Repository: econ-ark/HARK (language: python)

Issue:
[WIP] Initial distributions of permanent income and mNrm
#592 asks for "a plausible calibration of distributions at age of workforce entry of: -Permanent income -Assets". In discussions with @llorracc, we determined the best place to get such a calibration would be the SCF. This PR works on adding calibrations of these two distributions coming ---as directly and transparently as possible--- from the SCF.

An issue is that we clearly do not want to have the full SCF in the toolbox. The solution that I propose here is:
- Keep only a relatively light table with all (most of?) the summary statistics we will possibly need: `WealthIncomeStats.csv`. It currently contains survey-weighted means and standard deviations at multiple age-year-education combinations. I can easily expand it to contain, e.g., deciles in case one wants to avoid lognormality.
- Link to [a public repo that produces the table directly from raw SCF files](https://github.com/Mv77/SCF-IncWealthDist).
- Have functions that parse the contents of the table to produce the exact parameters that HARK's models need.

This clearly need more documentation, which I will add. I first wanted to check whether you (@llorracc @sbenthall ) like this solution of keeping only the table and linking to the external repo that reproduces it.

Please ensure your pull request adheres to the following guidelines:

<!--- Put an `x` in all the boxes that apply: -->
- [ ] Tests for new functionality/models or Tests to reproduce the bug-fix in code.
- [ ] Updated documentation of features that add new functionality.
- [ ] Update CHANGELOG.md with major/minor changes.



Discussion / hints:
@llorracc Chris, not sure if you merged this by accident, but it was work in progress. It's a bit far from complete. I just wanted initial feedback on how you all felt about the external repo solution. It needs at least:
- Documentation.
- A way to transform the moments to real dollars for any year one wants. I wanted to discuss the best way to do this in our call today.

Fix patch:
diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 3b83b45d7..1e4fbef90 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -20,10 +20,11 @@ Release Data: TBD
 * Centralizes the definition of value, marginal value, and marginal marginal value functions that use inverse-space
 interpolation for problems with CRRA utility. See [#888](https://github.com/econ-ark/HARK/pull/888).
 * MarkovProcess class used in ConsMarkovModel, ConsRepAgentModel, ConsAggShockModel [#902](https://github.com/econ-ark/HARK/pull/902) [#929](https://github.com/econ-ark/HARK/pull/929)
-* Adds SSA life tables and methods to extract survival probabilities from them [#986](https://github.com/econ-ark/HARK/pull/906).
-* Adds the U.S. CPI research series and tools to extract inflation adjustments from it [#930](https://github.com/econ-ark/HARK/pull/930).
 * replace HARKobject base class with MetricObject and Model classes [#903](https://github.com/econ-ark/HARK/pull/903/)
 * Add __repr__ and __eq__ methods to Model class [#903](https://github.com/econ-ark/HARK/pull/903/)
+* Adds SSA life tables and methods to extract survival probabilities from them [#986](https://github.com/econ-ark/HARK/pull/906).
+* Adds the U.S. CPI research series and tools to extract inflation adjustments from it [#930](https://github.com/econ-ark/HARK/pull/930).
+* Adds a module for extracting initial distributions of permanent income (`pLvl`) and normalized assets (`aNrm`) from the SCF [#932](https://github.com/econ-ark/HARK/pull/932).
 * Fix the return fields of `dcegm/calcCrossPoints`[#909](https://github.com/econ-ark/HARK/pull/909).
 * Corrects location of constructor documentation to class string for Sphinx rendering [#908](https://github.com/econ-ark/HARK/pull/908)
 * Adds a module for producing life-cycle profiles of income shock variances from [Sabelhaus and Song (2010)](https://www.sciencedirect.com/science/article/abs/pii/S0304393210000358). See [#921](https://github.com/econ-ark/HARK/pull/921).
diff --git a/HARK/datasets/SCF/WealthIncomeDist/README.md b/HARK/datasets/SCF/WealthIncomeDist/README.md
new file mode 100644
index 000000000..4437f8f8e
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/README.md
@@ -0,0 +1,36 @@
+# Summary statistics of wealth and permanent income in the United States
+
+The summary statistics in `WealthIncomeStats.csv` are computed using the
+Survey of Consumer Finances. The file can be replicated directly from the
+unprocessed SCF summary files using the repository [SCF-IncWealthDist](https://github.com/Mv77/SCF-IncWealthDist), created by [Mateo Velasquez-Giraldo](https://mv77.github.io/).
+
+**NOTE:** wealth and permanent income levels in `WealthIncomeStats.csv` are expressed in thousands of dollars before taking logarithms.
+
+# `WealthIncomeStats.csv`
+
+Both the file and this description come from the repository
+[SCF-IncWealthDist](https://github.com/Mv77/SCF-IncWealthDist).
+
+This is a table with summary statistics from the SCF. The file has the following
+columns, described by groups:
+- Demographic and sample-defining variables: these variables describe the sample on which the summary statistics were computed on. Their values should be read as filters applied
+  to the SCF's population before computing the summary statistics.
