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People versus machines in the UK: Minimum wages, labor reallocation and automatable jobs
5 selected findings · 9 of 14 quotations matched
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14Returned quotations from the admitted text projection. Select a field citation to locate its referenced passage.
Quotation: matched · Referenced by selected finding
Proposed locator: PLOS ONE 14(12): e0224789, page 1
RESEARCH ARTICLE People versus machines in the UK: Minimum wages, labor reallocation and automatable jobs Grace Lordan * Department of Psychological and Behavioural Science, The London School of Economics and Political Science, London, United Kingdom * g.lordan@lse.ac.uk
Proposed printed page: 1
Quotation: matched · Referenced by selected finding
Proposed locator: Introduction / Data, page 2
The empirical analysis draws on the Quarterly Labor Force Survey data (QLFS) from 1994– 2017. While I follow the Lordan and Neumark analysis as closely as possible, a lack of cross area variation in the UK means that the identification strategies do diverge.
Proposed printed page: 2
Quotation: matched · Referenced by selected finding
Proposed locator: Methodology, page 3
Throughout the analysis I consistently define low-skilled low-wage individuals as those who are working in the lowest-paid occupations, while also having low levels of education. I focus only on those born in the UK to circumvent the fact that immigration flows may cause the composition of this group to fluctuate over time. I calculate income for each occupation in each year, and based on this distribution only include in the analysis those in the bottom income quintile with a GCSE equivalent or less.
Proposed printed page: 3
Quotation: matched · Referenced by selected finding
Proposed locator: Methodology, page 4
Our analysis initially focuses on the following specification RSHiat ¼ b 1 LogMWt þ Aa λ þ þ At γ þ Ii φ þ Xiat þ ε iat ð 3 Þ where LogMWt denotes the natural log of the minimum wage at time t adjusted to 2015 prices (see data section for how minimum wage is defined). Eq (3) includes area ( Aa ) and industry ( Ii ) fixed effects. It also includes area specific time trends ( At) . Standard errors are two-way clustered by area crossed by industry and year.
Proposed printed page: 4
Quotation: matched
Proposed locator: Methodology, page 5
EMPjiat ¼ b 1 ð RSHjiatLogMWat Þ þ b 2 RSHjiat þ TiSs γ þ Ii φ þ ε jiat ð 5 Þ In Eq (5) Emp is the probability that the j th person is employed in industry i , area a , at time t + 1. It is assigned zero if a person was unemployed in t +1. The sample consists of those low- skill low-wage workers employed in period t , and either employed or dis-employed in period t +1.
Proposed printed page: 5
Quotation: matched · Referenced by selected finding
Proposed locator: Individual level analysis data / Results, page 6
The individual level analysis relies on the Longitudinal Labour Force Survey (LLFS), which contains a subset of individuals interviewed in the QLFS. In the LLFS respondents are inter- viewed five times in total–once every quarter for a year. In this analysis the first period is quar- ter 1 and the second is quarter 5.
Proposed printed page: 6
Quotation: not found · Referenced by selected finding
Proposed locator: Table 2, page 7
Table 2. Shares of employment analysis. (1) (2) Pooled Manufacturing Dependent Variable = Share of Automatable Employment MinWage -0.0112 -0.0288 (0.0012) (0.0027) N 4320 480
No unique location was verified in the admitted text projection. This quotation remains an unverified model proposal.
Proposed printed page: 7
Quotation: not found
Proposed locator: Table 3, page 8
40 years old + Min Wage -0.0251 -0.0549 (0.0050) (0.0130) N 4320 480
No unique location was verified in the admitted text projection. This quotation remains an unverified model proposal.
Proposed printed page: 8
Quotation: matched
Proposed locator: Results: Effects on remaining employed, page 9
From Table 5 Column (1), I find evidence of significant declines in the probability of remaining employed in the next period–and hence being dis-employed–for those who were previously in automatable jobs as compared with those in non-automatable jobs. Specifically, the estimates imply declines in the probability of employment, from a 10% minimum wage increase, of 0.19 percentage points, with declines specific to manufacturing being 0.28 percent- age points.
Proposed printed page: 9
Quotation: matched
Proposed locator: Results: Hours effects, page 10
The pooled estimates imply that minimum wage increases do decrease shares of hours for low-skilled low-wage workers in automatable employment significantly. For example, a 10% increase in the mini- mum wage causes a 0.04 percentage point decrease in the share of hours in automatable jobs done by low-skilled low-wage workers overall. The estimated declines in manufacturing are 0.19 percentage points.
Proposed printed page: 10
Quotation: not found
Proposed locator: Table 5, page 11
Table 5. Individual level estimates: Probability of being employed in the next period. (1) Pooled Full Sample Min Wage Automatable -0.0019 (0.0009) N 440614
No unique location was verified in the admitted text projection. This quotation remains an unverified model proposal.
Proposed printed page: 11
Quotation: not found
Proposed locator: Table 6, page 12
Table 6. Hours based analysis. Pooled Dependent Variable = Share of Automatable Hours Min Wage -0.0037 (0.0009) N 4320
No unique location was verified in the admitted text projection. This quotation remains an unverified model proposal.
