Data Entry Keyers: District of Columbia vs Ohio pay

Percentile	Published wages	District of Columbia	Ohio
10th	District of Columbia$48,930Ohio$29,220	$48,930	$29,220
25th	District of Columbia$50,910Ohio$36,060	$50,910	$36,060
Median	District of Columbia

Place	Employment	Jobs per 1,000	Location quotient	Employment RSE
District of Columbia	200Jobs per 1,000: 0.29 Location quotient: 0.35	0.29	0.35	24.4%
Ohio	4,530Jobs per 1,000: 0.82 Location quotient: 1	0.82	1	7.5%

75th	District of Columbia$64,100Ohio$47,270	$64,100	$47,270

90th	District of Columbia$73,090Ohio$54,990	$73,090	$54,990

Start with the answer

Data Entry Keyers pay in District of Columbia and Ohio

The May 2025 BLS OEWS median for Data Entry Keyers is $55,170 per year in District of Columbia. It is $40,220 per year in Ohio. District of Columbia is 37.2% above Ohio on the published median.

Both rows use SOC 43-9021 and the same annual wage metric. Median means the middle of each area's published wage spread. It is not one worker's pay. The larger median is not proof that one place gives a better job or a better result. It is one wage fact from one source period.

BLS lists 200 wage and salary jobs in District of Columbia and 4,530 in Ohio. Those are survey job counts, not open posts. Read pay, job scale, local share, and price level as separate facts before you make a choice.

Evidence: bls-oews-may-2025 · Rule: same-soc-same-metric-pair · nominal-median-gap · employment-not-openings

Pay range

Compare the full Data Entry Keyers wage ladder

A median can hide a wide range. The 10th, 25th, 50th, 75th, and 90th percentile points show more of each area's spread. A percentile is a rank point. It is not a career stage. The 10th point does not mean new-worker pay. The 90th point does not mean a wage that comes after a set number of years.

The lower half can differ from the upper half. One area can lead at the median and trail at another point. Read each field against the same field in the other area. Do not compare one area's 75th percentile with the other area's median. That would answer a different question.

A posted job range can sit below, across, or above these marks. That does not make the post wrong. A listing can differ by employer, level, hours, field, or work rules. Keep the listing tied to that employer. Keep the BLS range tied to the whole role-place row.

10th percentile: District of Columbia is $48,930 per year; Ohio is $29,220 per year. District of Columbia is 67.5% above Ohio at this point.
25th percentile: District of Columbia is $50,910 per year; Ohio is $36,060 per year. District of Columbia is 41.2% above Ohio at this point.
Median: District of Columbia is $55,170 per year; Ohio is $40,220 per year. District of Columbia is 37.2% above Ohio at this point.
75th percentile: District of Columbia is $64,100 per year; Ohio is $47,270 per year. District of Columbia is 35.6% above Ohio at this point.
90th percentile: District of Columbia is $73,090 per year; Ohio is $54,990 per year. District of Columbia is 32.9% above Ohio at this point.

Evidence: bls-oews-may-2025 · Rule: paired-percentile-fields · same-field-comparison · posted-pay-separation

Shape of pay

Mean and median tell two parts of the Data Entry Keyers story

District of Columbia has a mean of $58,250 per year and a median of $55,170. Ohio has a mean of $41,820 per year and a median of $40,220. Mean adds all wage values and splits the total by the number of jobs behind the estimate.

District of Columbia's mean is 5.6% above its median on the same published median. Ohio's mean is 4.0% above its median on the same published median. The gaps show that each wage spread has a shape. They do not explain why the shape differs.

Use median for the middle. Use mean to see how the full set pulls. Use percentiles to see more of the spread. None of the three is a pay promise. A person, team, or listing can sit far from all three for reasons that are outside these BLS rows.

Evidence: bls-oews-may-2025 · Rule: paired-mean-median · mean-median-gap · no-causal-inference

Job scale

How the two Data Entry Keyers job bases differ

District of Columbia has 200 estimated Data Entry Keyers jobs. Ohio has 4,530. The larger count shows a larger covered job base in this source. It does not show more open roles, faster hiring, or a better chance for one applicant.

