economic-unemployment_by_county

Overview

Source: /home/coolhand/datasets/us-inequality-atlas/economic/unemployment_by_county.csv

Saturn profiled 3,222 rows across 8 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).

[2]:

!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/datasets/us-inequality-atlas/economic/unemployment_by_county.csv",
    "--findings", "economic-unemployment_by_county.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset contains 3,222 US county-level labor market records with 8 columns covering county identifiers (FIPS, name, state) and workforce statistics (labor force, total 16+, unemployed, unemployment rate, participation rate). The unemployment rate averages 5.13% with a median of 4.69%, ranging up to 31.99%, so the right tail is worth inspecting for distressed counties. Population-based counts (labor_force, total_16_plus, unemployed) are extremely right-skewed (skew >13) with hundreds of outliers — expected when a few large metros sit alongside small rural counties, but it means you should likely log-transform before modeling. Texas (254), Georgia (159), and Virginia (133) contribute the most counties, reflecting state geography rather than any sampling bias. County names show a 39% duplicate rate driven by repeated names like Washington, Jefferson, and Franklin Counties across states — join on FIPS, not name.

citing: row_count · column_count · columns.unemployment_rate.stats · columns.labor_force.stats · columns.total_16_plus.stats · columns.unemployed.stats · columns.labor_force_participation_rate.stats · columns.state.top_values · columns.county_name.stats · columns.county_name.top_values

Out[4]:

saturn.schema() · 8 columns

column	kind	n	null%	unique	alerts
fips	numeric	3,222	0.0%	3,222
county_name	text	3,222	0.0%	1,960	short_text duplicates
state	categorical	3,222	0.0%	52
total_16_plus	numeric	3,222	0.0%	3,148	high_skew outliers
labor_force	numeric	3,222	0.0%	3,099	high_skew outliers
unemployed	numeric	3,222	0.0%	1,859	high_skew outliers
labor_force_participation_rate	numeric	3,222	0.0%	1,944
unemployment_rate	numeric	3,222	0.0%	950	high_skew

Fig 1.

unemployment_rate · Check the right tail beyond ~10% to find counties with persistent labor distress.

Show data table

Histogram bins for unemployment_rate (median: 4.69).
bin	count
0 – 0.7997	60
0.7997 – 1.599	90
1.599 – 2.399	197
2.399 – 3.199	333
3.199 – 3.999	464
3.999 – 4.798	539
4.798 – 5.598	492
5.598 – 6.398	361
6.398 – 7.198	217
7.198 – 7.997	142
7.997 – 8.797	85
8.797 – 9.597	61
9.597 – 10.4	36
10.4 – 11.2	36
11.2 – 12	13
12 – 12.8	18
12.8 – 13.6	13
13.6 – 14.4	12
14.4 – 15.2	11
15.2 – 15.99	8
15.99 – 16.79	5
16.79 – 17.59	4
17.59 – 18.39	2
18.39 – 19.19	1
19.19 – 19.99	3
19.99 – 20.79	3
20.79 – 21.59	4
21.59 – 22.39	3
22.39 – 23.19	2
23.19 – 23.99	3
23.99 – 24.79	1
24.79 – 25.59	0
25.59 – 26.39	0
26.39 – 27.19	0
27.19 – 27.99	0
27.99 – 28.79	0
28.79 – 29.59	0
29.59 – 30.39	0
30.39 – 31.19	1
31.19 – 31.99	2

Fig 2.

labor_force_participation_rate · Roughly symmetric around 58% — outliers below 30% flag counties with weak workforce engagement.

Show data table

Histogram bins for labor_force_participation_rate (median: 58.724999999999994).
bin	count
18.63 – 20.27	1
20.27 – 21.9	0
21.9 – 23.54	0
23.54 – 25.17	1
25.17 – 26.81	1
26.81 – 28.44	1
28.44 – 30.08	0
30.08 – 31.71	4
31.71 – 33.35	6
33.35 – 34.98	8
34.98 – 36.62	10
36.62 – 38.25	24
38.25 – 39.89	30
39.89 – 41.52	37
41.52 – 43.16	51
43.16 – 44.79	61
44.79 – 46.43	60
46.43 – 48.06	76
48.06 – 49.7	109
49.7 – 51.34	141
51.34 – 52.97	186
52.97 – 54.61	174
54.61 – 56.24	235
56.24 – 57.88	245
57.88 – 59.51	272
59.51 – 61.15	270
61.15 – 62.78	277
62.78 – 64.42	252
64.42 – 66.05	221
66.05 – 67.69	187
67.69 – 69.32	118
69.32 – 70.96	82
70.96 – 72.59	41
72.59 – 74.23	19
74.23 – 75.86	10
75.86 – 77.5	4
77.5 – 79.13	6
79.13 – 80.77	1
80.77 – 82.4	0
82.4 – 84.04	1

Fig 3.

state · Counts per state show TX, GA, and VA dominate the row count simply due to having more counties.

