Code
-terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
+terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
@@ -5759,7 +5759,7 @@ Table 4: Inequality term cluster
An overview of the respective criteria used for inclusion or exclusion can be found in Table 5. It restricts studies to those that comprise primary research published after 2000, with a focus on the narrowing criteria specified in Table 5.
Code
-inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
+inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
@@ -9294,7 +9294,7 @@ Figure 4: Available studies by primary type of intervention
def strength_for(val):
return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
-findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
+findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@@ -9558,7 +9558,7 @@ Table 6: Summary of main findings for institutional policies
Code
from src.model import validity
-findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
+findings_structural = pd.read_csv("data/supplementary/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@@ -9794,7 +9794,7 @@ Table 7: Summary of main findings for structural policies
Code
from src.model import validity
-findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
+findings_agency = pd.read_csv("data/supplementary/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@@ -22545,7 +22545,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
sns.set_style("whitegrid")
-DATA_DIR=Path("./02-data")
+DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
@@ -22835,7 +22835,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
```{python}
#| label: tbl-wow-terms
#| tbl-cap: World of work term cluster
-terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
+terms_wow = pd.read_csv("data/supplementary/terms_wow.csv")
md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
@@ -22857,7 +22857,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
```{python}
#| label: tbl-intervention-terms
#| tbl-cap: Intervention term cluster
-terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
+terms_policy = pd.read_csv("data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
@@ -22873,7 +22873,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
```{python}
#| label: tbl-inequality-terms
#| tbl-cap: Inequality term cluster
-terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
+terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
@@ -22897,7 +22897,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
```{python}
#| label: tbl-inclusion-criteria
-inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
+inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
@@ -22948,7 +22948,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
t3 = "`" * 3
-# FIXME use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
+# 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}
@@ -23145,7 +23145,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
def strength_for(val):
return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
-findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
+findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@@ -23378,7 +23378,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
#| label: tbl-findings-structural
from src.model import validity
-findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
+findings_structural = pd.read_csv("data/supplementary/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@@ -23615,7 +23615,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
#| label: tbl-findings-agency
from src.model import validity
-findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
+findings_agency = pd.read_csv("data/supplementary/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
diff --git a/05-final_paper/scoping_review.pdf b/final_document/scoping_review.pdf
similarity index 100%
rename from 05-final_paper/scoping_review.pdf
rename to final_document/scoping_review.pdf
diff --git a/manuscript/article.qmd b/manuscript/article.qmd
new file mode 100644
index 0000000..292c62b
--- /dev/null
+++ b/manuscript/article.qmd
@@ -0,0 +1,1460 @@
+---
+title: Inequalities in the World of Work
+subtitle: What do we know?
+author:
+ - name: Miguel Niño-Zarazúa
+ email: mn39@soas.ac.uk
+ affiliations:
+ - id: soas
+ name: SOAS University of London
+ department: Department of Economics
+ attributes:
+ corresponding: true
+ - name: Marty Oehme
+ email: mail@martyoeh.me
+date: last-modified
+abstract: |
+ We are researching the effectiveness of policies which target inequalities on the labour market.
+keywords:
+ - labour markets
+ - world of work
+ - inequality
+ - policy
+ - systematic scoping review
+lang: en
+crossref: # to fix the appendix crossrefs being separate from main
+ custom:
+ - kind: float
+ key: appatbl
+ latex-env: appatbl
+ reference-prefix: Table A
+ space-before-numbering: false
+ latex-list-of-description: Appendix A Table
+ - kind: float
+ key: appbtbl
+ latex-env: appbtbl
+ reference-prefix: Table B
+ space-before-numbering: false
+ latex-list-of-description: Appendix B Table
+---
+
+```{python}
+#| label: standard-imports
+#| echo: false
+#| output: false
+import src.globals as g
+from IPython.display import display, Markdown, HTML
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from tabulate import tabulate
+import seaborn as sns
+sns.set_style("whitegrid")
+
+# pyright: reportUnusedImport=false
+```
+
+```{python}
+#| label: load-dataframes
+#| echo: false
+#| output: false
+from src import df, df_by_intervention, validities
+```
+
+{{< portrait >}}
+
+# Introduction
+
+* Context and statement of the problem
+* Aims and rationale of the systematic scoping review
+* Summary of the main findings
+* Description of the structure of the paper
+
+# Conceptual framework
+
+* Theories, policies, mechanisms and outcomes
+
+# Review methodology
+
+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,
+as well as give an overview of the collected data.
+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.
+It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research,
+while limiting "bias by the systematic assembly, critical appraisal and synthesis" through applying scientific strategies to the review itself [@Cook1995].
+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.
+
+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.
+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].
+
+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.
+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.
+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].
+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].
+
+## Inclusion criteria
+
+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.
+iii) using appropriate quantitative methods to examine the links of intervention and impact on the given inequalities.
+The narrowing process makes use of the typology of inequalities, of forms of work, and of policy areas introduced above as its criteria.
+
+An overview of the respective criteria used for inclusion or exclusion can be found in @tbl-inclusion-criteria.
+It restricts studies to those that comprise primary research published after 2000,
+with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
+
+::: {#tbl-inclusion-criteria}
+
+```{python}
+inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
+Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
+```
+
+Source: Author's elaboration
+
+Study inclusion and exclusion scoping criteria
+
+:::
+
+## Search protocol
+
+The search protocol followed a three-staged process of execution: identification, screening and extraction.
+A detailed description of each review step and relevant terms can be found in the Appendix.
+
+First, in identification, the relevant policy, inequality and world of work related dimensions were 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.
+Second, in screening, duplicate results were removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
+language, title, abstract, full text and literature superseded through newer publications.
