class TaxCalcIO(object):
"""
Constructor for the Tax-Calculator Input-Output class.
TaxCalcIO class constructor call must be followed by init() call.
Parameters
----------
input_data: string or Pandas DataFrame
string is name of INPUT file that is CSV formatted containing
variable names in the Records.USABLE_READ_VARS set, or
Pandas DataFrame is INPUT data containing variable names in
the Records.USABLE_READ_VARS set. INPUT vsrisbles not in the
Records.USABLE_READ_VARS set can be present but are ignored.
tax_year: integer
calendar year for which taxes will be computed for INPUT.
reform: None or string
None implies no policy reform (current-law policy), or
string is name of optional REFORM file.
assump: None or string
None implies economic assumptions are standard assumptions,
or string is name of optional ASSUMP file.
Returns
-------
class instance: TaxCalcIO
"""
# pylint: disable=too-many-instance-attributes
def __init__(self, input_data, tax_year, reform, assump):
# pylint: disable=too-many-branches,too-many-statements
self.errmsg = ''
# check name and existence of INPUT file
inp = 'x'
if isinstance(input_data, six.string_types):
# remove any leading directory path from INPUT filename
fname = os.path.basename(input_data)
# check if fname ends with ".csv"
if fname.endswith('.csv'):
inp = '{}-{}'.format(fname[:-4], str(tax_year)[2:])
else:
msg = 'INPUT file name does not end in .csv'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of INPUT file
self.cps_input_data = input_data.endswith('cps.csv')
if not self.cps_input_data and not os.path.isfile(input_data):
msg = 'INPUT file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
elif isinstance(input_data, pd.DataFrame):
inp = 'df-{}'.format(str(tax_year)[2:])
else:
msg = 'INPUT is neither string nor Pandas DataFrame'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name and existence of REFORM file
ref = '-x'
if reform is None:
self.specified_reform = False
ref = '-#'
elif isinstance(reform, six.string_types):
self.specified_reform = True
# remove any leading directory path from REFORM filename
fname = os.path.basename(reform)
# check if fname ends with ".json"
if fname.endswith('.json'):
ref = '-{}'.format(fname[:-5])
else:
msg = 'REFORM file name does not end in .json'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of REFORM file
if not os.path.isfile(reform):
msg = 'REFORM file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
else:
msg = 'TaxCalcIO.ctor: reform is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name and existence of ASSUMP file
asm = '-x'
if assump is None:
asm = '-#'
elif isinstance(assump, six.string_types):
# remove any leading directory path from ASSUMP filename
fname = os.path.basename(assump)
# check if fname ends with ".json"
if fname.endswith('.json'):
asm = '-{}'.format(fname[:-5])
else:
msg = 'ASSUMP file name does not end in .json'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of ASSUMP file
if not os.path.isfile(assump):
msg = 'ASSUMP file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
else:
msg = 'TaxCalcIO.ctor: assump is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# create OUTPUT file name and delete any existing output files
self._output_filename = '{}{}{}.csv'.format(inp, ref, asm)
delete_file(self._output_filename)
delete_file(self._output_filename.replace('.csv', '.db'))
delete_file(self._output_filename.replace('.csv', '-doc.text'))
delete_file(self._output_filename.replace('.csv', '-tab.text'))
delete_file(self._output_filename.replace('.csv', '-atr.html'))
delete_file(self._output_filename.replace('.csv', '-mtr.html'))
# initialize variables whose values are set in init method
self.behavior_has_any_response = False
self.calc = None
self.calc_clp = None
self.param_dict = None
def init(self, input_data, tax_year, reform, assump, growdiff_response,
aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First four parameters are same as for TaxCalcIO constructor:
input_data, tax_year, reform, assump.
growdiff_response: Growdiff object or None
growdiff_response Growdiff object is used only by the
TaxCalcIO.growmodel_analysis method;
must be None in all other cases.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get parameter dictionaries from --reform and --assump files
paramdict = Calculator.read_json_param_objects(reform, assump)
# create Behavior object
beh = Behavior()
beh.update_behavior(paramdict['behavior'])
self.behavior_has_any_response = beh.has_any_response()
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
gdiff_response.update_growdiff(paramdict['growdiff_response'])
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
gdiff_response = None
msg = 'TaxCalcIO.more_init: growdiff_response is neither None '
msg += 'nor a Growdiff object'
self.errmsg += 'ERROR: {}\n'.format(msg)
if gdiff_response is not None:
some_gdiff_response = gdiff_response.has_any_response()
if self.behavior_has_any_response and some_gdiff_response:
msg = 'ASSUMP file cannot specify any "behavior" when using '
msg += 'GrowModel or when ASSUMP file has "growdiff_response"'
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
if gdiff_response is not None:
gdiff_response.apply_to(gfactors_ref)
# create Policy objects
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
try:
pol.implement_reform(paramdict['policy'])
self.errmsg += pol.reform_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_clp)
clp = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
clp.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records.cps_constructor(
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # if not cps_input_data
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_clp = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
con = Consumption()
con.update_consumption(paramdict['consumption'])
self.calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
behavior=beh,
sync_years=aging_input_data)
self.calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)
# remember parameter dictionary for reform documentation
self.param_dict = paramdict
def custom_dump_variables(self, tcdumpvars_str):
"""
Return set of variable names extracted from tcdumpvars_str, which
contains the contents of the tcdumpvars file in the current directory.