Show data table

Top values for state (20 unique shown, of 52 total).
value	count	share
TX	254	7.9%
GA	159	4.9%
VA	133	4.1%
KY	120	3.7%
MO	115	3.6%
KS	105	3.3%
IL	102	3.2%
NC	100	3.1%
IA	99	3.1%
TN	95	2.9%
NE	93	2.9%
IN	92	2.9%
OH	88	2.7%
MN	87	2.7%
MI	83	2.6%
MS	82	2.5%
PR	78	2.4%
OK	77	2.4%
AR	75	2.3%
WI	72	2.2%

Fig 4.

labor_force · Severe right skew (max 5.2M vs median 11.6K) — consider a log scale before any modeling.

Show data table

Histogram bins for labor_force (median: 11608.5).
bin	count
36 – 1.311e+05	2944
1.311e+05 – 2.621e+05	136
2.621e+05 – 3.931e+05	52
3.931e+05 – 5.241e+05	41
5.241e+05 – 6.551e+05	14
6.551e+05 – 7.862e+05	10
7.862e+05 – 9.172e+05	5
9.172e+05 – 1.048e+06	5
1.048e+06 – 1.179e+06	4
1.179e+06 – 1.31e+06	2
1.31e+06 – 1.441e+06	3
1.441e+06 – 1.572e+06	0
1.572e+06 – 1.703e+06	1
1.703e+06 – 1.834e+06	1
1.834e+06 – 1.965e+06	0
1.965e+06 – 2.096e+06	0
2.096e+06 – 2.227e+06	0
2.227e+06 – 2.358e+06	1
2.358e+06 – 2.489e+06	1
2.489e+06 – 2.62e+06	0
2.62e+06 – 2.751e+06	0
2.751e+06 – 2.882e+06	1
2.882e+06 – 3.013e+06	0
3.013e+06 – 3.145e+06	0
3.145e+06 – 3.276e+06	0
3.276e+06 – 3.407e+06	0
3.407e+06 – 3.538e+06	0
3.538e+06 – 3.669e+06	0
3.669e+06 – 3.8e+06	0
3.8e+06 – 3.931e+06	0
3.931e+06 – 4.062e+06	0
4.062e+06 – 4.193e+06	0
4.193e+06 – 4.324e+06	0
4.324e+06 – 4.455e+06	0
4.455e+06 – 4.586e+06	0
4.586e+06 – 4.717e+06	0
4.717e+06 – 4.848e+06	0
4.848e+06 – 4.979e+06	0
4.979e+06 – 5.11e+06	0
5.11e+06 – 5.241e+06	1

Fig 5.

unemployed · Heavy-tailed with 417 outliers; a handful of large counties account for most absolute unemployment.

Show data table

Histogram bins for unemployed (median: 589.0).
bin	count
0 – 9139	3018
9139 – 1.828e+04	96
1.828e+04 – 2.742e+04	53
2.742e+04 – 3.655e+04	23
3.655e+04 – 4.569e+04	8
4.569e+04 – 5.483e+04	4
5.483e+04 – 6.397e+04	3
6.397e+04 – 7.311e+04	4
7.311e+04 – 8.225e+04	4
8.225e+04 – 9.139e+04	3
9.139e+04 – 1.005e+05	1
1.005e+05 – 1.097e+05	2
1.097e+05 – 1.188e+05	0
1.188e+05 – 1.279e+05	0
1.279e+05 – 1.371e+05	0
1.371e+05 – 1.462e+05	0
1.462e+05 – 1.554e+05	0
1.554e+05 – 1.645e+05	1
1.645e+05 – 1.736e+05	0
1.736e+05 – 1.828e+05	0
1.828e+05 – 1.919e+05	0
1.919e+05 – 2.01e+05	1
2.01e+05 – 2.102e+05	0
2.102e+05 – 2.193e+05	0
2.193e+05 – 2.285e+05	0
2.285e+05 – 2.376e+05	0
2.376e+05 – 2.467e+05	0
2.467e+05 – 2.559e+05	0
2.559e+05 – 2.65e+05	0
2.65e+05 – 2.742e+05	0
2.742e+05 – 2.833e+05	0
2.833e+05 – 2.924e+05	0
2.924e+05 – 3.016e+05	0
3.016e+05 – 3.107e+05	0
3.107e+05 – 3.199e+05	0
3.199e+05 – 3.29e+05	0
3.29e+05 – 3.381e+05	0
3.381e+05 – 3.473e+05	0
3.473e+05 – 3.564e+05	0
3.564e+05 – 3.655e+05	1

