Resource misallocation denotes a situation in which capital and labor are poorly distributed so that less productive firms receive a larger share of capital and labor than they should according to their level of productivity. Such misallocation arises in the presence of distortions. For the purpose of this paper, misallocation implies that aggregate TFP could be higher given the same amount of capital, labor and firm-level TFP were financial frictions preventing factors of production from being allocated to their best use to be reduced or eliminated. For example, Vasconcelos (2017) and Busso et al. (2012) suggest that manufacturing TFP in Brazil could grow by 40 percent should distortions be reduced to benchmark US levels. This is a significant figure. The existence of financial frictions provides a rationale for government policies to reduce allocative inefficiency. One common policy intervention designed to improve access to credit for firms is the use of state-owned development banks and credit institutions, of which BNDES is a notable example.

Against this background, we want to test empirically the quantitative importance of financial frictions, access to BNDES loans and their interactions for the determination of the dispersion of distortions. While not original and only suggestive, our analysis aims to uncover additional channels through which BNDES can affect aggregate outcomes, therefore adding to the extant literature reviewed in the previous section. We do not, however, consider in our analysis the cost of setting up and running BNDES and the fiscal cost of subsidized credit; hence, our study is not an impact assessment.

BNDES provides a wide range of tools to support Brazilian firms. Our focus is on FINAME, the largest subsidized credit scheme aimed to support the acquisition of new domestically produced machines and equipment targeting firms of different size. The FINAME program is very large and accounts for approximately 45 percent of mean firm investment outlays in manufacturing firms. FINAME loans have, therefore, the potential to affect firms’ productivity since their extension is guided by improvements in production and/or creation of new and/or more sophisticated goods.

The analysis covers the period from 2003 to 2014, when BNDES significantly increased its role in the Brazilian economy, as discussed above. Data were drawn from the annual surveys of manufacturing (Pesquisa Industrial Annual; PIA) of the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística; IBGE). The PIA longitudinal sampling frame covers firms with more than 30 employees only; hence, our analysis is limited to medium-sized and large firms. During the period under analysis, about 4,800 firms received BNDES/FINAME loans per year in the manufacturing sector, whereas 40,000 firms per year on average were available to construct the counterfactual group.

Although the treated group had a reasonable size, potential drawbacks remain. First, we do not have clear policy changes to provide a natural experiment for our data frame. For example, the PSI policy experiment with reduced rates was horizontal across sectors and reached all firm types. Second, any of the firms under analysis (both granted and not) may be affected by other government interventions apart from BNDES loans. However, BNDES is the largest financial institution offering subsidized loans for long-term projects, which are the main type of subsidies to affect firms’ productivity. Third, there may be a time lag for any impact to be detected since outcomes do not necessarily appear immediately after the loan has been granted. Yet BNDES loans typically encompass six months before the project may 9