Also, builds self.errmsg if any custom variables are not valid.
"""
assert isinstance(tcdumpvars_str, six.string_types)
self.errmsg = ''
# change some common delimiter characters into spaces
dump_vars_str = tcdumpvars_str.replace(',', ' ')
dump_vars_str = dump_vars_str.replace(';', ' ')
dump_vars_str = dump_vars_str.replace('|', ' ')
# split dump_vars_str into a list of dump variables
dump_vars_list = dump_vars_str.split()
# check that all dump_vars_list items are valid
valid_set = Records.USABLE_READ_VARS | Records.CALCULATED_VARS
for var in dump_vars_list:
if var not in valid_set:
msg = 'invalid variable name in tcdumpvars file: {}'
msg = msg.format(var)
self.errmsg += 'ERROR: {}\n'.format(msg)
# add essential variables even if not on custom list
if 'RECID' not in dump_vars_list:
dump_vars_list.append('RECID')
if 'FLPDYR' not in dump_vars_list:
dump_vars_list.append('FLPDYR')
# convert list into a set and return
return set(dump_vars_list)
def tax_year(self):
"""
Return calendar year for which TaxCalcIO calculations are being done.
"""
return self.calc.policy.current_year
def output_filepath(self):
"""
Return full path to output file named in TaxCalcIO constructor.
"""
dirpath = os.path.abspath(os.path.dirname(__file__))
return os.path.join(dirpath, self._output_filename)
def analyze(self,
writing_output_file=False,
output_tables=False,
output_graphs=False,
output_ceeu=False,
dump_varset=None,
output_dump=False,
output_sqldb=False):
"""
Conduct tax analysis.
Parameters
----------
writing_output_file: boolean
whether or not to generate and write output file
output_tables: boolean
whether or not to generate and write distributional tables
to a text file
output_graphs: boolean
whether or not to generate and write HTML graphs of average
and marginal tax rates by income percentile
output_ceeu: boolean
whether or not to calculate and write to stdout standard
certainty-equivalent expected-utility statistics
dump_varset: set
custom set of variables to include in dump and sqldb output;
None implies include all variables in dump and sqldb output
output_dump: boolean
whether or not to replace standard output with all input and
calculated variables using their Tax-Calculator names
output_sqldb: boolean
whether or not to write SQLite3 database with dump table
containing same output as written by output_dump to a csv file
Returns
-------
Nothing
"""
# pylint: disable=too-many-arguments,too-many-branches
# in order to use print(), pylint: disable=superfluous-parens
if self.calc.policy.reform_warnings:
warn = 'PARAMETER VALUE WARNING(S): {}\n{}{}'
print(
warn.format('(read documentation for each parameter)',
self.calc.policy.reform_warnings,
'CONTINUING WITH CALCULATIONS...'))
