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manuscript/article.qmd
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---
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title: Inequalities in the World of Work
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subtitle: What do we know?
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author:
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- name: Miguel Niño-Zarazúa
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email: mn39@soas.ac.uk
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affiliations:
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- id: soas
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name: SOAS University of London
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department: Department of Economics
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attributes:
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corresponding: true
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- name: Marty Oehme
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email: mail@martyoeh.me
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date: last-modified
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abstract: |
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We are researching the effectiveness of policies which target inequalities on the labour market.
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keywords:
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- labour markets
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- world of work
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- inequality
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- policy
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- systematic scoping review
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lang: en
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crossref: # to fix the appendix crossrefs being separate from main
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custom:
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- kind: float
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key: appatbl
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latex-env: appatbl
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reference-prefix: Table A
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space-before-numbering: false
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latex-list-of-description: Appendix A Table
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- kind: float
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key: appbtbl
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latex-env: appbtbl
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reference-prefix: Table B
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space-before-numbering: false
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latex-list-of-description: Appendix B Table
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---
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```{python}
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#| label: standard-imports
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#| echo: false
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#| output: false
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import src.globals as g
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from IPython.display import display, Markdown, HTML
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from tabulate import tabulate
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import seaborn as sns
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sns.set_style("whitegrid")
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```
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```{python}
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#| label: load-dataframes
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#| echo: false
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#| output: false
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from src.process.generate_dataframes import df, df_by_intervention, validities
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```
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{{< portrait >}}
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# Introduction
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* Context and statement of the problem
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* Aims and rationale of the systematic scoping review
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* Summary of the main findings
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* Description of the structure of the paper
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# Conceptual framework
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* Theories, policies, mechanisms and outcomes
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# Review methodology
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The following section will discuss the methodology that is proposed to conduct the review of the literature on policy interventions that are expected to address inequalities in forms of work and labour market outcomes,
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as well as give an overview of the collected data.
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This study follows the principles of a systematic review framework, to systematically assess the impact of an array of policies on inequalities in the world of work.
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It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research,
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while limiting "bias by the systematic assembly, critical appraisal and synthesis" through applying scientific strategies to the review itself [@Cook1995].
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It thereby attempts to provide an improved basis for comparative analysis between studies through the rigorous application of systematic criteria and thus to avoid the potential bias of narrative reviews.
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Unlike purely systematic reviews which typically focus on specific policy questions and interventions, systematic scoping reviews focus on a wider spectrum of policies, where different study designs and research questions can be investigated.
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Since scoping reviews allow both broad and in-depth analyses, they are the most appropriate rigorous method to make a synthesis of the current evidence in this area [@Arksey2005].
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The scoping review allows broad focus to be given to a subject for which no unified path with clear edges has been laid out yet by prior reviews, as remains the case with policies targeting inequalities in the world of work.
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It does so through a breadth-first approach through a search protocol which favours working through a large body of literature to subsequently move toward a depth-favouring approach once the literature has been sufficiently delimited.
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Its purpose, clearly mapping a body of literature on a (broad) topic area, is thereby useful on its own or in combination with a systematic approach [@Arksey2005].
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With an increasingly adopted approach in recent years, with rigorous dichotomy of inclusion and exclusion criteria it provides a way of charting the relevance of literature related to its overall body that strives to be free of influencing biases which could affect the skew of the resulting literature sample [@Pham2014].
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## Inclusion criteria
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Concise narrowing criteria are applied to restrict the sample to studies looking at i) the effects of individual evidence-based policy measures or intervention initiatives ii) attempting to address a single or multiple of the defined inequalities in the world of work.
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iii) using appropriate quantitative methods to examine the links of intervention and impact on the given inequalities.
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The narrowing process makes use of the typology of inequalities, of forms of work, and of policy areas introduced above as its criteria.
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An overview of the respective criteria used for inclusion or exclusion can be found in @tbl-inclusion-criteria.
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It restricts studies to those that comprise primary research published after 2000,
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with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
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::: {#tbl-inclusion-criteria}
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```{python}
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inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
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Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
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```
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Source: Author's elaboration
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Study inclusion and exclusion scoping criteria
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:::
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## Search protocol
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The search protocol follows a three-staged process of execution: identification, screening and extraction.
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First, in identification, the relevant policy, inequality and world of work related dimensions are combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential grey literature.
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While the resulting study pools could be screened for in multiple languages, the search queries themselves are passed to the databases in English-language only.
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Relevant results are then complemented through the adoption of a 'snowballing' technique,
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in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.
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To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions.[^existingreviews]
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[^existingreviews]: TODO: citation of existing reviews used; ILO definitions if mentioned
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Identified terms comprising the world of work can be found in the Appendix tables @appatbl-wow-terms, @appatbl-intervention-terms, and @appatbl-inequality-terms,
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with the search query requiring a term from the general column and one other column of each table respectively.
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Each cluster is made up of a general signifier (such as “work”, “inequality” or “intervention”) which has to be labelled in a study to form part of the sample,
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as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as “domestic” work, “gender” inequality, “maternity leave” intervention).
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For the database query, a single term from the respective general category is required to be included in addition to one term from any of the remaining categories.
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Second, in screening, duplicate results are removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
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language, title, abstract, full text and literature supersession through newer publications.
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Properties in these characteristics are used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
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To facilitate the screening process, with the help of 'Zotero' reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,
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such as 'excluded::language', 'excluded::title', 'excluded::abstract', and 'excluded::superseded'.
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This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.
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To that end, a 'country::', 'region::' and 'income::' are used to disambiguate between the respective characteristics, such as 'region::LAC' for Latin America and the Caribbean, 'region::SSA' for Sub-Saharan Africa; as well as for example 'income::low-middle', 'income::upper-middle' or 'income::high'.
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These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings [@ILO2022].
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Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system (such as 'inequality::income' or 'inequality::gender').
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The complete process of identification and screening is undertaken with the help of the Zotero reference manager.
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Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with 'excluded::full-text' as explained above and relevant studies then ingested into the final sample pool.
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Should any literature reviews be identified as relevant during this screening process,
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they will in turn be crawled for cited sources in a 'snowballing' process.
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The sources will be added to the sample to undergo the same screening process explained above,
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ultimately resulting in the process represented in the PRISMA chart in @fig-prisma.
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```{mermaid}
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%%| label: fig-prisma
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%%| fig-cap: PRISMA flowchart for scoping process
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%%| file: ../data/processed/prisma.mmd
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```
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All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
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The results to be identified in the matrix include a study's: i) key outcome measures (dependent variables), ii) main findings, iii) main policy interventions (independent variables), iv) study design and sample size, v) dataset and methods of evaluation, vi) direction of relation and level of representativeness, vii) level of statistical significance, viii) main limitations.
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```{python}
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from src.model import prisma
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nr = prisma.PrismaNumbers()
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```
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The query execution results in an initial sample of `{python} nr.nr_database_query_raw` potential studies identified from the database search as well as `{python} nr.nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} nr.FULL_RAW_SAMPLE_NOTHING_REMOVED`.
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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
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Of these, `{python} nr.FULL_SAMPLE_DUPLICATES_REMOVED-nr.nr_out_title-nr.nr_out_abstract-nr.nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
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@fig-intervention-types shows the predominant interventions contained in the reviewed literature.
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Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.
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The entire spread of policies captures interventions aimed primarily at institutional and structural mechanisms, but also mechanisms focused on individual agency.
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```{python}
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#| label: fig-intervention-types
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#| fig-cap: Available studies by primary type of intervention
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sort_order = df_by_intervention["intervention"].value_counts().index
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fig = plt.figure()
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fig.set_size_inches(6, 3)
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ax = sns.countplot(df_by_intervention, x="intervention", order=df_by_intervention["intervention"].value_counts().index)
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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plt.show()
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del sort_order, fig, ax
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```
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# Synthesis of evidence
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Since policies employed in the pursuit of increased equality can take a wide form of actors, strategy approaches and implementation details,
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the following synthesis will first categorise between the main thematic area and its associated interventions.
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Individual observations are then descriptively distinguished between for the primary outcome variables (inequalities) of interest.
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Thus, in the following synthesis each reviewed study will be analysed through the primary policies or mechanisms they use as independent variables to analyse the effects on a variety of inequalities.
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One of the primary lenses of inequality in viewing policy interventions to reduce inequalities in the world of work is that of income,
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often measured for all people throughout a country (vertical inequality) or subsets thereof (horizontal inequality).
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At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities could lead to diminished intervention outcomes through adverse targeting.
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Each main thematic area will be preceded by a table presenting a summary of findings for the respective policies,
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their identified channels and an estimation of their strength of evidence base.
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Afterwards, the analytical lens will be inverted for the discussion and the reviewed studies discussed from a perspective of their analysed inequalities and limitations,
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to better identify areas of strong analytical lenses or areas of more limited analyses.
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## Institutional factors
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{{< portrait >}}
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::: {#tbl-findings-institutional}
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```{python}
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#| label: tbl-findings-institutional
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from src.model import validity
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study_strength_bins = {
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0.0: r"\-",
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5.0: r"\+",
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10.0: r"\++",
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}
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def strength_for(val):
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return list(study_strength_bins.keys())[
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list(study_strength_bins.values()).index(val)
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]
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findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-institutional.csv")
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outp = Markdown(
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tabulate(
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validity.add_to_findings(
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findings_institutional, df_by_intervention, study_strength_bins
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)[
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[
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"area of policy",
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"internal_validity",
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"external_validity",
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"findings",
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"channels",
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]
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].fillna(""),
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showindex=False,
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headers=[
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"area of policy",
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"internal strength",
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"external strength",
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"main findings",
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"channels",
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],
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tablefmt="grid",
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)
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)
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del findings_institutional
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outp
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```
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Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
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Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
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evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
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Summary of main findings for institutional policies
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:::
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{{< landscape >}}
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### Labour laws and regulatory systems
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@Adams2015 study the effects of labour, business and credit regulations and looks at their long-term correlations to income inequality in developing countries from 1970 to 2012.
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Additionally, the study looks at the effects of FDI and school enrolment, which will be reviewed in their respective policy sections.
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They find that in MENA, SSA, LAC and to some extend AP increased labour and business regulations are actually negatively related to equitable income distribution, with market regulation not having significant effects.
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The authors identify developing countries lacking in institutional capability to accomplish regulatory policies optimized for benefits and see the need for policies requiring more specific targeting of inequality reduction as their agenda.
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Overall, the authors suggest that regulatory policy in developing countries needs to be built for their specific contexts and not exported from developed countries due to their different institutional capabilities and structural make-up.
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The study is limited in its design focus relying purely on the macro-level regional analyses and can thus,
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when finding correlations towards income inequality, not necessarily drill down into their qualitative root causes.
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<!-- maternity leave and benefits -->
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@Broadway2020 study the introduction of universal paid maternal leave in Australia, looking at its impacts on mothers returning to work and the conditions they return under.
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The study finds that, while there is a short-term decrease of mothers returning to work since they make use of the introduced leave period, over the long-term (after six to nine months) there is a significant positive impact on return to work.
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Furthermore, there is a positive impact on returning to work in the same job and under the same conditions,
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the effects of which are stronger for more disadvantaged mothers (measured through income, education and access to employer-funded leave).
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This suggests that the intervention reduced the opportunity costs for delaying the return to work, and especially for those women that did not have employer-funded leave options, directly benefiting more disadvantaged mothers.
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Some potential biases of the study are its inability to account for child-care costs, as well as not being able to fully exclude selection bias into motherhood.
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There also remains the potential of results being biased through pre-birth labour supply effects or the results of the financial crisis, which may create a down-ward bias for either the short- or long-term effects.
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@Dustmann2012 analyse the long-run effects on children's outcomes of increasing the period of paid leave for mothers in Germany.
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While the study focuses on the children's outcomes, it also analyses the effects on the return to work rates and cumulative incomes of the policies within the first 40 months after childbirth.
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It finds that, while short-term increases of paid leave periods (up to 6 months) significantly increased incomes, over longer periods (10-36 months) the cumulative incomes in fact decreased significantly,
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marginally for low-wage mothers for 10 month periods, and across all wage segments for 36 month periods.
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For the share of mothers returning to work, it finds that there is a significant increase in the months away from work among all wage segments for all paid leave period increases, positively correlated with their length.
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Still similar numbers of mothers return once the leave period ends, though with significant decreases for leave periods from 18 to 36 months.
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For its analysis of long-term educational outcomes on children, however, it does not find any evidence for the expansions improving children's outcomes, even suggesting a possible decrease of educational attainment for the paid leave extension to 36 months.[^dustmann-childoutcomes]
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Some limitations of the study include its sample being restricted to mothers who go on maternity leave and some control group identification restrictions possibly introducing some sampling bias.
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[^dustmann-childoutcomes]: The authors suggest that the negative effect for children under the long-term paid leave program of 36 months may stem from the fact that children require more external stimuli (aside from the mother) before this period ends, as well as the negative long-term effects of the mother's significantly reduced income for the long-term leave periods.
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In a study on the effects of introductions of a variety of maternity leave laws in Japan, @Mun2018 look at the effects on employment numbers and job quality in managerial positions of women.
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Contrary to notions of demand-side mechanisms of the welfare state paradox, with women being less represented in high-authority employment positions due to hiring or workplace discrimination against them with increased maternity benefits,
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it finds that this is not the case for the Japanese labour market between 1992 and 2009.
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There were no increases in hiring discrimination against women, and either no significant change in promotions for firms not providing paid leave before the laws or instead a positive impact on promotions for firms that already provided paid leave.
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The authors suggest the additional promotions were primarily based on voluntary compliance of firms in order to maintain positive reputations,
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signalled through a larger positive response to incentive-based laws than for mandate-based ones.
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Additionally, the authors suggest that the welfare paradox may rather be due to supply-side mechanisms, based on individual career planning, as well as reinforced along existing gender divisions of household labour which may increase alongside the laws.
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Limitations of the study include foremost its limited generalizability due to the unique Japanese institutional labour market structure (with many employments, for example, being within a single firm until retirement), as well as no ability yet to measure the true causes and effects of adhering to the voluntary incentive-based labour policies, with lasting effects or done as symbolic compliance efforts and mere impression management.
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@Davies2022 conduct a study on the return to work ratios for high-skill women workers in public academic universities in the United Kingdom, comparing the results for those in fixed-term contract work versus those in open-ended contracts.
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It finds that there is a significantly decreased return to work probability for those working under fixed-term contracts, and most universities providing policies with more limited access to maternity payment for fixed-contract staff.
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This is possibly due to provisions in the policies implicitly working against utilization under fixed-terms:
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there are strict policies on payments if a contract ends before the maternity leave period is over, and obligations on repayments if not staying in the position long enough after rtw.
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Additionally, most policies require long-term continuous service before qualifying for enhanced payments in the maternity policies.
