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Receiver Profiles (CNAE) and DICT Users

Source: vignettes/receiver-profiles-dict-users.Rmd
receiver-profiles-dict-users.Rmd

This vignette covers the two endpoints added to the BCB PIX Open Data API and supported since pixr 0.1.1:

  • get_pix_dict_users(): the monthly series of users with at least one PIX key registered in the DICT (Directory of Transactional Account Identifiers), split into individuals (PF) and legal entities (PJ).
  • get_pix_cnae_stats(): PIX transaction statistics profiled by the receiver: economic activity (CNAE section), company size, and MEI (individual microentrepreneur) status.

Users registered in the DICT

Unlike the other endpoints, PixUsuariosCadastradosDICT takes no reference-date parameter: a single request returns the full monthly series. Use orderby and top to narrow it:

users <- get_pix_dict_users(orderby = "DataGraficosPix desc", top = 6)
users
#> # A tibble: 6 x 4
#>   DataGraficosPix qtdUsuariosPessoaFisica qtdUsuariosPessoaJuridica qtdUsuariosCadastrad...
#>   <date>                            <dbl>                     <dbl>                  <dbl>
#> 1 2026-09-30                    168245596                  18729138              186974734
#> 2 2026-08-31                    167721822                  18557780              186279602
#> 3 2026-07-31                    167141580                  18496255              185637835
#> 4 2026-06-30                    166529238                  18338313              184867551
#> 5 2026-05-31                    165981454                  18136372              184117826
#> 6 2026-04-30                    165307979                  17911400              183219379

The column DataGraficosPix is parsed as a Date (last day of each month), so the series is ready for time arithmetic and plotting:

users |>
  arrange(DataGraficosPix) |>
  mutate(
    monthly_growth = qtdUsuariosCadastradosDICTTotal -
      lag(qtdUsuariosCadastradosDICTTotal)
  ) |>
  select(DataGraficosPix, total = qtdUsuariosCadastradosDICTTotal, monthly_growth)
#> # A tibble: 6 x 3
#>   DataGraficosPix     total monthly_growth
#>   <date>              <dbl>          <dbl>
#> 1 2026-04-30      183219379             NA
#> 2 2026-05-31      184117826         898447
#> 3 2026-06-30      184867551         749725
#> 4 2026-07-31      185637835         770284
#> 5 2026-08-31      186279602         641767
#> 6 2026-09-30      186974734         695132

Plotting the full series:

library(ggplot2)
library(scales)

get_pix_dict_users() |>
  ggplot(aes(DataGraficosPix, qtdUsuariosCadastradosDICTTotal)) +
  geom_line(linewidth = 1) +
  scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  labs(
    x = NULL, y = "Users registered in DICT",
    title = "PIX adoption: users with at least one registered key"
  ) +
  theme_minimal()

Receiver profiles: CNAE section, company size, and MEI

get_pix_cnae_stats() profiles the receiving side of PIX transactions: which economic sectors get paid, how large the receiving companies are, and whether the receiver is an individual microentrepreneur (MEI).

pix_columns("cnae")

The database parameter selects a snapshot, not a month

As with get_pix_transactions_by_municipality(), the required database parameter selects the data snapshot, and the response can contain rows for several reference months (AnoMes). To analyze a single month, add an AnoMes filter:

cnae <- get_pix_cnae_stats(
  database = "202506",
  filter = "AnoMes eq 202506"
)

Where do MEI receive PIX payments? 

mei <- get_pix_cnae_stats(
  database = "202506",
  filter = "AnoMes eq 202506 and mei eq 'S'"
)

mei |>
  group_by(cnssecao) |>
  summarise(
    total_value = sum(vllanliq),
    transactions = sum(qtlanliq),
    .groups = "drop"
  ) |>
  arrange(desc(total_value)) |>
  slice_head(n = 5)
#> # A tibble: 5 x 3
#>   cnssecao                                                        total_value transactions
#>   <chr>                                                                 <dbl>        <dbl>
#> 1 Pessoa fisica                                                 129893546653.    545210144
#> 2 G - Comercio, reparacao de veiculos automotores e motocicletas  8253029010.     56506481
#> 3 I - Alojamento e alimentacao                                    2488863327.     31068838
#> 4 H - Transporte, armazenagem e correio                           2474709446.      7724758
#> 5 S - Outras atividades de servicos                               2317447612.     13550221

Value received by company size 

get_pix_cnae_stats(
  database = "202506",
  filter = "AnoMes eq 202506 and tipopessoa eq 'PJ'"
) |>
  group_by(porte) |>
  summarise(
    total_value = sum(vllanliq),
    transactions = sum(qtlanliq),
    .groups = "drop"
  ) |>
  arrange(desc(total_value))
#> # A tibble: 5 x 3
#>   porte         total_value transactions
#>   <chr>               <dbl>        <dbl>
#> 1 Demais            1.06e12   1141105592
#> 2 Microempresa      2.57e11   1190137127
#> 3 Pequeno Porte     1.14e11    348707396
#> 4 Outros            7.01e 7       119364
#> 5 NA                1.57e 6          930

Note how micro companies receive more transactions than all larger companies combined (“Demais”), while the larger companies concentrate most of the value – a typical signature of PIX as a retail payment method.

Other useful filters 

# Only Pix Saque / Pix Troco (cash withdrawal) transactions
saque <- get_pix_cnae_stats(
  database = "202506",
  filter = "AnoMes eq 202506 and finalidade ne 'Pix'"
)

# Payments between accounts of the same person
mesma_titularidade <- get_pix_cnae_stats(
  database = "202506",
  filter = "AnoMes eq 202506 and mesmatit eq 'S'"
)

Learn more