Merge pull request #1905 from martinholmer/check-pension-income

Add check of total>=taxable pension income

docs/index.htmx

@@ -464,9 +464,12 @@ above equations hold for each filing unit in the input file.</p>
 <p>In addition to this earnings-splitting data-preparation issue,
 Tax-Calculator expects that the value of ordinary dividends
 (<kbd>e00600</kbd>) will be no less than the value of qualified
-dividends (<kbd>e00650</kbd>) for each filing unit.  Again, it is your
-responsibility to prepare input data in a way that ensures this
-relationship is true for each filing unit.</p>
+dividends (<kbd>e00650</kbd>) for each filing unit.  And it also
+expects that the value of total pension and annuity income
+(<kbd>e01500</kbd>) will be no less than the value of taxable pension
+and annuity income (<kbd>e01700</kbd>) for each filing unit.  Again,
+it is your responsibility to prepare input data in a way that ensures
+these relationships are true for each filing unit.</p>
 
 <p id="marskids">Here's an example of how to specify a few stylized
 filing units with and without young children.

taxcalc/records.py

@@ -138,6 +138,12 @@ def __init__(self,
                            rtol=0.0, atol=tol):
             msg = 'expression "e00600 >= e00650" is not true for every record'
             raise ValueError(msg)
+        # check that total pension income is no less than taxable pension inc
+        nontaxable_pensions = np.maximum(0., self.e01500 - self.e01700)
+        if not np.allclose(self.e01500, self.e01700 + nontaxable_pensions,
+                           rtol=0.0, atol=tol):
+            msg = 'expression "e01500 >= e01700" is not true for every record'
+            raise ValueError(msg)
         # handle grow factors
         is_correct_type = isinstance(gfactors, Growfactors)
         if gfactors is not None and not is_correct_type:

taxcalc/records_variables.json

@@ -172,7 +172,7 @@
     },
     "e01500": {
       "type": "float",
-      "desc": "Pensions and annuities",
+      "desc": "Total pensions and annuities",
       "form": {"2013-2016": "1040 line 16a"},
       "availability": "taxdata_puf, taxdata_cps"
     },

taxcalc/tests/puf_var_wght_means_by_year.csv

@@ -22,7 +22,7 @@ e00900,Sch C business net profit/loss for filing unit,    1862,    1957,    2035
 e01100,Capital gain distributions not reported on Sch D,      14,      20,      25,      23,      25,      26,      26,      26,      27,      28,      28,      29,      30,      31,      32
 e01200,Other net gain/loss from Form 4797,    -133,    -184,    -236,    -205,    -225,    -229,    -225,    -231,    -235,    -239,    -247,    -254,    -261,    -269,    -278
 e01400,Taxable IRA distributions,    1556,    1671,    1767,    1840,    1929,    2033,    2137,    2246,    2357,    2475,    2596,    2719,    2852,    2987,    3126
-e01500,Pensions and annuities,    6170,    6420,    6687,    6843,    7039,    7275,    7516,    7762,    8028,    8305,    8591,    8877,    9173,    9490,    9811
+e01500,Total pensions and annuities,    6170,    6420,    6687,    6843,    7039,    7275,    7516,    7762,    8028,    8305,    8591,    8877,    9173,    9490,    9811
 e01700,Taxable pensions and annuities,    3936,    4092,    4246,    4327,    4430,    4557,    4688,    4823,    4971,    5124,    5279,    5440,    5601,    5774,    5953
 e02000,"Sch E total rental, royalty, partnership, S-corporation, etc, income/loss (includes e26270 and e27200)",    3781,    4192,    4165,    4093,    4036,    3978,    3957,    3962,    3993,    4052,    4148,    4254,    4363,    4486,    4612
 e02100,Farm net income/loss for filing unit from Sch F,     -48,     -43,     -21,     -15,     -12,     -18,     -22,     -27,     -32,     -35,     -38,     -39,     -40,     -40,     -39

taxcalc/tests/test_records.py

@@ -83,6 +83,10 @@ def test_correct_Records_instantiation(cps_subsample):
     (
         u'RECID,MARS,e00600,e00650\n'
         u'1,    1,        8,     9\n'
+    ),
+    (
+        u'RECID,MARS,e01500,e01700\n'
+        u'1,    1,        6,     7\n'
     )
 ])
 def test_read_data(csv):