Household month as unit of observation, "white-townsend" asset file with household monthly aggregated loan data.
Format
Household monthly asset and loans.
- id
Anonymized household ID (
tmid_hh). Matcheshhid_Numintstm_loans_panelfor households present in both files.- month
Survey month (unchanged; not an ID).
- agg_BS_1011
All rev-assets: livestock + household + Agri + business + land:
sum(agg_BS_08, agg_BS_10, agg_BS_11, agg_BS_12, agg_BS_13, na.rm = TRUE)- agg_BS_1012
Core 1 rev-assets: livestock + Agri + business + land, all are potentially revenue generating:
sum(agg_BS_08, agg_BS_11, agg_BS_12, agg_BS_13, na.rm = TRUE)- agg_BS_1021
Core 2 rev-assets: livestock + agri + business, exclude land:
sum(agg_BS_08, agg_BS_11, agg_BS_12, na.rm = TRUE)- agg_BS_1022
Core 3 rev-assets: agri + business, exclude livestock and land:
sum(agg_BS_11, agg_BS_12, na.rm = TRUE)- agg_BS_1023
Core 4 rev-assets: agri + business + land, exclude livestock :
sum(agg_BS_11, agg_BS_12, agg_BS_13, na.rm = TRUE)- agg_BS_1024
land alone:
sum(agg_BS_13, na.rm = TRUE)- agg_BS_1025
Livestock + agri assets combined, these are the combined agri assets:
sum(agg_BS_08, agg_BS_11, na.rm = TRUE)- agg_BS_2011
All agri-assets: livestock + agri + land:
sum(agg_BS_08, agg_BS_11, agg_BS_13, na.rm = TRUE)- agg_BS_2021
Core agri-assets: agri asset only:
sum(agg_BS_11, na.rm = TRUE)- agg_BS_3011
All biz-assets: household + business, household assets might faciliate business, a car for example.:
sum(agg_BS_10, agg_BS_12, na.rm = TRUE)- agg_BS_3021
core biz-assets: business asset only:
sum(agg_BS_12, na.rm = TRUE)- agg_BS_3022
core biz-assets: household asset alone:
sum(agg_BS_10, na.rm = TRUE)- agg_IS_1011
All income sum:
sum(agg_IS_07, na.rm = TRUE)- agg_IS_1012
Income minus wage:
sum(agg_IS_07, (-1) * agg_IS_05, na.rm = TRUE)- agg_IS_1013
income minus wage, minus other income, dont know what other income is:
sum(agg_IS_07, (-1) * sum(agg_IS_05, agg_IS_06, na.rm = TRUE ), na.rm = TRUE)- agg_IS_1021
income minus wage, minus other income, fish and livestock income:
sum(agg_IS_07, (-1) * sum(agg_IS_02, agg_IS_03, agg_IS_05, agg_IS_06, na.rm = TRUE), na.rm = TRUE)- agg_IS_1111
total income minus total costs:
sum(agg_IS_07, (-1) * agg_IS_16, na.rm = TRUE)- agg_IS_1112
total income minus total costs, excluding income and costs from labor:
sum(agg_IS_07, (-1) * (agg_IS_05), (-1) * (agg_IS_16), (agg_IS_14), na.rm = TRUE)- agg_IS_1113
total income minus total costs, excluding income and costs from labor and "others":
sum(agg_IS_07, (-1) * (agg_IS_05), (-1) * (agg_IS_06), (-1) * (agg_IS_16), (agg_IS_14), (agg_IS_15), na.rm = TRUE)- agg_IS_1114
Fish/shrimp + biz + cultivation only, no livestock:
sum(agg_IS_07, (-1) * (agg_IS_05), (-1) * (agg_IS_06), (-1) * (agg_IS_02), (-1) * (agg_IS_16), (agg_IS_14), (agg_IS_15), (agg_IS_09), na.rm = TRUE)- agg_IS_2011
cultivation + livestock + fish:
sum(agg_IS_01, agg_IS_02, agg_IS_03, na.rm = TRUE)- agg_IS_2012
net for (cultivation + livestock + fish):
sum(agg_IS_01, agg_IS_02, agg_IS_03, (-1) * (agg_IS_08), (-1) * (agg_IS_09), (-1) * (agg_IS_12), na.rm = TRUE)- agg_IS_2013
cultivation + livestock:
sum(agg_IS_01, agg_IS_02, na.rm = TRUE)- agg_IS_2014
net for cultivation + livestock:
sum(agg_IS_01, agg_IS_02, (-1) * (agg_IS_08), (-1) * (agg_IS_09), na.rm = TRUE)- agg_IS_2015
cultivation + shrimp:
sum(agg_IS_01, agg_IS_03, na.rm = TRUE)- agg_IS_2016
net for cultivation + shrimp:
sum(agg_IS_01, agg_IS_03, (-1) * (agg_IS_08), (-1) * (agg_IS_12), na.rm = TRUE)- agg_IS_2021
cultivation only:
sum(agg_IS_01, na.rm = TRUE)- agg_IS_2022
net for cultivation only:
sum(agg_IS_01, (-1) * (agg_IS_08), na.rm = TRUE)- agg_IS_3011
Business and labor income, labor if they pay themselves?:
sum(agg_IS_04, agg_IS_05, na.rm = TRUE)- agg_IS_3021
Pure business:
sum(agg_IS_04, na.rm = TRUE)- agg_IS_3022
Business revenue minus profits:
sum(agg_IS_04, (-1) * agg_IS_13, na.rm = TRUE)- agg_loanflow_all
Household-month total loan flow (all lenders); not an ID.