calc_clp_calculated = False
if output_dump or output_sqldb:
# might need marginal tax rates
(mtr_paytax, mtr_inctax,
_) = self.calc.mtr(wrt_full_compensation=False)
else:
# definitely do not need marginal tax rates
mtr_paytax = None
mtr_inctax = None
if self.behavior_has_any_response:
self.calc = Behavior.response(self.calc_clp, self.calc)
calc_clp_calculated = True
else:
self.calc.calc_all()
# optionally conduct normative welfare analysis
if output_ceeu:
if self.behavior_has_any_response:
ceeu_results = 'SKIP --ceeu output because baseline and '
ceeu_results += 'reform cannot be sensibly compared\n '
ceeu_results += ' '
ceeu_results += 'when specifying "behavior" with --assump '
ceeu_results += 'option'
elif self.calc.total_weight() <= 0.:
ceeu_results = 'SKIP --ceeu output because '
ceeu_results += 'sum of weights is not positive'
else:
self.calc_clp.calc_all()
calc_clp_calculated = True
cedict = self.calc_clp.ce_aftertax_income(
self.calc,
custom_params=None,
require_no_agg_tax_change=False)
ceeu_results = TaxCalcIO.ceeu_output(cedict)
else:
ceeu_results = None
# extract output if writing_output_file
if writing_output_file:
self.write_output_file(output_dump, dump_varset, mtr_paytax,
mtr_inctax)
self.write_doc_file()
# optionally write --sqldb output to SQLite3 database
if output_sqldb:
self.write_sqldb_file(dump_varset, mtr_paytax, mtr_inctax)
# optionally write --tables output to text file
if output_tables:
if not calc_clp_calculated:
self.calc_clp.calc_all()
calc_clp_calculated = True
self.write_tables_file()
# optionally write --graphs output to HTML files
if output_graphs:
if not calc_clp_calculated:
self.calc_clp.calc_all()
calc_clp_calculated = True
self.write_graph_files()
# optionally write --ceeu output to stdout
if ceeu_results:
print(ceeu_results)
def write_output_file(self, output_dump, dump_varset, mtr_paytax,
mtr_inctax):
"""
Write output to CSV-formatted file.
"""
if output_dump:
outdf = self.dump_output(dump_varset, mtr_inctax, mtr_paytax)
column_order = sorted(outdf.columns)
else:
outdf = self.minimal_output()
column_order = outdf.columns
assert len(outdf.index) == self.calc.records.dim
outdf.to_csv(self._output_filename,
columns=column_order,
index=False,
float_format='%.2f')
def write_doc_file(self):
"""
Write reform documentation to text file.
"""
doc = Calculator.reform_documentation(self.param_dict)
doc_fname = self._output_filename.replace('.csv', '-doc.text')
with open(doc_fname, 'w') as dfile:
dfile.write(doc)
def write_sqldb_file(self, dump_varset, mtr_paytax, mtr_inctax):
"""
Write dump output to SQLite3 database table dump.
"""
outdf = self.dump_output(dump_varset, mtr_inctax, mtr_paytax)
assert len(outdf.index) == self.calc.records.dim
db_fname = self._output_filename.replace('.csv', '.db')
dbcon = sqlite3.connect(db_fname)
outdf.to_sql('dump', dbcon, if_exists='replace', index=False)
dbcon.close()
def write_tables_file(self):
"""
Write tables to text file.
"""
# pylint: disable=too-many-locals
tab_fname = self._output_filename.replace('.csv', '-tab.text')
# create list of nontax column results
# - weights don't change with reform
# - expanded_income may change, but always use baseline expanded income
nontax_cols = ['s006', 'expanded_income']
nontax = [getattr(self.calc_clp.records, col) for col in nontax_cols]
# specify column names for taxes
tax_cols = ['iitax', 'payrolltax', 'lumpsum_tax', 'combined']
all_cols = nontax_cols + tax_cols
# create DataFrame with taxes under the reform
reform = [getattr(self.calc.records, col) for col in tax_cols]
dist = nontax + reform # using expanded_income under baseline policy
distdf = pd.DataFrame(data=np.column_stack(dist), columns=all_cols)
# skip tables if there are not some positive weights
if distdf['s006'].sum() <= 0.:
with open(tab_fname, 'w') as tfile:
msg = 'No tables because sum of weights is not positive\n'
tfile.write(msg)
return
# create DataFrame with tax differences (reform - baseline)
base = [getattr(self.calc_clp.records, col) for col in tax_cols]
change = [(reform[idx] - base[idx]) for idx in range(0, len(tax_cols))]
diff = nontax + change # using expanded_income under baseline policy
diffdf = pd.DataFrame(data=np.column_stack(diff), columns=all_cols)
# write each kind of distributional table
with open(tab_fname, 'w') as tfile:
TaxCalcIO.write_decile_table(distdf, tfile, tkind='Reform Totals')
tfile.write('\n')
TaxCalcIO.write_decile_table(diffdf, tfile, tkind='Differences')
@staticmethod
def write_decile_table(dfx, tfile, tkind='Totals'):
"""
Write to tfile the tkind decile table using dfx DataFrame.
