chore(script): Clean leftover todo annotations
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@ -247,7 +247,7 @@ The identified channels here are different social supports, gender role expectat
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<!-- gender -->
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Kumari [-@Kumari2018] looks at the relationship of both economic growth and gender disparity on the labour supply in investigating their effects on female work participation.
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<!-- TODO explain U-shape -->
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<!-- TODO further explanation of U-shape necessary? -->
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They see a U-shaped participation rate and some evidence of cross-sector gender pay disparity which is affected by demographic factors such as migration, marriage, child care and fertility, as well as economic factors such as per capita income, unemployment, infrastructure and the prevalence of non-farm jobs.
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Ultimately, they argue that the labour supply inequalities are based on inequality between the sexes and, while regulatory measures such as adequate family and childcare policies, tax regimes and the presence of subsidized healthcare help, changes to the female labour force participation fundamentally require the replacement of such a traditional value system itself.
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@ -290,7 +290,8 @@ There is also low evidence for return to work being increased by education, work
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# Methodology and data
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This section will discuss the systematic scoping review 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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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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@ -306,12 +307,10 @@ With an increasingly adopted approach in recent years, with rigorous dichotomy o
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## The search protocol
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<!-- TODO need correct above definitions -->
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The search protocol was carried out based on the introduced areas of policies as well as the possible combination of definitions and outcomes in the world of work.
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For each dimension of definitions, a cluster containing possible utilized terms will be created, that is for: definitions of work and labour, forms of work, definitions of inequality, forms of vertical and forms of horizontal inequalities, labour market outcomes, and definitions of policy.
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Each of the clusters contains synonymous terms as well as term-adjacent phrase combinations which are in turn used to refine or broaden the search scope to best encapsulate each respective cluster, based on the above definitions.
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<!-- TODO Why WOS database? -->
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The search protocol then 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 gray 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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@ -479,26 +478,15 @@ For a full list of validity ranks, see @apptbl-validity-external and @apptbl-val
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## Data
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```{python}
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#| label: calculate-relevant-studies
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#| echo: false
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# TODO Remove redundant 'relevant' studies observation below once all studies are extracted.
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nr_relevant = len([1 for kw in all_keywords if "relevant" in kw])
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```
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The query execution results in an initial sample of `{python} nr_database_query_raw` potential studies identified from the database search as well as `{python} nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} 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_relevant` have been identified as potentially relevant studies for the purposes of this scoping review, from which `{python} nr_extraction_done` have been extracted.
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<!-- {{++ FIXME: Update description for changing study pool ++}} -->
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Of these, `{python} FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract-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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from which in turn `{python} nr_extraction_done` have ultimately been extracted.
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The currently identified literature rises somewhat in volume over time,
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with first larger outputs identified from 2014,
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as can be seen in @fig-publications-per-year.
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<!-- {{++ FIXME: give full year scale ++}} -->
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<!-- TODO Should this be sub-divided by region or subdivision later per-section? -->
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```{python}
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#| label: fig-publications-per-year
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#| fig-cap: Publications per year
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@ -509,20 +497,21 @@ df_study_years = (
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.reset_index()
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.drop_duplicates()
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)
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# plot by year TODO decide if we want to distinguish by literature type/region/etc as hue
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# FIXME should be timeseries plot so no years are missing
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# FIXME potentially timeseries plot so no years are missing
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ax = sns.countplot(df_study_years, x="year")
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ax.tick_params(axis='x', rotation=45)
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ax.set_xlabel("")
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ax.set_ylabel("Count")
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ax.set_xlabel("Year")
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plt.tight_layout()
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plt.show()
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df_study_years = None
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```
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Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.
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While this can mean a decreased interest or different focus points within academia during those time spans,
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it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions,
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and should thus be kept in mind for future scoping efforts.
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Anomalies such as the relatively significant dips in output in 2016 and 2017 become especially interesting against the strong overall increase of output.
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While this can mean a decreased interest or different focus points within academia during the time span,
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it may also point towards newly arising alternative term clusters, or a re-focus towards different interventions,
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either of which may not be completely captured in the data and should thus be kept in mind for future scoping efforts.
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The predominant amount of literature is based on white literature, with only a marginal amount solely published as gray literature.
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This represents a gap which seems reasonable and not surprising since the database query efforts were primarily aimed at finding the most current versions of white literature.
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@ -530,7 +519,7 @@ Such a stark gap speaks to a well targeted identifaction procedure, with more up
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@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
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From the literature sample, several patterns emerge:
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First, in general, citation counts are slightly decreasing - as should generally be expected with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.
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First, in general, citation counts are slightly decreasing --- as should generally be expected for newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.
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```{python}
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#| label: fig-citations-per-year-avg
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@ -541,7 +530,12 @@ fig, ax = plt.subplots()
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ax.bar(grpd["year"], grpd["zot_cited"])
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sns.regplot(x=grpd["year"], y=grpd["zot_cited"], ax=ax)
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#ax = sns.lmplot(data=grpd, x="year", y="zot_cited", fit_reg=True)
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years_list = np.arange(2000, 2024).tolist()
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ax.set_xticks(years_list)
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ax.tick_params(axis='x', rotation=45)
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ax.set_ylabel("Citations")
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ax.set_xlabel("Year")
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plt.tight_layout()
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plt.show()
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```
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@ -551,8 +545,10 @@ This suggests, first, no overall decrease in academic interest in the topic over
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and second, no linearly developing concentration or centralization of knowledge output and dissemination,
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though it also throws into question a clear-cut increase of *relevant* output over time.