Fig 6.

Per-column null rate across the corpus. Columns are ordered by input position.

Show data table

Per-column null rate across the corpus.
column	kind	null %
fips	numeric	0.0%
county_name	text	0.0%
state	categorical	0.0%
total_16_plus	numeric	0.0%
labor_force	numeric	0.0%
unemployed	numeric	0.0%
labor_force_participation_rate	numeric	0.0%
unemployment_rate	numeric	0.0%

Fig 7.

Pearson correlation across numeric columns (sampled, bounded).

Show data table

Pearson correlation across 6 numeric columns (values clipped to 2 decimals).
	fips	total_16_plus	labor_force	unemployed	labor_force_participation_rate	unemployment_rate
fips	+1.00	-0.07	-0.06	-0.07	-0.11	+0.10
total_16_plus	-0.07	+1.00	+1.00	+0.98	+0.21	+0.05
labor_force	-0.06	+1.00	+1.00	+0.98	+0.22	+0.04
unemployed	-0.07	+0.98	+0.98	+1.00	+0.17	+0.08
labor_force_participation_rate	-0.11	+0.21	+0.22	+0.17	+1.00	-0.44
unemployment_rate	+0.10	+0.05	+0.04	+0.08	-0.44	+1.00

fips numeric identifier

This is the U.S. county FIPS code, evidenced by every one of the 3222 rows being unique with no nulls and values spanning 1001 to 72153 — the standard 5-digit state+county encoding. The distribution is essentially uniform across the FIPS range (skew 0.16, kurtosis -0.63, no outliers), which is expected for an identifier rather than a measured quantity.

Treatment: Treat as a categorical county key; left-join on this id rather than feeding it as a numeric feature.

anthropic:claude-opus-4-7 · confidence high

Out[13]:

saturn.columns["fips"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	3,222
min	1,001
max	72,153
mean	3.138e+04
median	30,022
std	1.63e+04
q1	1.903e+04
q3	4.61e+04
iqr	27,075
skew	0.1574
kurtosis	-0.6314
n_outliers	0
outlier_rate	0
zero_rate	0

Fig 8.

Distribution of fips. Vertical dash marks the median.

Show data table

Histogram bins for fips (median: 30022.0).
bin	count
1001 – 2780	97
2780 – 4559	15
4559 – 6337	133
6337 – 8116	59
8116 – 9895	14
9895 – 1.167e+04	4
1.167e+04 – 1.345e+04	226
1.345e+04 – 1.523e+04	5
1.523e+04 – 1.701e+04	49
1.701e+04 – 1.879e+04	189
1.879e+04 – 2.057e+04	204
2.057e+04 – 2.235e+04	184
2.235e+04 – 2.413e+04	39
2.413e+04 – 2.59e+04	15
2.59e+04 – 2.768e+04	170
2.768e+04 – 2.946e+04	196
2.946e+04 – 3.124e+04	150
3.124e+04 – 3.302e+04	27
3.302e+04 – 3.48e+04	21
3.48e+04 – 3.658e+04	95
3.658e+04 – 3.836e+04	153
3.836e+04 – 4.013e+04	155
4.013e+04 – 4.191e+04	46
4.191e+04 – 4.369e+04	67
4.369e+04 – 4.547e+04	51
4.547e+04 – 4.725e+04	161
4.725e+04 – 4.903e+04	268
4.903e+04 – 5.081e+04	29
5.081e+04 – 5.259e+04	133
5.259e+04 – 5.436e+04	94
5.436e+04 – 5.614e+04	95
5.614e+04 – 5.792e+04	0
5.792e+04 – 5.97e+04	0
5.97e+04 – 6.148e+04	0
6.148e+04 – 6.326e+04	0
6.326e+04 – 6.504e+04	0
6.504e+04 – 6.682e+04	0
6.682e+04 – 6.86e+04	0
6.86e+04 – 7.037e+04	0
7.037e+04 – 7.215e+04	78