"""
dfx = add_quantile_bins(dfx,
'expanded_income',
10,
weight_by_income_measure=False)
gdfx = dfx.groupby('bins', as_index=False)
rtns_series = gdfx.apply(unweighted_sum, 's006')
xinc_series = gdfx.apply(weighted_sum, 'expanded_income')
itax_series = gdfx.apply(weighted_sum, 'iitax')
ptax_series = gdfx.apply(weighted_sum, 'payrolltax')
htax_series = gdfx.apply(weighted_sum, 'lumpsum_tax')
ctax_series = gdfx.apply(weighted_sum, 'combined')
# write decile table to text file
row = 'Weighted Tax {} by Baseline Expanded-Income Decile\n'
tfile.write(row.format(tkind))
rowfmt = '{}{}{}{}{}{}\n'
row = rowfmt.format(' Returns', ' ExpInc', ' IncTax',
' PayTax', ' LSTax', ' AllTax')
tfile.write(row)
row = rowfmt.format(' (#m)', ' ($b)', ' ($b)',
' ($b)', ' ($b)', ' ($b)')
tfile.write(row)
rowfmt = '{:9.2f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}\n'
for decile in range(0, 10):
row = '{:2d}'.format(decile)
row += rowfmt.format(rtns_series[decile] * 1e-6,
xinc_series[decile] * 1e-9,
itax_series[decile] * 1e-9,
ptax_series[decile] * 1e-9,
htax_series[decile] * 1e-9,
ctax_series[decile] * 1e-9)
tfile.write(row)
row = ' A'
row += rowfmt.format(rtns_series.sum() * 1e-6,
xinc_series.sum() * 1e-9,
itax_series.sum() * 1e-9,
ptax_series.sum() * 1e-9,
htax_series.sum() * 1e-9,
ctax_series.sum() * 1e-9)
tfile.write(row)
def write_graph_files(self):
"""
Write graphs to HTML files.
"""
pos_wght_sum = self.calc.records.s006.sum() > 0.
atr_fname = self._output_filename.replace('.csv', '-atr.html')
atr_title = 'ATR by Income Percentile'
if pos_wght_sum:
fig = self.calc_clp.atr_graph(self.calc)
write_graph_file(fig, atr_fname, atr_title)
else:
reason = 'No graph because sum of weights is not positive'
TaxCalcIO.write_empty_graph_file(atr_fname, atr_title, reason)
mtr_fname = self._output_filename.replace('.csv', '-mtr.html')
mtr_title = 'MTR by Income Percentile'
if pos_wght_sum:
fig = self.calc_clp.mtr_graph(self.calc,
alt_e00200p_text='Taxpayer Earnings')
write_graph_file(fig, mtr_fname, mtr_title)
else:
reason = 'No graph because sum of weights is not positive'
TaxCalcIO.write_empty_graph_file(mtr_fname, mtr_title, reason)
@staticmethod
def write_empty_graph_file(fname, title, reason):
"""
Write HTML graph file with title but no graph for specified reason.
"""
txt = ('<html>\n'
'<head><title>{}</title></head>\n'
'<body><center<h1>{}</h1></center></body>\n'
'</html>\n').format(title, reason)
with open(fname, 'w') as gfile:
gfile.write(txt)
def minimal_output(self):
"""
Extract minimal output and return it as Pandas DataFrame.
"""
varlist = ['RECID', 'YEAR', 'WEIGHT', 'INCTAX', 'LSTAX', 'PAYTAX']
odict = dict()
crecs = self.calc.records
odict['RECID'] = crecs.RECID # id for tax filing unit
odict['YEAR'] = self.tax_year() # tax calculation year
odict['WEIGHT'] = crecs.s006 # sample weight
odict['INCTAX'] = crecs.iitax # federal income taxes
odict['LSTAX'] = crecs.lumpsum_tax # lump-sum tax
odict['PAYTAX'] = crecs.payrolltax # payroll taxes (ee+er)
odf = pd.DataFrame(data=odict, columns=varlist)
return odf
@staticmethod
def ceeu_output(cedict):
"""
Extract --ceeu output and return as text string.
"""
text = ('Aggregate {} Pre-Tax Expanded Income and '
'Tax Revenue ($billion)\n')
txt = text.format(cedict['year'])
txt += ' baseline reform difference\n'
fmt = '{} {:12.3f} {:10.3f} {:12.3f}\n'
txt += fmt.format('income', cedict['inc1'], cedict['inc2'],
cedict['inc2'] - cedict['inc1'])
alltaxdiff = cedict['tax2'] - cedict['tax1']
txt += fmt.format('alltax', cedict['tax1'], cedict['tax2'], alltaxdiff)
txt += ('Certainty Equivalent of Expected Utility of '
'After-Tax Expanded Income ($)\n')
txt += ('(assuming consumption equals ' 'after-tax expanded income)\n')
txt += 'crra baseline reform pctdiff\n'
fmt = '{} {:17.2f} {:10.2f} {:11.2f}\n'
for crra, ceeu1, ceeu2 in zip(cedict['crra'], cedict['ceeu1'],
cedict['ceeu2']):
txt += fmt.format(crra, ceeu1, ceeu2,
100.0 * (ceeu2 - ceeu1) / ceeu1)
if abs(alltaxdiff) >= 0.0005:
txt += ('WARN: baseline and reform cannot be '
'sensibly compared\n')
text = (' because "alltax difference" is '
'{:.3f} which is not zero\n')
txt += text.format(alltaxdiff)
txt += ('FIX: adjust _LST or another reform policy parameter '
'to bracket\n')
txt += (' "alltax difference" equals zero and '
'then interpolate')
else:
txt += 'NOTE: baseline and reform can be sensibly compared\n'
txt += ' because "alltax difference" is essentially zero'
return txt
def dump_output(self, dump_varset, mtr_inctax, mtr_paytax):
"""
Extract dump output and return it as Pandas DataFrame.