+  - `Educ`: education level. It can take the levels `NoHS` for individuals without a high-school diploma, `HS` for individuals with a high-school diploma but no college degree, and
+    `College` for individuals with a college degree. `All` marks rows in which individuals from all educational attainment levels were used.
+  - `Age_grp`: age group. I split the sample in 5-year brackets according to their age and this variable indicates which bracket the statistics correspond to. Note that the left
+     extreme of brackets is not included, so `(20,25]` corresponds to ages `{21,22,23,24,25}`. `All` marks rows in which all age groups were used.
+  - `YEAR`: survey wave of the SCF. It indicates which waves of the SCF were used in calculating the row's statistics. `All` marks rows in which all waves were pooled. **NOTE:**
+     when combining multiple waves,  I do not re-weight observations: I continue to use the weight variable as if all observations came from the same wave.
+
+ - Summary statistics:
+  - `lnPermIncome.mean` and `lnPermIncome.sd`: survey-weighted mean and standard deviation of the natural logarithm of "permanent income", as measured by the variable `norminc`
+    in the SCF's summary files. Note that this measure contains, among others, capital gains and pension-fund withdrawals and might therefore substantially differ from the
+    popular concept of the "permanent component" of labor income, especially at older ages
+    (see [the SCF's website for exact definitions](https://www.federalreserve.gov/econres/scfindex.htm)).
+
+# SCFDistTools.py
+
+Contains functions to read the table and its columns, and convert them to
+parameters to be used by HARK's `AgentType` classes in their `simBirth()`
+methods to produce realistically calibrated distributions of initial permanent
+income and wealth levels.
diff --git a/HARK/datasets/SCF/WealthIncomeDist/SCFDistTools.py b/HARK/datasets/SCF/WealthIncomeDist/SCFDistTools.py
new file mode 100644
index 000000000..887e4150d
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/SCFDistTools.py
@@ -0,0 +1,204 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan  8 15:36:14 2021
+
+@author: Mateo
+"""
+
+import numpy as np
+import pandas as pd
+from warnings import warn
+from HARK.datasets.cpi.us.CPITools import cpi_deflator
+import os
+
+scf_sumstats_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+def get_scf_distr_stats():
+    """
+    A function to read the full table of SCF summary statistics as a Pandas
+    DataFrame
+
+    Returns
+    -------
+    table : pandas DataFrame
+        A pandas representation of file WealthIncomeStats.csv. See ./README.md
+        for an explanation of the variables in the table and its source.
+    """
+
+    # Form the file name
+    filename = os.path.join(scf_sumstats_dir, "WealthIncomeStats.csv")
+
+    # Read csv
+    table = pd.read_csv(filename, sep=",")
+
+    return table
+
+
+def parse_scf_distr_stats(age=None, education=None, wave=None):
+    """
+    A funtion to retreive SCF summary statistics regarding wealth and
+    permanent income for a specific SCF wave, age bracket, and education
+    level.
+
+    Parameters
+    ----------
+    age : int, optional
+        Age for which to retreive summary statistics. The statistics are
+        calculated using 5-year age bins. Therefore, for instance, Age = 23
+        will return statistics computed on ages (20,25].
+        The default is None. In such case, the function will return statistics
+        for the group without any age filtration.
+    education : str, optional
+        Education level for which to retreive summary statistics. Must be one
+        of 'NoHS' (no high-school or GED), 'HS' (high-school or GED), or
+        'College'.
+        The default is None. In such case, no education filtration is applied
+        (all groups are pooled).
+    wave : int, optional
+        SCF wave to use for summary statistics. Must be one of 1995, 1998,
+        2001, 2004, 2007, 2010, 2013, 2016, 2019.
+        The default is None. In such case, all waves are used.
+
+    Returns
+    -------
+    row_dict : dict
+        Dictionary with summary statistics for wealth and permanent income
+        for the specified group. Its fields correspond to the columns of
+        ./WealthIncomeStats.csv, which are described in ./README.md.
+
+    """
+
+    # Pre-process year to make it a five-year bracket as in the table
+    if age is not None:
+
+        u_bound = int(np.ceil(age / 5) * 5)
+        l_bound = u_bound - 5
+        age_bracket = "(" + str(l_bound) + "," + str(u_bound) + "]"
+
+        warn("Returning SCF summary statistics for ages " + age_bracket + ".")
+
+    else:
+
+        # If no age is given, use all age brackets.
+        age_bracket = "All"
+
+    # Check whether education is in one of the allowed categories
+    if education is not None:
+
+        message = (
+            "If an education level is provided, it must be one of "
+            + "'NoHS', 'HS', or 'College'."
+        )
+        assert education in ["NoHS", "HS", "College"], message
+
+    else:
+
+        education = "All"
+
+    # Parse the wave
+    wave_str = "All" if wave is None else str(int(wave))
+
+    # Read table
+    filename = os.path.join(scf_sumstats_dir, "WealthIncomeStats.csv")
+
+    # Read csv
+    table = pd.read_csv(
+        filename,
+        sep=",",
+        index_col=["Educ", "YEAR", "Age_grp"],
+        dtype={"Educ": str, "YEAR": str, "Age_grp": str, "BASE_YR": int},
+    )
+
+    # Try to access the requested combination
+    try:
+
+        row = table.loc[(education, wave_str, age_bracket)]
+
+    except KeyError as e:
+
+        message = (
+            "The summary statistics do not contain the "
+            + "Age/Wave/Education combination that was requested."
