|**Table 3: Examples**<br> <br>|**of aggregati**<br> <br>|**ng different sets of policy restrictions (**ρ= 3)<br>|
|---|---|---|
|**Example set of**<br>**measure scores**<br>**(**mi**)**|**Aggregate**<br>**index**<br>**(**SS<sup>k</sup>**)**|<br>**Comment**|
|0+0+0+0.50|0.50|The aggregate STRI is driven by actual restrictions and<br>independent of the number of measures_not applied_in a given<br>context.|
|0.50+0.50|0.63|Adding a major restriction (0.5) to an already existing major<br>restriction increases restrictiveness, but by less than 0.5.|
|0.50+0.50+0.50|0.72|The marginal contribution to restrictiveness is falling: the<br>second 0.5 measure adds 0.13 and the third 0.5 measure adds<br>0.09. Taken together, the presence of three such restrictions<br>is roughly equivalent to the presence of one very restrictive<br>measure that would be scored 0.75 on a stand-alone basis.|
|0.25+0.25+0.50<br>|0.54<br>|With two 0.5 measures in place, adding a third such measure<br>adds merely 0.09 to overall restrictiveness (previous row),<br>whereas with two 0.25 measures in place (aggregating to<br>0.32), adding a 0.5 measure adds 0.22, commensurate with<br>the fact that this addition renders the bundle considerably more<br>restrictive. Viewed from a different angle, adding two minor<br>0.25 measures to a pre-existing 0.5 measure leaves<br>restrictiveness virtually unchanged, because minor restrictions<br>matter lessinthe presence ofamajor restriction.<br>|
|Large values of the<br>are mostly substitut<br>as quotas and econo<br>already applies to en<br>increase overall rest<br>captured by the quo<br>with a high value of<br>least in part substitu<br>Conversely, measur<br>|ρparameter a<br>able. For insta<br>mic needs tes<br>try in a given<br>rictiveness by<br>ta measure.<br> ρ. More gen<br>tes should be<br>es that are re<br>|re appropriate for aggregation if the policy measures in question<br>nce, different forms of quantitative market entry limitations such<br>ts are likely to exert a similar effect on market entry. If a quota<br>sector and mode, then applying an economics needs test may not<br>much, since the main contribution to restrictiveness is already<br>This result will be achieved by aggregating both measure scores<br>erally, measures with similar effects that could be considered at<br>combined using a high value ofρ.<br>garded as adding significantly to the restrictiveness of already<br>|

Conversely, measures that are regarded as adding significantly to the restrictiveness of already applied measures, e.g. operational restrictions that apply after any market entry limitations, would call for a lower value of the aggregation parameter, . As is well known, CES functions of the type outlined in equation (1) can be nested to accommodate ρρ As is well known, CES functions of the type outlined in equation (1) can be nested to accommodate different layers of aggregation, with each nest potentially having a different CES parameter.<sup>25</sup> We take advantage of this modular property and break the aggregation of scores across the universe of all measures into several steps, so that bundles of policy measures that bear a similar relationship with each other in terms of substitutability or complementarity correspond to a CES nest with a suitably chosen parameter.

> 25 For instance, in modeling production processes, it is not uncommon to have an upper-layer CobbDouglas version for combining a numeraire good with a bunch of differentiated intermediate inputs, which emerges in equation (1) as the limit case of going to zero, and another nest that would aggregate the differentiated inputs using the CES structure within that Cobb-Douglas aggregator. ρρ

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