"""
if dump_varset is None:
varset = Records.USABLE_READ_VARS | Records.CALCULATED_VARS
else:
varset = dump_varset
# create and return dump output DataFrame
odf = pd.DataFrame()
for varname in varset:
vardata = self.calc.array(varname)
if varname in Records.INTEGER_VARS:
odf[varname] = vardata
else:
odf[varname] = vardata.round(2) # rounded to nearest cent
# specify mtr values in percentage terms
if 'mtr_inctax' in varset:
odf['mtr_inctax'] = (mtr_inctax * 100).round(2)
if 'mtr_paytax' in varset:
odf['mtr_paytax'] = (mtr_paytax * 100).round(2)
# specify tax calculation year
odf['FLPDYR'] = self.tax_year()
return odf
@staticmethod
def growmodel_analysis(input_data,
tax_year,
reform,
assump,
aging_input_data,
exact_calculations,
writing_output_file=False,
output_tables=False,
output_graphs=False,
output_ceeu=False,
output_dump=False):
"""
High-level logic for dynamic analysis using GrowModel class.
Parameters
----------
First six parameters are same as the first six parameters of
the TaxCalcIO.init method.
Last five parameters are same as the first five parameters of
the TaxCalcIO.analyze method.
Returns
-------
Nothing
"""
# pylint: disable=too-many-arguments,too-many-locals
progress = 'STARTING ANALYSIS FOR YEAR {}'
gdiff_dict = {Policy.JSON_START_YEAR: {}}
for year in range(Policy.JSON_START_YEAR, tax_year + 1):
print(progress.format(year)) # pylint: disable=superfluous-parens
# specify growdiff_response using gdiff_dict
growdiff_response = Growdiff()
growdiff_response.update_growdiff(gdiff_dict)
gd_dict = TaxCalcIO.annual_analysis(
input_data, tax_year, reform, assump, aging_input_data,
exact_calculations, growdiff_response, year,
writing_output_file, output_tables, output_graphs, output_ceeu,
output_dump)
gdiff_dict[year + 1] = gd_dict
@staticmethod
def annual_analysis(input_data, tax_year, reform, assump, aging_input_data,
exact_calculations, growdiff_response, year,
writing_output_file, output_tables, output_graphs,
output_ceeu, output_dump):
"""
Conduct static analysis for specifed year and growdiff_response.
Parameters
----------
First six parameters are same as the first six parameters of
the TaxCalcIO.init method.
Last five parameters are same as the first five parameters of
the TaxCalcIO.analyze method.
Returns
-------
gd_dict: Growdiff sub-dictionary for year+1
"""
# pylint: disable=too-many-arguments
# instantiate TaxCalcIO object for specified year and growdiff_response
tcio = TaxCalcIO(input_data=input_data,
tax_year=year,
reform=reform,
assump=assump)
tcio.init(input_data=input_data,
tax_year=year,
reform=reform,
assump=assump,
growdiff_response=growdiff_response,
aging_input_data=aging_input_data,
exact_calculations=exact_calculations)
if year == tax_year:
# conduct final tax analysis for year equal to tax_year
tcio.analyze(writing_output_file=writing_output_file,
output_tables=output_tables,
output_graphs=output_graphs,
output_ceeu=output_ceeu,
output_dump=output_dump)
gd_dict = {}
else:
# conduct intermediate tax analysis for year less than tax_year
tcio.analyze()
# build dict in gdiff_dict key:dict pair for key equal to next year
# ... extract tcio results for year needed by GrowModel class
# >>>>> add logic here <<<<<
# ... use extracted results to advance GrowModel to next year
# >>>>> add logic here <<<<<
# ... extract next year GrowModel results for next year gdiff_dict
# >>>>> add logic here <<<<<
gd_dict = {} # TEMPORARY CODE
return gd_dict
class TaxCalcIO(object):
"""
Constructor for the Tax-Calculator Input-Output class.