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There is high internal heterogeneity between the universities, primarily due to the diverging maternity policy documents, only a small number of the overall dataset providing favourable conditions for fixed-term work within.
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### Minimum wage laws
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@Chao2022, in a study looking at the effects of minimum wage increases on a country's income inequality, analyse the impacts in a sample of 43 countries, both LMIC and HIC.
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Using a general-equilibrium model, it finds that there are differences between the short-term and long-term effects of the increase:
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In the short term it leads to a reduction of the skilled-unskilled wage gap, however an increase in unemployment and welfare,
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while in the long term the results are an overall decrease in wage inequality as well as improved social welfare.
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It finds those results primarily stem from LMIC which experience significant effects driven by a long-term firm exit from the urban manufacturing sector thereby increasing available capital for the rural agricultural sector, while in HIC the results remain insignificant.
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The study uses the Gini coefficient for identifying a country's inequality.
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Some limitations of the study include the necessity to omit short-term urban firm exit for the impact to be significant, as well as requiring the, reasonable but necessary, prior assumption of decreased inequality through increased rural agricultural capital.
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@Alinaghi2020 conduct a study using a microsimulation to estimate the effects of a minimum wage increase in New Zealand on overall income inequality and further disaggregation along gender and poverty lines.
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It finds limited redistributional effects for the policy, with negligible impact on overall income inequality and the possibility of actually increasing inequalities among lower percentile income households.
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Additionally, while it finds a significant reduction in some poverty measures for sole parents that are in employment, when looking at sole parents overall the effects become insignificant again.
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The authors suggest this points to bad programme targeting, which at best has negligible positive impact on income equality and at worst worsens income inequality in lower income households, due to may low-wage earners being the secondary earners of higher-income households but low-wage households often having no wage earners at all.
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A pertinent limitation of the study includes its large sample weights possibly biasing the impacts on specific groups such as sole parents and thus being careful not to overestimate their significance.
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In a study on the impacts of minimum wage increases in Ecuador @Wong2019 specifically looks at the income and hours worked of low-wage earners to analyse the policies effectiveness.
|
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The study finds that, generally, there was a significant increase on the income of low-wage earners and also a significant increase on wage workers hours worked which would reflect positively on a decrease in the country's income inequality.
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At the same time, it finds some potential negative effects on the income of high earners, suggesting an income-compression effect as employers freeze or reduce high-earners wages to offset low-earners new floors.
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The findings hide internal heterogeneity, however:
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For income the effect is largest for agricultural workers while for women the effect is significantly smaller than overall affected workers.
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For hours worked there is a significant negative impact on women's hours worked, a fact which may point to a decreased intensive margin for female workers and thus also affect their lower income increases.
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Limitations of the study include some sort-dependency in their panel data and only being able to account for effects during a period of economic growth.
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Thus, while overall income inequality seems well targeted in the intervention, it may exacerbate the gender gap that already existed at the same time.
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<!-- non-spatial policy but spatial effects -->
|
||||
@Gilbert2001 undertake a study looking at the distributional effects of introducing a minimum wage in Britain, with a specific spatial component.
|
||||
Overall it finds little effect on income inequality in the country.
|
||||
It finds that the effects on rural areas differ depending on their proximity to urban areas.
|
||||
While overall income inequality only decreases a small amount, the intervention results in effective targeting with remote rural households having around twice the reduction in inequality compared to others.
|
||||
Rural areas that are accessible to urban markets are less affected, with insignificant impacts to overall income inequality reduction.
|
||||
One limit of the study is that it has to assume no effects on employment after the enaction of the minimum wage for its results to hold.
|
||||
|
||||
In a study on the impacts of minimum wage and direct cash transfers in Brazil on the country's income inequality,
|
||||
@SilveiraNeto2011 especially analyse the way the policies interact with spatial inequalities.
|
||||
It finds that incomes between regions have converged during the time frame and overall the cash transfers under the 'Bolsa Familia' programme and minimum wage were accounting for 26.2% of the effect.
|
||||
Minimum wage contributed 16.6% of the effect to overall Gini reduction between the regions while cash transfers accounted for 9.6% of the effect.
|
||||
The authors argue that this highlights the way even non-spatial policies can have a positive (or, with worse targeting or selection, negative) influence on spatial inequalities,
|
||||
as transfers occurring predominantly to poorer regions and minimum wages having larger impacts in those regions created quasi-regional effects without being explicitly addressed in the policies.
|
||||
Some limitations include limited underlying data only making it possible to estimate the cash transfer impacts for the analysis end-line,
|
||||
and minimum wage effects having to be constructed from the effects wages equal to minimum wage.
|
||||
|
||||
@Militaru2019 conduct an analysis of the effects of minimum wage increases on income inequality in Romania.
|
||||
They find that, generally, minimum wage increases correlate with small wage inequality decreases, but carry a larger impact for women.
|
||||
The channels for the policies effects are two-fold in that there is an inequality decrease as the number of wage earners in total number of employees increases,
|
||||
as well as the concentration of workers at the minimum level mattering --- the probable channel for a larger impact on women since they make up larger parts of low-income and minimum wage households in Romania.
|
||||
Limitations to the study are some remaining unobservables for the final inequality outcomes (such as other wages or incomes), the sample over-representing employees and not being able to account for any tax evasion or behavioural changes in the model.
|
||||
|
||||
@Sotomayor2021 conducts a study on the impact of subsequent minimum wage floor introductions on poverty and income inequality in Brazil.
|
||||
He finds that in the short-term (3 months) wage floor increases reduced poverty by 2.8% and reduced income inequality by 2.4%.
|
||||
Over the longer-term though these impacts decrease,
|
||||
the minimum wage increases only show diminishing returns when the legal minimum is already high in relation to median earnings.
|
||||
It suggests that additional unemployment costs, created through new job losses through the introduction, are offset by the increased benefits --- the higher wages for workers.
|
||||
The authors also suggest an inelastic relationship between increases and poverty incidence.
|
||||
One limitation of the study is the limit of tracking individuals in the underlying data which can not account for people moving household to new locations.
|
||||
The data can only track individual dwellings --- instead of the households and inhabitants within --- and thus resembles repeated cross-sectional data more than actual panel data.
|
||||
|
||||
### Collective bargaining
|
||||
|
||||
@Alexiou2023 study the effects of both political orientation of governments' parties and a country's trade unionisation on its income inequality.
|
||||
They find that, generally, strong unionisation is strongly related to decreasing income inequality, most likely through a redistribution of political power through collective mobilization in national contexts of stronger unions.
|
||||
It also suggests that in contexts of weaker unionisation, post-redistribution income inequality is higher, thus also fostering unequal redistributive policies.
|
||||
Lastly, it finds positive relations between right-wing orientation of a country's government and its income inequality, with more mixed results for centrist governments pointing to potential fragmentations in their redistributive policy approaches.
|
||||
The study is mostly limited in not being able to account for individual drivers (or barriers) and can thus not disaggregate for the effects for example arbitration or collective bargaining.
|
||||
|
||||
@Dieckhoff2015 undertake a study on the effect of trade unionisation in European labour markets, with a specific emphasis on its effects on gender inequalities.
|
||||
It finds, first of all, that increased unionisation is related to the probability of being employed on a standard employment contract for both men and women.
|
||||
It also finds no evidence that men seem to carry increased benefits from increased unionisation alone,
|
||||
although in combination with temporary contract and family policy re-regulations, men can experience greater benefits than women.
|
||||
At the same time women's employment under standard contracts does not decrease, such that there is no absolute detrimental effect for either gender.
|
||||
It does, however, leave open the question of the allocation of relative benefits between the genders through unionisation efforts.
|
||||
The study is limited in that, by averaging outcomes across European nations, it can not account for nation-specific labour market contexts or gender disaggregations.
|
||||
|
||||
@Cardinaleschi2019 study the wage gap in the Italian labour market, looking especially at the effects of collective negotiation practices.
|
||||
It finds that the Italian labour market's wage gap exists primarily due to occupational segregation between the genders, with women often working in more 'feminized' industries, and not due to educational lag by women in Italy.
|
||||
It also finds that collective negotiation practices targeting especially managerial representation and wages do address the gender pay gap, but only marginally significantly.
|
||||
The primary channel for only marginal significance stems from internal heterogeneity in that only the median part of wage distributions is significantly affected by the measures.
|
||||
Instead, the authors recommend a stronger mix of policy approaches, also considering the human-capital aspects with for example active labour-market policies targeting it.
|
||||
|
||||
@Ferguson2015 conducts a study on the effects of a more unionised workforce in the United States, on the representation of women and minorities in the management of enterprises.
|
||||
It finds that while stronger unionisation is associated both with more women and more minorities represented in the overall workforce and in management, this effect is only marginally significant.
|
||||
Additionally, there are drivers which may be based on unobservables and not a direct effect ---
|
||||
it may be a selection effect of more unionised enterprises.
|
||||
It uses union elections as its base of analysis, and thus can not exclude self-selection effects of people joining more heavily unionised enterprises rather than unionisation increasing representation in its conclusions.
|
||||
|
||||
@Ahumada2023 on the other hand create a study on the effects of unequal distributions of political power on the extent and provision of collective labour rights.
|
||||
It is a combination of quantitative global comparison with qualitative case studies for Argentina and Chile.
|
||||
It finds that, for societies in which power is more unequally distributed, collective bargaining possibilities are more limited and weaker.
|
||||
It suggests that, aside from a less entrenched trade unionisation in the country, the primary channel for its weakening are that existing collective labour rights are often either restricted or disregarded outright.
|
||||
Employers were restricted in their ability to effectively conduct lobbying, and made more vulnerable to what the authors suggest are 'divide-and-conquer' strategies by government with a strongly entrenched trade unionisation, due to being more separate and uncoordinated.
|
||||
A limit is the strong institutional context of the two countries which makes generalizable application of its underlying channels more difficult to the overarching quantitative analysis of inequality outcomes.
|
||||
|
||||
### Workfare programmes
|
||||
|
||||
@Whitworth2021 analyse the spatial consequences of a UK work programme on spatial factors of job deprivation or opportunity increases.
|
||||
The programme follows a quasi-marketized approach of rewarding employment-favourable results of transitions into employment and further sustained months in employment.
|
||||
The author argues, however, that the non-spatial implementation of the policy leads to spatial outcomes.
|
||||
Founded on the approach of social 'creaming' and 'parking' and applied to the spatial dimension,
|
||||
the study shows that already job-deprived areas indeed experience further deprivations under the programme,
|
||||
while non-deprived areas are correlated with positive impacts, thereby further deteriorating spatial inequality outcomes.
|
||||
This occurs because of providers in the programme de-prioritizing the already deprived areas ('parking') in favour prioritizing wealthier areas for improved within-programme results.
|
||||
|
||||
@Li2022 conduct a study on the effects of previous inequalities on the outcomes of a work programme in India intended to provide job opportunity equality for already disadvantages population.
|
||||
It specifically looks at the NREGA programme in India, and takes the land-ownership inequality measured through the Gini coefficient as its preceding inequality.[^nrega]
|
||||
The study finds that there is significantly negative relationship between the Gini coefficient and the provision of jobs through the work programme.
|
||||
In other words, the workfare policy implemented at least in part to reduce a district's inequality seems to be less effective if there is a larger prior capital inequality.
|
||||
The authors see the primary channel to be the landlords' opposition to broad workfare programme introduction since they are often followed by overall wage increases in the districts.
|
||||
They suggest that in more inequally distributed channels the landlords can use a more unequal power structure to lobby and effect political power decreasing the effectiveness of the programmes,
|
||||
in addition to often reduced collective bargaining power on the side of labour in these districts.
|
||||
The results show the same trends for measurement of land inequality using the share of land owned by the top 10 per cent largest holdings instead.
|
||||
|
||||
[^nrega]: The National Rural Employment Guarantee Scheme (NREGA) is a workfare programme implemented in India, the largest of its kind, which seeks to provide 100 days of employment for each household per year. It was rolled out from 2005 over several phases until it reached all districts in India in 2008.
|
||||
|
||||
### Social protection
|
||||
|
||||
<!-- TODO Should we include Pi2016 on social security? -->
|
||||
|
||||
<!-- social assistance benefits and wages -->
|
||||
@Wang2016 undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries.
|
||||
It finds that real minimum income benefit levels generally increased in most countries from 1990 to 2009, with only a few countries, mostly in Eastern European welfare states, showing decreases during the time frame.
|
||||
The majority of changes in real benefit levels are from deliberate policy changes and the study calculates them by a comparison of the changes in benefit levels to the changes in consumer prices.
|
||||
Secondly, it finds that changes for income replacement rates are more mixed, with rates decreasing even in some countries which have increasing real benefits levels.
|
||||
The study suggests this is because benefit levels are in most cases not linked to wages and policy changes also do not take changes in wages into account resulting in diverging benefit levels and wages, which may lead to exacerbating inequality gaps between income groups.
|
||||
|
||||
<!-- conditional cash transfer -->
|
||||
@Debowicz2014 conduct a study looking at the impact of the cash transfer programme Oportunidades in Mexico, conditioned on a household's children school attendance, on income inequality among others.
|
||||
It finds that a combination of effects raises the average income of the poorest households by 23 percent.
|
||||
The authors argue in the short run this benefits households through the direct cash influx itself, as well as generating a positive wage effect benefitting those who keep their children at work.
|
||||
For the estimation of income inequality it uses the Gini coefficient.
|
||||
Additionally, over the long-term for the children in the model there is a direct benefit for those whose human capital is increased due to the programme, but also an indirect benefit for those who did not increase their human capital, because of the increased scarcity of unskilled labor as a secondary effect.
|
||||
Due to the relatively low cost of the programme if correctly targeted, it seems to have a significantly positive effect on the Mexican economy and its income equality.
|
||||
|
||||
In a study on the labour force impacts for women @Hardoy2015 look at the effects of reducing overall child care costs in Norway through subsidies.
|
||||
It finds that overall the reductions in child care cost increased the female labour supply in the country (by about 5 per cent),
|
||||
while there were no significant impacts on mothers which already participated in the labour market.
|
||||
It also finds some internal heterogeneity, with the impact being strongest for low-education mothers and low-income households,
|
||||
a finding the authors expected due to day care expenditure representing a larger part of those households' budgets thus creating a larger impact.
|
||||
Though it may alternatively also be generated by the lower average pre-intervention employment rate for those households.
|
||||
Interestingly when disaggregating by native and immigrant mothers there is only a significant impact on native mothers,
|
||||
though the authors do not form an inference on why this difference would be.
|
||||
A limitation of the study is that there was a simultaneous child care capacity increase in the country,
|
||||
which may bias the labour market results due to being affected by both the cost reduction and the capacity increase.
|
||||
|
||||
<!-- health care -->
|
||||
@Carstens2018 conduct an analysis of the potential factors influencing mentally ill individuals in the United States to participate in the labour force, using correlation between different programmes of Medicaid and labour force status.