county_name text metadata

This column holds US county-level place names: 3,222 rows with 1,960 unique values, all between 10 and 46 characters and averaging ~2 words. The vocabulary is dominated by 'county' (2,999 occurrences) but also includes 'municipio' (78, Puerto Rico), 'parish' (64, Louisiana), and 'city' (47), so the field mixes several jurisdiction types. Note the 39.2% duplicate rate — recurring names like Washington County (30), Jefferson County (25), and Franklin County (24) appear across many states, so this name alone does not uniquely identify a county.

Treatment: Pair with a state code to form a unique key before joining or aggregating.

anthropic:claude-opus-4-7 · confidence high

Out[16]:

saturn.columns["county_name"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	1,960
len_min	10
len_max	46
len_mean	14.17
len_median	14
len_p95	18
word_mean	2.083
word_median	2
n_empty	0
n_duplicates	1,262
duplicate_rate	0.3917
vocab_size	1,963
readability_flesch_mean	33.36
emoji_rate	0
url_rate	0
one_word_rate	0
allcaps_rate	0
boilerplate_rate	0
alert: short_text	95th-percentile length under 20 chars
alert: duplicates	39.2% duplicate strings

Fig 9.

Character-length distribution for county_name.

Show data table

Character-length distribution for county_name (mean: 14.172253258845437).
chars	count
10 – 11	29
11 – 12	255
12 – 13	465
13 – 14	682
14 – 14	588
14 – 15	493
15 – 16	291
16 – 17	219
17 – 18	67
18 – 19	0
19 – 20	49
20 – 21	23
21 – 22	16
22 – 23	14
23 – 24	8
24 – 24	4
24 – 25	5
25 – 26	2
26 – 27	1
27 – 28	0
28 – 29	1
29 – 30	0
30 – 31	0
31 – 32	2
32 – 32	1
32 – 33	1
33 – 34	1
34 – 35	1
35 – 36	0
36 – 37	0
37 – 38	0
38 – 39	0
39 – 40	0
40 – 41	2
41 – 42	1
42 – 42	0
42 – 43	0
43 – 44	0
44 – 45	0
45 – 46	1

state categorical feature

Two-letter US state/territory codes across 3,222 rows with 52 distinct values and no nulls — consistent with the 50 states plus DC and likely Puerto Rico. Distribution tracks county counts rather than population: TX leads at 254 (7.88%), followed by GA (159), VA (133) and KY (120), suggesting one row per county/equivalent. High entropy ratio (0.93) confirms a fairly even spread across states.

Treatment: one-hot or target-encode for modelling; useful as a join key to state-level reference tables.

anthropic:claude-opus-4-7 · confidence high

Out[19]:

saturn.columns["state"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	52
top_value	TX
top_rate	0.07883
cardinality	52
entropy	5.314
entropy_ratio	0.9322

Fig 10.

Top values for state.

Show data table

Top values for state (20 unique shown, of 52 total).
value	count	share
TX	254	7.9%
GA	159	4.9%
VA	133	4.1%
KY	120	3.7%
MO	115	3.6%
KS	105	3.3%
IL	102	3.2%
NC	100	3.1%
IA	99	3.1%
TN	95	2.9%
NE	93	2.9%
IN	92	2.9%
OH	88	2.7%
MN	87	2.7%
MI	83	2.6%
MS	82	2.5%
PR	78	2.4%
OK	77	2.4%
AR	75	2.3%
WI	72	2.2%

total_16_plus numeric feature

This is a numeric population-style count of people aged 16+, with 3222 non-null rows and 3148 unique values spanning 50 to 8,086,852. The distribution is severely right-skewed (skew 13.49, kurtosis 305.88): the median is 21,167.5 but the mean is 83,549.93 and 13.7% of rows (443) flag as outliers. The std of 265,514 dwarfs the IQR of 45,507.75, consistent with a long upper tail typical of geographic aggregates.