Parameters
----------
input_data: string or Pandas DataFrame
string is name of INPUT file that is CSV formatted containing
variable names in the Records.USABLE_READ_VARS set, or
Pandas DataFrame is INPUT data containing variable names in
the Records.USABLE_READ_VARS set. INPUT vsrisbles not in the
Records.USABLE_READ_VARS set can be present but are ignored.
tax_year: integer
calendar year for which taxes will be computed for INPUT.
reform: None or string
None implies no policy reform (current-law policy), or
string is name of optional REFORM file.
assump: None or string
None implies economic assumptions are standard assumptions,
or string is name of optional ASSUMP file.
growdiff_response: Growdiff object or None
growdiff_response Growdiff object used in dynamic analysis;
must be None when conducting static analysis.
aging_input_data: boolean
whether or not to age record data from data year to tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
Raises
------
ValueError:
if input_data is neither string nor pandas DataFrame.
if input_data string does not have .csv extension.
if file specified by input_data string does not exist.
if reform is neither None nor string.
if reform string does not have .json extension.
if file specified by reform string does not exist.
if assump is neither None nor string.
if assump string does not have .json extension.
if growdiff_response is not a Growdiff object or None
if file specified by assump string does not exist.
if tax_year before Policy start_year.
if tax_year after Policy end_year.
if behavior in --assump ASSUMP has any response.
if growdiff_response in --assump ASSUMP has any response.
Returns
-------
class instance: TaxCalcIO
"""
def __init__(
self,
input_data,
tax_year,
reform,
assump,
growdiff_response, # =None in static analysis
aging_input_data,
exact_calculations):
"""
TaxCalcIO class constructor.
"""
# pylint: disable=too-many-arguments
# pylint: disable=too-many-locals
# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
# check for existence of INPUT file
if isinstance(input_data, six.string_types):
# remove any leading directory path from INPUT filename
fname = os.path.basename(input_data)
# check if fname ends with ".csv"
if fname.endswith('.csv'):
inp = '{}-{}'.format(fname[:-4], str(tax_year)[2:])
else:
msg = 'INPUT file named {} does not end in .csv'
raise ValueError(msg.format(fname))
# check existence of INPUT file
if not os.path.isfile(input_data):
msg = 'INPUT file named {} could not be found'
raise ValueError(msg.format(input_data))
elif isinstance(input_data, pd.DataFrame):
inp = 'df-{}'.format(str(tax_year)[2:])
else:
msg = 'INPUT is neither string nor Pandas DataFrame'
raise ValueError(msg)
# construct output_filename and delete old output file if it exists
if reform is None:
self._reform = False
ref = ''
elif isinstance(reform, six.string_types):
self._reform = True
# remove any leading directory path from REFORM filename
fname = os.path.basename(reform)
# check if fname ends with ".json"
if fname.endswith('.json'):
ref = '-{}'.format(fname[:-5])
else:
msg = 'REFORM file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor reform is neither None nor str'
raise ValueError(msg)
if assump is None:
asm = ''
elif isinstance(assump, six.string_types):
# remove any leading directory path from ASSUMP filename
fname = os.path.basename(assump)
# check if fname ends with ".json"
if fname.endswith('.json'):
asm = '-{}'.format(fname[:-5])
else:
msg = 'ASSUMP file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor assump is neither None nor str'
raise ValueError(msg)
self._output_filename = '{}{}{}.csv'.format(inp, ref, asm)
delete_file(self._output_filename)
# get parameter dictionaries from --reform and --assump files
param_dict = Calculator.read_json_param_files(reform, assump)
# make sure no behavioral response is specified in --assump
beh = Behavior()
beh.update_behavior(param_dict['behavior'])
if beh.has_any_response():
msg = '--assump ASSUMP cannot assume any "behavior"'
raise ValueError(msg)
# make sure no growdiff_response is specified in --assump
gdiff_response = Growdiff()
gdiff_response.update_growdiff(param_dict['growdiff_response'])
if gdiff_response.has_any_response():
msg = '--assump ASSUMP cannot assume any "growdiff_response"'
raise ValueError(msg)
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(param_dict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
msg = 'TaxCalcIO.ctor growdiff_response is neither None nor {}'
raise ValueError(msg.format('a Growdiff object'))
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
# create Policy object and implement reform if specified
if self._reform:
pol = Policy(gfactors=gfactors_ref)
pol.implement_reform(param_dict['policy'])
clp = Policy(gfactors=gfactors_clp)
else:
pol = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
raise ValueError(msg.format(tax_year, pol.start_year))
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
raise ValueError(msg.format(tax_year, pol.end_year))
# set policy to tax_year
pol.set_year(tax_year)
if self._reform:
clp.set_year(tax_year)
# read input file contents into Records object(s)
if aging_input_data:
if self._reform:
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else:
recs = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
exact_calculations=exact_calculations,
gfactors=None,
adjust_ratios=None,
weights=None,
start_year=tax_year)
if self._reform:
recs_clp = copy.deepcopy(recs)
# create Calculator object(s)
con = Consumption()
con.update_consumption(param_dict['consumption'])
self._calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
sync_years=aging_input_data)
if self._reform:
self._calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)
def tax_year(self):
"""
Returns year for which TaxCalcIO calculations are being done.