|
||||
In trying to find labour force participation predictors it finds employment motivating factors in reduced depression and anxiety, increased responsibility and problem-solving and stress management being positive predictors.
|
||||
In turn increased stress, discrimination based on their mental, loss of free time, loss of government benefits and tests for illegal drugs were listed as barriers negatively associated with labour force participation.
|
||||
For the government benefits, it finds significant variations for the different varieties of Medicaid programmes, with the strongest negative labour force participation correlated to Medicaid ABD, a programme for which it has to be demonstrated that an individual cannot work due to their disability.
|
||||
The authors suggest this shows the primary channel of the programme becoming a benefit trap, with disability being determined by not working and benefits disappearing when participants enter the labour force, creating dependency to the programme as a primary barrier.
|
||||
Two limitations of the study are its small sample size due to a low response rate, and an over-representation of racial minorities, women and older persons in the sample mentioned as introducing possible downward bias for measured labour force participation rates.
|
||||
|
||||
<!-- UBI -->
|
||||
<!-- TODO Potentially mention single sentence of Standing also looking into UBI -->
|
||||
@Cieplinski2021 undertake a simulation study on the income inequality effects of both a policy targeting a reduction in working time and the introduction of a UBI in Italy.
|
||||
It finds that while both decrease overall income inequality, measured through Gini coefficient, they do so through different channels.
|
||||
While provision of a UBI sustains aggregate demand, thereby spreading income in a more equitable manner,
|
||||
working time reductions significantly decrease aggregate demand through lower individual income but significantly increases labour force participation and thus employment.
|
||||
It also finds that through these channels of changing aggregate demand, the environmental outcomes are oppositional, with work time reduction decreasing and UBI increasing the overall ecological footprint.
|
||||
One limitation of the study is the modelling assumption that workers will have to accept both lower income and lower consumption levels under a policy of work time reduction through stable labour market entry for the results to hold.
|
||||
|
||||
## Structural factors
|
||||
|
||||
## Agency factors
|
||||
|
||||
# Robustness of evidence
|
||||
|
||||
## Output chronology
|
||||
|
||||
The identified literature rises in volume over time between 2000 and 2023,
|
||||
with first larger outputs identified from 2014 onwards,
|
||||
as can be seen in @fig-publications-per-year.
|
||||
While fluctuating overall, with a significantly smaller outputs 2017 and in turn significantly higher in 2021,
|
||||
the overall output volume strongly increased during this period.
|
||||
|
||||
```{python}
|
||||
#| label: fig-publications-per-year
|
||||
#| fig-cap: Publications per year
|
||||
|
||||
df_study_years = (
|
||||
df.groupby(["author", "year", "title"])
|
||||
.first()
|
||||
.reset_index()
|
||||
.drop_duplicates()
|
||||
["year"].value_counts()
|
||||
.sort_index()
|
||||
)
|
||||
# use order to ensure all years are displayed, even ones without values
|
||||
years_range = list(range(df_study_years.index.min(), df_study_years.index.max()+1))
|
||||
ax = sns.barplot(df_study_years, order=years_range)
|
||||
|
||||
ax.set_ylabel("Count")
|
||||
ax.set_xlabel("Year")
|
||||
plt.tight_layout()
|
||||
ax.tick_params(axis='x', rotation=90)
|
||||
ax.set_ylabel("Citations")
|
||||
ax.set_xlabel("Year")
|
||||
plt.show()
|
||||
del df_study_years
|
||||
```
|
||||
|
||||
Such anomalies can point to a dispersed or different focus during the time span,
|
||||
newly arising alternative term clusters which have not been captured by the search query
|
||||
or a diversion of efforts towards different interventions or policies.
|
||||
Their temporary nature, however, makes non-permanent causes more likely than fundamental changes to approaches or terms which could signal more biased results for this review.
|
||||
|
||||
The literature is predominantly based on white literature, with only a marginal amount solely published as grey literature.
|
||||
Such a gap in volume seems expected with the database query efforts primarily aimed at finding the most current versions of white literature.
|
||||
It also points to a well targeted identification procedure, with more up-to-date white literature correctly superseding potential previous grey publications.
|
||||
@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
|
||||
|
||||
```{python}
|
||||
#| label: fig-citations-per-year-avg
|
||||
#| fig-cap: Average citations per year
|
||||
df["zot_cited"] = df["zot_cited"].dropna().astype("int")
|
||||
df_avg_citations = df.groupby(["year"], as_index=False)["zot_cited"].mean()
|
||||
fig, ax = plt.subplots()
|
||||
ax.bar(df_avg_citations["year"], df_avg_citations["zot_cited"])
|
||||
sns.regplot(x=df_avg_citations["year"], y=df_avg_citations["zot_cited"], ax=ax)
|
||||
#ax = sns.lmplot(data=df_avg_citations, x="year", y="zot_cited", fit_reg=True)
|
||||
|
||||
ax.set_ylabel("Citations")
|
||||
ax.set_xlabel("Year")
|
||||
plt.tight_layout()
|
||||
years_range = list(range(df_avg_citations["year"].min(), df_avg_citations["year"].max()+1))
|
||||
ax.set_xticks(years_range)
|
||||
ax.tick_params(axis='x', rotation=90)
|
||||
plt.show()
|
||||
del df_avg_citations
|
||||
```
|
||||
|
||||
From the literature sample, several patterns emerge:
|
||||
First, in general, citation counts are slightly decreasing over time ---
|
||||
a trend which should generally be expected as less time has passed to allow newer studies' contents to be distributed and fewer repeat citations to have occurred.
|
||||
Second, larger changes between individual years appear more erratically.
|
||||
Taken together, this suggests that, though no overall decrease in academic interest in the topic over time occurred,
|
||||
it may point to the volume of relevant output not necessarily rising as steadily as overall output.
|
||||
|
||||
Early outliers also suggest clearly influential individual studies having been produced during those years,
|
||||
a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013).
|
||||
This is because, as @fig-publications-per-year showed, the overall output was nowhere near as rich as in the following years, allowing single influential works to skew the visible means for those years.
|
||||
|
||||
## Validity ranking
|
||||
|
||||
Finally, following @Maitrot2017, the relevant studies are ranked for their validity.
|
||||
Here, a 2-dimensional approach is taken to separate the external validity from the internal validity of the studies.
|
||||
The ranking process then uses the representativeness of a study's underlying dataset,
|
||||
from a non-representative survey sample, through a sub-nationally representative sample, a nationally representative and the use of census data,
|
||||
to arrive at a ranking between 2.0 and 5.0 respectively.
|
||||
Similarly, the studies are ranked for internal validity using the study design,
|
||||
with only quasi-experimental and experimental studies receiving similar rankings between 2.0 and 5.0 depending on the individually applied methods due to their quantifiability,
|
||||
while observational and qualitative studies go without an internal validity rank (0.0) due to the more contextual nature of their analyses.
|
||||
For a full list of validity ranks, see @appbtbl-validity-external and @appbtbl-validity-internal.
|
||||
|
||||
Using the validity ranking separated into internal and external validity for each study,
|
||||
it is possible to identify the general make-up of the overall sample,
|
||||
the relationship between both dimensions and the distribution of studies within.
|
||||
|
||||
@fig-validity-relation shows the relation between each study's validity on the internal dimension and the external dimension,
|
||||
with experimental studies additionally distinguished.
|
||||
Generally, studies that have a lower internal validity, between 2.0 and 3.5, rank higher on their external validity,
|
||||
while studies with higher internal validity in turn do not reach as high on the external validity ranking.
|
||||
|
||||
```{python}
|
||||
#| label: fig-validity-relation
|
||||
#| fig-cap: "Relation between internal and external validity"
|
||||
|
||||
plt.figure().set_figheight(5)
|
||||
sns.violinplot(
|
||||
data=validities,
|
||||
x="Internal Validity", y="External Validity", hue="design",
|
||||
cut=0, bw_method="scott",
|
||||
orient="x"
|
||||
)
|
||||
sns.swarmplot(
|
||||
data=validities,
|
||||
x="Internal Validity", y="External Validity", legend=False,
|
||||
color="darkmagenta",
|
||||
s=4
|
||||
)
|
||||
```
|
||||
|
||||
Studies with an internal validity ranking of of 3.0 (primarily made up of difference-in-difference approaches) and an internal ranking of 5.0 (randomized control trials) have the same tight clustering around an external validity between 4.0 (national) and 5.0 (census-based), and 2.0 (local) and 3.0 (subnational), respectively.
|
||||
This clearly shows the expected overall relationship of studies with high internal validity generally ranking lower on their external validity.
|
||||
|
||||
The situation is less clear-cut with the internal rankings of 2.0 (primarily ordinary least squares) and 4.0 (primarily instrumental variable),
|
||||
which show a larger external validity spread.
|
||||
For 2.0-ranked studies, there is an overall larger spread with most using nationally representative data,
|
||||
while a significant amount makes use of census-based data and others in turn only being subnationally representative.
|
||||
Studies ranked 4.0 internally have a higher heterogeneity with the significant outlier of @Thoresen2021,
|
||||
which had the limitation of its underlying data being non-representative.
|
||||
|
||||
Looking at the overall density of studies along their external validity dimension,
|
||||
@fig-validity-distribution reiterates this overall relationship with internal validity.
|
||||
It additionally shows that studies with low internal validity make up the dominant number of nationally representative analyses and the slight majority of census-based analyses,
|
||||
while locally or non-representative samples are almost solely made up of internally highly valid (ranking 4.0 or above) analyses,
|
||||
again with the exception of @Thoresen2021 already mentioned.
|
||||
|
||||
```{python}
|
||||
#| label: fig-validity-distribution
|
||||
#| fig-cap: "Distribution of internal validities"
|
||||
|
||||
sns.displot(
|
||||
data=validities,
|
||||
x="External Validity", hue="Internal Validity",
|
||||
kind="kde",
|
||||
multiple="fill", clip=(0, None),
|
||||
palette="ch:rot=-0.5,hue=1.5,light=0.9",
|
||||
bw_adjust=.65, cut=0,
|
||||
warn_singular = False
|
||||
)
|
||||
```
|
||||
|
||||
Looking at the data per region, census-based studies are primarily spread between Latin America and the Caribbean, as well as Europe and Central Asia.
|
||||
Meanwhile, studies using nationally, subnationally or non-representative data then to have a larger focus on North America, as well as East Asia and the Pacific.
|
||||
A slight trend towards studies focusing on evidence-based research in developing countries is visible,
|
||||
though with an overall rising output as could be seen in @fig-publications-per-year, and the possibly a reliance on more recent datasets, this would be expected.
|
||||
|
||||
## Regional spread
|
||||
|
||||
As can be seen in @fig-region-counts, taken by region for the overall study sample,
|
||||
the evidence base receives a relatively even split between the World Bank regional country groupings with the exception of the Middle East and North Africa (MENA) region,
|
||||
in which fewer studies have been identified.
|
||||
|
||||
```{python}
|
||||
#| label: fig-region-counts
|
||||
#| fig-cap: Studies by regions analysed
|
||||
|
||||
by_region = (
|
||||
df[["region"]]
|
||||
.assign(
|
||||
region = lambda _df: (_df["region"]
|
||||
.str.replace(r" ?; ?", ";", regex=True)
|
||||
.str.strip()
|
||||
.str.split(";")
|
||||
)
|
||||
)
|
||||
.explode("region")
|
||||
.reset_index(drop=True)
|
||||
)
|
||||
ax = sns.countplot(by_region, x="region", order=by_region["region"].value_counts().index)
|
||||
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
|
||||
rotation_mode="anchor")
|
||||
plt.show()
|
||||
del by_region
|
||||
|
||||
def regions_for_inequality(df, inequality:str):
|
||||
df_temp = df.loc[(df["inequality"] == inequality)]
|
||||
return sns.countplot(df_temp, x="region", order=df_temp["region"].value_counts().index)
|
||||
```
|
||||
|
||||
Most studies come from a context of East Asia and the Pacific, though with an almost equal amount analysing Europe and Central Asia.
|
||||
With slightly fewer studies, the contexts of North America, Sub-Saharan Africa follow for amount of anlalyses,
|
||||
and in turn Latin America and the Caribbean and South Asia with an equal amount of studies for each region.
|
||||
|
||||
The lower amount of studies stemming from a MENA context can point to a variety of underlying causes:
|
||||
First, it is possible that there is simply not as much evidence-based analysis undertaken for countries in the region as for other national or subnational contexts,
|
||||
with research either following a more theoretical trajectory, or missing the underlying data collection that is available for other regional contexts.
|
||||
|
||||
However, it cannot be ruled out that the search protocol itself did not capture the same depth of analytical material as for other contexts,
|
||||
with each region often having both a specific focus in policy-orientations and academically,
|
||||
and in some cases also differing underlying term bases.
|
||||
Such a contextual term differences may then not be captured adequately by the existing query terms and would point to a necessity to re-align it to the required specifics.
|
||||
|
||||
One reason for such a differentiation could be a larger amount of grey literature captured compared to other regions,
|
||||
which may be utilising less established terms than the majority of captured literature for policy implementations.
|
||||
Another reason could be the actual implementation of different policy programmes which are then equally not captured by existing term clusters.
|
||||
|
||||
# Discussion
|
||||
|
||||
# Conclusions
|
||||
|
||||
# Bibliography
|
||||
|
||||
::: {#refs}
|
||||
|
||||
:::
|
||||
|
||||
# Appendices {.appendix .unnumbered}
|
||||
|
||||
## Appendix A - Term clusters {.unnumbered}
|
||||
|
||||
::: {#appatbl-wow-terms}
|
||||
|
||||
```{python}
|
||||
terms_wow = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_wow.csv")
|
||||
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
World of work term cluster
|
||||
|
||||
:::
|
||||
|
||||
::: {#appatbl-intervention-terms}
|
||||
|
||||
```{python}
|
||||
terms_policy = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_policy.csv")
|
||||
# different headers to include 'social norms'
|
||||
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
|
||||
Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
|
||||
```
|
||||
|
||||
Policy intervention term cluster
|
||||
|
||||
:::
|
||||
|
||||
::: {#appatbl-inequality-terms}
|
||||
|
||||
```{python}
|
||||
terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
|
||||
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
Inequality term cluster
|
||||
|
||||
:::
|
||||
|
||||
## Appendix B - Validity rankings {#sec-appendix-validity-rankings .unnumbered}
|
||||
|
||||
::: {#appbtbl-validity-external}
|
||||
|
||||
| Representativeness | Ranking |
|
||||
| --- | --- |
|
||||
| non-representative survey/dataset | 2.0 |
|
||||
| subnationally representative survey/dataset | 3.0 |
|
||||
| nationally representative survey/dataset | 4.0 |
|
||||
| census-based dataset | 5.0 |
|
||||
|
||||
External validity ranking. Adapted from @Maitrot2017.