+        )
+        raise Exception(message).with_traceback(e.__traceback__)
+
+    # Check for NAs
+    if any(row.isna()):
+        warn(
+            "There were not enough observations in the requested "
+            + "Age/Wave/Education combination to compute all summary"
+            + "statistics."
+        )
+
+    # to_dict transforms BASE_YR to float from int. Manually fix this
+    row_dict = row.to_dict()
+    row_dict["BASE_YR"] = int(row_dict["BASE_YR"])
+
+    return row_dict
+
+
+def income_wealth_dists_from_scf(base_year, age=None, education=None, wave=None):
+    """
+    Finds and formats parameters for the initial distributions of permanent
+    income and normalized wealth from the SCF's summary statistics.
+    
+    Many of HARK's models (e.g. PerfForesightConsumerType.simBirth(),
+    GenIncProcessConsumerType.simBirth()) assume the initial distribution
+    of permanent income (pLvl) and normalized wealth (aNrm) are log-normal.
+    They construct these distributions from their means and standard
+    deviations, which are parameters to the models. This fuction assigns these
+    parameters using summary statistics from the SCF.
+
+    Parameters
+    ----------
+    base_year : int
+        Base year to use for nominal quantities.
+    age : int, optional
+        Age for which to retreive summary statistics. See
+        parse_scf_distr_stats(). The default is None.
+    education : str, optional
+        Edcuational attainment level for which to retreive summary
+        statistics. See parse_scf_distr_stats(). The default is None.
+    wave : int, optional
+        SCF wave to use for summary statistics. See parse_scf_distr_stats().
+        The default is None.
+
+    Returns
+    -------
+    param_dict : dict
+        Dictionary with means and standard deviations of the distributions
+        of permanent income and normalized wealth.
+    """
+
+    # Extract summary statistics from the SCF table
+    stats = parse_scf_distr_stats(age, education, wave)
+
+    # Find the deflator to adjust nominal quantities. The SCF summary files
+    # use the september CPI measurement to deflate, so use that.
+    deflator = cpi_deflator(
+        from_year=stats["BASE_YR"], to_year=base_year, base_month="SEP"
+    )[0]
+
+    # log(X*deflator) = log(x) + deflator.
+    # Therefore, the deflator does not apply to:
+    # - NrmWealth: it's the ratio of two nominal quantities, so unaltered by base changes.
+    # - sd(ln(Permanent income)): the deflator is an additive shift to log-permanent income
+    #   so the standard deviation is unchanged.
+
+    log_deflator = np.log(deflator)
+    param_dict = {
+        "aNrmInitMean": stats[
+            "lnNrmWealth.mean"
+        ],  # Mean of log initial assets (only matters for simulation)
+        "aNrmInitStd": stats[
+            "lnNrmWealth.sd"
+        ],  # Standard deviation of log initial assets (only for simulation)
+        "pLvlInitMean": stats["lnPermIncome.mean"]
+        + log_deflator,  # Mean of log initial permanent income (only matters for simulation)
+        "pLvlInitStd": stats[
+            "lnPermIncome.sd"
+        ],  # Standard deviation of log initial permanent income (only matters for simulation)
+    }
+
+    return param_dict
diff --git a/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv b/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv
new file mode 100644
index 000000000..f6a3564eb
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv
@@ -0,0 +1,681 @@
+Educ,YEAR,Age_grp,w.obs,obs,lnWealth.mean,lnWealth.sd,lnNrmWealth.mean,lnNrmWealth.sd,lnPermIncome.mean,lnPermIncome.sd,BASE_YR
+NoHS,1995,"(15,20]",254159.0751330069,45,2.0390492989171642,1.8723637720094584,-1.1415928747499353,1.2123861616412062,3.1806421736670996,0.9174337184338497,2019
+NoHS,1995,"(20,25]",471238.6505663131,79,2.061075096679409,1.6103075901874595,-1.0611984728537684,1.475816500147777,3.122273569533178,0.7264931123240703,2019
+NoHS,1995,"(25,30]",567079.7571388013,91,2.699733637877628,1.209762491580228,-0.8866317828863428,1.2315962819299662,3.586365420763971,0.6698676085870335,2019
+NoHS,1995,"(30,35]",561122.6223094718,101,2.41600588350625,1.5320710288200308,-0.8548681211202709,1.1991765252775426,3.270874004626521,0.5956165986325258,2019