"""
return self._calc.policy.current_year
def output_filepath(self):
"""
Return full path to output file named in TaxCalcIO constructor.
"""
dirpath = os.path.abspath(os.path.dirname(__file__))
return os.path.join(dirpath, self._output_filename)
def static_analysis(self,
writing_output_file=False,
output_graph=False,
output_ceeu=False,
output_dump=False):
"""
Conduct STATIC tax analysis for INPUT and write or return OUTPUT.
Parameters
----------
writing_output_file: boolean
output_graph: boolean
whether or not to generate and show HTML graphs of average
and marginal tax rates by income percentile
output_ceeu: boolean
whether or not to calculate and write to stdout standard
certainty-equivalent expected-utility statistics
output_dump: boolean
whether or not to replace standard output with all input and
calculated variables using their Tax-Calculator names
Returns
-------
Nothing
"""
# pylint: disable=too-many-arguments,too-many-locals,too-many-branches
# conduct STATIC tax analysis
if output_dump:
(mtr_paytax, mtr_inctax,
_) = self._calc.mtr(wrt_full_compensation=False)
else: # do not need marginal tax rates
self._calc.calc_all()
# optionally conduct normative welfare analysis
if output_ceeu:
self._calc_clp.calc_all()
cedict = ce_aftertax_income(self._calc_clp,
self._calc,
require_no_agg_tax_change=False)
ceeu_results = TaxCalcIO.ceeu_output(cedict)
else:
ceeu_results = None
# extract output if writing_output_file
if writing_output_file:
if output_dump:
outdf = self.dump_output(mtr_inctax, mtr_paytax)
else:
outdf = self.standard_output()
assert len(outdf.index) == self._calc.records.dim
outdf.to_csv(self._output_filename,
index=False,
float_format='%.2f')
# optionally write --graph output to HTML files
if output_graph:
atr_data = atr_graph_data(self._calc_clp, self._calc)
atr_plot = xtr_graph_plot(atr_data)
atr_fname = self._output_filename.replace('.csv', '-atr.html')
atr_title = 'ATR by Income Percentile'
write_graph_file(atr_plot, atr_fname, atr_title)
mtr_data = mtr_graph_data(self._calc_clp,
self._calc,
alt_e00200p_text='Taxpayer Earnings')
mtr_plot = xtr_graph_plot(mtr_data)
mtr_fname = self._output_filename.replace('.csv', '-mtr.html')
mtr_title = 'MTR by Income Percentile'
write_graph_file(mtr_plot, mtr_fname, mtr_title)
# optionally write --ceeu output to stdout
if ceeu_results:
print(ceeu_results) # pylint: disable=superfluous-parens
def standard_output(self):
"""
Extract standard output and return as pandas DataFrame.
"""
varlist = ['RECID', 'YEAR', 'WEIGHT', 'INCTAX', 'LSTAX', 'PAYTAX']
odict = dict()
crecs = self._calc.records
odict['RECID'] = crecs.RECID # id for tax filing unit
odict['YEAR'] = self.tax_year() # tax calculation year
odict['WEIGHT'] = crecs.s006 # sample weight
# pylint: disable=protected-access
odict['INCTAX'] = crecs._iitax # federal income taxes
odict['LSTAX'] = crecs.lumpsum_tax # lump-sum tax
odict['PAYTAX'] = crecs._payrolltax # payroll taxes (ee+er)
odf = pd.DataFrame(data=odict, columns=varlist)
return odf
@staticmethod
def ceeu_output(cedict):
"""
Extract --ceeu output and return as text string.
