The avatar for @jzavala-gonzalez
José M. Zavala González
Public
Edited
Aug 6, 2024
2 forks
8
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db_client
from cbc_variable
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Inputs.table(cbc_labels)
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cbc_labels = {
let thedata = [...cbc_variable_interes, iir_variable_interes]

thedata = thedata.map(d => {
let dnew = {};
for (const k of ['cbc', 'variable_en', 'variable_es']) {
dnew[k] = d[k]
}
return dnew
})

return thedata
}
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cbc_variable_interes = {
let thedata = await db_client.query(`
from cbc_variable
where ${cbc_variable_interes_sql_list}.list_contains(cbc)
`)

const variables_with_aggregate = ['ETR', 'TTR', 'QDMTT'];
thedata.forEach(d => {
d.needs_aggregate = variables_with_aggregate.includes(d.cbc),
d.variable_en = (cbc_variable_en[d.cbc] ?? d.variable)
d.variable_es = cbc_variable_es[d.cbc]
})
return d3.sort(thedata, d => cbc_variable_interes_list.indexOf(d.cbc))
}
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iir_variable_interes = Object({
cbc: 'IIR', variable: 'Income inclusion rule',
needs_aggregate: true,
variable_en: 'Income Inclusion Rule (IIR)', variable_es: 'Regla de inclusión de ingresos',
})
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cbc_variable_interes_list = ['TOT_REV', 'PROFIT', 'TAX_ACCRUED', 'EMPLOYEES', 'ENTITIES_COUNT', 'ASSETS', 'ETR', 'EARNINGS', 'PAYROLL', 'TAX_BASE', 'SBIE', 'EXCESS_PROFIT', 'ETR', 'TTR', 'QDMTT']
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cbc_variable_interes_no_aggregate_list = cbc_variable_interes.filter(d => !d.needs_aggregate).map(d => d.cbc)
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cbc_variable_interes_list.map((d,i) => [d,i])
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cbc_variable_needs_aggregate_sql = 'tax_accrued / profit as ETR, .15 - ETR as TTR, TTR * TAX_BASE as QDMTT'
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panel_to_profit_label = Object({
'_T': 'Total', 'PANELAI': 'Positive', 'PANELAII': 'Negative'
})
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[...pr_iir_projections, ...iir_projections]
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totals_by_year_data
Type Table, then Shift-Enter. Ctrl-space for more options.

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downloadbuttonCSV(totals_by_year_data, `oecd_puerto_rico_totals_by_year_profit_${panel_to_profit_label[profit_grouping].toLowerCase()}.csv`)
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`oecd_puerto_rico_totals_by_year_profit_${panel_to_profit_label[profit_grouping].toLowerCase()}.csv`
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totals_by_year_data = await db_client.query(`\
with initial as (
select time_period,
-- sum(TOT_REV) as TOT_REV,
${cbc_variable_interes_no_aggregate_list.map(d => `sum(${d}) as ${d}`).join(', ')}
from oecd_puerto_rico
group by time_period
order by time_period
)

from initial select *, ${cbc_variable_needs_aggregate_sql}
`)
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summary_variables_yearly = await db_client.query(`\
with initial as (
select time_period,
-- sum(TOT_REV) as TOT_REV,
${cbc_variable_interes_no_aggregate_list.map(d => `sum(${d}) as ${d}`).join(', ')}
from oecd_puerto_rico
group by time_period
order by time_period
),

totals_by_year as (
from initial select *, ${cbc_variable_needs_aggregate_sql}
),

first_unpivot as (

unpivot totals_by_year
on columns(* exclude (time_period))
into
name cbc
value value
)