District of Columbia has 0.29 Data Entry Keyers jobs per 1,000 local jobs. Ohio has 0.82 per 1,000. This rate helps compare places of different size. It is not a job-post rate and does not use the MeritLog catalog.

The employment RSE is 24.4% in District of Columbia and 7.5% in Ohio. RSE means relative standard error. It is a survey precision flag. It is not the chance that the job count or one person's pay is right.

Evidence: bls-oews-may-2025 · Rule: paired-employment · paired-jobs-per-thousand · employment-rse-field-scope

Local share

Where Data Entry Keyers takes a larger share of jobs

The location quotient is 0.35 in District of Columbia and 1 in Ohio. A value of 1 means the role has the same share of local jobs as it has across the nation. A value above 1 means the local share is larger. A value below 1 means it is smaller.

District of Columbia's quotient is 65.0% below Ohio's. That gap is about the job mix. It is not a wage gap, a job-count gap, or a growth claim.

Read location quotient with employment. A small place can have a high share and few jobs. A large place can have many jobs and a share near the national mark. Then read pay as its own field. The three measures do not form one score.

Evidence: bls-oews-may-2025 · Rule: paired-location-quotient · quotient-gap · metric-separation

BEA price view

How price levels differ between District of Columbia and Ohio

BEA's 2024 all-items price index is 109.9 in District of Columbia and 92.8 in Ohio. The U.S. level is 100. A higher number means the broad local price level is higher than the national mark. It does not say a place is good or bad.

Housing is 155.0 in District of Columbia and 73.0 in Ohio. Goods are 106.5 and 93.7. Utilities are 112.8 and 95.8. Other services are 103.0 and 98.9. The parts can move in different ways.

The wage period is May 2025. The price period is 2024. WageScope keeps those clocks visible. It uses the all-items index for one clear proxy. It does not mix BEA prices into the BLS source row, and it does not claim the two releases describe the exact same month.

Evidence: bea-rpp-2024 · bls-oews-may-2025 · Rule: paired-rpp-fields · rpp-us-baseline · source-period-separation

Price-level proxy

Compare the medians in national-price dollars

WageScope divides District of Columbia's $55,170 median by 109.9 divided by 100. The result is $50,200 per year. It divides Ohio's $40,220 median by 92.8 divided by 100. That result is $43,353 per year.

District of Columbia's proxy is 15.8% above Ohio's. The nominal gap is 37.2%. The order can stay the same or change after the price-level step. Both results must keep their labels.

The proxy is a narrow math tool. It is not take-home pay. It does not add taxes, benefits, household size, debt, commute, schools, safety, climate, or taste. It does not set a wage target. Use it to see how one broad price index changes the wage view, then check your own costs outside WageScope.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-bounded-comparison-v1 · Rule: price-level-adjusted-formula · nominal-adjusted-gap-separation · proxy-limits

Same-proxy pay

What equal price-level pay would look like in the other place

$55,170 per year in District of Columbia has the same national-price-dollar proxy as $46,572 per year in Ohio. The math keeps District of Columbia's proxy fixed, then applies Ohio's all-items price index.

$40,220 per year in Ohio has the same proxy as $47,645 per year in District of Columbia. This reverse view is useful because the answer depends on which wage you start with. It is not a claim about an offer in either place.

Equivalent nominal pay uses one broad price measure. It does not know what a person buys. It does not know rent, family size, debt, taxes, travel, benefits, or savings. Treat the result as a clean source-backed comparison, not a full move plan or a number an employer must match.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-bounded-comparison-v1 · Rule: equivalent-nominal-value-forward · equivalent-nominal-value-reverse · proxy-limits

Data care

Check the survey marks before you use the gap

The mean-wage RSE is 4.2% in District of Columbia and 1.3% in Ohio. The employment RSE is 24.4% and 7.5%. RSE is a precision flag for the field it sits beside. It is not a score for the whole page.

BLS does not publish the same RSE field for the median and each percentile. WageScope does not move a mean RSE to the median. It does not use an employment RSE as a wage care mark. Each value stays tied to its source field. Missing or top-coded values also keep their source state.

A later BLS or BEA release can change the result. MeritLog checks the official source clocks before promotion. The page cannot call its data current when a newer complete release is waiting for review. Current job cards have their own seen date and do not make the government data newer.