"""
text = ('Aggregate {} Pre-Tax Expanded Income and '
'Tax Revenue ($billion)\n')
txt = text.format(cedict['year'])
txt += ' baseline reform difference\n'
fmt = '{} {:12.3f} {:10.3f} {:12.3f}\n'
txt += fmt.format('income', cedict['inc1'], cedict['inc2'],
cedict['inc2'] - cedict['inc1'])
alltaxdiff = cedict['tax2'] - cedict['tax1']
txt += fmt.format('alltax', cedict['tax1'], cedict['tax2'], alltaxdiff)
txt += ('Certainty Equivalent of Expected Utility of '
'After-Tax Expanded Income ($)\n')
txt += ('(assuming consumption equals ' 'after-tax expanded income)\n')
txt += 'crra baseline reform pctdiff\n'
fmt = '{} {:17.2f} {:10.2f} {:11.2f}\n'
for crra, ceeu1, ceeu2 in zip(cedict['crra'], cedict['ceeu1'],
cedict['ceeu2']):
txt += fmt.format(crra, ceeu1, ceeu2,
100.0 * (ceeu2 - ceeu1) / ceeu1)
if abs(alltaxdiff) >= 0.0005:
txt += ('WARN: baseline and reform cannot be '
'sensibly compared\n')
text = (' because "alltax difference" is '
'{:.3f} which is not zero\n')
txt += text.format(alltaxdiff)
txt += ('FIX: adjust _LST or another reform policy parameter '
'to bracket\n')
txt += (' "alltax difference" equals zero and '
'then interpolate')
else:
txt += 'NOTE: baseline and reform can be sensibly compared\n'
txt += ' because "alltax difference" is essentially zero'
return txt
def dump_output(self, mtr_inctax, mtr_paytax):
"""
Extract --dump output and return as pandas DataFrame.
"""
odf = pd.DataFrame()
varset = Records.USABLE_READ_VARS | Records.CALCULATED_VARS
for varname in varset:
vardata = getattr(self._calc.records, varname)
odf[varname] = vardata
odf['mtr_inctax'] = mtr_inctax
odf['mtr_paytax'] = mtr_paytax
return odf
@staticmethod
def dynamic_analysis(input_data,
tax_year,
reform,
assump,
aging_input_data,
exact_calculations,
writing_output_file=False,
output_graph=False,
output_ceeu=False,
output_dump=False):
"""
High-level logic for dyanamic tax analysis.
Parameters
----------
First six parameters are same as the first six parameters of
the TaxCalcIO constructor.
Last four parameters are same as the first four parameters of
the TaxCalcIO static_analysis method.
Returns
-------
Nothing
"""
# pylint: disable=too-many-arguments
# pylint: disable=superfluous-parens
progress = 'STARTING ANALYSIS FOR YEAR {}'
gdiff_dict = {Policy.JSON_START_YEAR: {}}
for year in range(Policy.JSON_START_YEAR, tax_year + 1):
print(progress.format(year))
# specify growdiff_response using gdiff_dict
growdiff_response = Growdiff()
growdiff_response.update_growdiff(gdiff_dict)
gd_dict = TaxCalcIO.annual_analysis(
input_data, tax_year, reform, assump, aging_input_data,
exact_calculations, growdiff_response, year,
writing_output_file, output_graph, output_ceeu, output_dump)
gdiff_dict[year + 1] = gd_dict
@staticmethod
def annual_analysis(input_data, tax_year, reform, assump, aging_input_data,
exact_calculations, growdiff_response, year,
writing_output_file, output_graph, output_ceeu,
output_dump):
"""
Conduct static analysis for specifed year and growdiff_response.
Parameters
----------
First six parameters are same as the first six parameters of
the TaxCalcIO constructor.
Last four parameters are same as the first four parameters of
the TaxCalcIO static_analysis method.
Returns
-------
gd_dict: Growdiff sub-dictionary for year+1
"""
# pylint: disable=too-many-arguments
# instantiate TaxCalcIO object for specified year and growdiff_response
tcio = TaxCalcIO(input_data=input_data,
tax_year=year,
reform=reform,
assump=assump,
growdiff_response=growdiff_response,
aging_input_data=aging_input_data,
exact_calculations=exact_calculations)
if year == tax_year:
# conduct final tax analysis for year equal to tax_year
tcio.static_analysis(writing_output_file=writing_output_file,
output_graph=output_graph,
output_ceeu=output_ceeu,
output_dump=output_dump)
gd_dict = {}
else:
# conduct intermediate tax analysis for year less than tax_year
tcio.static_analysis()
# build dict in gdiff_dict key:dict pair for key equal to next year
# ... extract tcio results for year needed by GrowModel class
# >>>>> add logic here <<<<<
# ... use extracted results to advance GrowModel to next year
# >>>>> add logic here <<<<<
# ... extract next year GrowModel results for next year gdiff_dict
# >>>>> add logic here <<<<<
gd_dict = {} # TEMPORARY CODE
return gd_dict