+NoHS,1995,"(35,40]",980859.5982391366,175,2.9307459801903732,2.2791542495407664,-0.6016033428134426,1.9953057547195632,3.5323493230038157,0.7997432616998629,2019
+NoHS,1995,"(40,45]",982487.0230649995,156,2.9176029574671842,1.8728583152618197,-0.7363269281003587,1.596555880516999,3.6539298855675426,0.6911510104816453,2019
+NoHS,1995,"(45,50]",633992.145392498,105,3.4199687960730243,2.309363786159219,-0.4190314243418286,1.9555160932013003,3.8390002204148526,0.7271902980487227,2019
+NoHS,1995,"(50,55]",1052664.078934471,181,3.9631314091344145,1.8344938408406024,0.2990282993921788,1.3378004535780876,3.664103109742236,0.8777065641211838,2019
+NoHS,1995,"(55,60]",1081293.7543381394,161,4.1504868798536725,1.911276924541766,0.19315782814324792,1.6593916577375334,3.957329051710425,0.6181058008509499,2019
+NoHS,1995,"(60,65]",1640435.4511792683,235,3.959522759912683,1.6962859319952326,0.6456781133666262,1.5088286804187747,3.313844646546057,0.9056069353413314,2019
+NoHS,1995,"(65,70]",1911024.4533374945,270,4.530708167607755,1.7998103033830657,1.1542216733317936,1.5364293575243984,3.3764864942759614,0.8101756999385431,2019
+NoHS,1995,"(70,75]",1869302.2394106474,262,4.72787522403732,1.4587629273517413,1.539672904646723,1.3450234888023387,3.188202319390597,0.7480723782422178,2019
+NoHS,1995,"(75,80]",1759377.6172246474,238,3.8352902921380116,1.9322708397465354,1.0709321873867144,1.747853603356272,2.7643581047512975,1.1130351079024319,2019
+NoHS,1995,"(80,85]",913703.7079329834,130,3.8389114311169017,1.8296407346983814,1.0226254223620126,1.8725773690147332,2.816286008754889,0.9604688582067351,2019
+NoHS,1995,"(85,90]",440249.00659021793,57,3.9876507914831483,2.0256176194100175,0.9362979207019342,1.7117255209925282,3.051352870781214,0.6286227833855311,2019
+NoHS,1995,"(90,95]",138470.92246808432,28,4.197219923126586,2.4858484887741232,1.2994659267630355,2.637533758630498,2.897753996363551,0.7106677267823902,2019
+NoHS,1998,"(15,20]",111297.21483771286,19,1.6351231450084378,2.3115122394284016,-1.5265284687826273,1.9864058689100768,3.161651613791065,0.3903841497164726,2019
+NoHS,1998,"(20,25]",648729.817998217,109,1.6213284658040783,1.853579365617018,-1.4577789856136514,1.9964709753027032,3.0791074514177295,0.9282504430113926,2019
+NoHS,1998,"(25,30]",864616.4014484201,141,2.41934701916797,1.7972472421394228,-0.886000236199471,1.5496466037080503,3.305347255367441,0.7834627135418694,2019
+NoHS,1998,"(30,35]",758060.7347963224,126,2.227525027493211,1.8547399538499112,-1.4113028332665893,1.6358556545763951,3.6388278607598004,0.6846238155897263,2019
+NoHS,1998,"(35,40]",1252330.0085377665,196,3.3239295510622715,2.2840788362948157,-0.34838544297338986,2.0737273959986937,3.6723149940356614,0.7396151580516056,2019
+NoHS,1998,"(40,45]",754737.3267919559,136,3.60169061209688,1.392766963470353,-0.05051686847472791,1.3563440875464057,3.652207480571608,0.9061352082622736,2019
+NoHS,1998,"(45,50]",756064.1495596407,123,3.782762178862576,1.4105031448515455,0.10711096578327702,1.258835604021331,3.675651213079299,0.902520307324946,2019
+NoHS,1998,"(50,55]",961974.562666491,152,4.134130925390506,1.9248975453855477,0.5334697275351005,1.597859102854461,3.600661197855406,0.999785069338481,2019
+NoHS,1998,"(55,60]",1265772.2073251319,205,4.190847084639045,1.9443863465095592,0.5740938575743559,1.6705295585981568,3.6167532270646885,0.7924869612113813,2019
+NoHS,1998,"(60,65]",859910.6181941511,136,3.8495684074305894,2.0743432534325668,0.3880216519464565,1.9943288377636523,3.461546755484133,0.6459499717094644,2019
+NoHS,1998,"(65,70]",1114864.7143920055,155,3.3628176580211537,2.514723627985684,0.5380463660580382,2.074666847600282,2.824771291963115,1.3035400795133012,2019
+NoHS,1998,"(70,75]",1435101.324025731,234,4.072073144930316,1.8432857490634431,0.9958494689716326,1.5084762571127888,3.0762236759586834,0.7764276667779851,2019
+NoHS,1998,"(75,80]",1308262.7749921551,165,4.182986218730126,2.1756194479749986,1.1070407224155705,2.0960048832978866,3.075945496314555,0.769146152324175,2019
+NoHS,1998,"(80,85]",1090401.7043019878,130,4.668332238387552,1.4042815590357296,1.7078153966433638,1.4127278910345957,2.960516841744188,0.7304158804291525,2019