from first_unpivot
`)
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cbc_plot_options = Object({
'TOT_REV': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 130, tickPadding: 70,
bar_threshold: 1.8e9, outtext_spacing: 3.5e9,
},
'PROFIT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 80,
bar_threshold: 700e6, outtext_spacing: 1e9,
},
'TAX_ACCRUED': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 100, tickPadding: 55,
bar_threshold: 15e6, outtext_spacing: 30e6,
},
'EMPLOYEES': {
tickFormat: d => d3.format(',.0f')(d),
marginLeft: 95, tickPadding: 55,
bar_threshold: 1200,
},
'ENTITIES_COUNT': {
marginLeft: 70, tickPadding: 45,
bar_threshold: 17,
},
'ETR': {
tickFormat: d => d3.format('.2%')(d),
bar_threshold: 0.1, outtext_spacing: 0.12,
},
'ASSETS': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
bar_threshold: 5e6, marginLeft: 90,
},
'EARNINGS': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 5000e6, outtext_spacing: 3000e6,
},
'PAYROLL': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 5e6,
},
'TAX_BASE': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 750e6, outtext_spacing: 1000e6,
},
'SBIE': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 100e6, outtext_spacing: 1000e6,
},
'EXCESS_PROFIT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 3000e6, outtext_spacing: 3000e6,
},
'TTR': {
tickFormat: d => d3.format('.2%')(d),
bar_threshold: 0.1, outtext_spacing: 0.12,
},
'QDMTT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 150e6, outtext_spacing: 170e6,
},
'IIR': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 100e6, outtext_spacing: 150e6,
},
})
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cbc_plot_options_profit_positive = Object({
'TOT_REV': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 150, tickPadding: 70,
bar_threshold: 2.5e9, outtext_spacing: 3.0e9,
},
'PROFIT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 80,
bar_threshold: 700e6, outtext_spacing: 1e9,
},
'TAX_ACCRUED': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 100, tickPadding: 55,
bar_threshold: 25e6, outtext_spacing: 30e6,
},
'EMPLOYEES': {
tickFormat: d => d3.format(',.0f')(d),
marginLeft: 95, tickPadding: 55,
bar_threshold: 1200,
},
'ENTITIES_COUNT': {
marginLeft: 70, tickPadding: 45,
bar_threshold: 10, outtext_spacing: 5,
},
'ETR': {
tickFormat: d => d3.format('.2%')(d),
bar_threshold: 0.02, outtext_spacing: 0.015,
},
'ASSETS': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
bar_threshold: 700e6, outtext_spacing: 700e6,
marginLeft: 90,
},
'EARNINGS': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 5000e6, outtext_spacing: 3000e6,
},
'PAYROLL': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 90e6, outtext_spacing: 90e6,
},
'TAX_BASE': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 750e6, outtext_spacing: 1000e6,
},
'SBIE': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 70e6, outtext_spacing: 70e6,
},
'EXCESS_PROFIT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 3000e6, outtext_spacing: 3000e6,
},
'TTR': {
tickFormat: d => d3.format('.2%')(d),
bar_threshold: 0.02, outtext_spacing: 0.02,
},
'QDMTT': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 150e6, outtext_spacing: 170e6,
},
'IIR': {
tickFormat: d => d3.format('$,.1f')(d/1e6)+'M',
marginLeft: 140, tickPadding: 70,
bar_threshold: 100e6, outtext_spacing: 150e6,
},
})
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d3.sort(m.get(2016), d => -d['TOT_REV'])
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region_labels = {
let themap = Object({
'EU': ['Europe', 'Europa'],
'US': ['United States', 'Estados Unidos'],
'OTHER': ['Other countries', 'Otros países'],
})

let thedata = Object.entries(themap).map(d => Object({region: d[0], region_en: d[1][0], region_es: d[1][1]}))
return thedata
}
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db_client
select distinct cou, ultimate_parent_jurisdiction, region
from oecd_puerto_rico
left join cou_regions
using (cou)
order by cou
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totals_by_region_data
Type Table, then Shift-Enter. Ctrl-space for more options.

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totals_by_region_data = {
await db_client.query(`
create or replace table totals_by_region_data as (
with initial as (
select region, time_period,
${cbc_variable_interes_no_aggregate_list.map(d => `sum(${d}) as ${d}`).join(', ')}
from oecd_puerto_rico
left join cou_regions
using (cou)
group by all
order by all
)

from initial select *, ${cbc_variable_needs_aggregate_sql},
sum(tax_accrued) over (partition by time_period) / sum(profit) over (partition by time_period) as ETR_total,
(.15 - ETR_total) * TAX_BASE as IIR,
order by region, time_period
)
`)

let thedata = await db_client.query(`from totals_by_region_data`)
return thedata
}
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Inputs.table(totals_by_region_data)
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iir_projections
Type Table, then Shift-Enter. Ctrl-space for more options.