Treatment: log-transform before regression to tame the heavy right tail.

anthropic:claude-opus-4-7 · confidence high

Out[22]:

saturn.columns["total_16_plus"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	3,148
min	50
max	8.087e+06
mean	8.355e+04
median	2.117e+04
std	2.655e+05
q1	8986
q3	5.449e+04
iqr	4.551e+04
skew	13.49
kurtosis	305.9
n_outliers	443
outlier_rate	0.1375
zero_rate	0
alert: high_skew	skew=+13.49
alert: outliers	13.7% rows beyond 1.5 IQR

Fig 11.

Distribution of total_16_plus. Vertical dash marks the median.

Show data table

Histogram bins for total_16_plus (median: 21167.5).
bin	count
50 – 2.022e+05	2947
2.022e+05 – 4.044e+05	134
4.044e+05 – 6.066e+05	56
6.066e+05 – 8.087e+05	37
8.087e+05 – 1.011e+06	12
1.011e+06 – 1.213e+06	10
1.213e+06 – 1.415e+06	7
1.415e+06 – 1.617e+06	4
1.617e+06 – 1.82e+06	3
1.82e+06 – 2.022e+06	4
2.022e+06 – 2.224e+06	2
2.224e+06 – 2.426e+06	0
2.426e+06 – 2.628e+06	1
2.628e+06 – 2.83e+06	1
2.83e+06 – 3.033e+06	0
3.033e+06 – 3.235e+06	0
3.235e+06 – 3.437e+06	0
3.437e+06 – 3.639e+06	2
3.639e+06 – 3.841e+06	0
3.841e+06 – 4.043e+06	0
4.043e+06 – 4.246e+06	1
4.246e+06 – 4.448e+06	0
4.448e+06 – 4.65e+06	0
4.65e+06 – 4.852e+06	0
4.852e+06 – 5.054e+06	0
5.054e+06 – 5.256e+06	0
5.256e+06 – 5.459e+06	0
5.459e+06 – 5.661e+06	0
5.661e+06 – 5.863e+06	0
5.863e+06 – 6.065e+06	0
6.065e+06 – 6.267e+06	0
6.267e+06 – 6.469e+06	0
6.469e+06 – 6.672e+06	0
6.672e+06 – 6.874e+06	0
6.874e+06 – 7.076e+06	0
7.076e+06 – 7.278e+06	0
7.278e+06 – 7.48e+06	0
7.48e+06 – 7.683e+06	0
7.683e+06 – 7.885e+06	0
7.885e+06 – 8.087e+06	1

labor_force numeric feature

This column appears to be the size of the labor force per record, likely at a US county or similar geographic unit given the 3,222 rows and 3,099 unique values. The distribution is severely right-skewed (skew 13.29, kurtosis 295.22) with a median of 11,608.5 but a max of 5,240,842, and 14.2% of values flagged as outliers. No nulls or zeros, but the gap between Q3 (31,930.5) and the maximum signals a long tail of very large jurisdictions.

Treatment: log-transform before modelling to tame the heavy right tail.

anthropic:claude-opus-4-7 · confidence high

Out[25]:

saturn.columns["labor_force"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	3,099
min	36
max	5.241e+06
mean	5.287e+04
median	1.161e+04
std	1.742e+05
q1	4777
q3	3.193e+04
iqr	2.715e+04
skew	13.29
kurtosis	295.2
n_outliers	459
outlier_rate	0.1425
zero_rate	0
alert: high_skew	skew=+13.29
alert: outliers	14.2% rows beyond 1.5 IQR

Fig 12.

Distribution of labor_force. Vertical dash marks the median.

