We adapt the procedure to account for our quality assessment of the data and for auxiliary data. We first review the quality of the data used to construct our initial estimate of the input-output table. To assess the quality, we evaluate whether we believe the data are representative of what they are supposed to report. That is, to what extent do we consider the underlying data complete and accurate?

We distinguish three levels of quality: high, medium and low. The purpose is to have more adjustments in data of low quality and fewer adjustments in data of high quality. This is one way (albeit indirect) of correcting for problems in data.

We begin with data of high quality. The first are data on imports and exports, which in turn are based on customs data. These data are typically collected with lots of detail and in a format consistent with input-output tables. The second are the data on total GDP. This data point can be and is calculated in several ways, and should be identical independently of the method used. This provides a check on the data. Moreover, GDP data is widely used and thus frequently reviewed.

As data of medium quality we include industrial structure, measured as output by sector. These data are in part based on commodity balances and on wage data by sector, both of which are typically collected with lots of details.

As data of low quality we include the structure of intermediate demand, and household, government, and investment expenditure. These data are to some extent based on surveys. In this sense they are less complete and must subsequently be scaled appropriately to become representative. Also, the surveys used are not always consistent with the classifications of the input-output table.

We account for data quality in the objective function by assigning weights to the data elements depending on quality. High, medium and low quality data are assigned the weights of 5, 3, and 1, respectively.

This setup implies that a given adjustment in data carry different penalties depending on what data element is adjusted. For example, a given adjustment in imports carries a five times higher penalty in the objective function than an identical adjustment in intermediate demand. Obviously, this results in more and larger adjustments in data with low weights (quality) than in data with high weights (quality). This is exactly the purpose of the design.

This brings us to our second adaptation of our balancing procedure: the use of auxiliary data. The use of weights implies that most adjustments will take place in intermediate demand<sup>39</sup> . Now, we want to avoid that intermediate demand is adjusted in an unrealistic way. To achieve this, we install lower and upper bounds based on GTAP data on the variables representing intermediate demand<sup>40</sup> .

Specifically, we aggregate version 7 of the GTAP database to the level of aggregation in the Armenian supply-use table. We then compute coefficients of intermediate demand by

> 39 Low weights also apply to several final demands, but they also make up GDP, which is assigned a high weight, and they are therefore less likely to change. Also, adjustments in intermediate demands may in some cases serve two purposes simultaneously: Reduce excess demand in one sector, and reduce excess supply in another sector. The reason is that intermediate demand is part of domestic supply. 40 See Narayanan and Walmsley (2008) for documentation of the GTAP data.

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