|
||||
|
||||
:::
|
||||
|
||||
::: {#appbtbl-validity-internal}
|
||||
|
||||
| Method | Ranking |
|
||||
| --- | --- |
|
||||
| ordinary least squares & fixed-effects | 2.0 |
|
||||
| discontinuity matching | 3.0 |
|
||||
| difference in difference (& triple difference) | 3.0 |
|
||||
| propensity score matching | 3.5 |
|
||||
| instrumental variable | 4.0 |
|
||||
| general method of moments | 4.0 |
|
||||
| regression discontinuity | 4.5 |
|
||||
| randomised control trial | 5.0 |
|
||||
|
||||
Internal validity ranking. Adapted from @Maitrot2017.
|
||||
|
||||
:::
|
||||
|
||||
## Appendix C - Boolean search query {.unnumbered}
|
||||
|
||||
```{python}
|
||||
#| label: full-search-query
|
||||
#| echo: false
|
||||
#| output: asis
|
||||
with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
|
||||
query = f.read()
|
||||
|
||||
t3 = "`" * 3
|
||||
print(f"""
|
||||
```sql
|
||||
{query}
|
||||
{t3}
|
||||
""")
|
||||
```
|
||||
365
manuscript/meeting_eoy.qmd
Normal file
365
manuscript/meeting_eoy.qmd
Normal file
|
|
@ -0,0 +1,365 @@
|
|||
---
|
||||
bibliography: ../data/intermediate/zotero-library.bib
|
||||
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
|
||||
papersize: A4
|
||||
linestretch: 1.5
|
||||
fontfamily: lmodern
|
||||
fontsize: "12"
|
||||
geometry:
|
||||
- left=2.2cm
|
||||
- right=3.5cm
|
||||
- top=2.5cm
|
||||
- bottom=2.5cm
|
||||
lang: en
|
||||
title: "Scoping Review: Preliminary findings"
|
||||
subtitle: Addressing inequalities in the World of Work
|
||||
---
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
from pathlib import Path
|
||||
import re
|
||||
## standard imports
|
||||
from IPython.core.display import Markdown as md
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from matplotlib import pyplot as plt
|
||||
import seaborn as sns
|
||||
from tabulate import tabulate
|
||||
import bibtexparser
|
||||
|
||||
sns.set_style("whitegrid")
|
||||
|
||||
from src import globals as g
|
||||
|
||||
bib_string=""
|
||||
for partial_bib in g.RAW_DATA.glob("**/*.bib"):
|
||||
with open(partial_bib) as f:
|
||||
bib_string+="\n".join(f.readlines())
|
||||
bib_sample_raw_db = bibtexparser.parse_string(bib_string)
|
||||
|
||||
bib_string=""
|
||||
for partial_bib in g.WORKING_DATA.glob("**/*.bib"):
|
||||
with open(partial_bib) as f:
|
||||
bib_string+="\n".join(f.readlines())
|
||||
bib_sample = bibtexparser.parse_string(bib_string)
|
||||
|
||||
# load relevant studies
|
||||
from src.extract import load_data
|
||||
|
||||
# load zotero-based metadata: citations and uses
|
||||
zot_df = pd.DataFrame([
|
||||
[
|
||||
entry["doi"] if "doi" in entry.fields_dict else None,
|
||||
entry["times-cited"] if "times-cited" in entry.fields_dict else None,
|
||||
entry["usage"] if "usage" in entry.fields_dict else None,
|
||||
entry["keywords"] if "keywords" in entry.fields_dict else None,
|
||||
]
|
||||
for entry in bib_sample.entries
|
||||
], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
|
||||
|
||||
# Add WB country grouping definitions (income group, world region)
|
||||
WB_COUNTRY_GROUPS_FILE = Path(f"{g.SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
|
||||
df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
|
||||
|
||||
bib_df = (load_data.from_yml(f"{g.PROCESSED_DATA}/relevant")
|
||||
.assign(
|
||||
doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
|
||||
zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
|
||||
zot_usage=lambda _df: _df["doi"].map(zot_df["usage"]),
|
||||
zot_keywords=lambda _df: _df["doi"].map(zot_df["keywords"]),
|
||||
date = lambda _df: pd.to_datetime(_df["year"], format="%Y"),
|
||||
year = lambda _df: _df["date"].dt.year,
|
||||
region = lambda _df: _df["country"].map(df_country_groups["Region"]),
|
||||
income_group = lambda _df: _df["country"].map(df_country_groups["Income group"]),
|
||||
)
|
||||
.query("year >= 2000")
|
||||
)
|
||||
zot_df = None
|
||||
df_country_groups = None
|
||||
```
|
||||
|
||||
# The data sample
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| output: asis
|
||||
|
||||
FULL_RAW_SAMPLE_NOTHING_REMOVED = 2396
|
||||
nr_database_query_raw = len(bib_sample_raw_db.entries)
|
||||
nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - len(bib_sample.entries)
|
||||
nr_other_sources = (len(bib_sample.entries) + nr_out_duplicates) - nr_database_query_raw
|
||||
|
||||
all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()]
|
||||
nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + 400
|
||||
nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + 400
|
||||
nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + 300
|
||||
nr_out_language = len([1 for kw in all_keywords if "out::language" in kw])
|
||||
nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
|
||||
|
||||
t3 = "`" * 3
|
||||
# FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
|
||||
# from: https://github.com/quarto-dev/quarto-cli/discussions/6508
|
||||
print(f"""
|
||||
```{{mermaid}}
|
||||
%%| label: fig-prisma
|
||||
%%| fig-cap: "Sample sorting process through identification and screening"
|
||||
%%| fig-width: 6
|
||||
flowchart TD;
|
||||
search_db["Records identified through database searching (n={nr_database_query_raw})"] --> starting_sample;
|
||||
search_prev["Records identified through other sources (n={nr_other_sources})"] --> starting_sample["Starting sample (n={FULL_RAW_SAMPLE_NOTHING_REMOVED})"];
|
||||
|
||||
starting_sample -- "Duplicate removal ({nr_out_duplicates} removed) "--> dedup["Records after duplicates removed (n={len(bib_sample.entries)})"];
|
||||
|
||||
dedup -- "Title screening ({nr_out_title} excluded)" --> title_screened["Records after titles screened (n={len(bib_sample.entries) - nr_out_title})"];
|
||||
|
||||
title_screened -- "Abstract screening ({nr_out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={len(bib_sample.entries)-nr_out_title-nr_out_abstract})"];
|
||||
|
||||
abstract_screened -- " Language screening ({nr_out_language} excluded) "--> language_screened["Records after language screened (n={len(bib_sample.entries)-nr_out_title-nr_out_abstract-nr_out_language})"];
|
||||
|
||||
language_screened -- " Full-text screening ({nr_out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={nr_extraction_done}) STILL OUTSTANDING: {len(bib_sample.entries)-nr_out_title-nr_out_abstract-nr_out_language - nr_extraction_done}"];
|
||||
{t3}
|
||||
""")
|
||||
```
|
||||
|
||||
- strongest focus on income inequality (vertical), with many horizontal inequality studies including aspect of income inequality
|
||||
- horizontal inequalities: strongest focus on income - gender inequalities (horizontal)
|
||||
- interventions:
|
||||
- strongest research base on labour rights protection interventions
|
||||
- second on infrastructural interventions
|
||||
- third on agency-strengthening ones: training, financial access, education programmes
|
||||
|
||||
- formalization & social protection research rarely goes into inequality outcomes beyond 'income' effects; most excluded for that reason
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| label: fig-inequality-types-whole-sample
|
||||
#| fig-cap: Overall inequality types in sample
|
||||
|
||||
# load zotero-based metadata: citations and uses
|
||||
pi = (pd.DataFrame([
|
||||
[
|
||||
entry["doi"] if "doi" in entry.fields_dict else None,
|
||||
entry["times-cited"] if "times-cited" in entry.fields_dict else None,
|
||||
entry["usage"] if "usage" in entry.fields_dict else None,
|
||||
entry["keywords"] if "keywords" in entry.fields_dict else None,
|
||||
]
|
||||
for entry in bib_sample.entries
|
||||
], columns = ["doi", "cited", "usage", "keywords"])
|
||||
.drop_duplicates("doi")
|
||||
.assign(
|
||||
inequality=lambda _df: _df["keywords"].str.replace("\\", "").str.extract('inequality::([\w\_]+),?')
|
||||
).dropna(subset="inequality")
|
||||
.assign(
|
||||
inequality=lambda _df: _df["inequality"].str.replace("_", " "),
|
||||
projected = 1
|
||||
).reset_index()
|
||||
)
|
||||
pi
|
||||
|
||||
inequality = (pd.concat([
|
||||
bib_df.groupby(["author", "year", "title"])
|
||||
.agg(
|
||||
{
|
||||
"inequality": lambda _col: "; ".join(_col),
|
||||
}
|
||||
)
|
||||
.assign(
|
||||
projected=0
|
||||
|
||||
)
|
||||
.reset_index()
|
||||
.drop_duplicates() , pi])
|
||||
.assign( inequality=lambda _df: _df["inequality"].apply(
|
||||
lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
|
||||
),
|
||||
)
|
||||
.explode("inequality")
|
||||
.drop_duplicates()
|
||||
)
|
||||
|
||||
sort_order = inequality["inequality"].value_counts().index
|
||||
i = inequality[inequality["inequality"].str.contains(r"(?:structural|institutional|agency)") == False]
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(6, 3)
|
||||
ax = sns.countplot(i, x="inequality", hue="projected" ,order=i["inequality"].value_counts().index)
|
||||
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
|
||||
rotation_mode="anchor")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
# Preliminary findings
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| label: fig-inequality-types
|
||||
#| fig-cap: Finished and projected inequality types
|
||||
inequality = (pd.concat([
|
||||
bib_df.groupby(["author", "year", "title"])
|
||||
.agg(
|
||||
{
|
||||
"inequality": lambda _col: "; ".join(_col),
|
||||
}
|
||||
)
|
||||
.assign(
|
||||
projected=0
|
||||
|
||||
)
|
||||
.reset_index()
|
||||
.drop_duplicates() , pi[pi["keywords"].str.contains("relevant") == True]])
|
||||
.assign( inequality=lambda _df: _df["inequality"].apply(
|
||||
lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
|
||||
),
|
||||
)
|
||||
.explode("inequality")
|
||||
.drop_duplicates()
|
||||
)
|
||||
|
||||
sort_order = inequality["inequality"].value_counts().index
|
||||
i = inequality[inequality["inequality"].str.contains(r"(?:structural|institutional|agency)") == False]
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(6, 3)
|
||||
ax = sns.countplot(i, x="inequality", hue="projected" ,order=i["inequality"].value_counts().index)
|
||||
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
|
||||
rotation_mode="anchor")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
- interventions most strongly target gender-income divide
|
||||
- most studies here recommend further scale-integration between agency/structural approaches
|
||||
- most studies also only focus on analysing a single scale however
|
||||
- interventions often have intersectional impacts even if not targeted at them
|
||||
- most visible for institutional/structural interventions and spatial inequalities
|
||||
- studies analysing intersectional inequalities near unanimously recommend intersectional targeting
|
||||
|
||||
- individual agency-based interventions (training, subsidies, maternity benefits, transfers, microcredit, etc):
|
||||
- seem most effective for targeting WoW outcomes of disability inequalities
|
||||
- seem marginally effective for targeting WoW outcomes of gender inequalities
|
||||
- require additional mediating scales for other inequalities
|
||||
- more structural interventions (education, infrastructural, ubi, trade liberalization, collective action):
|
||||
- seem most effective for spatial, income, education-generational inequalities
|
||||
- often show longer-term impacts, requiring longer periods of analyses
|
||||
- can work without additional agency-based interventions, few studies analyse both at same time
|
||||
|
||||
# Preliminary limitations
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| label: fig-intervention-types
|
||||
#| fig-cap: Finished and projected intervention types
|
||||
|
||||
# load zotero-based metadata: citations and uses
|
||||
pi = (pd.DataFrame([
|
||||
[
|
||||
entry["doi"] if "doi" in entry.fields_dict else None,
|
||||
entry["times-cited"] if "times-cited" in entry.fields_dict else None,
|
||||
entry["usage"] if "usage" in entry.fields_dict else None,
|
||||
entry["keywords"] if "keywords" in entry.fields_dict else None,
|
||||
]
|
||||
for entry in bib_sample.entries
|
||||
], columns = ["doi", "cited", "usage", "keywords"])
|
||||
.drop_duplicates("doi")
|
||||
.assign(
|
||||
intervention=lambda _df: _df["keywords"].str.replace("\\", "").str.extract('type::([\w\_]+),?')
|
||||
).dropna(subset="intervention")
|
||||
.assign(
|
||||
intervention=lambda _df: _df["intervention"].str.replace("_", " "),
|
||||
projected = 1
|
||||
).reset_index()
|
||||
)
|
||||
pi
|
||||
|
||||
by_intervention = (pd.concat([
|
||||
bib_df.groupby(["author", "year", "title"])
|
||||
.agg(
|
||||
{
|
||||
"intervention": lambda _col: "; ".join(_col),
|
||||
}
|
||||
)
|
||||
.assign(
|
||||
projected=0
|
||||
|
||||
)
|
||||
.reset_index()
|
||||
.drop_duplicates() , pi[pi["keywords"].str.contains("relevant") == True]])
|
||||
.assign( intervention=lambda _df: _df["intervention"].apply(
|
||||
lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
|
||||
),
|
||||
)
|
||||
.explode("intervention")
|
||||
.drop_duplicates()
|
||||
)
|
||||
|
||||
sort_order = by_intervention["intervention"].value_counts().index
|
||||
i = by_intervention[by_intervention["intervention"].str.contains(r"(?:structural|institutional|agency)") == False]
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(6, 3)
|
||||
ax = sns.countplot(i, x="intervention", hue="projected" ,order=i["intervention"].value_counts().index)
|
||||
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
|
||||
rotation_mode="anchor")
|
||||
plt.show()
|
||||
```
|
||||
|
||||
- stronger institutional-structural research focus in developed countries, with more structural-agency based in developing countries
|
||||
- employment creation as a category is often subsumed in other structural/institutional analyses
|
||||
- little evidence-based research on effect of interventions targeting education on world of work outcomes
|
||||
- spatial inequality most evenly geographically spread evidence base
|
||||
- empirical base on interventions targeting disability inequalities strongly restricted on developed countries, especially United States
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| label: fig-countries
|
||||
#| fig-cap: Country spread
|
||||
#| column: screen
|
||||
|
||||
# load zotero-based metadata: citations and uses
|
||||
pi = (pd.DataFrame([
|
||||
[
|
||||
entry["doi"] if "doi" in entry.fields_dict else None,
|
||||
entry["times-cited"] if "times-cited" in entry.fields_dict else None,
|
||||
entry["usage"] if "usage" in entry.fields_dict else None,
|
||||
entry["keywords"] if "keywords" in entry.fields_dict else None,
|
||||
]
|
||||
for entry in bib_sample.entries
|
||||
], columns = ["doi", "cited", "usage", "keywords"])
|
||||
.drop_duplicates("doi")
|
||||
.assign(
|
||||
country=lambda _df: _df["keywords"].str.replace("\\", "").str.extract('country::([\w\_]+),?')
