coverage, and then validated it against the standard aggregated public finance data source in Brazil.

We then illustrated the potential uses of this new data in two applications, uncovering new facts about local public finances in Brazil that are only measurable using the granular data we provide. First, we show that, on average, municipalities purchase 15%-25% of their goods and materials from suppliers located in the same municipality. Furthermore, we document wide variation behind that average - several municipalities purchase close to 50% from local suppliers. This average share is not meaningfully different when local officers use noncompetitive, discretionary tender methods. But it is systematically correlated with the size of municipalities – those with larger populations tend to buy more locally, perhaps reflecting the existence of a larger pool of suppliers.

Second, we produce new descriptive evidence on delays in payments to government suppliers. We show that approximately 15% of payments are delayed, meaning they are paid over the maximum allowed limit of 30 days. Furthermore, 20% of municipality-year observations have an average delay above 30 days – suggesting they are systematically late payers. Payment timeliness is also correlated with local per capita GDP, with higher-income municipalities paying their suppliers faster. These findings open several additional research questions. For example, if suppliers know that some municipalities tend to be late payers, they might include that "financial risk" of mismatched assets and liabilities in their decisions when selling to these governments and increase prices. Some firms, particularly smaller and liquidityconstrained ones, might also decide not to sell to these governments in order to avoid that financial risk. Other possible questions are whether these delays are systematically different depending on government functions (are health expenses paid faster than education for example?); whether they systematically vary with the business and/or political cycles<sup>28</sup> ; and whether some suppliers benefit from better payment terms than others (Ricca, 2019). All of these have important implications for competition and value-for-money in the public sector and deserve further investigation, which is possible using the granular data we provide.

Several other research questions related to local public finances can be explored using these novel data, particularly when matched with other administrative data available in Brazil. Ash et al. (2021), for example, combine the well-known audit courts data that reveal corruption at the municipal level (Ferraz and Finan, 2011) with aggregate data from SICONFI to predict out-of-sample corruption. The new dataset we provide could be used to compute additional measures of local budget and procurement decisions – such as payment delays or shares of purchases from local and/or politically connected firms – which could improve machine learning models used to predict mismanagement and corruption. The granular data available can also shed new light on how subnational entities adjust their expenditures throughout the business cycle, the political cycle (Foremny et al., 2018) and in response to fiscal rules that might 28In Figure A.6, we document that the share of late payments was much higher in the 2014-2016 period, when Brazil faced a severe recession, and then improved substantially in more recent years.

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