diff --git a/HARK/datasets/SCF/WealthIncomeDist/README.md b/HARK/datasets/SCF/WealthIncomeDist/README.md
new file mode 100644
index 000000000..9bc2587f9
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/README.md
@@ -0,0 +1,8 @@
+# Summary statistics of wealth and permanent income in the United States
+
+# TODO
+
+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/).
diff --git a/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv b/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv
new file mode 100644
index 000000000..ab18b64f1
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/WealthIncomeStats.csv
@@ -0,0 +1,680 @@
+Educ,YEAR,Age_grp,w.obs,obs,lnNrmWealth.mean,lnNrmWealth.sd,lnPermIncome.mean,lnPermIncome.sd
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+All,2019,"(15,20]",4011862.89453125,129,-0.851484047599156,1.4438570780180975,3.062375955190159,0.9304557125926853
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+NoHS,All,"(15,20]",5470007.394042969,182,-1.6349347878455271,1.762626039177238,3.067364038228524,0.6659073133584634
+NoHS,All,"(20,25]",22656197.228027344,760,-1.7672867777463928,1.8824889588153533,3.298277958635653,0.6910041652327881
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+NoHS,All,"(35,40]",42835407.29420471,1702,-1.0372838216718565,1.8707456755894514,3.607655109907486,0.7129757741522655
+NoHS,All,"(40,45]",46623620.31907654,1796,-0.7415806419375326,1.8924316925866085,3.6481002702433067,0.6952315562349962
+NoHS,All,"(45,50]",43829173.671424866,1755,-0.5724204628934191,1.9100843595076533,3.665182455069601,0.8425731584240043
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+NoHS,All,"(60,65]",49239258.554885864,1737,0.20765292811690317,1.951914862113778,3.319023528163061,0.7916043476470646
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+HS,All,"(15,20]",30071967.91500473,1045,-0.7989399025076641,1.9899049223907448,2.8251772301787947,0.8817558623243203
+HS,All,"(20,25]",133175516.49902344,4530,-1.1951827940004625,1.7573453170629987,3.486897496845292,0.7452779608572472
+HS,All,"(25,30]",189277425.88919067,6719,-0.8864233795099057,1.5507096333065833,3.8593198124487693,0.677418692676724
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+HS,All,"(50,55]",243825743.8276329,10651,0.3426549980227443,1.6386451797546673,4.1947826593725415,0.7642058886877242
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+College,All,"(60,65]",120592582.89935732,10246,1.5783467590107076,1.2721993312919462,4.833049027495653,0.9537982930449117
+College,All,"(65,70]",102350006.36816311,8057,1.7271476949535256,1.411378691321138,4.586043542087677,0.9894262353933774
+College,All,"(70,75]",76384759.14373875,5542,1.9380629862151486,1.2364629388286523,4.428587472494311,0.9598003448938114
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+College,All,"(80,85]",34986047.190751076,1995,1.9560916918273707,1.102859911116208,4.197827304402279,0.8940877082404807
+College,All,"(85,90]",14265743.893580914,774,2.1352136976593936,1.1805737876753815,4.038196128248264,0.8403076672873403
+College,All,"(90,95]",6236168.387966156,363,1.8374676478826804,1.6556908660638283,4.054980398899089,0.8716371298324342
+NoHS,1995,All,76287300.48362637,2314,0.2767715763305479,1.8121803156902991,3.329737740629483,0.8959151664951726
+NoHS,1998,All,68531861.37744665,2100,0.1412074178942138,1.983600264007263,3.311012674745849,0.907535132583704
+NoHS,2001,All,69667517.49018097,2182,0.18348901422568617,1.9907411041582075,3.3629898726033223,0.808738731574151
+NoHS,2004,All,69232062.00756073,2049,-0.059720303080970746,2.0760171566612815,3.387074218934852,0.744992259293126
+NoHS,2007,All,65667552.10536194,1816,0.07171177756136687,2.0997792951724414,3.454688473153436,0.7637591789633575
+NoHS,2010,All,59741837.409088135,2757,-0.3322151582008037,2.1265781162370416,3.44247245749613,0.6958487133782794