Evidence: bls-oews-may-2025 · Rule: paired-rse-field-scope · no-rse-transfer · independent-source-freshness

Use the result

A short check list for District of Columbia and Ohio

Start with the same Data Entry Keyers wage field. Then read the full percentile spread. Check job count, jobs per 1,000, and location quotient. Add the 2024 all-items and housing price levels. Read the national-price-dollar proxy with its limits. Keep the BLS and BEA dates in view.

Next, inspect one current employer-title match in each place when an exact public place match exists. A job post can show work level, hours, location rules, and pay that the BLS row cannot. The post is dated employer evidence. It does not change the survey median or prove a broad trend.

Last, use your own facts. Compare the job needs with work you have done and can support. A wage gap cannot show fit. A price-level gap cannot show fit. A job count cannot show fit. MeritLog's résumé path starts only after you choose a listing and confirm the work facts you want to use.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-public-job-boundary-v1 · Rule: comparison-checklist · job-source-separation · confirmed-resume-evidence

Limits

What this Data Entry Keyers comparison does not decide

The page does not choose District of Columbia or Ohio for you. It does not say what one worker earns. It does not promise a job, offer, raise, or move result. It does not say why the medians differ. It does not turn the larger wage, job count, quotient, or proxy into a place score.

The comparison leaves out people who work for themselves and other work outside the OEWS scope. It does not add taxes, take-home pay, benefits, stock, bonus pay, hours, travel, remote work, or a household budget. Current listings are separate and can be empty even when the government rows are complete.

Use this page for a bounded choice. Compare the same May 2025 BLS role and metric across two exact areas. Add the separate 2024 BEA price view. Inspect dated employer-title matches. Then build a résumé from facts you have confirmed. Keep each source tied to the question it can answer.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-public-job-boundary-v1 · Rule: no-place-choice · no-personal-pay-claim · source-purpose-separation

Data Entry Keyers: District of Columbia vs Ohio

Same occupation, same annual wage metric

The winner can change when local prices differ

Housing and all-items indexes explain the adjustment

Equivalent nominal pay, using price levels only

Employment scale and local concentration

Compare another occupation or pair

Data Entry Keyers: District of Columbia and Ohio guide

Data Entry Keyers in District of Columbia and Ohio: common questions

Compare Data Entry Keyers listings by place

District of Columbia

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Ohio

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Current jobs

Tailor for a real Data Entry Keyers job, not a generic wage number.

Data Entry Keyers pay in District of Columbia and Ohio

Compare the full Data Entry Keyers wage ladder

Mean and median tell two parts of the Data Entry Keyers story

How the two Data Entry Keyers job bases differ

Where Data Entry Keyers takes a larger share of jobs

How price levels differ between District of Columbia and Ohio

Compare the medians in national-price dollars

What equal price-level pay would look like in the other place

Check the survey marks before you use the gap

A short check list for District of Columbia and Ohio

What this Data Entry Keyers comparison does not decide

Compare Data Entry Keyers listings by place

District of Columbia

We could not load these listings.

Ohio

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Privacy choices

Data Entry Keyers: District of Columbia vs Ohio

Same occupation, same annual wage metric

The winner can change when local prices differ

Housing and all-items indexes explain the adjustment

Equivalent nominal pay, using price levels only

Employment scale and local concentration

Compare another occupation or pair

Data Entry Keyers: District of Columbia and Ohio guide

Data Entry Keyers in District of Columbia and Ohio: common questions

Compare Data Entry Keyers listings by place

District of Columbia

We could not load these listings.

Ohio

We could not load these listings.

Current jobs

Tailor for a real Data Entry Keyers job, not a generic wage number.

Data Entry Keyers pay in District of Columbia and Ohio

Compare the full Data Entry Keyers wage ladder

Mean and median tell two parts of the Data Entry Keyers story

How the two Data Entry Keyers job bases differ

Where Data Entry Keyers takes a larger share of jobs

How price levels differ between District of Columbia and Ohio

Compare the medians in national-price dollars

What equal price-level pay would look like in the other place

Check the survey marks before you use the gap

A short check list for District of Columbia and Ohio

What this Data Entry Keyers comparison does not decide

Compare Data Entry Keyers listings by place

District of Columbia

We could not load these listings.

Ohio

We could not load these listings.