- agg_loanflow_inf
Household-month informal loan flow; not an ID.
- agg_loanflow_for
Household-month formal loan flow; not an ID.
Source
Regenerated by
vignettes/ffv_gen_asset_loan.qmd
(consolidates legacy
data-raw/ffs_hfid_gen_a_data.R and
data-raw/ffs_hfid_gen_b_data.R;
PrjThaiHFID-#5).
Upstream inputs:
data-raw/whitem160aggregate_wthhhkey_loanamount.rda (anonymized via
data-raw/id_anonymize/02c_anonymize_whitem160.R) and in-vignette
tstm_loans_amount (from data/tstm_loans.rda).
Set bl_replace_data_output <- TRUE in the vignette to overwrite
data/tstm_asset_loan.rda; FALSE writes to gitignored data-temp/ for
validation. The vignette also writes local-only CSV/DTA copies to
inst/extdata/tstm_asset_loan.{csv,dta}; that folder is git-ignored and
excluded from the build (.Rbuildignore), so it is not shipped — installed
users load the canonical data via data(tstm_asset_loan).
Anonymized in data-raw/id_anonymize/;
true IDs in data-raw/id_anonymize/tstm_asset_loan_true_id.rda.
Downstream vignettes: Group B gateway input to ffv_invest_loan_bridge /
ffv_invest_return_bridge (issue
#32); household
start/span source for ffv_loan_overlap (issue
#36); and input
to ffv_invest_freq_sizes (issue
#9).
Details
The packaged object stores anonymized household IDs: id holds tmid_hh
from data-raw/tm_key_id.rda (via data-raw/id_anonymize/), not the raw
composite province–village–household code. A small number of household-month rows
without a crosswalk match were dropped when building this file; true IDs are in
data-raw/id_anonymize/tstm_asset_loan_true_id.rda.
Examples
data(tstm_asset_loan)
ffp_preview_dataset(tstm_asset_loan)
#>
#> ── tstm_asset_loan ─────────────────────────────────────────────────────────────
#> Dimensions: 112718 rows × 41 columns(34.8 Mb)
#>
#> ── Column names (41) ──
#>
#> • 1. id
#> • 2. month
#> • 3. agg_BS_01
#> • 4. agg_BS_03
#> • 5. agg_BS_17
#> • 6. agg_BS_1011
#> • 7. agg_BS_1012
#> • 8. agg_BS_1021
#> • 9. agg_BS_1022
#> • 10. agg_BS_1023
#> • 11. agg_BS_1024
#> • 12. agg_BS_1025
#> • 13. agg_BS_2011
#> • 14. agg_BS_2021
#> • 15. agg_BS_3011
#> • 16. agg_BS_3021
#> • 17. agg_BS_3022
#> • 18. agg_IS_1011
#> • 19. agg_IS_1012
#> • 20. agg_IS_1013
#> • 21. agg_IS_1021
#> • 22. agg_IS_1111
#> • 23. agg_IS_1112
#> • 24. agg_IS_1113
#> • 25. agg_IS_1114
#> • 26. agg_IS_1115
#> • 27. agg_IS_1116
#> • 28. agg_IS_2011
#> • 29. agg_IS_2012
#> • 30. agg_IS_2013
#> • 31. agg_IS_2014
#> • 32. agg_IS_2015
#> • 33. agg_IS_2016
#> • 34. agg_IS_2021
#> • 35. agg_IS_2022
#> • 36. agg_IS_3011
#> • 37. agg_IS_3021
#> • 38. agg_IS_3022