|
||||
).dropna(subset="country")
|
||||
.assign(
|
||||
country=lambda _df: _df["country"].str.replace("_", " ").str.replace("US", "United States").str.replace("Britain", "United Kingdom"),
|
||||
projected = 1
|
||||
).reset_index()
|
||||
)
|
||||
pi
|
||||
|
||||
by_country = (pd.concat([
|
||||
bib_df.groupby(["author", "year", "title"])
|
||||
.agg(
|
||||
{
|
||||
"country": lambda _col: "; ".join(_col),
|
||||
}
|
||||
)
|
||||
.assign(
|
||||
projected=0
|
||||
|
||||
)
|
||||
.reset_index()
|
||||
.drop_duplicates() , pi[pi["keywords"].str.contains("relevant") == True]])
|
||||
.assign( country=lambda _df: _df["country"].apply(
|
||||
lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
|
||||
),
|
||||
)
|
||||
.explode("country")
|
||||
.drop_duplicates()
|
||||
)
|
||||
|
||||
sort_order = by_country["country"].value_counts().index
|
||||
i = by_country[by_country["country"].str.contains(r"(?:structural|institutional|agency)") == False]
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(12, 5)
|
||||
ax = sns.countplot(i, x="country", hue="projected" ,order=i["country"].value_counts().index)
|
||||
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
|
||||
rotation_mode="anchor")
|
||||
plt.show()
|
||||
```
|
||||
914
manuscript/notes.qmd
Normal file
914
manuscript/notes.qmd
Normal file
|
|
@ -0,0 +1,914 @@
|
|||
---
|
||||
bibliography: ../data/intermediate/zotero-library.bib
|
||||
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
|
||||
papersize: A4
|
||||
linestretch: 1.5
|
||||
fontfamily: lmodern
|
||||
fontsize: "12"
|
||||
geometry:
|
||||
- left=2.2cm
|
||||
- right=3.5cm
|
||||
- top=2.5cm
|
||||
- bottom=2.5cm
|
||||
toc: true
|
||||
link-citations: true
|
||||
link-bibliography: true
|
||||
number-sections: true
|
||||
lang: en
|
||||
title: Inequalities and the world of work
|
||||
subtitle: Conceptual Definitions and Key Terms
|
||||
---
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
from src import globals as g
|
||||
|
||||
## standard imports
|
||||
from IPython.core.display import Markdown as md
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from matplotlib import pyplot as plt
|
||||
import seaborn as sns
|
||||
from tabulate import tabulate
|
||||
|
||||
sns.set_style("whitegrid")
|
||||
```
|
||||
|
||||
# Definitions
|
||||
|
||||
## Defining the world of work
|
||||
|
||||
Work
|
||||
: "any activity performed by persons of any sex and age to produce goods or to provide services for use by others or for own use" [@ILO2013, p. 2].
|
||||
: "Work is defined irrespective of its formal or informal character or the legality of the activity" [@ILO2013, p. 2].
|
||||
|
||||
Employment
|
||||
: those in employment are "of working age who, during a reference period, were engaged in any activity to produce goods or provide services for pay or profit" [@ILO2013, 6].
|
||||
: "For pay or profit refers to work done as part of a transaction in exchange for remuneration payable in the form of wages or salaries for time worked or work done, or in the form of profits derived from the goods and services produced through market transactions" [@ILO2013, 6].
|
||||
|
||||
| key concepts | |
|
||||
| --- | --- |
|
||||
| production of goods | production/processing/collection of agricultural, fishing, hunting, gathering, mining, forestry, water, household goods, effecting major repairs/building additions to dwelling,farm buildings, etc. |
|
||||
| provision of services | provision of accounting, management, transport, meal preparation/serving, waste disposal/recycling, cleaning, decorating, dwelling/goods maintenance, childcare/-instruction, elderly/dependent person/pet/domestic animal care
|
||||
| use by others or own-use | whether final products are destined *mainly* for final use by producer as capital formation or final consumption, or by others |
|
||||
| for pay or profit | as part of transaction in exchange for remuneration (wages/salary), or in form of profits from goods/services through market transactions |
|
||||
|
||||
Informal economy
|
||||
: "economic activities carried out by workers and economic units that are --- in law or in practice --- not covered or insufficiently covered by formal arrangements." [@ILO2002, p.14; @ILO2015, p.4,25]
|
||||
: "all remunerative work [...] that is not registered, regulated or protected by existing legal or regulatory frameworks, as well as non-remunerative work undertaken in an income-producing enterprise." [@ilo2003]
|
||||
: "does not cover illicit activities, in particular the provision of services or the production, sale, possession or use of goods forbidden by law" [@ILO2015, p.4]
|
||||
|
||||
Formal economy
|
||||
: sufficiently covered by formal arrangements, defined as "procedures established by the government to regulate the actions and functions of economic units and workers, as well as protecting their legal rights." [ILO, 2021, p.10]
|
||||
|
||||
Informal employment *outside* the informal sector
|
||||
: comprises employees holding informal jobs in formal sector enterprises, as paid domestic workers employed by households, contributing family workers working in formal sector enterprises, and own-account workers producing exclusively for own final use by their household [@ILO2023b]
|
||||
|
||||
<!--
|
||||
- with access to contributory social insurance schemes via formal arrangements:
|
||||
- employees of public sector
|
||||
- armed forces
|
||||
- employees/casual/seasonal workers of formal enterprises
|
||||
- independent workers operating in formal economy
|
||||
- with occasional access to non-contributory social insurance schemes via formal (legal) arrangements:
|
||||
- employers/employees of informal sector enterprises
|
||||
- workers hired off-the-books by formal sector enterprises
|
||||
- independent workers operating in informal economy
|
||||
-->
|
||||
|
||||
Labour underutilization
|
||||
: are "mismatches between labour supply and demand which translate into an unmet need for employment among the population" [@ILO2013, 9]
|
||||
: can be "*time-related underemployment*, when the working time of persons in employment is insufficient in relation to alternative employment situations in which they are willing and available to engage" [@ILO2013, 9]
|
||||
: can be "*unemployment*, reflecting an active job search by persons not in employment" [@ILO2013, 9]
|
||||
: can be "*potential labour force*, [those] not in employment who express an interest in this form of work but for whom existing conditions limit their active job search and/or their availability." [@ILO2013, 9]
|
||||
|
||||
| key concepts | |
|
||||
| --- | --- |
|
||||
| remuneration | see 'for pay or profit' above |
|
||||
| formal arrangements | protection of legal rights, regulation of actions/functions of economic units |
|
||||
| legality of activity | while concept of 'work' covers acitivities regardless of illicit status, informal economy excludes illicit activities |
|
||||
| utilization of labor | described various states of labour supply and demand mismatch such as unemployment, time-related underemployment, and potential labour force |
|
||||
|
||||
## Defining forms of work
|
||||
|
||||
"five mutually exclusive forms of work are identified for separate measurement" [@ILO2013, 3]
|
||||
|
||||
own-use production work
|
||||
: comprising production of goods and services for own final use
|
||||
: "all those of working age who, during a short reference period, performed any activity to produce goods or provide services for own final use." [@ILO2013, 5]
|
||||
|
||||
employment work
|
||||
: comprising work performed for others in exchange for pay or profit
|
||||
: "all those of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit" [@ILO2013, 6]
|
||||
|
||||
unpaid trainee work
|
||||
: comprising work performed for others without pay to acquire workplace experience or skills
|
||||
: "all those of working age who, during a short reference period, performed any unpaid activity to produce goods or provide services for others, in order to acquire workplace experience or skills in a trade or profession" [@ILO2013, 7]
|
||||
|
||||
volunteer work
|
||||
: comprising non-compulsory work performed for others without pay
|
||||
: "all those of working age who, during a short reference period, performed any unpaid, non-compulsory activity to produce goods or provide services for others"
|
||||
|
||||
other work activities
|
||||
: not defined in this resolution
|
||||
|
||||
| key concepts | |
|
||||
| --- | --- |
|
||||
| intensity of participation | how many hours/days work occupies in a certain time frame |
|
||||
| own-use/other-use | see above |
|
||||
| for pay or profit | see above |
|
||||
| non-compulsory | undertaken without civil, legal, administrative requirement and different from fulfilment of social responsibilities of communal, cultural or religious nature |
|
||||
|
||||
## Defining inequality
|
||||
|
||||
Vertical inequality
|
||||
: "income inequality between all households in a country" [@ILO2021]
|
||||
: "the debate on vertical inequalities has increasingly focused on how, in many countries, the richest 1 per cent or the top 10 per cent of income earners have improved their situation compared to the poorest 99 per cent or bottom 90 per cent." [@ILO2021a]
|
||||
|
||||
Horizontal inequality
|
||||
: "Horizontal inequalities occur when some groups within the population find themselves disadvantaged and discriminated against on the basis of their visible identity, for example their gender, colour or beliefs, among others" [@ILO2021a]
|
||||
: "public attention has incerasingly been devoted to racial or ethnic inequalities, and to the rifts between migrants and nationals."
|
||||
: "Spatial inequalities between rural and urban areas and, more recently, between large mega-cities and smaller, more peripheral, cities have also been studied with increasing concern"
|
||||
: "also refers to disparities in employment outcomes, labour rights and opportunities between groups depending on their gender, age, nationality, ethnicity, health status, disability or other characteristics"
|
||||
|
||||
Intersectionality
|
||||
: "captures the complex way in which inequalities based on different personal characteristics overlap and accumulate [and] particular dynamics of inequality appear where people belong to multiple disadvantaged groups." [@ILO2021a]
|
||||
|
||||
Equality of opportunities
|
||||
: "seeks to level the playing field so that gender, ethnicity, birthplace, family background and other caracteristics that are beyond an individual's control do not influence or harm a person's future perspective" [@ILO2021a]
|
||||
: "ensuring that all people are 'equally enabled to make the best of such powers as they possess'" [@ILO2021a]
|
||||
|
||||
Equality of outcomes
|
||||
: "a focus on opportunities [...] should not distract from the importance of observed inequality of outcomes." [@ILO2021a]
|
||||
: "high levels of inequality make it much more difficult to ensure equal opportunities for the next generation [since] high levels of inequality today tend to reduce social mobility tomorrow." [@ILO2021a]
|
||||
|
||||
| key concepts | |
|
||||
| --- | --- |
|
||||
| within-group/between-group inequality | the horizontal or vertical nature of inequality, existing as income inequality between all households in a country (vertical) or when some population groups are disadvantaged/discriminated against (horizontal) |
|
||||
| overlapping characteristics | all inequalities can be intersectional through individuals' overlapping disadvantaged characteristics or situations |
|
||||
| enabling of opportunity/outcome equality | two philosophies of seeking equality, by either providing a 'level playing field' (opportunity) or ensuring equality in the resulting situations (outcome) |
|
||||
|
||||
Missing:
|
||||
|
||||
- difference between relative and absolute inequality (see @Ravallion2018, 637)
|
||||
|
||||
## Inequalities in the world of work
|
||||
|
||||
### Income Inequality
|
||||
|
||||
- main focus point of many inequality measurements (e.g. Gini Coefficient, Palma Ratio) [UN, 2023, A call to action to save SDG10, Policy Brief]
|
||||
- "labour income is the main source of income for most households in the world [thus] unequal access to work and working poverty are major drivers of inequalities" [@ILO2021]
|
||||
- "Income inequality, inequality of employment outcomes more generally and inequality of opportunities are intimately related" [@ILO2022b]
|
||||
- "To some extent, therefore, income inequality is like a prism, which reveals many other forms of inequality, including those generated in the world of work" [@ILO2021a, 13].
|
||||
- "Throughout the world, earnings inequality is also determined by a set of other factors, including status in employment (whether a worker is a wage employee or self-employed), sector of activity and occupation, enterprise type, type of contract (for wage earners), and often formality" [@ILO2019].
|
||||
|
||||
### Other forms of inequality
|
||||
|
||||
Inequalities are always multi-faceted, complex and display intersectional qualities [@ILO2021a]:
|
||||
|
||||
#### Gender inequality
|
||||
|
||||
These are inequalities that arise because of an individual's gender:
|
||||
|
||||
- while type and extent of inequalities varies by country, "gender inequalities, despite some progress over the past decades, remain persistent and pervasive" [@ILO2021].
|
||||
- "women everywhere still face high barriers in entering, remaining and progressing in the labour market, while continuing to bear most of the responsibility for unpaid care work" [@ILO2022b].
|
||||
- "hinders not only access to education, training and lifelong learning, but also access to quality jobs, housing, mobility, land and capital, as well as social protection" [@ILO2021].
|
||||
- more women, globally, work in underemployment, contribute disproportionally to family work, work shorter hours in employment but have longer working days when including unpaid work, are increasingly employed in services sectors, and still suffer a substantial wage gap [@ILO2016]
|
||||
- "Domestic work is female-dominated, with women accounting for 76.2 per cent of domestic workers" and domestic work, in turn is overwhelmingly informal employment globally [@ILO2023a, 6].
|
||||
- "Disparities in the gendered division of unpaid care work and paid work are the result of deeply rooted inequalities based on gender roles, income, age, education and place of residence" [@ILO2019].
|
||||
|
||||
#### Socio-demographic inequalities
|
||||
|
||||
These are inequalities that, like gender inequality, are based on the innate, often visible, identification of a person.
|
||||
|
||||
Examples are: ethnic, racial inequalities, or those based on religion and beliefs, migrant status, age, sex, or disabilities. [@ILO2021a, 11-12]
|
||||
|
||||
- "the incidence of temporary employment is generally higher among youths" [@ILO2019].
|
||||
- "Women and young people fare significantly worse in labour markets, an indication of the large inequalities within the world of work in many countries." [@ILO2023]
|
||||
- "In the EU28, some 7% of workers felt they had been discriminated against in the 12 months prior to the survey on grounds of sex, race, religion, age, nationality, disability or sexual orientation" [@ILO2019].