+NoHS,2013,All,58091158.66171265,2316,-0.24886525952588584,2.0923916356956065,3.359771488543526,0.6993632847083737
+NoHS,2016,All,72656551.38700867,2942,-0.5016225459245605,2.2441521901052295,3.4391803487233434,0.7612556172342962
+NoHS,2019,All,62662988.78552246,2295,-0.5015422640122742,2.251880762248853,3.4289948622316535,0.7655035934244159
+HS,1995,All,252364339.23076773,9683,0.2875386741425905,1.6323753264865741,3.8898927406262365,0.8306632843671315
+HS,1998,All,261501213.12968063,9410,0.35580816536559573,1.684030912464507,3.930003912986418,0.8040877550901718
+HS,2001,All,267830643.92208385,9318,0.3190843525453584,1.7067811769180736,4.024033431712365,0.7703415108113507
+HS,2004,All,282691489.91774845,9412,0.3102050898863799,1.7019330797261736,3.9983496890891432,0.7685135122939907
+HS,2007,All,298389501.37011003,9299,0.41783656922104223,1.7526477004197747,3.988417167035632,0.7821407683254537
+HS,2010,All,287588162.8185663,14041,0.14520580722597254,1.7773693609143142,3.9955234136865116,0.7560852834246589
+HS,2013,All,301195576.2418499,12915,0.06383466271414409,1.8541671386301877,3.902218304988406,0.7697886714802871
+HS,2016,All,292827228.88671875,12616,0.11277245906323351,1.9217075318215233,3.9170943950133945,0.7939457281334957
+HS,2019,All,303052873.4500284,11612,0.17325582486183533,1.8755024471895327,3.9343437795261105,0.8324001267737489
+College,1995,All,117439154.02269363,7892,0.6530735748386791,1.4244771186149254,4.466381654540479,0.8553062556416903
+College,1998,All,126003428.80296755,8077,0.8094976888845535,1.4686031597454927,4.602230772116712,0.8310745492186921
+College,2001,All,143813752.32436228,8966,0.9663331086373025,1.4125792653480922,4.7152042692286225,0.8555205944436873
+College,2004,All,158573081.9644227,9425,0.979157637735482,1.4042718273169101,4.754138068440446,0.8845158173094193
+College,2007,All,159171465.9269204,9257,1.0703128313951413,1.413969315519551,4.749139309836472,0.8782587942642424
+College,2010,All,163369694.56777763,11728,0.8686037540622702,1.5389482324821726,4.722292536879898,0.9335111886959762
+College,2013,All,173700230.89420795,11384,0.9786063764148358,1.423501302429209,4.688629051485133,0.9075369252745765
+College,2016,All,191822569.94490433,12451,0.9874264925814195,1.4567127351350462,4.743600191453276,0.9343974855237215
+College,2019,All,207089539.5801239,12243,1.0637967691099335,1.4182741006593422,4.697008545826145,0.9276408463877193
+NoHS,All,All,602538829.7075086,20771,-0.09630773829467425,2.093249356595968,3.389251259386837,0.7915380473894505
+HS,All,All,2547441028.967554,98306,0.23943809934885812,1.7782048200162501,3.953486579968092,0.7910761108501261
+College,All,All,1440982918.0283804,91423,0.9471998082983402,1.4452120296874236,4.691844865867459,0.8991236790570938
+All,1995,All,446090793.7370877,19889,0.38192913740377693,1.6214841820333408,3.9458671249936224,0.9263606642802306
+All,1998,All,456036503.31009483,19587,0.4489135343217659,1.6935642015919639,4.022720761624648,0.9268871725796918
+All,2001,All,481311913.7366271,20466,0.4928525043610466,1.6994540583218298,4.13486926685767,0.9150002784432887
+All,2004,All,510496633.8897319,20886,0.46783035930917344,1.7127093738679093,4.1502171574061935,0.9221373254627077
+All,2007,All,523228519.4023924,20372,0.5728862890531313,1.7416807218833659,4.152851250092322,0.9172925015988294
+All,2010,All,510699694.7954321,28526,0.32076748632449115,1.7968194207339507,4.163316287359667,0.9131728763276665
+All,2013,All,532986965.7977705,26615,0.32787668647790486,1.814865417832679,4.099387036886441,0.9226597004477293
+All,2016,All,557306350.2186317,28009,0.3337255528544858,1.8949325817387324,4.139268212763788,0.9606101679642679
+All,2019,All,572805401.8156748,26150,0.4213974251250882,1.8486769931893534,4.1547907609435315,0.9650806263448962
+All,All,"(15,20]",35661067.225063324,1232,-0.9226540253689112,1.9779221308491004,2.861565047350938,0.8556053317187928
