 and 124:
Union. In fact, the level of regula
Page 125 and 126:
Table 2 List of the Commonly Raised
Page 127 and 128:
Table 6 Predicted improvement (show
Page 129 and 130:
SB_PR all procedures required to re
Page 131 and 132:
AFGHANISTAN, ALBANIA, ALGERIA, ANGO
Page 133 and 134:
Brunetti, Aymo; Kisunko, Gregory an
Page 135 and 136:
Hines, J.R. (1995). Forbidden Payme
Page 137 and 138:
PellegriniI, Lorenzo and Gerlagh, R
Page 139:
World Bank (2005). Doing Business i
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