**Table 3. Tariff and Non Tariff Measures in BICs -  Total Trade**

|||||||_Weig_|_hted Aver_|_aged Tar_|_iffs in Tot_|_al Trade_|
|---|---|---|---|---|---|---|---|---|---|---|
||_1990_|_1995_|_2000_|_2001_|_2002_|_2003_|_2004_|_2005_|_2006_|_2007_|
|Brazil|19.0|12.7|12.7|10.4|10.0|9.4|9.0|8.5|8.5|8.7|
|China|32.2|23.8|14.7|14.1|10.3|6.5|6.0|4.9|4.4|4.7|
|India|49.6|23.2|27.5|26.5|25.3|24.1|22.8|13.4|11.9|10.4|
|Korea|9.5|6.8|7.2|8.6|10.0|9.6|9.2|8.3|7.4|7.4|
||||||_OTRI_<sup>_1_</sup> _( _|_MFN app _|_lied tariffs _|_plus Non_|_Tariff M_|_easures)_|
|||||_2001_||||_2005_|_2006_||
|Brazil||||27.2||||22.3|21.9||
|China||||21.1||||11.2|10.1||
|India||||32.2||||19.7|21.9||
|Korea||||10.0||||-|10.0||

Source: World Integrated Trade Solution and World Trade Indicators 1OTRI (MFN applied tariff+NTMs)- The OTRI is expressed as a tariff rate and applies to all goods. This index summarizes the impact of each country's non-discriminatory trade policies on its aggregate imports. It is the uniform equivalent tariff that would maintain the country’s aggregate import volume at its current level (given heterogeneous tariffs) including non-tariff measures. It captures the trade distortions that each country’s MFN tariffs impose on its import bundle using estimated elasticities to calculate the impact of a tariff schedule on a country’s imports. These measures are based on actual or current trade patterns and thus do not capture restrictions facing new or potential trade. They also do not take into account domestic subsidies or export taxes.

Table 3 complements tariff information with a proxy for non-tariff measures (OTRI) and summarizes the trends in trade liberalization that increased competition in the BICs and Korea. Evidently, in the early 1990s, at 10 percent Korean tariff and non-tariff barriers were already quite low; they have stabilized since at those levels. In 2006, the OTRI rates in Brazil and India were twice as high as those of Korea and China. Evidently, the latter liberalized relatively rapidly while Brazil and India did it more slowly. If the industries of the three BICs were significantly and equally far from the Korean technological frontier, then Aghion et al.’s model would predict that liberalization would hurt these industries. Slower and belated competition without export orientation in Brazil and India perpetuated uncompetitive and inefficient industries.

Table 4 summarizes some of the outcome indicators of the main distance-shortening policies of the BICs and Korea. In the absence of a time series, we rely on recent statistics to track progress and identify several noteworthy issues that are highlighted in Table 4.

First, nearly every cell in table 4 is populated with statistics which suggests that government-led innovation interventions were at work in all four countries. Second, the data suggests that governments value the strategic-nature of distance reducing policies and are willing to spend on them. However, the data does not indicate that there is a formula for combining the precise mix of public and private interventions to spur innovation. A few examples are noted below.

Consider the case of R&D financing. Consistently high scores of GERD and BERD in Korea and China point to the importance of R&D expenditures for distance shortening policies. The university-industry linkages scores which suggest a more efficient role of the private sector 29