ACS Japanese Speaker Data (Under Construction)

I needed some language data, so I ended up making something similar to this site: https://probablyhelpful.com/languages/Japanese.html. Except with newer data, more visualizations, searchable tables, and some helpful statistics.

Why

There are a number of factors unique to Japanese that create upward wage pressure for bilingual speakers (me).
From the demand side:

Japan is and has been the country with the most direct investment in the US. src1, src2
Japan is the fourth largest economy (investment target) src3, fifth when adjusting for PPP src4

On the supply side:

Japan ranks poorly in the “English Proficiency Index”. src5
Japanese is among the hardest language to learn for English speakers (along with Arabic, Chinese, Korean). src6
Japan has a small emigrant population relative to its population size. src7 (Source available through wayback machine)
Regional concentrations/disparities within the US (the purpose of this analysis). src8=myself+ACS data

Together, these factors make Japanese a uniquely valuable language in the US. China/Chinese is similar in some respects, but lacks, basically any, direct investments into the US. For reference, in 2025 Austria had more direct investment into the US than China.

As an aside, I wish the BLS would release language specific data on Interpreters and Translators. Rather than the general translator bucket. (How am I supposed to interpret the wage data!)
There is also the complication of specializations that require language ability as an addition, but are not a “translator” role. With the right data I imagine they could create an average % wage premium that a language adds.
An example wherein they identified the wage premium a foreign language skill provided (in Poland).

Data

The ACS question:

The data originates from https://data.census.gov/.

U.S. Census Bureau. “Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over.” American Community Survey, ACS 5-Year Estimates Detailed Tables, Table B16001.

full questionnare: https://www.census.gov/programs-surveys/acs/about/forms-and-instructions.html

Observations

Alaska, Utah, Nevada stood out as locations not traditionally associated with a Japanese diaspora, but had relatively high populations of Japanese speakers.

Visualizations 見える化

Code

import pandas as pd
import geopandas as gpd
import folium
import itables
import geoplot

jpmap = gpd.read_file("../../census/jpmap.gpkg")
# jpmap = jpmap.rename({"STUSPS":"State", "NAME":"State Full", "osmid":"ID", "url":"URL"}, axis=1)

itables.show(pd.DataFrame(jpmap[jpmap.columns[:13]]), maxBytes=200000, style="width:100%", 
                                    buttons=["pageLength", {"extend": "colvisGroup","text": "Hide Errors","hide": [2,4,6,8,10]},
                                                            {"extend": "colvisGroup","text": "Show all","show": "*"}],
                                    columnDefs=[{"className":"dt-left", "targets":[0]}, 
                                                {"targets":[2,4,6,8,10], "visible":False}],
                                    columnControl=["order"],
                                    ordering={"indicators": False, "handler": False})

^ⓘstate	total	etotal	eng	eeng	jpn	ejpn	bilingual	ebilingual	jpnbad	ejpnbad	percentjpn	percentbilingual
alabama	4793030	601	4503218	5359	2528	541	1228	414	1300	327	0.052743	0.025621
alaska	689070	169	585162	2727	1494	390	1033	281	461	254	0.216814	0.149912
arizona	6981320	285	5184054	11544	7952	907	5119	706	2833	511	0.113904	0.073324
arkansas	2869111	548	2630451	4441	935	266	495	195	440	218	0.032588	0.017253
california	37121454	487	20642638	54450	133419	4594	78900	3190	54519	2222	0.359412	0.212546
colorado	5549558	577	4650283	9223	5893	658	4494	563	1399	266	0.106189	0.080979
connecticut	3442817	90	2638366	6723	3281	640	2322	504	959	329	0.095300	0.067445
delaware	966580	46	820336	3432	805	272	603	229	202	102	0.083283	0.062385
district of columbia	640841	66	518478	3204	1054	285	692	244	362	145	0.164471	0.107983
florida	21289728	707	14769458	25976	12222	1183	8153	993	4069	647	0.057408	0.038295
(41 more rows not shown)

Code

itables.show(pd.DataFrame(jpmap.loc[:,["total","eng","jpn","bilingual","jpnbad","region"]]), maxBytes=200000, 
                                    style="width:100%", buttons=["pageLength"],
                                    columnDefs=[{"className":"dt-left", "targets":[0]}],
                                    columnControl=["order"],
                                    ordering={"indicators": False, "handler": False})

^ⓘtotal	eng	jpn	bilingual	jpnbad	region
4793030	4503218	2528	1228	1300	south
689070	585162	1494	1033	461	west
6981320	5184054	7952	5119	2833	west
2869111	2630451	935	495	440	south
37121454	20642638	133419	78900	54519	west
5549558	4650283	5893	4494	1399	west
3442817	2638366	3281	2322	959	northeast
966580	820336	805	603	202	south
640841	518478	1054	692	362	south
21289728	14769458	12222	8153	4069	south
(41 more rows not shown)

Code

regionallang = jpmap.loc[:,["total","eng","jpn","bilingual","jpnbad","region"]].groupby(["region"], as_index=False).sum()
regionallang["percentjpn"] = (regionallang.jpn * 100) / regionallang.total
regionallang["percentbilingual"] = (regionallang.bilingual * 100) / regionallang.total
itables.show(regionallang, maxBytes=200000, style="width:100%",
                                    columnDefs=[{"className":"dt-left", "targets":[0]}],
                                    columnControl=["order"],
                                    ordering={"indicators": False, "handler": False})

^ⓘregion	total	eng	jpn	bilingual	jpnbad	percentjpn	percentbilingual
midwest	65194303	57229624	58473	33954	24519	0.089690	0.052081
northeast	54393479	41199327	68163	41873	26290	0.125315	0.076982
south	121811243	96711452	96036	59928	36108	0.078840	0.049197
west	74743523	50626636	239733	148271	91462	0.320741	0.198373

Code

# m = folium.Map([37, -100], zoom_start=4)

# marker_cluster = MarkerCluster(disableClusteringAtZoom=10).add_to(m)

# for idx, row in nodirgdf.iterrows():
#     popup_html = f"<style> th\
#                 {{border:1px solid black;\}}</style>\
#                 <table><tr>\
#                     <th>State: </th>\
#                     <th>{row['State Full']}</th>\
#                 </tr><tr>\
#                     <th>ALPR ID: </th>\
#                     <th>{row['ALPR ID']}</th>\
#                 </tr><tr>\
#                     <th text-align='center' colspan='2'>{row['url']}</th>\
#                 </tr></table>"
#     folium.Circle(
#         location=[row.geometry.y, row.geometry.x], 
#         popup=popup_html, fill=True, radius=30, weight=9,color="#9E2F02",opacity=0.8,fillOpacity=0.5
#     ).add_to(marker_cluster)

# m

Other Stats

203,771,037 / 316,142,548 = 64.5%	of the general populace is working age (18 to 64)
8,030,466 / 11,320,253 = 70.9%	among those who Speak Asian and Pacific Island language
4,734,803 / 8,030,466 = 59.0%	speak english “very well” among working age API lang speakers
4,734,803 / 11,320,253 = 41.8%	working age speak english “very well” among all API lang speakers
2024 ACS 5-Year Estimates, Table B16004