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iir_projections = {
// Dependencies:
totals_by_region_data;
iir_projections_scale_factors;
let thedata = await db_client.query(`
with iir_2021 as (
select region, time_period, iir,
from totals_by_region_data
where time_period = 2021
),

data_with_scale_factors as (
select iir_2021.*, sf.time_period as new_time_period, sf.scale_factor
from iir_2021
left join iir_projections_scale_factors as sf
on iir_2021.region = sf.region
)

select region, new_time_period as time_period, IIR, IIR * scale_factor as IIR_scaled,
from data_with_scale_factors
`)

return thedata
}
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iir_projections_scale_factors = {
// For scaling down projections segun el
// numero de paises por region que aplican IIR

const region_data = [
{region: 'US', 2024: 0, 2025: .92746},
{region: 'EU', 2024: .8589, 2025: 7/7},
{region: 'OTHER', 2024: .9934, 2025: .9934}, // solo Japon tiene IIR
// {region: 'US', 2024: 1, 2025: 1},
// {region: 'EU', 2024: 1, 2025: 1},
// {region: 'OTHER', 2024: 1, 2025: 1},
]

let thedata = [];
for (let o of region_data) {
const region = o['region'];
thedata.push(...[
{region, time_period: 2024, scale_factor: o[2024]},
{region, time_period: 2025, scale_factor: o[2025]},
])
}

await db_client.query(`
create or replace table iir_projections_scale_factors (region VARCHAR, time_period INT, scale_factor FLOAT)
`)
await db_client.query(`insert into iir_projections_scale_factors values ` +
(thedata.map(d => `('${d.region}', ${d.time_period}, ${d.scale_factor})`)).join(', ')
)
return thedata
}
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await db_client.query('from iir_projections_scale_factors')
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cou_regions = {
const themap = Object({
'ARE': 'OTHER',
'SAU': 'OTHER',
'BMU': 'OTHER',
'CHE': 'EU',
'CYM': 'OTHER',
'DEU': 'EU',
'DNK': 'EU',
'ESP': 'EU',
'GRC': 'EU',
'HKG': 'OTHER',
'IND': 'OTHER',
'ITA': 'EU',
'JPN': 'OTHER',
'LUX': 'EU',
'MEX': 'OTHER',
'PAN': 'OTHER',
'PER': 'OTHER',
'USA': 'US',
})

const thedata = Object.entries(themap).map(d => Object({cou: d[0], region: d[1]}));

return thedata
}
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{
const cou_in_profit_making = new Set(["CHE", "CYM", "DEU", "ESP", "GRC", "ITA", "JPN", "LUX", "SAU", "USA"]);

const cou_in_total = new Set(["ARE", "BMU", "CHE", "CYM", "DEU", "ESP", "GRC", "HKG", "ITA", "JPN", "LUX", "MEX", "SAU", "USA"]);

return cou_in_profit_making
}
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totals_by_country_data.filter(d => d.time_period === 2021).map(d => d.cou)
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totals_by_country_data
Type Table, then Shift-Enter. Ctrl-space for more options.

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totals_by_country_data = {
let thedata = await db_client.query(`
with initial as (
select cou, time_period,
${cbc_variable_interes_no_aggregate_list.map(d => `sum(${d}) as ${d}`).join(', ')}
from oecd_puerto_rico
group by all
order by all
)

select *, ${cbc_variable_needs_aggregate_sql},
sum(tax_accrued) over (partition by time_period) / sum(profit) over (partition by time_period) as ETR_total,
(.15 - ETR_total) * TAX_BASE as IIR,
from initial
left join cou_regions
using (cou)
order by cou, time_period
`)
return thedata
}
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cou_to_name = {
let thedata = await db_client.query(`
select distinct cou, ultimate_parent_jurisdiction as label_en, pais as label_es
from oecd_puerto_rico
left join countries
on oecd_puerto_rico.cou = countries."alpha-3"
group by all
order by all
`)