|
||||
|
||||
#### Spatial inequality
|
||||
|
||||
These are inequalities that arise because of an individual's location relative to others:
|
||||
|
||||
- "between urban, rural and peripheral areas and richer and poorer regions [...] contribute to inequalities in the world of work, as well as to a growing sense of fractured societies" [@ILO2021]
|
||||
- due to "unequal access to economic and decent work opportunities, to finance, quality public services, quality education and relevant training, essential social services infrastructures and digital infrastructure" [@ILO2021]
|
||||
|
||||
#### Pre-existing inequalities
|
||||
|
||||
These are inequalities that exist *before* the labor market enters the picture for an individual and, while intertwined with socio-demographic inequalities, may be useful to differentiate:
|
||||
|
||||
- "some inequalities arise well before individuals enter the world of work and addressing them is key to reducing inequalities in the labour market and beyond" [@ILO2021a]
|
||||
- "inequality in household incomes [...] reflects many other correlated or underlying forms of inequality [such as] inequality of opportunity, or inequality of access to health services or education, for example." [@ILO2021a, 13]
|
||||
- "the world of work plays an important role in reducing inequalities, including in terms of intergenerational social mobility" [@ILO2021]
|
||||
- "they also relate to the characteristics [...] such as the level of education, poverty or productivity and, of course, their underlying factors." [@ILO2021a]
|
||||
- "underlying factors are numerous and include the lack of formal recognition as an individual (the lack of a birth certificate or identity card), the lack of property rights or of clear ownership of assets, or the lack of access to formal banking, all of which are both a form of inequality and increase other forms of inequality." [@ILO2021a]
|
||||
|
||||
### The scale of inequalities
|
||||
|
||||
- globally, between countries (vertical)
|
||||
- national inequalities, between all households in one country (vertical)
|
||||
- regional, between urban/rural divides; poor/rich regions (horizontal: spatial)
|
||||
- households, between households with different access to education/essential services/infrastructures (horizontal: spatial)
|
||||
- individuals, between persons based on (visible/invisible) characteristics (horizontal: gender, spatial, pre-existing, ...)
|
||||
|
||||
### COVID-19 influence
|
||||
|
||||
- "[Post COVID-19] recovery patterns vary significantly across regions, countries and sectors [and] the impact has been particularly serious for developing nations that experienced higher levels of inequality, more divergent working conditions and weaker social protection systems even before the pandemic." [@ILO2022a]
|
||||
- "The pandemic is deepening various forms of inequality, from exacerbating gender inequity to widening the digital divide." [@ILO2022a]
|
||||
|
||||
## Outcomes of inequalities
|
||||
|
||||
- "[inequalities] slow economic growth and poverty reduction, undermine social mobility and increase the risk of social unrest and political instability [as well as] contribute to the intergenerational transmission of poverty and social exclusion" [@ILO2022b]
|
||||
- "forms of inequality can be also among the root causes of child labour and forced or compulsory labour in all its forms." [@ILO2021]
|
||||
|
||||
### Inequalities' impact on employment outcomes
|
||||
|
||||
- **unemployment**: forecloses income prospects
|
||||
- **underemployment**:
|
||||
- low wages make meeting basic needs impossible (esp. food, healthcare, education, decent housing)
|
||||
- split into 'time-related underemployment' (wanting more hourly paid work) and 'potential labour force' (not actively looking or not able to work)
|
||||
- **inequality of job quality** (achievement of decent work)
|
||||
- "concerns first and foremost those working in the informal economy", who may experience reduced social protection, productivity, job security, wages and earnings [@ILO2021a]
|
||||
- "many are in forms of work, such as part-time work, fixed-term contracts and working through private employment agencies, that can offer a stepping stone to employment [but] may give rise to decent work deficits when, among other reasons, they are not regulated well" or used to circumvent legal obligations or without adequate labour/social protection [@ILO2021]
|
||||
- "Job quality features are also positively associated with enterprise performance, productivity and innovation, [...] reducing sickness absence and the loss of productivity due to working while sick. In addition, job quality contributes to developing organisational commitment and motivation among workers, as well as shaping a climate that is supportive of creativity and the development of the workforce" [@ILO2019].
|
||||
- other employment outcomes affected:
|
||||
- overall labor force participation: exclusion from labour market or the ability towards full utilization of labour market opportunities [@ILO2021a; @ILO2019]
|
||||
- ultimately resulting in income inequalities, in turn becoming driver of subsequent inequal outcomes and barriers in the labour market (as well as outside the labour market), reducing inter-generational social mobility [@ILO2021]
|
||||
|
||||
# ILO Policy typology
|
||||
|
||||
identified in @ILO2021 and @ILO2022b:
|
||||
|
||||
- attention to root causes
|
||||
- addressing both distribution and redistribution
|
||||
- original distribution highly affected by inequalities on labour market
|
||||
- preventing both vertical/horizontal inequalities requires redistribution through taxes and transfers
|
||||
- fundamental principles and rights and international labour standards
|
||||
- social dialogue and tripartism
|
||||
- interconnectedness, integration and monitoring
|
||||
- country-specific approaches
|
||||
|
||||
## Policy areas
|
||||
|
||||
<!--
|
||||
generally, [from UN, 2023, A call to action to save SDG10, Policy Brief], separated into:
|
||||
|
||||
- fiscal policies
|
||||
- labour policies
|
||||
- other policies
|
||||
- both from distributive and redistributive levers of action
|
||||
-->
|
||||
guiding principles
|
||||
|
||||
main policy areas identified [@ILO2022b]:
|
||||
|
||||
Policy *areas*, identified by @ILO2022b:
|
||||
|
||||
- employment creation
|
||||
- business sustainability promotion
|
||||
- pro-employment framework
|
||||
- gender-transformative framework
|
||||
- promote:
|
||||
- business sustainability
|
||||
- productivity increases
|
||||
- reduction in productivity gaps
|
||||
- digital infrastructure
|
||||
- technology for decent work
|
||||
- tackling digital divide
|
||||
- access to education
|
||||
- quality of education/training/skills development
|
||||
- relevance of education/training/skills development
|
||||
- green transition
|
||||
- digital transition
|
||||
- gender-transformative career guidance
|
||||
- improvements of public services/social protection
|
||||
- work-life balance ('juggle paid work and family care')
|
||||
- targeted support for disadvantaged groups
|
||||
- labour right protection
|
||||
- promotion of rights for all workers
|
||||
- minimum wage
|
||||
- collective bargaining systems
|
||||
- equal pay for work of equal value
|
||||
- wage transparency
|
||||
- inclusive labour market institutions
|
||||
- formalization
|
||||
- approaching informality:
|
||||
- gender-responsive
|
||||
- country-tailored
|
||||
- comprehensive
|
||||
- non-discriminatory
|
||||
- increase decent work in formal economy
|
||||
- absorb informal workers / economic units
|
||||
- gender equality
|
||||
- removal of stereotypes
|
||||
- removal of discriminatory law
|
||||
- removal of discriminatory practice
|
||||
- promotion of equality of treatment
|
||||
- promotion of equality of opportunity
|
||||
- data disaggregated by
|
||||
- gender
|
||||
- age
|
||||
- disability
|
||||
- race
|
||||
- ethnicity
|
||||
- migrant status
|
||||
- occupational gender segregation
|
||||
- unequal pay for work of equal value
|
||||
- gender-based violence
|
||||
- gender-based harassment
|
||||
- gender unequal division of unpaid care work
|
||||
- trade development
|
||||
- avoid severe economic fluctuations
|
||||
- ensure price stability
|
||||
- promotion of high volume of trade
|
||||
- promotion of steady volume of trade
|
||||
- fundamental principles and rights at work
|
||||
- responsible business practices
|
||||
- social protection
|
||||
- extend reach of social protection schemes
|
||||
- reach those not adequately protected
|
||||
- ensure access for everyone to:
|
||||
- comprehensive SP
|
||||
- adequate SP
|
||||
- sustainable SP
|
||||
|
||||
## Employment creation
|
||||
|
||||
- pro-employment, gender-transformative macroeconomic framework
|
||||
- enabling business environment promoting sustainable enterprise,productivity increases, reductions in productivity gaps
|
||||
- digital infrastructure investments for potential of technology for decent work and tackling digital divide
|
||||
- just transition minimizing impacts of environmental changes on employment
|
||||
- effective active labour market policies enabling employment for vulnerable and disadvantaged
|
||||
|
||||
## Equal access to education/training/quality public services from early childhood
|
||||
|
||||
- improvements to quality and relevance of education, training, skills development
|
||||
- responsive to labour market needs, changing WoW demands, green/digital transitions, demographic changes
|
||||
- gender-transformative career guidance on e.g. STEM
|
||||
- improvements to quality of public services, social protection to juggle paid work & family care
|
||||
- targeted support for disadvantaged groups
|
||||
|
||||
## Adequate protection of all workers and a fair share of the fruits of growth
|
||||
|
||||
- promotion of fundamental principles and rights at work for all workers
|
||||
- adequate minimum wage (Minimum Wage Fixing Convention, 1970, No 131)
|
||||
- implementation of collective bargaining systems
|
||||
- equal pay for work of equal value, wage transparency
|
||||
- effective/inclusive labour market institutions, e.g. relevant inspectorates
|
||||
|
||||
## Transition to the formal economy
|
||||
|
||||
- comprehensive, country-tailored, gender-responsive, non-discriminatory strategies tackling drivers of informality
|
||||
- combination of interventions increasing ability of formal economy to provide decent work opportunities, absorption of current informal workers&economic units
|
||||
- strengthening ability of people/enterprises to enter formal economy through incentives and elimination of barriers
|
||||
|
||||
## Gender equality and non-discrimination, equality for all, diversity and inclusion
|
||||
|
||||
- removal of stereotypes, discriminatory laws and practices, including at workplace
|
||||
- promotion of positive/transformative measures ensuring equality of treatment&opportunities
|
||||
- more available data disaggregated by gender,age,disability,race,ethnicity,migrant status to monitor policy impacts
|
||||
- combined policy responses within/-out labour market against: occupational gender segregation, unequal pay for work of equal value, gender-based violence/harassment, gender unequal division of unpaid care work
|
||||
|
||||
## Trade and development for a fair globalization and shared prosperity
|
||||
|
||||
- full cooperation with relevant international bodies to avoid severe economic fluctuations, ensure price stability
|
||||
- promotion of high and steady volume of intl. trade
|
||||
- promotion/application of fundamental principles and rights at work through trade agreements/in supply chains, alongside responsible business practices
|
||||
|
||||
## Universal and adequate social protection
|
||||
|
||||
- extension of reach of national social protection systems
|
||||
- reach those not adequately protected
|
||||
- ensure access for everyone to comprehensive, adequate, sustainable social protection over life cycle
|
||||
|
||||
# Search Protocol
|
||||
|
||||
## Inclusion criteria
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| label: tbl-inclusion-criteria
|
||||
#| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}
|
||||
|
||||
inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
|
||||
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
not currently used as criteria:
|
||||
- we are probably including qualitative studies (to be tagged)
|
||||
- perhaps studies <2000 (to be tagged) to count quantity?
|
||||
|
||||
## Tagging system
|
||||
|
||||
Tagging:
|
||||
|
||||
- inequality(ies) analysed: `inequality::`
|
||||
- intersectionality: `intersectional`
|
||||
- intervention: `intervention::`
|
||||
- outcome: `outcome::`
|
||||
- review: `review::` (meta, systematic, scoping, narrative, ..)
|
||||
- design: `design::` (qualitative, mixed, quasi-experimental, experimental)
|
||||
- country: `country::`
|
||||
- relevancy: `relevant`, `out::` (could be transformed to `excluded::` in end step)
|
||||
- status: `TODO`, `done`, `integrated`
|
||||
|
||||
see Screening Tool document for exact keywords used during screening.
|
||||
|
||||
## Matrix extraction properties
|
||||
|
||||
For up-to-date usage of extraction keys please see `extraction_template.yml` in data directory.
|
||||
Information kept here for full-text descriptions of each option, to be migrated
|
||||
to extraction metadata sheet.
|
||||
|
||||
| Publication info | Description |
|
||||
| --- | --- |
|
||||
| author | |
|
||||
| year | |
|
||||
| title | |
|
||||
| publisher | |
|
||||
| pubtype | article/working paper - publication type |
|
||||
| url/doi | |
|
||||
| ?discipline? | The overall discipline the study falls under |
|
||||
|
||||
| Context info | Description |
|
||||
| --- | --- |
|
||||
| country | What is the primary/are the primary country/countries under analysis in the study? (set:txt) |
|
||||
| country_world_region | Which ILO region does the country belong to? (set:txt) |
|
||||
| country_income_class | Which UN Bank income category does the country belong to? (set:txt) |
|
||||
| period_of_analysis | What is the main period of analysis for the study (in years, e.g. 2010-2012)? (timedelta) |
|
||||
| observation_length | What is the main length of observation for the study, if mentioned (in months)? (timedelta) |
|
||||
| observation_length_max | What is the max length of observation for the study, if it diverges from the average length (in months)? (timedelta) |
|
||||
| explicit_targeting | is intervention specifically (explicitly) targeted at population/group? (bool:0/1) |
|
||||
| target_group | who is the intervention targeted at (explicitly/implicitly)? (list:txt) |
|
||||
| data | What dataset/database/collection does the data stem from, if mentioned? (list:txt) |
|
||||
|
||||
| Results info | Description |
|
||||
| --- | --- |
|
||||
| interventions | what are the independent variables for the study? (list:txt) |
|
||||
| intervention_institutional | Is the intervention part of the institutional category? (bool:0/1) |
|
||||
| intervention_structural | Is the intervention part of the structural category? (bool:0/1) |
|
||||
| intervention_agency | Is the intervention part of the agency and social norms category? (bool:0/1) |
|
||||
| inequality_type | which inequalities/dimensions of inequality are objects of analysis for the study? (list:txt) |
|
||||
| inequality_direction | Is the main inequality looked at of horizontal or vertical type? (0: vertical; 1: horizontal) |
|
||||
| outcome_measures | what are the dependent variables looked at in the study? (list:txt) |
|
||||
| findings | what are the main findings for the dependent variables? (list:txt) |
|
||||
| channels | What are the main channels for outcomes identified, if mentioned? (list:txt) |
|
||||
| theory | What is the main theoretical argument/grounding for the study, if mentioned? (list:txt) |
|
||||
| limitations | What are the main limitations of the study, if mentioned? (list:txt) |
|
||||
|
||||
| Statistical info | Description |
|
||||
| --- | --- |
|
||||
| study_design | Is the study mainly of experimental, quasi-experimental, qualitative, mixed design? (set:txt) |
|
||||
| study_method | What is the main method of the study? (list:txt) |
|
||||
| indicator_relative | Is the main indicator used relative or absolute? (0: absolute, 1: relative) |
|
||||
| sample_size | What is the main sample size/observation number of the study? (numeric) |
|
||||
| sample_unit | What is the main sample unit (person,household,firm, ...)? (txt) |
|
||||
| representativeness | At what level is the study mainly representative? (national, subnational, rural, urban..) (list:txt) |
|
||||
| direction | What is the main direction of relation between independent/dependent variables? (0: negative, 1: positive) |
|
||||
| significance | What is the main level of statistical significance? (2: significant, 1: marginally significant, 0: non significant) |
|
||||
|
||||
- annotation, quick 100-300wd written summary of major properties found above for each study
|
||||
-> ~34 observations per study
|
||||
|
||||
## Search Term clusters
|
||||
|
||||
These lists have been used to create data-driven term cluster files in the supplementary data directory.