+All,All,"(20,25]",191506993.86631775,6540,-1.1655197405858353,1.7420523769276761,3.5000559145001446,0.7623891510021877
+All,All,"(25,30]",323960395.4660721,11453,-0.7075478077421268,1.5169076975674285,3.9705932854570523,0.7195903893799949
+All,All,"(30,35]",384168172.7016449,14189,-0.435578259587435,1.5132026662675304,4.189271745812854,0.7516750217231464
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+All,All,"(40,45]",471457640.103632,21454,0.13867941729313105,1.5492113146786328,4.358158923407542,0.8302768026794269
+All,All,"(45,50]",470198294.5209675,23327,0.38269222603155983,1.5524007995920974,4.414930664224103,0.8809170489084311
+All,All,"(50,55]",452379960.7693362,24712,0.5777126253608125,1.5943390285333134,4.3982048764809685,0.9408043543403601
+All,All,"(55,60]",403179997.5655031,22769,0.8438089338948742,1.619848674192003,4.305567502912483,0.9887761144222413
+All,All,"(60,65]",360878440.4648757,20159,0.9916128810708348,1.6985411259718377,4.155555614611303,1.0075799342095844
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+All,All,"(70,75]",266572401.67756367,12160,1.3644575791729392,1.5870615777439467,3.7924437262322397,0.9423562938594875
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+All,All,"(80,85]",151102090.4521284,5605,1.4707375855154317,1.6233255207233075,3.574977931210382,0.8464880148529895
+All,All,"(85,90]",70303448.4290576,2499,1.5577574289625318,1.6510718492207406,3.4534801858323267,0.838809596460012
+All,All,"(90,95]",28573344.09506607,1065,1.508008571520737,1.8960565684726924,3.3697614420154136,0.8403050679224577
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/WealthIncomeDist/parser.py b/HARK/datasets/SCF/WealthIncomeDist/parser.py
new file mode 100644
index 000000000..1148eefcd
--- /dev/null
+++ b/HARK/datasets/SCF/WealthIncomeDist/parser.py
@@ -0,0 +1,97 @@
+# -*- 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
+import os
+
+scf_sumstats_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+def get_scf_distr_stats():
+    """
+    
+    """
+
+    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, year = None
+):
+    
+    # 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) + ']'
+    
+    else:
+        
+        # If no year is given, use all years.
+        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 year
+    year_str = 'All' if year is None else str(int(year))
+    
+    # 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})
+    
+    # Try to access the requested combination
+    try:
+        
+        row = table.loc[(education, year_str, age_bracket)]
+        
+    except KeyError as e:
+        
+        message = ("The summary statistics do not contain the "+
+                   "Age/Year/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/Year/Education combination to compute all summary" +
+             "statistics.")
+    
+    return row.to_dict()
+
+def income_wealth_dists_from_scf(age = None, education = None, year = None):
+
+    stats = parse_scf_distr_stats(age, education, year)
+    
+    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'],# 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
\ No newline at end of file
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..3b0aa8a29
--- /dev/null
+++ b/examples/Calibration/SCF_distributions.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jan 18 13:57:50 2021
+
+@author: Mateo
+"""
+
+from HARK.datasets.SCF.WealthIncomeDist.parser 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
+
+# %% Get the distribution of aNrm and pLvl at each year x education
+params = list(product([age],educ_lvls, years))
+age, education, year = list(zip(*params))
+
+frame = pd.DataFrame({'age': age, 'education': education, 'year': 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 = ['age', 'education','year'])
+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, "year", "value", alpha=.7)
+g.add_legend()
\ No newline at end of file