// const cou_labels_es = Object({
// 'ARE': 'Reino de Arabia Saudita',
// })
// thedata.forEach(d => {
// const cou = d.cou;
// // if (cou_labels_es[d.cou] !== undefined) {
// d.label_es = cou_labels_es[d.cou] ?? undefined;
// // }
// })
return thedata
}
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db_client
select sum(entities_count) as entities_count,
-- (ACT_ADMIN+ACT_DOR+ACT_FIN+ACT_HOLDING+ACT_IGF+ACT_INS+ACT_IP+ACT_MAN+ACT_OTHER+ACT_PUR+ACT_RD+ACT_SALES+ACT_SER) as total_in_act,
sum(list_value(ACT_ADMIN,ACT_DOR,ACT_FIN,ACT_HOLDING,ACT_IGF,ACT_INS,ACT_IP,ACT_MAN,ACT_OTHER,ACT_PUR,ACT_RD,ACT_SALES,ACT_SER).list_aggregate('sum')) as total_in_act,
sum(columns('ACT_*'))
from oecd_puerto_rico
where time_period = 2021
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Plot.plot({
projection: "equirectangular",
r: {transform: (r) => Math.pow(10, r)}, // Richter to amplitude
marks: [
Plot.geo(land, {fill: "currentColor", fillOpacity: 0.2}),
Plot.sphere(),
// Plot.geo(earthquakes, {
// r: "mag",
// fill: "red",
// fillOpacity: 0.2,
// stroke: "red",
// title: "title",
// href: "url",
// target: "_blank"
// })
]
})
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`
select time_period,
sum(TOT_REV) as TOT_REV,
${cbc_variable_interes_list.map(d => `sum(${d}) as ${d},`)}
from oecd_puerto_rico
group by time_period
`
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db_client.query(`
select time_period,
sum(TOT_REV) as TOT_REV,
${cbc_variable_interes_no_aggregate_list.map(d => `sum(${d}) as ${d}`).join(', ')}
from oecd_puerto_rico
group by time_period
`)
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db_client
from oecd_puerto_rico
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db_client
from cbc_variable
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db_client
from countries
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cbc_variable_en = Object({
'ETR': 'Effective Tax Rate (ETR)',
'QDMTT': 'Qualifying Domestic Minimum Top-up Tax (QDMTT)',
'SBIE': 'Substance-Based Income Exclusion (SBIE)',
'TTR': 'Top-up Tax Rate (TTR)',
'IIR': 'Income Inclusion Rule (IIR)'
})
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cbc_variable_es = Object({
'TOT_REV': 'Ingresos totales',
'PROFIT': 'Beneficios',
'TAX_ACCRUED': 'Impuestos devengados',
'EMPLOYEES': 'Empleos',
'ENTITIES_COUNT': 'Subsidiarias',
'ETR': 'Tasa efectiva contributiva',
'ASSETS': 'Activos tangibles',
'EARNINGS': 'Ganancias acumuladas',
'PAYROLL': 'Nómina',
'TAX_BASE': 'Base tributaria',
'SBIE': 'Exclusión de ingresos basada en la sustancia',
'EXCESS_PROFIT': 'Beneficio excedente',
'TTR': 'Impuesto complementario',
'QDMTT': 'Impuesto complementario mínimo nacional calificado (QDMTT)',
})
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duckdb = import(
"https://cdn.jsdelivr.net/npm/@duckdb/duckdb-wasm@1.28.1-dev232.0/+esm"
)
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db_client
from cou_regions
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db_instance = {
// Pick a bundle
const bundles = duckdb.getJsDelivrBundles();
const bundle = await duckdb.selectBundle(bundles);

// Initialize the database
const logger = new duckdb.ConsoleLogger();
const worker = await duckdb.createWorker(bundle.mainWorker);
const db = new duckdb.AsyncDuckDB(logger, worker);
await db.instantiate(bundle.mainModule);
const connection = await db.connect()

// Import dataset files
// await insertFile(db, 'oecd_puerto_rico', oecd_puerto_rico_file)
// await insertFile(db, 'cbc_variable', cbc_variable_file)

// Insert object arrays manually
await db.registerFileBuffer('oecd_puerto_rico.csv', encoder.encode( d3.csvFormat(await oecd_puerto_rico_file.csv({typed: true})) ))
await connection.insertCSVFromPath('oecd_puerto_rico.csv', {name: 'oecd_puerto_rico_file'})

await db.registerFileBuffer('cbc_variable.csv', encoder.encode( d3.csvFormat(await cbc_variable_file.csv({typed: true})) ))
await connection.insertCSVFromPath('cbc_variable.csv', {name: 'cbc_variable_file'})

await db.registerFileBuffer('ISO.csv', encoder.encode(
d3.csvFormat(cou_iso) ))
await connection.insertCSVFromPath('ISO.csv', {name: 'countries'})
await db.registerFileBuffer('cou_regions.csv', encoder.encode(d3.csvFormat(cou_regions)))
await connection.insertCSVFromPath('cou_regions.csv', {name: 'cou_regions'})
// const buffer = await oecd_cbcr_file.arrayBuffer();
// await db_instance.registerFileBuffer(oecd_cbcr_file.name, new Uint8Array(buffer));
// await client.query(`create or replace view 'oecd_cbcr' as select * from parquet_scan(${oecd_cbcr_file.name})`)
return db
}
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cou_iso = {
const cou_iso = await iso_file.csv({typed: true});
cou_iso.forEach(d => {
d['pais'] = cou_alpha2_map[d['alpha-2']]
})
return cou_iso