Show data table

Histogram bins for labor_force (median: 11608.5).
bin	count
36 – 1.311e+05	2944
1.311e+05 – 2.621e+05	136
2.621e+05 – 3.931e+05	52
3.931e+05 – 5.241e+05	41
5.241e+05 – 6.551e+05	14
6.551e+05 – 7.862e+05	10
7.862e+05 – 9.172e+05	5
9.172e+05 – 1.048e+06	5
1.048e+06 – 1.179e+06	4
1.179e+06 – 1.31e+06	2
1.31e+06 – 1.441e+06	3
1.441e+06 – 1.572e+06	0
1.572e+06 – 1.703e+06	1
1.703e+06 – 1.834e+06	1
1.834e+06 – 1.965e+06	0
1.965e+06 – 2.096e+06	0
2.096e+06 – 2.227e+06	0
2.227e+06 – 2.358e+06	1
2.358e+06 – 2.489e+06	1
2.489e+06 – 2.62e+06	0
2.62e+06 – 2.751e+06	0
2.751e+06 – 2.882e+06	1
2.882e+06 – 3.013e+06	0
3.013e+06 – 3.145e+06	0
3.145e+06 – 3.276e+06	0
3.276e+06 – 3.407e+06	0
3.407e+06 – 3.538e+06	0
3.538e+06 – 3.669e+06	0
3.669e+06 – 3.8e+06	0
3.8e+06 – 3.931e+06	0
3.931e+06 – 4.062e+06	0
4.062e+06 – 4.193e+06	0
4.193e+06 – 4.324e+06	0
4.324e+06 – 4.455e+06	0
4.455e+06 – 4.586e+06	0
4.586e+06 – 4.717e+06	0
4.717e+06 – 4.848e+06	0
4.848e+06 – 4.979e+06	0
4.979e+06 – 5.11e+06	0
5.11e+06 – 5.241e+06	1

unemployed numeric feature

This is a numeric count of unemployed persons per record, with 3222 rows, no nulls, and 1859 unique values. The distribution is severely right-skewed (skew 16.82, kurtosis 450.4): the median is 589 while the mean is 2827 and the max reaches 365544, and 417 rows (12.9%) flag as outliers. Only 0.56% of records are zero, so sparsity is not the issue—a few extreme values are.

Treatment: Log-transform (or winsorize) before any modelling to tame the heavy right tail.

anthropic:claude-opus-4-7 · confidence high

Out[28]:

saturn.columns["unemployed"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	1,859
min	0
max	365,544
mean	2827
median	589
std	1.083e+04
q1	223
q3	1706
iqr	1482
skew	16.82
kurtosis	450.4
n_outliers	417
outlier_rate	0.1294
zero_rate	0.005587
alert: high_skew	skew=+16.82
alert: outliers	12.9% rows beyond 1.5 IQR

Fig 13.

Distribution of unemployed. Vertical dash marks the median.

Show data table

Histogram bins for unemployed (median: 589.0).
bin	count
0 – 9139	3018
9139 – 1.828e+04	96
1.828e+04 – 2.742e+04	53
2.742e+04 – 3.655e+04	23
3.655e+04 – 4.569e+04	8
4.569e+04 – 5.483e+04	4
5.483e+04 – 6.397e+04	3
6.397e+04 – 7.311e+04	4
7.311e+04 – 8.225e+04	4
8.225e+04 – 9.139e+04	3
9.139e+04 – 1.005e+05	1
1.005e+05 – 1.097e+05	2
1.097e+05 – 1.188e+05	0
1.188e+05 – 1.279e+05	0
1.279e+05 – 1.371e+05	0
1.371e+05 – 1.462e+05	0
1.462e+05 – 1.554e+05	0
1.554e+05 – 1.645e+05	1
1.645e+05 – 1.736e+05	0
1.736e+05 – 1.828e+05	0
1.828e+05 – 1.919e+05	0
1.919e+05 – 2.01e+05	1
2.01e+05 – 2.102e+05	0
2.102e+05 – 2.193e+05	0
2.193e+05 – 2.285e+05	0
2.285e+05 – 2.376e+05	0
2.376e+05 – 2.467e+05	0
2.467e+05 – 2.559e+05	0
2.559e+05 – 2.65e+05	0
2.65e+05 – 2.742e+05	0
2.742e+05 – 2.833e+05	0
2.833e+05 – 2.924e+05	0
2.924e+05 – 3.016e+05	0
3.016e+05 – 3.107e+05	0
3.107e+05 – 3.199e+05	0
3.199e+05 – 3.29e+05	0
3.29e+05 – 3.381e+05	0
3.381e+05 – 3.473e+05	0
3.473e+05 – 3.564e+05	0
3.564e+05 – 3.655e+05	1

labor_force_participation_rate numeric feature

Numeric column capturing labor force participation rate, almost certainly expressed as a percentage given the 18.63 to 84.04 range and mean of 57.89. The distribution is moderately left-skewed (-0.58) with a tight IQR of 10.695 around a median of 58.72, and only 1.18% of values flagged as outliers. No nulls or zeros across 3,222 rows, and 1,944 unique values suggest a continuous measurement rather than a coded category.