+NoHS,1998,"(85,90]",344003.1951173016,48,3.909228323621559,2.8281610120906855,1.1610533294774716,2.455615497034932,2.748174994144087,0.8397716495821361,2019
+NoHS,1998,"(90,95]",180245.52656928354,25,3.983900468639974,2.3201530686499336,1.1995053363755193,2.241927810135738,2.7843951322644545,0.7574024553920288,2019
+NoHS,2001,"(15,20]",113545.30896558195,15,2.972124036173893,0.7804681866901584,-0.29239860127400746,1.4910691854195197,3.2645226374479006,0.7262349698935744,2019
+NoHS,2001,"(20,25]",569595.5813429527,91,2.0234621318327597,1.9306124627122412,-1.5126010457095127,1.7064770166421845,3.5360631775422724,0.6024643003988907,2019
+NoHS,2001,"(25,30]",670218.2362365621,110,2.558756877598683,1.5898884713544965,-0.8151757914382627,1.4786712364239873,3.3739326690369458,0.5563910843677695,2019
+NoHS,2001,"(30,35]",866248.2430145431,141,3.2209582772358805,1.6673190555855242,-0.40994959187587315,1.5680159789424912,3.630907869111754,0.7691850206744024,2019
+NoHS,2001,"(35,40]",667179.1448082414,114,2.313197610116022,1.6156028265725733,-1.1091096927379844,1.5790189116119764,3.4223073028540063,0.5333332458056308,2019
+NoHS,2001,"(40,45]",952755.1173981486,168,3.3073532264712933,2.1099675472253763,-0.3565839420255624,1.9983224270782682,3.663937168496856,0.7807178849721856,2019
+NoHS,2001,"(45,50]",945795.4036474751,175,3.214826140728113,2.1107567903907105,-0.4526053776406059,1.6306962306388808,3.667431518368719,0.8682241627012787,2019
+NoHS,2001,"(50,55]",1167689.4011403143,202,3.5862214469901725,2.113620388208285,0.28027376854774466,2.084971096217472,3.3059476784424278,1.1222000920119382,2019
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+NoHS,2016,"(15,20]",80300.48512592616,15,0.25372757806475815,2.0450029911030274,-2.465303605389512,1.2777368483051788,2.71903118345427,0.7891505606750878,2019
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+HS,2007,"(65,70]",3565404.0829834747,620,5.30870113892258,1.7266966928045242,1.499534010568521,1.4164341153974869,3.809167128354059,0.7613635930524145,2019
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+HS,2010,"(15,20]",604375.7980270141,120,2.2837784532118035,1.783269860254688,-0.14129416546863285,1.5081560629243151,2.4250726186804363,0.8847830904125265,2019
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+HS,2010,"(50,55]",6503014.218686916,1702,4.739679064823001,1.820588279213891,0.4873471803482028,1.5366162985980518,4.252331884474798,0.6859210138090632,2019
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+College,2010,"(15,20]",NA,NA,NA,NA,NA,NA,NA,NA,2019
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+College,2013,"(15,20]",NA,NA,NA,NA,NA,NA,NA,NA,2019
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+College,2016,"(15,20]",NA,NA,NA,NA,NA,NA,NA,NA,2019
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+College,2019,"(15,20]",NA,NA,NA,NA,NA,NA,NA,NA,2019
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+NoHS,1998,All,13706372.281554274,2100,3.616249255138931,2.1264560284052343,0.3052365797974567,1.9836002629631881,3.3110126753414746,0.9075351314645086,2019
+NoHS,2001,All,13933503.509655267,2182,3.710508045705039,2.1716338416958876,0.3475181738570871,1.9907411038143876,3.362989871847952,0.8087387307900494,2019
+NoHS,2004,All,13846412.3810685,2049,3.4913830784345716,2.2607758564388267,0.10430885900992695,2.076017156358422,3.387074219424645,0.7449922588601968,2019
+NoHS,2007,All,13133510.420500483,1816,3.690429410717965,2.3315607594583234,0.23574093799266943,2.099779293781707,3.4546884727252953,0.7637591784906118,2019
+NoHS,2010,All,11948367.480269527,2757,3.2742864591932244,2.2884887384617483,-0.1681859984882998,2.1265781168599465,3.442472457681524,0.6958487135578102,2019
+NoHS,2013,All,11618231.741009122,2316,3.274935389885691,2.244125567154438,-0.08483609789340421,2.092391635963999,3.3597714877790956,0.6993632845939471,2019
+NoHS,2016,All,14531310.28324085,2942,3.1015869627603587,2.553319134385985,-0.33759338669044747,2.244152190026329,3.4391803494508064,0.761255616910468,2019
+NoHS,2019,All,12532597.754865196,2295,3.091481759229589,2.487688577777353,-0.33751310311906774,2.2518807634991274,3.4289948623486564,0.7655035930088917,2019
+HS,1995,All,50472867.84298034,9683,4.341460575592293,1.8064946727275035,0.4515678345936135,1.6323753266231444,3.8898927409986785,0.8306632845889547,2019