}
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cou_alpha2_map = {
const paises = await paises_file.json()
let thedata = {}
paises.forEach(d => {
thedata[d.code] = d.country
})
return thedata
}
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import {world, land} from "@observablehq/plot-live-earthquake-map"
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import {DuckDBClientCompat} from "@jzavala-gonzalez/duckdb-tpc-h"
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weighted_avg_salary = {
let salary = 41840.46 // el valor original calculado
salary = salary * 1.20 // prima de 20%
salary = Math.round(salary * 100) / 100 // round a dos decimales

return salary
}
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db_client = {
const client = await new DuckDBClientCompat(db_instance)

await client.query(`create or replace table oecd_puerto_rico as (
select *,
EMPLOYEES * ${weighted_avg_salary} as PAYROLL,
PROFIT - .08*ASSETS - .10*PAYROLL as TAX_BASE,
.10*PAYROLL + .08*ASSETS as SBIE,
TOT_REV - SBIE as EXCESS_PROFIT,
from (from oecd_puerto_rico_file where profit_grouping = '${profit_grouping}')
)`)

// await client.query(`create or replace table cbc_variable_backup as (from cbc_variable)`);
await client.query(`create or replace table cbc_variable (cbc varchar primary key, variable varchar)`);
await client.query(`insert into cbc_variable select * from cbc_variable_file`)

await client.query(`insert into cbc_variable values
('PAYROLL', 'Payroll'),
('TTR', 'Top-up tax rate'),
('TAX_BASE', 'Tax base'),
('SBIE', 'Substance based income exclusion'),
('EXCESS_PROFIT', 'Excess profit'),
('QDMTT', 'Qualifying domestic minimum top-up tax'),
('IIR', 'Income inclusion rule')
`)


return client
}
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db_client
from cbc_variable
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async function insertFile(database, name, file, options) {
const url = await file.url();
if (url.startsWith("blob:")) {
const buffer = await file.arrayBuffer();
await database.registerFileBuffer(file.name, new Uint8Array(buffer));
} else {
await database.registerFileURL(file.name, new URL(url, location).href, 4); // duckdb.DuckDBDataProtocol.HTTP
}
const connection = await database.connect();
try {
switch (file.mimeType) {
case "text/csv":
case "text/tab-separated-values": {
return await connection.insertCSVFromPath(file.name, {
name,
schema: "main",
...options
}).catch(async (error) => {
// If initial attempt to insert CSV resulted in a conversion
// error, try again, this time treating all columns as strings.
if (error.toString().includes("Could not convert")) {
return await insertUntypedCSV(connection, file, name);
}
throw error;
});
}
case "application/json":
return await connection.insertJSONFromPath(file.name, {
name,
schema: "main",
...options
});
default:
if (/\.arrow$/i.test(file.name)) {
const buffer = new Uint8Array(await file.arrayBuffer());
return await connection.insertArrowFromIPCStream(buffer, {
name,
schema: "main",
...options
});
}
if (/\.parquet$/i.test(file.name)) {
return await connection.query(
`CREATE VIEW '${name}' AS SELECT * FROM parquet_scan('${file.name}')`
);
}
throw new Error(`unknown file type: ${file.mimeType}`);
}
} finally {
await connection.close();
}
}
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encoder.encode('abcd').buffer
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cbc_variable_file = FileAttachment("cbc_variable@1.csv")
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oecd_puerto_rico_file = FileAttachment("oecd_puerto_rico@4.csv")
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iso_file = FileAttachment("ISO.csv")
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paises_file = FileAttachment("paises.json")
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encoder = new TextEncoder();
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oecd_puerto_rico_file.csv({typed: true})
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estilos = htl.html`
<style>
h1 {
font-family: "jaf-bernino-sans-comp", sans-serif;
font-weight: 700;
font-style: normal;
}

h2 {
font-family: "jaf-bernino-sans-narrow", sans-serif;
font-weight: 700;
font-style: normal;
}

span {
font-family: "jaf-bernino-sans", sans-serif;
font-weight: 400;
font-style: normal;
}

span[class$="-swatch"] {
font-size: 16px;
}
</style>
`
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import { EA_COLORES_MAP } from "22dc6182207c44c8"
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import {downloadbuttonCSV} from "@pavlos/download-buttons"
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Purpose-built for displays of data

Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
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