#> • 39. agg_loanflow_all
#> • 40. agg_loanflow_inf
#> • 41. agg_loanflow_for
#>
#> ── Summary statistics (all variables) ──
#>
#> id month agg_BS_01 agg_BS_03
#> Min. :1003 Min. : 0.0 Min. : 0 Min. : 0
#> 1st Qu.:3181 1st Qu.: 40.0 1st Qu.: 58905 1st Qu.: 920
#> Median :5602 Median : 81.0 Median : 174051 Median : 6000
#> Mean :5514 Mean : 80.4 Mean : 461068 Mean : 72084
#> 3rd Qu.:7764 3rd Qu.:121.0 3rd Qu.: 459166 3rd Qu.: 35204
#> Max. :9996 Max. :160.0 Max. :42502796 Max. :7507694
#> agg_BS_17 agg_BS_1011 agg_BS_1012 agg_BS_1021
#> Min. : -56500 Min. : 0 Min. : 0 Min. : 0.0
#> 1st Qu.: 4000 1st Qu.: 133716 1st Qu.: 92429 1st Qu.: 337.3
#> Median : 34000 Median : 422049 Median : 369404 Median : 6747.9
#> Mean : 100992 Mean : 1377999 Mean : 1294427 Mean : 44868.2
#> 3rd Qu.: 90200 3rd Qu.: 1003873 3rd Qu.: 924348 3rd Qu.: 31362.6
#> Max. :7000000 Max. :141635206 Max. :141319628 Max. :8877259.0
#> agg_BS_1022 agg_BS_1023 agg_BS_1024
#> Min. : 0.0 Min. : 0 Min. : 0
#> 1st Qu.: 34.8 1st Qu.: 87400 1st Qu.: 73000
#> Median : 3155.8 Median : 366527 Median : 339350
#> Mean : 35225.4 Mean : 1284785 Mean : 1249559
#> 3rd Qu.: 24716.0 3rd Qu.: 918709 3rd Qu.: 870000
#> Max. :7685191.9 Max. :141319628 Max. :141015200
#> agg_BS_1025 agg_BS_2011 agg_BS_2021 agg_BS_3011
#> Min. : 0.0 Min. : 0 Min. : 0 Min. : 0
#> 1st Qu.: 173.7 1st Qu.: 87989 1st Qu.: 0 1st Qu.: 13795
#> Median : 4547.2 Median : 361330 Median : 1843 Median : 35611
#> Mean : 33319.0 Mean : 1282878 Mean : 23676 Mean : 95121
#> 3rd Qu.: 23792.4 3rd Qu.: 914000 3rd Qu.: 17707 3rd Qu.: 93231
#> Max. :8591977.0 Max. :141319628 Max. :4564772 Max. :7492854
#> agg_BS_3021 agg_BS_3022 agg_IS_1011 agg_IS_1012
#> Min. : 0 Min. : 0 Min. :-837762 Min. : -32820
#> 1st Qu.: 0 1st Qu.: 12883 1st Qu.: 380 1st Qu.: 50
#> Median : 0 Median : 32835 Median : 4630 Median : 400
#> Mean : 11549 Mean : 83572 Mean : 25067 Mean : 19765
#> 3rd Qu.: 0 3rd Qu.: 84396 3rd Qu.: 17700 3rd Qu.: 7389
#> Max. :7397000 Max. :2369422 Max. :6625000 Max. :6625000
#> agg_IS_1013 agg_IS_1021 agg_IS_1111 agg_IS_1112
#> Min. : -36000 Min. : 0 Min. :-1209220 Min. :-1219960.1
#> 1st Qu.: 0 1st Qu.: 0 1st Qu.: 200 1st Qu.: 0.0
#> Median : 150 Median : 0 Median : 3265 Median : 235.7
#> Mean : 19140 Mean : 13125 Mean : 12956 Mean : 8359.9
#> 3rd Qu.: 6130 3rd Qu.: 2100 3rd Qu.: 11257 3rd Qu.: 3505.8
#> Max. :6625000 Max. :4050000 Max. : 4050998 Max. : 4050247.6
#> agg_IS_1113 agg_IS_1114 agg_IS_1115 agg_IS_1116
#> Min. :-627942.3 Min. :-627942 Min. :-1234999.8 Min. :-658280.9
#> 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: -140.2 1st Qu.: -949.9