Treatment: Use as-is for modelling; optionally standardize since values are bounded percentages.

anthropic:claude-opus-4-7 · confidence high

Out[31]:

saturn.columns["labor_force_participation_rate"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	1,944
min	18.63
max	84.04
mean	57.89
median	58.72
std	8.041
q1	52.97
q3	63.66
iqr	10.7
skew	-0.5766
kurtosis	0.4502
n_outliers	38
outlier_rate	0.01179
zero_rate	0

Fig 14.

Distribution of labor_force_participation_rate. Vertical dash marks the median.

Show data table

Histogram bins for labor_force_participation_rate (median: 58.724999999999994).
bin	count
18.63 – 20.27	1
20.27 – 21.9	0
21.9 – 23.54	0
23.54 – 25.17	1
25.17 – 26.81	1
26.81 – 28.44	1
28.44 – 30.08	0
30.08 – 31.71	4
31.71 – 33.35	6
33.35 – 34.98	8
34.98 – 36.62	10
36.62 – 38.25	24
38.25 – 39.89	30
39.89 – 41.52	37
41.52 – 43.16	51
43.16 – 44.79	61
44.79 – 46.43	60
46.43 – 48.06	76
48.06 – 49.7	109
49.7 – 51.34	141
51.34 – 52.97	186
52.97 – 54.61	174
54.61 – 56.24	235
56.24 – 57.88	245
57.88 – 59.51	272
59.51 – 61.15	270
61.15 – 62.78	277
62.78 – 64.42	252
64.42 – 66.05	221
66.05 – 67.69	187
67.69 – 69.32	118
69.32 – 70.96	82
70.96 – 72.59	41
72.59 – 74.23	19
74.23 – 75.86	10
75.86 – 77.5	4
77.5 – 79.13	6
79.13 – 80.77	1
80.77 – 82.4	0
82.4 – 84.04	1

unemployment_rate numeric feature

This is a county- or region-level unemployment rate expressed as a percentage, with values from 0.0 to 31.99 and a median of 4.69. The distribution is heavily right-skewed (skew 2.55, kurtosis 12.81) with 154 outliers (4.78%) pulling the mean above the median, and a small share of zero readings (0.56%).

Treatment: Apply a log or winsorising transform before regression to tame the right tail.

anthropic:claude-opus-4-7 · confidence high

Out[34]:

saturn.columns["unemployment_rate"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	950
min	0
max	31.99
mean	5.127
median	4.69
std	2.926
q1	3.42
q3	6.08
iqr	2.66
skew	2.545
kurtosis	12.81
n_outliers	154
outlier_rate	0.0478
zero_rate	0.005587
alert: high_skew	skew=+2.55

Fig 15.

Distribution of unemployment_rate. Vertical dash marks the median.

Show data table

Histogram bins for unemployment_rate (median: 4.69).
bin	count
0 – 0.7997	60
0.7997 – 1.599	90
1.599 – 2.399	197
2.399 – 3.199	333
3.199 – 3.999	464
3.999 – 4.798	539
4.798 – 5.598	492
5.598 – 6.398	361
6.398 – 7.198	217
7.198 – 7.997	142
7.997 – 8.797	85
8.797 – 9.597	61
9.597 – 10.4	36
10.4 – 11.2	36
11.2 – 12	13
12 – 12.8	18
12.8 – 13.6	13
13.6 – 14.4	12
14.4 – 15.2	11
15.2 – 15.99	8
15.99 – 16.79	5
16.79 – 17.59	4
17.59 – 18.39	2
18.39 – 19.19	1
19.19 – 19.99	3
19.99 – 20.79	3
20.79 – 21.59	4
21.59 – 22.39	3
22.39 – 23.19	2
23.19 – 23.99	3
23.99 – 24.79	1
24.79 – 25.59	0
25.59 – 26.39	0
26.39 – 27.19	0
27.19 – 27.99	0
27.99 – 28.79	0
28.79 – 29.59	0
29.59 – 30.39	0
30.39 – 31.19	1
31.19 – 31.99	2

economic unemployment by county

Overview

Summary confidence: high

fips numeric identifier

county_name text metadata

state categorical feature

total_16_plus numeric feature

labor_force numeric feature

unemployed numeric feature

labor_force_participation_rate numeric feature

unemployment_rate numeric feature

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