# **B Data Quality**

This section briefly describes some aspects of the quality of our newly created dataset on local procurement and budget execution. Despite the large effort in harmonizing a variety of original sources as described in Section 2, there are still some issues that researchers using our data should be aware of.

First, we plot the percentage of observations we cannot uniquely identify for each table in Figure B.1, Figure B.2, Figure B.3, and Figure B.4. After harmonizing and stacking each state’s information we create unique identifiers for each observation. For example, for the _tender table_ , to create the id_licitacao_bd variable, we concatenate the following original variables: tender number, agency identifier, and state identifier.<sup>1</sup> This identifier can be null in rows where (1) any of these components are missing or (2) this combination of variables still does not uniquely identify it. In Figure B.1 we report that CE was the only state in which we could not uniquely identify all observations - the percentage of null tenders identifiers exceeds 3% in 2010 but later remains stable around 1%.

Similarly, for the _commitment_ table, to create the id_empenho_bd variable, we concatenate the following original variables: number, agency identifier, unit identifier, municipality identifier, year, and month. In Figure B.2 we document that the vast majority of observations are uniquely identified, i.e. the percentage of null commitment identifiers is never above 2%. When adding the total value committed from observations with null identifiers this percentage is at most about 5% in SP between 2008 and 2012.

The situation is worse for verifications and payments. In Figure B.3 we report that is was impossible to uniquely identify observations in the states of CE and PB, while the state of RS has a percentage of up to 60% in the early years. For payments in Figure B.4 we show that the states of CE, RS, and SP have values up to 10%. In other states such as MG or PR we are able to fully identify observations.

Second, in Figure B.5 and Figure B.6 we plot the percentage of missing municipalities for each table and state. The relevant pattern observed is that for most states the number of missing municipalities is stable and close to zero over time. The exceptions are the state of CE, ranging around 5%-10% missing, the state of PE in 2013 in the _payment_ table, the state of PR in 2022 in the _payment_ table, and the state of PE in 2012 in the _tender_ and _tender-participant_ tables.

Finally, we compare the number of municipalities present in each of our budget execution tables to the number in SICONFI in Figure B.7, Figure B.8, and Figure B.9. We document that for most states and most years both datasets have similar coverage or at most a 10% difference. Some exceptions are the state of MG in 2014 or the state of RS in 2021, for which our dataset has very little coverage.

> 1The combination of these variables might vary depending on the state. In CE, for example, there is no agency identifier, instead we use the municipality identifier. Also, in RS, we had to include the year and an additional identifier for the type of purchase.

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