+HS,1998,All,52300242.63687403,9410,4.449841239847236,1.8715067875358766,0.5198373265415285,1.6840309128768676,3.9300039133057068,0.8040877554951151,2019
+HS,2001,All,53566128.784247935,9318,4.507146944854224,1.9251250201447914,0.4831135131060285,1.706781176911158,4.024033431748195,0.770341510388271,2019
+HS,2004,All,56538298.01162146,9412,4.472583939245569,1.9623200064520758,0.47423425018227683,1.701933079241637,3.998349689063292,0.7685135117756638,2019
+HS,2007,All,59677900.260387,9299,4.5702828969418965,1.9899878198521144,0.5818657302140878,1.7526477010580324,3.988417166727809,0.7821407681665381,2019
+HS,2010,All,57517632.556592494,14041,4.30475838245568,2.021313275424282,0.30923496825232416,1.7773693604973464,3.9955234142033556,0.7560852833378277,2019
+HS,2013,All,60239115.25652231,12915,4.130082128660598,2.1146451876984638,0.22786382380957487,1.8541671387604222,3.9022183048510235,0.7697886716201534,2019
+HS,2016,All,58565445.763883896,12616,4.193896014870771,2.200774470084733,0.2768016199143905,1.9217075315183432,3.9170943949563806,0.7939457283976683,2019
+HS,2019,All,60610574.71648153,11612,4.271628764985156,2.156163988935388,0.337284985434576,1.8755024480291267,3.9343437795505793,0.8324001271926555,2019
+College,1995,All,23487830.801927734,7892,5.283484389446383,1.6904379193545076,0.8171027346722197,1.4244771184883567,4.466381654774164,0.8553062555826004,2019
+College,1998,All,25200685.790815603,8077,5.57575762374777,1.7857992370346263,0.9735268510145542,1.4686031580156753,4.602230772733217,0.8310745489785812,2019
+College,2001,All,28762750.45081884,8966,5.845566539697895,1.7821386460706663,1.1303622697109537,1.4125792651250266,4.715204269986942,0.855520594647326,2019
+College,2004,All,31714616.400228985,9425,5.897324867237596,1.7806073665188766,1.14318679862933,1.4042718272019357,4.754138068608266,0.884515817392817,2019
+College,2007,All,31834293.173382215,9257,5.9834813027537725,1.7805254340510206,1.2343419928341566,1.4139693149985728,4.749139309919616,0.8782587941697739,2019
+College,2010,All,32673938.955177713,11728,5.754925453726742,1.9228039401128574,1.032632916387292,1.538948232144489,4.72229253733945,0.9335111883790715,2019
+College,2013,All,34740046.15847195,11384,5.831264588607021,1.801676392993434,1.1426355372906463,1.4235013020983596,4.688629051316375,0.9075369253616025,2019
+College,2016,All,38364513.99648692,12451,5.895055846178219,1.886911892271184,1.1514556541949448,1.456712734594602,4.743600191983274,0.9343974855235341,2019
+College,2019,All,41417907.91436649,12243,5.924834476622033,1.8126440629751805,1.227825930295157,1.4182741003282031,4.697008546326876,0.92764084685772,2019
+NoHS,All,All,120507765.9554234,20771,3.45697268181314,2.28594119767371,0.0677214223945202,2.093249356469876,3.3892512594186197,0.7915380469528671,2019
+HS,All,All,509488205.829591,98306,4.356953840174459,2.0207692664248853,0.40346726013342565,1.7782048201212055,3.953486580041033,0.7910761108878736,2019
+College,All,All,288196583.6416764,91423,5.8030738357742475,1.821822296136964,1.1112289695595763,1.445212029264702,4.691844866214671,0.8991236790958228,2019
+All,1995,All,89218158.74816826,19889,4.4918254229109404,1.8776685760067322,0.5459582976927327,1.6214841820362338,3.9458671252182076,0.926360664353655,2019
+All,1998,All,91207300.70924391,19587,4.635663458159626,1.9972025256796966,0.6129426959584617,1.693564201230259,4.022720762201164,0.9268871726100931,2019
+All,2001,All,96262382.74472204,20466,4.791750931651483,2.059022709950208,0.6568816648343252,1.6994540583416962,4.134869266817157,0.915000278444205,2019
+All,2004,All,102099326.79291895,20886,4.782076677798243,2.1154725084753756,0.6318595201333184,1.7127093733904277,4.150217157664925,0.9221373251276149,2019
+All,2007,All,104645703.8542697,20372,4.889766699922423,2.1230135803814902,0.7369154500772356,1.7416807219907098,4.152851249845187,0.9172925015293492,2019
+All,2010,All,102139938.99203973,28526,4.648112935927783,2.185144430979905,0.48479664785796567,1.7968194206078985,4.163316288069817,0.9131728763045562,2019
+All,2013,All,106597393.15600339,26615,4.591292883851544,2.223419458211226,0.4919058473688643,1.8148654178933838,4.09938703648268,0.9226597005463111,2019