Proposed printed page: 12
Quotation: matched
Proposed locator: Contemporary analysis, page 13
To explore this, similar to Lordan and Neumark [1] I re-esti- mate Eq 3 for the share of employment analysis and Eq 5 for the probability of remaining employed analysis for the period 2004 to 2017. The estimates are documented in Table 8 and it is notable that they are roughly double the size of those documented in Tables 2, 3 and 5
Proposed printed page: 13
Quotation: not found
Proposed locator: Table 8, page 14
Table 8. Contemporary analysis. (1) Pooled Dependent Variable = Share of Employment Min Wage -0.0131 (0.0039) N 2340
No unique location was verified in the admitted text projection. This quotation remains an unverified model proposal.
Proposed printed page: 14
Economic context
1 / 5research estimate · proposed mapping
Share of Automatable Employment
Reported expression: -0.0112. Assignment: reported.
Finding role: main estimate · Reference period: 1994–2017 · Intervention: natural log of the minimum wage at time t adjusted to 2015 prices
Original generated description
Pooled effect of minimum wage on share of automatable employment
In the pooled shares of employment regression across 1994–2017, the estimated coefficient on log minimum wage is -0.0112 (s.e. 0.0012), indicating that a 10% increase in the minimum wage leads to a 0.11 percentage point decrease in the share of automatable jobs done by low-skilled workers.
Structured-field presentation; field attribution and economic meaning are not independently validated.
- Outcome
- Share of Automatable Employmentreported · proposed · source support incompleteSource: e7
- Intervention
- natural log of the minimum wage at time t adjusted to 2015 pricesreported · proposed · source support incompleteSource: e4e7
- Population
- low-skilled low-wage individuals (bottom income quintile with a GCSE equivalent or less, born in the UK)reported · proposed · source support incompleteSource: e3e7
- Estimator
- OLSreported · proposed · source support incompleteSource: e7
- Reported expression
- -0.0112reported · proposed · source support incompleteSource: e7
- Uncertainty
- (0.0012)reported · proposed · source support incompleteSource: e7
- Sample count
- 4320reported · proposed · source support incompleteSource: e7
15 unresolved fields
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- Identification relies on area-specific time trends rather than cross-area variation because of a lack of cross-area variation in UK minimum wage rates.
AI-generated interpretation. Matched quotations establish text locations; economic validity and publisher attribution remain unverified.
The saved source receipt reports a durable archive. An older generated limitation below says request-scoped. That historical text is preserved in the original download.
Source and processing receipt: 1 passed · 2 partial
Source, document identity and inspected coverage
- Supplied document
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- Input
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- Study
- People versus machines in the UK: Minimum wages, labor reallocation and automatable jobsreported · proposedSource: e1
- Authors
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- Publisher
- PLOS ONEreported · proposedSource: e1
- Publication time
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- Document kind
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- Selected findings are not an exhaustive inventory. Publisher authority and semantic mappings are not established by this analysis.
- Physical PDF page indices differ from printed page labels. Text order and table header/note association are not independently validated.
- The native PDF is supplied to Gemini. Figure, scan and layout interpretations without matched text anchors remain unverified.
APPLICATION SCHEMA: passed
Tool arguments match the versioned draft schema, field scopes and knowledge-state invariants.
SOURCE ANCHORS: partial
9 of 14 quotations uniquely match the admitted text projection. This checks location, not meaning.
FIELD SUPPORT: partial
34 populated finding fields lack matched literal/source support. Field attribution, table association and entailment are not assessed.
openucem.publication-record/0.3.0 · Gemini · gemini-3.8-flash
2026-11-01T06:24:10.736Z · 1 model request
- Selected findings are not an exhaustive inventory. Publisher authority and semantic mappings are not established by this analysis.
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- The native PDF is supplied to Gemini. Figure, scan and layout interpretations without matched text anchors remain unverified.
- The draft presents a selective extraction of 5 core findings across employment shares, individual retention, hours, and contemporary sub-periods, and does not exhaustively tabulate every demographic and industry subgroup.
- All numerical entries preserve exact signed values, precision, and parenthetical standard errors reported in the study tables.
- All assignments are proposed. Matching quotations and tokens do not certify economic meaning, causal claims, independent support or compatibility for calculation.
- Source bytes are request-scoped, not archived. Downloaded hashes and quotations do not guarantee future source recovery.
- Findings and repeated specifications from one publication are potentially dependent. No coefficient conversion, pooling or statistical inference was performed.
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"quote": "RESEARCH ARTICLE People versus machines in the UK: Minimum wages, labor reallocation and automatable jobs Grace Lordan * Department of Psychological and Behavioural Science, The London School of Economics and Political Science, London, United Kingdom * g.lordan@lse.ac.uk",
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"quote": "Our analysis initially focuses on the following specification RSHiat ¼ b 1 LogMWt þ Aa λ þ þ At γ þ Ii φ þ Xiat þ ε iat ð 3 Þ where LogMWt denotes the natural log of the minimum wage at time t adjusted to 2015 prices (see data section for how minimum wage is defined). Eq (3) includes area ( Aa ) and industry ( Ii ) fixed effects. It also includes area specific time trends ( At) . Standard errors are two-way clustered by area crossed by industry and year.",
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"quote": "Table 2. Shares of employment analysis. (1) (2) Pooled Manufacturing Dependent Variable = Share of Automatable Employment MinWage -0.0112 -0.0288 (0.0012) (0.0027) N 4320 480",
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"quote": "From Table 5 Column (1), I find evidence of significant declines in the probability of remaining employed in the next period–and hence being dis-employed–for those who were previously in automatable jobs as compared with those in non-automatable jobs. Specifically, the estimates imply declines in the probability of employment, from a 10% minimum wage increase, of 0.19 percentage points, with declines specific to manufacturing being 0.28 percent- age points.",
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