#> Median : 46.7 Median : 0 Median : 2179.4 Median : -236.5
#> Mean : 8027.8 Mean : 6244 Mean : 11091.4 Mean : 6163.1
#> 3rd Qu.: 2562.9 3rd Qu.: 1200 3rd Qu.: 9481.1 3rd Qu.: 1202.1
#> Max. :4050107.6 Max. :4050000 Max. : 4050707.4 Max. :4049817.4
#> agg_IS_2011 agg_IS_2012 agg_IS_2013 agg_IS_2014
#> Min. : -36000 Min. :-697270.0 Min. : -36000.0 Min. :-513306.9
#> 1st Qu.: 0 1st Qu.: -0.3 1st Qu.: 0.0 1st Qu.: -5.2
#> Median : 40 Median : 0.0 Median : 0.0 Median : 0.0
#> Mean : 11779 Mean : 6519.7 Mean : 9855.1 Mean : 5599.4
#> 3rd Qu.: 870 3rd Qu.: 471.6 3rd Qu.: 519.2 3rd Qu.: 230.0
#> Max. :6625000 Max. :3873583.1 Max. :6625000.0 Max. :3873583.1
#> agg_IS_2015 agg_IS_2016 agg_IS_2021 agg_IS_2022
#> Min. : 0 Min. :-697270 Min. : 0 Min. :-151765
#> 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
#> Median : 0 Median : 0 Median : 0 Median : 0
#> Mean : 7687 Mean : 4736 Mean : 5763 Mean : 3816
#> 3rd Qu.: 320 3rd Qu.: 250 3rd Qu.: 0 3rd Qu.: 0
#> Max. :3900000 Max. :3873583 Max. :3900000 Max. :3873583
#> agg_IS_3011 agg_IS_3021 agg_IS_3022 agg_loanflow_all
#> Min. :-837962 Min. : 0 Min. :-612800 Min. : 0
#> 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
#> Median : 1080 Median : 0 Median : 0 Median : 0
#> Mean : 12663 Mean : 7362 Mean : 1508 Mean : 5274
#> 3rd Qu.: 8420 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0
#> Max. :4050750 Max. :4050000 Max. :4050000 Max. :7000000
#> agg_loanflow_inf agg_loanflow_for
#> Min. : 0 Min. : 0
#> 1st Qu.: 0 1st Qu.: 0
#> Median : 0 Median : 0
#> Mean : 1325 Mean : 3949
#> 3rd Qu.: 0 3rd Qu.: 0
#> Max. :1130000 Max. :7000000
#> ── Sample rows (first 6) ──
#>
#> # A tibble: 6 × 41
#> id month agg_BS_01 agg_BS_03 agg_BS_17 agg_BS_1011 agg_BS_1012 agg_BS_1021
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3078 0 283 0 0 152226. 151000 0
#> 2 3078 1 1186 0 0 152205. 151000 0
#> 3 3078 2 1953 0 0 152183. 151000 0
#> 4 3078 3 31 0 0 152162. 151000 0
#> 5 3078 4 0 0 0 152141. 151000 0
#> 6 3078 5 834 0 0 152121. 151000 0
#> # ℹ 33 more variables: agg_BS_1022 <dbl>, agg_BS_1023 <dbl>, agg_BS_1024 <dbl>,
#> # agg_BS_1025 <dbl>, agg_BS_2011 <dbl>, agg_BS_2021 <dbl>, agg_BS_3011 <dbl>,
#> # agg_BS_3021 <dbl>, agg_BS_3022 <dbl>, agg_IS_1011 <dbl>, agg_IS_1012 <dbl>,
#> # agg_IS_1013 <dbl>, agg_IS_1021 <dbl>, agg_IS_1111 <dbl>, agg_IS_1112 <dbl>,
#> # agg_IS_1113 <dbl>, agg_IS_1114 <dbl>, agg_IS_1115 <dbl>, agg_IS_1116 <dbl>,
#> # agg_IS_2011 <dbl>, agg_IS_2012 <dbl>, agg_IS_2013 <dbl>, agg_IS_2014 <dbl>,
#> # agg_IS_2015 <dbl>, agg_IS_2016 <dbl>, agg_IS_2021 <dbl>, …