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Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest, a possible sign of still somewhat unfocused research production which does not approach from a single coherent perspective yet.
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It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.
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Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest,
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a possible sign of still somewhat unfocused research production which does not yet approach from a single coherent perspective.
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It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the literature review process,
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a possible sign of more focused knowledge production and thus valuable to more closely review during screening.
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It may also suggest that clearly influential studies have been produced during those years, a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013).
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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.
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@ -560,10 +556,6 @@ This is because, as @fig-publications-per-year showed, the overall output was no
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In all of these cases, such outliers should provide clear points of interest during the screening process for eventual re-evaluation of utilized scoping term clusters and for future research focus.
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Should they point towards gaps (or over-optimization) of specific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking future identification queries to better align with the prevailing literature output.
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<!-- {{++ TODO: Add breakdown by thematic area++}} -->
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<!-- {{++ TODO: describe intervention types with complete dataset ++}} -->
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# Synthesis: A multitude of lenses
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This section will present a synthesis of evidence from the scoping review,
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@ -814,7 +806,7 @@ The results show the same trends for measurement of land inequality using the sh
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### Social protection
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<!-- TODO Include part of Pi2016 on social security -->
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<!-- TODO Should we include Pi2016 on social security? -->
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<!-- social assistance benefits and wages -->
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@Wang2016 undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries.
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@ -851,8 +843,7 @@ The authors suggest this shows the primary channel of the programme becoming a b
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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.
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<!-- UBI -->
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<!-- TODO! Add to main findings and possibly env protection/labour regulation -->
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<!-- TODO Add quick sentence of Standing also looking into UBI -->
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<!-- TODO Potentially mention single sentence of Standing also looking into UBI -->
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@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.
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It finds that while both decrease overall income inequality, measured through Gini coefficient, they do so through different channels.
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While provision of a UBI sustains aggregate demand, thereby spreading income in a more equitable manner,
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@ -1175,8 +1166,7 @@ Some limitations to the study include a relatively small overall sample size, as
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### Unconscious bias and discriminatory norms
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<!-- TODO include discussion of Gates2000 on social components of disability rtw -->
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<!-- TODO include hiring / firing patterns of Mun2018? mat laws -->
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<!-- TODO should we include hiring / firing patterns of Mun2018? mat laws -->
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@Al-Mamun2014 conduct a study on the impacts of an urban micro-finance programme in Malaysia on the economic empowerment of women.
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The programme introduced the ability for low-income urban individuals to receive collateral-free credit.
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@ -1301,17 +1291,19 @@ validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=T
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validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
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#validities["external_validity"] = validities["external_validity"].astype('category')
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validities["internal_validity"] = validities["internal_validity"].astype('category')
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validities["External Validity"] = validities["external_validity"]
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validities["Internal Validity"] = validities["internal_validity"]
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plt.figure().set_figheight(5)
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sns.violinplot(
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data=validities,
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x="internal_validity", y="external_validity", hue="design",
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x="Internal Validity", y="External Validity", hue="design",
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cut=0, bw_method="scott",
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orient="x"
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)
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sns.swarmplot(
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data=validities,
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x="internal_validity", y="external_validity", legend=False,
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x="Internal Validity", y="External Validity", legend=False,
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color="darkmagenta",
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s=4
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)
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@ -1339,7 +1331,7 @@ again with the exception of @Thoresen2021 already mentioned.
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sns.displot(
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data=validities,
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x="external_validity", hue="internal_validity",
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x="External Validity", hue="Internal Validity",
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kind="kde",
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multiple="fill", clip=(0, None),
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palette="ch:rot=-0.5,hue=1.5,light=0.9",
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@ -1436,7 +1428,6 @@ Similarly, rarely do studies delineate generational outcomes from income, gender
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[^education-studies]: For gender inequalities within education paths themselves, see @Stepanenko2021. For possible ways to integrate gender-transformative interventions into professional education, see @Newman2016. For the effects of prior inequalities on taxation preferences, school enrolment and educational choices, see @Gutierrez2009 and @Zamfir2017. For interactions between policies for the knowledge translation of sexual education and their barriers, see @Curran2022.
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<!-- frameworks/qualitative discussion -->
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<!-- explanatory framework; see data/processed/irrelevant/Eckardt2022 TODO connect with study results above -->
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The effects of automation on income inequality are more clearly put into focus by @Eckardt2022 by studying income inequality and under the effects of various kinds of automation and a minimum wage within the economy.
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He considers several types of automation, with automation on the extensive margin (automation of more tasks) leading to decreased wage inequality between low-skill and high-skill earners if it results in decreased overall outputs due to wage compression, and vice versa for increased total outputs.
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Automation on the intensive margin (increased productivity of automating existing tasks) has ambiguous effects on the employment share of low-skill workers (who are possibly displaced) and a higher minimum wage here decreases the inequality between low-skill wages and higher-skill wages.
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@ -1463,7 +1454,7 @@ findings - channels - policy recc
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- supply-side effects, esp maternal (family planning; care work)
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- organisational disadvantagement in new economy (networking needs, self-promotion, managerial discretions)
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TODO include unionisation effects on gender
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TODO perhaps add specific section for unionisation effects on gender?
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-->
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Due to its persistent characteristics, gender inequality is an often analysed horizontal dimension of workplace inequality in the study sample,
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