+All,2016,All,111461270.04361166,28009,4.63702292682612,2.361186872004425,0.4977547137841735,1.8949325816117706,4.139268213041946,0.9606101681283918,2019
+All,2019,All,114561080.38571322,26150,4.7402173470114155,2.290207929826731,0.5854265859057156,1.8486769937748637,4.1547907611057004,0.9650806267262096,2019
+All,All,"(15,20]",7132213.449212611,1232,2.1029401817689797,1.9274894027075171,-0.7586248653329717,1.9779221299706335,2.8615650471019514,0.8556053324294248,2019
+All,All,"(20,25]",38301398.75744594,6540,2.4985653340509306,1.8658499307539316,-1.0014905801071365,1.7420523772181444,3.500055914158067,0.7623891509006964,2019
+All,All,"(25,30]",64792079.07813065,11453,3.427074639001534,1.7742179139653085,-0.5435186464439437,1.5169076966510147,3.9705932854454775,0.7195903894016917,2019
+All,All,"(30,35]",76833634.53744955,14189,3.917722646733151,1.8287432863032087,-0.27154909891866724,1.5132026668926462,4.189271745651818,0.7516750220591969,2019
+All,All,"(35,40]",86517746.44482443,18305,4.282892409931282,1.9833943471605797,-0.012964411813856791,1.5814023840126317,4.295856821745138,0.7972930213641523,2019
+All,All,"(40,45]",94291528.01046914,21454,4.66086750151682,1.9808076654670315,0.3027085778445724,1.549211315204094,4.3581589236722476,0.8302768026135345,2019
+All,All,"(45,50]",94039658.93626265,23327,4.9616520510120745,2.0065668333295283,0.5467213868347744,1.552400799389431,4.4149306641773,0.88091704883655,2019
+All,All,"(50,55]",90475992.19361311,24712,5.139946662796708,2.0926897000667295,0.7417417860861684,1.5943390280863738,4.39820487671054,0.94080435445468,2019
+All,All,"(55,60]",80635999.51867823,22769,5.313405597286107,2.130101106872632,1.0078380942641254,1.6198486749131025,4.305567503021981,0.9887761144684385,2019
+All,All,"(60,65]",72175688.12508897,20159,5.311197656508757,2.2153758193293367,1.1556420417849282,1.6985411269059931,4.155555614723829,1.007579934510979,2019
+All,All,"(65,70]",64928607.12400947,16404,5.32668516989954,2.140382737024916,1.3505144300685186,1.6649854778639714,3.976170739831021,0.9698781703286417,2019
+All,All,"(70,75]",53314480.33127927,12160,5.320930466703414,2.010736531728035,1.5284867403976614,1.5870615770449719,3.792443726305753,0.9423562939713019,2019
+All,All,"(75,80]",44757752.32382846,8627,5.207328407993907,1.9676264648651671,1.5831188338381752,1.5939484248404006,3.624209574155732,0.8976720799085824,2019
+All,All,"(80,85]",30220418.089825574,5605,5.2097446787348485,1.973746751353384,1.634766747188284,1.6233255196734766,3.5749779315465644,0.8464880145047097,2019
+All,All,"(85,90]",14060689.69350626,2499,5.175266777012485,1.9833120094064205,1.721786590880081,1.65107184899677,3.4534801861324045,0.8388095964180435,2019
+All,All,"(90,95]",5714668.813066513,1065,5.041799177335537,2.174409006543606,1.672037734329221,1.8960565668076024,3.3697614430063156,0.8403050685245416,2019
+All,All,All,918192555.4266908,210500,4.69273539454396,2.154842475208252,0.5815503047931327,1.7678957935676622,4.111185089750827,0.9334031199699846,2019
diff --git a/HARK/datasets/SCF/WealthIncomeDist/__init__.py b/HARK/datasets/SCF/WealthIncomeDist/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HARK/datasets/SCF/__init__.py b/HARK/datasets/SCF/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/Calibration/SCF_distributions.py b/examples/Calibration/SCF_distributions.py
new file mode 100644
index 000000000..a14c82eeb
--- /dev/null
+++ b/examples/Calibration/SCF_distributions.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jan 18 13:57:50 2021
+
+@author: Mateo
+"""
+
+from HARK.datasets.SCF.WealthIncomeDist.SCFDistTools import income_wealth_dists_from_scf
+import seaborn as sns
+from itertools import product, starmap
+import pandas as pd
+
+# List the education levels and years
+educ_lvls = ["NoHS", "HS", "College"]
+years = list(range(1995, 2022, 3))
+
+age = 25
+base_year = 1992
+
+# %% Get the distribution of aNrm and pLvl at each year x education
+params = list(product([base_year], [age], educ_lvls, years))
+base_year, age, education, year = list(zip(*params))
+
+frame = pd.DataFrame(
+    {"base_year": base_year, "age": age, "education": education, "wave": year}
+)
+
+results = list(starmap(income_wealth_dists_from_scf, params))
+frame = pd.concat([frame, pd.DataFrame(results)], axis=1)
+
+# %% Plot time trends at different education levels.
