professional/wow-inequalities
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

refactor(script): Update synthesis intro

Browse source
This commit is contained in:
Marty Oehme 2024-02-18 16:59:26 +01:00
parent b453afd112
commit 1d90133049
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
1 changed files with 13 additions and 28 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

41
scoping_review.qmd
View file

@ -570,39 +570,24 @@ plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
plt.show()
```
@fig-intervention-types shows the predominant interventions for the literature reviewed.
Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and training.
The overall spread of interventions captures primarily institutional and structural, but also agency-driven programmes.
One of the primary lenses through which policy interventions to reduce inequalities in the world of work are viewed is that of income inequality, often measured for all people throughout a country or subsets thereof.
At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities may lead to adverse targeting or intervention outcomes.
In the following synthesis each reviewed study will be analysed through the primary policies they concern themselves with.
@fig-intervention-types shows the predominant interventions contained in the reviewed literature.
Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.
The entire spread of policies captures interventions aimed primarily at institutional and structural mechanisms, but also mechanisms focused on individual agency.
Since policies employed in the pursuit of increased equality can take a wide form of actors, strategy approaches and implementation details,
the following synthesis will first categorize between the main thematic area and its associated interventions,
which are then descriptively distinguished between for their primary outcome inequalities.
the following synthesis will first categorise between the main thematic area and its associated interventions.
Individual observations are then descriptively distinguished between for the primary outcome variables (inequalities) of interest.
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.
Each main thematic area will be preceded by a table prsenting the overall inequalities reviewed,
main findings and accompanying channels that could be identified.
Afterwards, the analytical lens will be inverted for the discussion (Section 5)
and the reviewed studies discussed from a perspective of their analysed inequalities and limitations,
One of the primary lenses of inequality in viewing policy interventions to reduce inequalities in the world of work is that of income,
often measured for all people throughout a country (vertical inequality) or subsets thereof (horizontal inequality).
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.
Each main thematic area will be preceded by a table presenting a summary of findings for the respective policies,
their identified channels and an estimation of their strength of evidence base.
Afterwards, the analytical lens will be inverted for the discussion (Section 5) and the reviewed studies discussed from a perspective of their analysed inequalities and limitations,
to better identify areas of strong analytical lenses or areas of more limited analyses.
```{python}
#| label: fig-validity
from src.model import validity
validities = validity.calculate(by_intervention)
validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
g = sns.PairGrid(validities[["internal_validity", "external_validity", "identifier"]].drop_duplicates(subset="identifier"),
x_vars=["internal_validity", "external_validity"], y_vars = ["identifier"]
)
# Create a stacked histplot using Seaborn
#sns.scatterplot(data=validities, x='external_validity', y='internal_validity', hue='intervention')
```
## Institutional
{{< portrait >}}