|
||||
The lists have been kept here for historic documentation but should not be used for up-to-date term changes, use the csv files instead.
|
||||
|
||||
### World-of-work cluster
|
||||
|
||||
- ILO:
|
||||
- work
|
||||
- production of goods, provision of services
|
||||
- use by others or own-use
|
||||
- regardless of legality
|
||||
- labour
|
||||
- production of goods, provision of services,
|
||||
- use by others
|
||||
- of working age
|
||||
- for pay or profit (remuneration, wages, salaries for time worked work done, profits derived from goods or services, through market transactions)
|
||||
|
||||
### forms of work cluster
|
||||
|
||||
- ILO
|
||||
- own-use
|
||||
- employment
|
||||
- unpaid trainee
|
||||
- volunteer
|
||||
- other work activities
|
||||
- domestic
|
||||
- wage-employed
|
||||
- self-employed
|
||||
- formality
|
||||
- (unpaid) care work
|
||||
|
||||
### LM outcome cluster
|
||||
|
||||
- ILO:
|
||||
- employment outcomes
|
||||
- labour rights
|
||||
- opportunities between groups
|
||||
- equality of opportunity/outcome
|
||||
|
||||
@Finlay2021:
|
||||
|
||||
- labour force participation [also, @Pinto2021]
|
||||
- job quality
|
||||
- career advancement
|
||||
- hours worked
|
||||
|
||||
@Silvaggi2020:
|
||||
|
||||
- labour force exit
|
||||
- retrurning to work issues
|
||||
|
||||
### intervention cluster
|
||||
|
||||
- general terms:
|
||||
- intervention (@ILO2022b)
|
||||
- policy (@ILO2022b)
|
||||
- distributive (@ILO2022b)
|
||||
- redistributive (@ILO2022b)
|
||||
- regulatory (@ILO2022b)
|
||||
|
||||
### policy cluster
|
||||
|
||||
- institutional promotion:
|
||||
- institutional support for childcare (@Perez2022)
|
||||
- labour rights (@ILO2022b)
|
||||
- minimum wage (@ILO2022b)
|
||||
- collective bargaining (@ILO2022b)
|
||||
- employment creation ?? (@ILO2022b)
|
||||
- business sustainability promotion ?? (@ILO2022b)
|
||||
- work-life balance promotion ?? (@ILO2022b)
|
||||
- equal pay for work of equal value ?? (@ILO2022b)
|
||||
- removal of (discriminatory) law (@ILO2022b)
|
||||
- law reformation (@ILO2022b)
|
||||
- social dialogue (ILO requested)
|
||||
- guaranteed income (@Perez2022)
|
||||
- universal basic income (@Perez2022)
|
||||
- provision of living wage (@Perez2022)
|
||||
- maternity leave (@Chang2021)
|
||||
|
||||
- structural promotion:
|
||||
- cash benefits
|
||||
- services in kind
|
||||
- green transition (@ILO2022b)
|
||||
- digital infrastructure (@ILO2022b)
|
||||
- (physical) infrastructure (@ILO2022b)
|
||||
- quality of education (@ILO2022b)
|
||||
- public service improvement (@ILO2022b)
|
||||
- lowering of gender segregation (@ILO2022b)
|
||||
- price stability intervention (@ILO2022b)
|
||||
- extended social protection scheme (@ILO2022b)
|
||||
- comprehensive social protection (@ILO2022b)
|
||||
- sustainable social protection (@ILO2022b)
|
||||
- supported employment (@Lettieri2017)
|
||||
- vocational rehabilitation (@Silvaggi2020, @Lettieri2017)
|
||||
- unionization (ILO requested)
|
||||
|
||||
- agency & social norms:
|
||||
- credit programs (@Perez2022)
|
||||
- career guidance (@ILO2022b)
|
||||
- vocational guidance (@Nevala2015)
|
||||
- vocational counselling (@Nevala2015)
|
||||
- counteracting of stereotypes (@ILO2022b)
|
||||
- commuting subsidies (@Perez2022)
|
||||
- housing mobility programs (@Perez2022)
|
||||
- encouraging re-situation/migration (@Perez2022)
|
||||
- encouraging self-advocacy (@Nevala2015)
|
||||
- cognitive behavioural therapy (@Lettieri2017)
|
||||
- computer-assisted therapy (@Lettieri2017)
|
||||
- work organization (@Nevala2015)
|
||||
- special transportation (@Nevala2015)
|
||||
- collective action (ILO requested)
|
||||
|
||||
### inequality cluster
|
||||
|
||||
- ILO:
|
||||
- inequality/-ies
|
||||
- barrier(s)
|
||||
- (dis)advantaged
|
||||
- discriminated
|
||||
- disparity/-ies
|
||||
- horizontal / vertical inequality
|
||||
|
||||
### vertical inequalities cluster
|
||||
|
||||
- income:
|
||||
- Palma ratio [@DFI2023]
|
||||
- Gini coefficient [@DFI2023]
|
||||
- Log deviation [our quant indicators]
|
||||
- Theil [our quant indicators]
|
||||
- Atkinson [our quant indicators]
|
||||
- class @Kalasa2021
|
||||
- fertility @Kalasa2021
|
||||
- NOT identified by previous reviews, need to find sources:
|
||||
- bottom percentile
|
||||
- top percentile
|
||||
|
||||
### horizontal inequalities cluster
|
||||
|
||||
identified by ILO:
|
||||
|
||||
- identity
|
||||
- demographic inequalities
|
||||
- demographic markers
|
||||
- gender
|
||||
- colour
|
||||
- beliefs
|
||||
- racial
|
||||
- ethnic
|
||||
- migrant
|
||||
- spatial
|
||||
- rural
|
||||
- urban
|
||||
- mega-cities
|
||||
- small cities
|
||||
- peripheral cities
|
||||
- age
|
||||
- nationality
|
||||
- ethnicity
|
||||
- health status
|
||||
- disability
|
||||
- characteristics
|
||||
|
||||
# Notes on previous reviews
|
||||
|
||||
## Perez2022
|
||||
|
||||
summary: multi-disciplinary systematic review of association between income, employment, urban poverty. n=243 articles, academic focus on advanced economies; finds significant role of employment in life of urban poor;
|
||||
|
||||
findings: most relevant barriers for improving labour market outcomes: lack of access to public transport, geographical segregation, labour informality, inadequate human capital
|
||||
|
||||
[@Perez2022] identify a multitude of factors which ultimately affect income, employment and urban poverty.
|
||||
Among them:
|
||||
gender inequality, through traditional gender roles and lack of empowerment, a lack of childcare, or inequal domestic work;
|
||||
low human capital, which can originate through pre-existing inequalities,
|
||||
spatial inequality, through lack of access to transportation, residential segregation or discrimination, limited access to work,
|
||||
the inter-generational persistence of poverty as well as the impacts of pre-existing inequalities such as lower human capital or larger household sizes;
|
||||
|
||||
and external factors such as extreme weather events or inflation.
|
||||
|
||||
Strategies to reduce poverty/unemployment are:
|
||||
participation in informal sectors or illegal activities,
|
||||
credit programs,
|
||||
consumption from informal food sources,
|
||||
family and institutional support for childcare,
|
||||
guaranteed minimum income or universal basic income and/or living wage,
|
||||
income diversification,
|
||||
commuting subsidies,
|
||||
housing mobility programs, and
|
||||
migration.
|
||||
|
||||
## Zeinali2021
|
||||
|
||||
systematic review of female leadership in health sector (LMICs) using intersectional analysis
|
||||
|
||||
main findings: main barriers at intersection of gender and social identity of professional cadre, race/ethnicity, financial status, culture;
|
||||
main barriers limiting women's access to career development resources: mentorship, sponsorship opportunities, reduce value, recognition, respect at work for women;
|
||||
channels: increased likelihood for women to take on 'dual burdens' professional work and childcare/domestic work, biased views effectiveness of men/women's leadership styles.
|
||||
|
||||
## Pinto2021
|
||||
|
||||
systematic review of impact of basic income interventions (n=86; 10 different interventions) on labour market, health, educational, housing and other outcomes
|
||||
|
||||
main findings: workforce participation was main dependent variable for studies, evaluation shift over time to include wider array of outcomes reflecting reigning perspective of BI investments possibly lowering health & social care spending; large focus on advanced economies (US)
|
||||
|
||||
## Finlay2001
|
||||
|
||||
narrative review of connection between women's reproductive health and women's economic activity (and gender equality); looking at (causal) effect of fertility (timing, spacing, number of children) on female labour force participation changes (career advancement, job quality, hours worked); separation between LI,MI,HI countries
|
||||
|
||||
main findings:
|
||||
|
||||
- low-inc countries women have to adopt individual strategies of balancing child rearing and labour force part. through selection of job type, relying on other household women for childcare, birth spacing due to mostly informal work
|
||||
- middle-inc countries women have to juggle child rearing, labour force part. with overall income inequality; early childbearing and lone motherhood perpetuate poverty
|
||||
- high-inc countries, SP policies can assist women in managing childrearing and work balance but underlying issues of gender inequality remain
|
||||
- all: childbearing interrupts career advancement
|
||||
|
||||
## Chaudhuri2021
|
||||
|
||||
systematic review on effects of food insecurity (common byproduct of poverty) on health and social outcomes, focusing on women and children
|
||||
|
||||
main findings:
|
||||
- female coping behaviours are non-food (livelihood alterations: outdoor employment, asset base selling, borrowing food/money, purchasing food on credit) or food-based (reducing daily intake sizes/frequency, food rationing; nutritional switch; food sharing);
|
||||
- (obligatory) outdoor employment mostly as farm labourers, can result in time poverty
|
||||
- children coping behaviours are begging, stealing, food seeking (with relatives/friends/charitable institutions), dropping out of school
|
||||
- health outcome includes disrupted socio-cognitive development among children
|
||||
|
||||
## Chang2021
|
||||
|
||||
systematic qualitative review of effects of return to paid employment and breast-feeding (n=26)
|
||||
|
||||
main findings: women experienced physical and emotional difficulties, described gender and employment inequalities in accessing and receiving the support they needed; importance of having workplace legislation in place (and individual motivation) to facilitate breastfeeding during employment; support from employers/colleagues/family members & access to convenient child care helped facilitate breast feeding on return to paid employment
|
||||
|
||||
channels:
|
||||
- gender role expectations viewing women as responsible for domestic work or childcare (especially in LMICs)
|
||||
- shorter maternity leave times discourage decision towards breast-feeding
|
||||
|
||||
## Silvaggi2020
|
||||
|
||||
systematic review looking at effect of brain tumors on on work ability of those affected (and BT survivors) (n=7)
|
||||
|
||||
main findings: impact of neuropsychological functioning on work productivity, change of employment status for long-term survivors (?most often? job loss), issues related to return to work process
|
||||
|
||||
channels: depressive symptoms/cognitive deficits, high short-term mortality, environmental barriers
|
||||
|
||||
policy recc: vocational rehabilitation
|
||||
|
||||
## dePaz-Banez2020
|
||||
|
||||
systematic review of effects of UBI on labour supply (n=38)
|
||||
|
||||
main finding: not found any evidence of significant reduction in labour supply, instead labour supply increases globally among adults, men, women, young, old; some insignificant (functional) reductions for: children, elderly, sick, those with disabilities, women with young children to look after, young people who continued studying - do not reduce overall supply since offset by otherwise increased supply
|
||||
|
||||
## Lindsay2018a
|
||||
|
||||
systematic review of role of gender in employment for disabled young adults (n=48)
|
||||
|
||||
main finding:
|
||||
- majority (21) reported young men with disabilities better employment outcomes than women, fewer (8) showed reverse, minority (5) reported no difference
|
||||
- men with disabilities often work more hours and have higher wages
|
||||
- youth with disabilities half as likely to be employed as typically developing peers; starting life with disability often compounds disadvantages
|
||||
|
||||
channels:
|
||||
- social supports
|
||||
- gender role expectations
|
||||
- lowered expectations
|
||||
- overprotection from parents/guardians discouraging independence
|
||||
|
||||
## Kumari2018
|
||||
|
||||
systematic review looking at relationship of female labour force participation and economic growth, gender disparity in work participation
|
||||
|
||||
main findings: U-shaped part. rate; evidence of gender pay disparity across sectors
|
||||
|
||||
channels affecting FLFP:
|
||||
- demographic factors (fertility, migration, marriages, child care)
|
||||
- economic factors (unemployment, per capita income, non-farm job, infrastructure)
|
||||
- regulatory context (family and childcare policies, tax regimes, presence of subsidized healthcare)
|
||||
|
||||
policy recc: changes to FLFP require replacement of traditional value system based on inequality of sexes (with females playing subordinate role)
|
||||
|
||||
## Ugur2017
|
||||
|
||||
systematic review of effects of technology adoption on employment (in LMIC/LIC 'less developed countries')
|
||||
|
||||
main findings: positive effect more likely when technology adoption favours product innovation not process innovation and when it is is skill based
|
||||
|
||||
additional:
|
||||
- techn. adoption *less* likely to create employment when: related to farm employment not firm/industry employment; related to low-income countries not LMICs; related to data from after 2001 instead of pre-2001
|
||||
- intl trade, weak forward/backward linkages, weaknesses in governance & labor market institutions can weak job-creating effects of technology adoption
|
||||
|
||||
inequality:
|
||||
- existing income inequalities makes effect of technology adoption on employment creation more ambiguous (potentially widening rift of demand for skilled versus unskilled labour)
|
||||
- green revolution technologies tend to reduce income/wealth inequality; also negative effect on on-farm employment
|
||||
|
||||
## Lettieri2017
|
||||
|
||||
meta-review of barriers (and drivers) of inclusion into the labour market for people with disabilities (mental illness)
|
||||
|
||||
main findings: employment outcomes seem increased for individuals able to hide their mental illness, practice of concealment of identity
|
||||
|
||||
channels:
|
||||
- prejudices: of missing skills, danger, unpredictability; of hiring as act of charity due to being unproductive; of work stress as contradicting requirements of mental health
|
||||
- discriminatory hiring practices
|
||||
- generally low-skilled individuals due to discrimination/cultural/social barriers for training and work inclusion
|
||||
|
||||
policy recc:
|
||||
- supported employment (environmental)
|
||||
- cognitive behavioural/computer-assisted therapies (cognitive)
|
||||
- vocational rehabilitation programs (human capital)
|
||||
|
||||
## Taukobong2016
|
||||
|
||||
(narrative?) review of effects of dimensions of female 'empowerment' on health outcomes and development outcomes, such as access to and use of financial services
|
||||
|
||||
main findings:
|
||||
- gender inequalities highly contextual (and intersectional), requires identification of variations at start of interventions where inequalities exist, overlap and work as barriers to its implementation
|
||||
- strong association with improved outcomes across multiple outcome sectors: control over income/assets/resources, decision-making power, education
|
||||
- relation with health/family planning outcomes: mobility, personal safety, equitable interpersonal relationships
|
||||
|
||||
## Ruhindwa2016
|
||||
|
||||
(narrative) review of barriers to workforce inclusion (paid/volunteer work) for people with disabilities; summary of findings
|
||||
|
||||
main findings:
|
||||