+
+# Formatting
+frame = frame.melt(id_vars=["base_year", "age", "education", "wave"])
+aux = frame["variable"].str.split("(Mean|Std)", n=1, expand=True)
+frame["variable"] = aux[0]
+frame["stat"] = aux[1]
+
+# Plot
+g = sns.FacetGrid(frame, col="stat", row="variable", hue="education", sharey=True)
+g.map(sns.scatterplot, "wave", "value", alpha=0.7)
+g.add_legend()


Test patch:
diff --git a/HARK/datasets/tests/test_load_data.py b/HARK/datasets/tests/test_load_data.py
index 2f82827fc..229fb21bb 100644
--- a/HARK/datasets/tests/test_load_data.py
+++ b/HARK/datasets/tests/test_load_data.py
@@ -1,6 +1,8 @@
 import unittest
 from HARK.datasets import load_SCF_wealth_weights
 from HARK.datasets.cpi.us.CPITools import cpi_deflator
+from HARK.datasets.SCF.WealthIncomeDist.SCFDistTools import income_wealth_dists_from_scf
+
 
 class test_load_SCF_wealth_weights(unittest.TestCase):
     def setUp(self):
@@ -10,25 +12,85 @@ def test_shape(self):
         self.assertEqual(self.SCF_wealth.shape, (3553,))
         self.assertEqual(self.SCF_weights.shape, (3553,))
 
+
 # %% US CPI tests
 class test_cpi_deflators(unittest.TestCase):
-    
     def test_month_deflators(self):
-        
+
         # Same year test
-        defl_same_year = cpi_deflator(2000, 2000, 'SEP')
+        defl_same_year = cpi_deflator(2000, 2000, "SEP")
         self.assertEqual(defl_same_year[0], 1.0)
-        
+
         # Different year test
         defl_diff_year = cpi_deflator(1998, 2019, 'SEP')
         self.assertAlmostEqual(defl_diff_year[0], 1.57279534)
         
     def test_avg_deflators(self):
-        
+
         # Same year test
         defl_same_year = cpi_deflator(2000, 2000)
         self.assertEqual(defl_same_year[0], 1.0)
-        
+
         # Different year test
         defl_diff_year = cpi_deflator(1998, 2019)
-        self.assertAlmostEqual(defl_diff_year[0], 1.57202505)
\ No newline at end of file
+        self.assertAlmostEqual(defl_diff_year[0], 1.57202505)
+
+
+# %% Tests for Survey of Consumer finances initial distributions
+class test_SCF_dists(unittest.TestCase):
+    def setUp(self):
+        self.BaseYear = 1992
+
+    def test_at_21(self):
+
+        # Get stats for various groups and test them
+        NoHS = income_wealth_dists_from_scf(
+            self.BaseYear, age=21, education="NoHS", wave=1995
+        )
+        self.assertAlmostEqual(NoHS["aNrmInitMean"], -1.0611984728537684)
+        self.assertAlmostEqual(NoHS["aNrmInitStd"], 1.475816500147777)
+        self.assertAlmostEqual(NoHS["pLvlInitMean"], 2.5413398571226233)
+        self.assertAlmostEqual(NoHS["pLvlInitStd"], 0.7264931123240703)
+
+        HS = income_wealth_dists_from_scf(
+            self.BaseYear, age=21, education="HS", wave=2013
+        )
+        self.assertAlmostEqual(HS["aNrmInitMean"], -1.0812342937817578)
+        self.assertAlmostEqual(HS["aNrmInitStd"], 1.7526704743231725)
+        self.assertAlmostEqual(HS["pLvlInitMean"], 2.806605268756435)
+        self.assertAlmostEqual(HS["pLvlInitStd"], 0.6736467457859727)
+
+        Coll = income_wealth_dists_from_scf(
+            self.BaseYear, age=21, education="College", wave=2019
+        )
+        self.assertAlmostEqual(Coll["aNrmInitMean"], -0.6837248150760165)
+        self.assertAlmostEqual(Coll["aNrmInitStd"], 0.8813676761170798)
+        self.assertAlmostEqual(Coll["pLvlInitMean"], 3.2790838587291127)
+        self.assertAlmostEqual(Coll["pLvlInitStd"], 0.746362502979793)
+
+    def test_at_60(self):
+
+        # Get stats for various groups and test them
+        NoHS = income_wealth_dists_from_scf(
+            self.BaseYear, age=60, education="NoHS", wave=1995
+        )
+        self.assertAlmostEqual(NoHS["aNrmInitMean"], 0.1931578281432479)
+        self.assertAlmostEqual(NoHS["aNrmInitStd"], 1.6593916577375334)
+        self.assertAlmostEqual(NoHS["pLvlInitMean"], 3.3763953392998705)
+        self.assertAlmostEqual(NoHS["pLvlInitStd"], 0.61810580085094993)
+
+        HS = income_wealth_dists_from_scf(
+            self.BaseYear, age=60, education="HS", wave=2013
+        )
+        self.assertAlmostEqual(HS["aNrmInitMean"], 0.6300862955841334)
+        self.assertAlmostEqual(HS["aNrmInitStd"], 1.7253736778036055)
+        self.assertAlmostEqual(HS["pLvlInitMean"], 3.462790681398899)
+        self.assertAlmostEqual(HS["pLvlInitStd"], 0.8179188962937205)
+
+        Coll = income_wealth_dists_from_scf(
+            self.BaseYear, age=60, education="College", wave=2019
+        )
+        self.assertAlmostEqual(Coll["aNrmInitMean"], 1.643936802283761)
+        self.assertAlmostEqual(Coll["aNrmInitStd"], 1.2685135110865389)
+        self.assertAlmostEqual(Coll["pLvlInitMean"], 4.278905678818748)
+        self.assertAlmostEqual(Coll["pLvlInitStd"], 1.0776403992280614)