- "effective practice takes an inclusive approach and allows clients to take ownership of solutions in relation to addressing the challenges they experience in the employment sector"
|
||||
|
||||
policy recc:
|
||||
- employment support practices
|
||||
- campaigns to encourage disclosing disability
|
||||
|
||||
## Kirsh2016
|
||||
|
||||
review of factors influencing LM outcomes of supported employment interventions for people with disabilities
|
||||
|
||||
main findings:
|
||||
- most employment support literature only looks at overall efficacy of interventions, with little prudence for intersectional inequality variations
|
||||
|
||||
inequalities:
|
||||
- men more likely to be employed (argue possibly due to manual labour of many jobs)
|
||||
- older people less likely to be employed (age+, change-)
|
||||
- older women more likely to be employed than men
|
||||
- education very important in employment outcomes
|
||||
|
||||
policy recc:
|
||||
- vocational rehabilitation
|
||||
|
||||
## Hastbacka2016
|
||||
|
||||
scoping review of linkages between societal participation and people with disabilities for identity of participant, type of participation, type of facilitators and barriers; focus on European countries (n=32, between 2012-2013)
|
||||
|
||||
main findings: strongest focus on labour market participation; social participation viewed through lens of disabled people as one group instead of intersectional
|
||||
|
||||
main barriers: financial factors, attitudes, health issues, unemployment
|
||||
main facilitators: legislation and disability policies; support from people in close contact with disabled people, attitudes in society and employment opportunities for people with disabilities
|
||||
|
||||
## Nevala2015
|
||||
|
||||
systematic review looking at effectiveness of workplace accommodation (vocational counselling/guidance, education/self-advocacy, help of others, changes in work schedules, work organization, special transportation) on employment, work ability, cost-benefit, rtw (n=11)
|
||||
|
||||
main findings:
|
||||
- moderate evidence that employment among physically disabled persons promoted by: vocational counselling/guidance, education/self-advocacy, help of others, changes in work schedules, work organisation, special transportation
|
||||
- low evidence that rtw increased for physical/cognitive disabilities by: liaison (btw employer and other professionals), education, work aids, work techniques
|
||||
|
||||
barrier/facilitators: self-advocacy, support of employer and community, amount of training/counselling, flexibility of work schedules/organisation
|
||||
|
||||
# Database Query
|
||||
|
||||
## Other reviews queried databases
|
||||
|
||||
from @Pinto2021:
|
||||
|
||||
- Scopus
|
||||
- Embase
|
||||
- Medline
|
||||
- CINAHL
|
||||
- WOS
|
||||
- ProQuest
|
||||
- EBSCOhost Research DB
|
||||
- PsycINFO
|
||||
|
||||
## WOS
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
#| output: asis
|
||||
with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
|
||||
query = f.read()
|
||||
|
||||
t3 = "`" * 3
|
||||
print(f"""
|
||||
```sql
|
||||
{query}
|
||||
{t3}
|
||||
""")
|
||||
```
|
||||
|
||||
# Findings and Updates from query
|
||||
|
||||
## Preliminary source pool
|
||||
|
||||
- initial query pool (no deduplication): 1643
|
||||
- snowballing pool (from 29 reviews): 530
|
||||
|
||||
## Additional concept research
|
||||
|
||||
- utilizing Joanna Briggs Institute JBI Scoping Review methodology
|
||||
|
||||
## Preliminary findings income
|
||||
|
||||
- potential drivers: (Zhuan2023)
|
||||
- inverted-U hypothesis (Kuznets, 1955)/ dual economy model (Lewis, 1954)
|
||||
- technological progress
|
||||
- globalization
|
||||
- deregulation/market-oriented reform
|
||||
- financialization
|
||||
- population aging
|
||||
- widening spatial inequality between subsistence/growth economy (i.e. dual economy)
|
||||
- growing gap to super-rich (top 1 percentile)
|
||||
- potential channels:
|
||||
- declining labor income share/ growing capital income share
|
||||
- widening skilled/non-skilled wage differentials
|
||||
- growing spatial inequality
|
||||
- limited taxation/transfer income redistribution
|
||||
|
||||
## Potential additional search terms
|
||||
|
||||
- Matthew effect (lower socio-economic position households send fewer children to formal childcare in HIC)
|
||||
- issue: currently in many cases looking at *health* and *health inequality* outcomes
|
||||
|
||||
## Issues raised by ILO
|
||||
|
||||
- only english: Query itself is English only. If Spanish/French fall into grid, may include
|
||||
- no purely qualitative: might prove too much; how to ensure rigour?
|
||||
- no pre-2000: Can include?
|
||||
|
||||
\pagebreak
|
||||
|
||||
# Relevant references
|
||||
|
||||
::: {#refs}
|
||||
:::
|
||||
169
manuscript/presentation_summary.md
Normal file
169
manuscript/presentation_summary.md
Normal file
|
|
@ -0,0 +1,169 @@
|
|||
---
|
||||
number-sections: true
|
||||
---
|
||||
|
||||
# Text summary of review search and extraction process
|
||||
|
||||
## WHAT we search for and WHY
|
||||
|
||||
We are undertaking a systematic scoping review mapping out the current academic state of the art for policies explicitly or implicitly aimed at reducing inequalities in the world of work.
|
||||
To arrive at a mapping which is as unbiased as possible, we closely follow the scoping review methodology proposed by Arksey and O'Malley (2005) and extended by Pham et al. (2014),
|
||||
as well as those points from the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA, 2020) guidelines that are applicable to scoping reviews.
|
||||
|
||||
The goal of this review strategy is to capture any possible coherent mechanisms of policy-making which positively influence the reduction of inequalities in the world of work and reach some measure of clarity on the extent and robustness of evidence in these areas.
|
||||
|
||||
ACCOMPANYING SLIDE @sec-clusters
|
||||
|
||||
To achieve this, we follow the general typologies formulated by ILO for the world of work to find a definition of work, various forms of work and labour market adjacent outcomes to be measured.
|
||||
These provide one 'cluster' of terms for the world of work which will later be used for the actual search strategy.
|
||||
A second cluster is provided by terms considering a variety of terms posing a definition of a policy,
|
||||
as well as possible implementations of it in an institutional perspective, a structural perspective and the perspective of personal or collective agency.
|
||||
The last cluster is made up of terms defining the concept of inequality, and terms describing dimensions of vertical and horizontal inequalities.
|
||||
|
||||
These three clusters, taken together, then describe all domains which are of special interest in finding policies to reduce inequalities in the world of work.
|
||||
|
||||
ACCOMPANYING SLIDE @sec-terms-example
|
||||
|
||||
They, together with specific inclusion criteria, provide the semantic baseline for the search:
|
||||
the terms and concepts for which studies will first be searched and later included or excluded from consideration in the actual search implementation.
|
||||
|
||||
## HOW we search
|
||||
|
||||
ACCOMPANYING SLIDE @sec-search
|
||||
|
||||
The scoping review process itself consists of three consecutive steps: identification, screening and extraction.
|
||||
|
||||
The goal of the identification process is to allow a wide-breadth net to be cast,
|
||||
including all possibly relevant material published on the topic to be identified and included in an initial sample pool of sources.
|
||||
To facilitate this process, two search methods are used:
|
||||
|
||||
ACCOMPANYING SLIDE @sec-identification
|
||||
|
||||
Boolean-based searches of the extended World of Science corpus is the primary source of database literature.
|
||||
Then, relevant existing reviews are identified within and outside of this sample and used for a 'snowballing' technique, which adds all sources mentioned within those reviews, and any overlapping citations from those sources in turn.
|
||||
Through this identification technique, a wide breadth of sources are identified, generally covering all relevant literature making up the current state of the art.
|
||||
|
||||
ACCOMPANYING SLIDE @sec-screening
|
||||
|
||||
In the second scoping review step of screening, sources are systematically considered for their relevance from far-reading to close-reading techniques:
|
||||
first, sources are considered only based title; next, based on abstract; and only those sources remaining are screened for full-text relevance for the final extraction.
|
||||
|
||||
The criteria applied within each screening step are the aforementioned inclusion/exclusion criteria,
|
||||
which are repeatedly applied in each step.
|
||||
A source which can not unambiguously be assigned to any one of the criteria which would exclude it will be left included in the sample pool for another closer-reading consideration at a later step, until the full-text review.
|
||||
|
||||
Alongside the final full-text screening step,
|
||||
sources are assigned specific keyword 'tags' to ease their later organisation into domains of policies, inequalities, as well as countries and regions with the help of the internal keyword abilities of the *Zotero* reference manager.
|
||||
|
||||
## HOW we extract from it
|
||||
|
||||
Extraction with the help of the extraction tool follows a strict grid of relevant data to extract from each source identified and screened as relevant.
|
||||
The extraction data to be pulled from each relevant source can be categorized into 4 overall dimensions:
|
||||
publication data, contextual data, results, and statistical data.
|
||||
|
||||
ACCOMPANYING SLIDE @sec-extraction
|
||||
|
||||
Publication data captures the relevant information to uniquely identify the study under review,
|
||||
as well as identify its publication type and location.
|
||||
Results capture the primary of findings of a study, along with any suggested channels or mechanisms of operation, the theory they are basing them on if provided, and any limitations.
|
||||
They also capture the type of intervention under review as well as the types of inequalities, as well as any specific outcome measures.
|
||||
|
||||
Contextual data represents all information given within the source as to the intervention's respective required contexts:
|
||||
what the primary country (or countries) of analysis were, which world region and country income class they represent, which target group was targeted by the intervention if any, which dataset the study at hand made use of if provided and importantly when and for how long the intervention and its study were undertaken.
|
||||
|
||||
Statistical data captures all study findings of statistical relevance: its sample size, level of representativeness, significance, but and if it is using relative or absolute indicators.
|
||||
It also captures the design (whether it was undertaken experimentally or observationally) and the methods used by the study.
|
||||
|
||||
# Bullet-points / Possible Slides
|
||||
|
||||
## Term cluster areas {#sec-clusters}
|
||||
|
||||
- *world of work* cluster:
|
||||
- Definition of work
|
||||
- Forms of work
|
||||
- Labour market adjacent outcomes
|
||||
- *policy* cluster:
|
||||
- definition of policy
|
||||
- institutional implementations
|
||||
- structural implementations
|
||||
- implementations of personal/collective agency
|
||||
- *inequality* cluster:
|
||||
- definition of inequalities
|
||||
- vertical inequalities
|
||||
- horizontal inequalities
|
||||
|
||||
## Inequalities term cluster example {#sec-terms-example}
|
||||
|
||||
| Defintion | Vertical inequality | Horizontal inequality |
|
||||
| --- | --- | --- |
|
||||
| inequality | income | identity |
|
||||
| barrier | class | demographic |
|
||||
| advantaged | Gini | gender |
|
||||
| disadvantaged | Palma | colour |
|
||||
| discriminated | Theil | beliefs |
|
||||
| disparity | Atkinson | racial |
|
||||
| horizontal inequality | log deviation | ethnic |
|
||||
| vertical inequality | fertility | migrant |
|
||||
| | bottom percentile | spatial |
|
||||
| | top percentile | rural |
|
||||
| | | urban |
|
||||
| | | small cities |
|
||||
| | | peripherial cities |
|
||||
| | | age |
|
||||
| | | nationality |
|
||||
| | | ethnicity |
|
||||
| | | health status |
|
||||
| | | disability |
|
||||
| | | characteristics |
|
||||
|
||||
## Inclusion criteria {#sec-criteria}
|
||||
|
||||
| Parameter | Inclusion criteria | Exclusion criteria |
|
||||
| --- | --- | --- |
|
||||
| Time frame | study published in or after 2000 | study published before 2000 |
|
||||
| Study type | primary research | opinion piece, editorial, commentary, news article, literature review |
|
||||
| Study recency | most recent publication of study | gray literature superseded by white literature publication |
|
||||
| Study focus | inequality or labour market outcomes as primary outcome (dependent variable) | neither inequality nor labour market outcomes as dependent variable |
|
||||
| | policy measure or strategy as primary intervention (independent variable) | no policy measure/strategy as intervention or relationship unclear |
|
||||
| | specifically relates to some dimension of world of work | exists outside world of work for both independent and dependent variables |
|
||||
| | focus on dimension of inequality in analysis | no focus on mention of inequality in analysis |
|
||||
|
||||
## The Search Process {#sec-search}
|
||||
|
||||
- identification: create a sample of *all* possibly relevant sources from current literature
|
||||
- screening: separate irrelevant from relevant sources and map source characteristics
|
||||
- extraction: pull out the relevant outcomes for which data were sought based on extraction tool from relevant studies only
|
||||
|
||||
## Identification {#sec-identification}
|
||||
|
||||
- Database sources:
|
||||
- World of Science (Extended Corpus): white literature
|
||||
- Google Scholar: possibly relevant grey literature
|
||||
- Snowballing:
|
||||
- starting from existing relevant reviews contained in initial database results & additional relevant ones
|
||||
- all contained citations extracted and added to identified sources
|
||||
- Deduplication:
|
||||
- automated deduplication for exact source matches
|
||||
- manual deduplication for inexact matches or superseding literature
|
||||
|
||||
## Screening {#sec-screening}
|
||||
|
||||
- Repeated sorting out of irrelevant literature:
|
||||
- repeated process from far-reading to close-reading (title, abstract, full-text)
|
||||
- using inclusion/exclusion criteria for each round of separations
|
||||
- Pre-sorting of literature with keyword tagging:
|
||||
- reason for exclusion (title, abstract, superseded, or note for full-text reason)
|
||||
- types of inequalities, types of policies, types of outcomes measured
|
||||
|
||||
## Extraction {#sec-extraction}
|
||||
|
||||
- Extraction of relevant data for current review:
|
||||
- from all sources identified as relevant during full-text screening
|
||||
- using extraction tool to unify extracted data
|
||||
- Relevant data:
|
||||
- Publication information (author, year, title, publisher, publication type)
|
||||
- Contextual data (country, region, income class, period & length of analysis, target group, dataset)
|
||||
- Results data (intervention, inequalities, outcome measures, main findings, channels, limitations)
|
||||
- Statistical data (sample size, representativeness, methods, significance, absolute indicator)
|
||||
|
||||
{{< pagebreak >}}
|
||||
1774
manuscript/scoping_review.qmd
Normal file
1774
manuscript/scoping_review.qmd
Normal file
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