+Properties in these characteristics were used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
+
+Should any literature reviews be identified as relevant during this screening process,
+they will in turn be crawled for cited sources in a 'snowballing' process.
+The sources are then be added to the sample to undergo the same screening process,
+ultimately resulting in the process represented in the PRISMA chart in @fig-prisma.
+
+```{mermaid}
+%%| label: fig-prisma
+%%| fig-cap: PRISMA flowchart for scoping process
+%%| file: ../data/processed/prisma.mmd
+```
+
+All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
+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.
+
+```{python}
+from src.model import prisma
+p = prisma.PrismaNumbers()
+```
+
+The query execution results in an initial sample of
+`{python} p.raw_db`
+potential studies identified from the database search as well as
+`{python} p.raw_snowball`
+potential studies from other sources,
+leading to a total initial number of
+`{python} p.raw_full`.
+This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
+Of these,
+`{python} p.dedup_full - p.out_title - p.out_abstract - p.out_language`
+have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
+from which in turn
+`{python} p.final_extracted`
+have ultimately been extracted.
+
+@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.
+
+```{python}
+#| label: fig-intervention-types
+#| fig-cap: Available studies by primary type of intervention
+
+sort_order = df_by_intervention["intervention"].value_counts().index
+
+fig = plt.figure()
+fig.set_size_inches(6, 3)
+ax = sns.countplot(df_by_intervention, x="intervention", order=df_by_intervention["intervention"].value_counts().index)
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+ rotation_mode="anchor")
+plt.show()
+del sort_order, fig, ax
+```
+
+# Synthesis of evidence
+
+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 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.
+
+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 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.
+
+## Institutional factors
+
+{{< portrait >}}
+
+::: {#tbl-findings-institutional}
+
+```{python}
+# | label: tbl-findings-institutional
+from src.model import validity
+from src.model.validity import strength_for # Careful: ruff org imports will remove
+
+findings_institutional = pd.read_csv(
+ f"{g.SUPPLEMENTARY_DATA}/findings-institutional.csv"
+)
+fd_df = validity.add_to_findings(findings_institutional, df_by_intervention)
+
+outp = Markdown(
+ tabulate(
+ fd_df[
+ [
+ "area of policy",
+ "internal_validity",
+ "external_validity",
+ "findings",
+ "channels",
+ ]
+ ].fillna(""),
+ showindex=False,
+ headers=[
+ "area of policy",
+ "internal strength",
+ "external strength",
+ "main findings",
+ "channels",
+ ],
+ tablefmt="grid",
+ )
+)
+del findings_institutional, fd_df
+outp # type: ignore[ReportUnusedExpression]
+```
+
+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.
+Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
+evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
+
+Summary of main findings for institutional policies
+
+:::
+
+{{< landscape >}}
+
+### Labour regulation and paid leave
+
+
+@Dustmann2012 analyse the long-run effects of a series of increases in the period of paid leave for mothers in Germany,
+first up to 18 months and then extending unpaid leave up to 36 months.
+Though primarily focused on children's outcomes,
+it also analyses the policy's effects on the return to work rates and cumulative incomes of the mothers.[^dustmann-childoutcomes]
+While increases of paid leave periods (up to 6 months) significantly increased incomes,
+longer periods (up to 10 months) saw a decrease with marginal significance for low-income mothers.
+Further increases, including the unpaid but job-protected increase to 36 months,
+significantly decreased cumulative incomes across income brackets.[^cumulative]
+For those returning to work, there is a significant increase in the months away from work among all wage segments for all paid leave period increases,
+roughly corresponding to the respective provided leave length.
+Still, similar numbers of mothers return once the leave period ends,
+with significant decreases for the longer leave periods from 18 to 36 months.
+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.
+
+[^cumulative]: Cumulative income being defined as the sum of mother's income until the child is 40 months old, combined from monthly earnings if working or monthly child benefit if not working but eligible for paid leave.
+
+[^dustmann-childoutcomes]: 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. 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.
+
+@Mun2018, taking a look at hiring discrimination due to introducing maternity leave laws in Japan,
+find similar results:[^laws-japan]
+no increase in hiring discrimination or job promotions was visible and the laws in fact had a partly positive impact on job promotions.
+They argue these positive impacts may predominantly be due to voluntary firm compliance to maintain positive reputations,
+arguing for an incentive-based approach over mandated ones though no causal analysis was undertaken.[^welfare-paradox]
+Their analysis focused on women in managerial positions which may bias findings away from lower income brackets.
+
+[^laws-japan]: The study focuses on the 1992 introduced Childcare Leave Act which, as the first major childcare policy, mandated one year childcare leave per child for both men and women,
+and the 2005 introduced Act on Advancement of Measures to Support Raising Next-Generation Children which focused on yielding incentives for companies to provide paid leave to at least 70 percent of its female employees and have at least one male employee taking paid leave.
+
+[^welfare-paradox]: These results run 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. 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.
+
+@Broadway2020 study the introduction of universal paid maternal leave in Australia,
+analysing the impacts on mothers' return to work as well as the conditions they return under.
+They also find a short-term decrease of mothers returning to work since they make use of the introduced leave period,
+but a long-term (after six to nine months) significant positive impact on return to work.
+Furthermore, there is a positive impact on returning to the same job and under the same conditions,
+the effects of which are stronger for more disadvantaged mothers.[^aus-disadvantaged]
+This suggests that the intervention reduced the opportunity costs for delaying the return to work,
+especially for those women that did not have employer-funded leave options.
+The study cannot account for child-care costs or completely exclude selection bias into motherhood or through exogenous shocks.
+
+[^aus-disadvantaged]: Disadvantages measured as a combination of income, education and access to employer-funded leave.
+
+@Davies2022 focus on the difference in return to work ratios between working under fixed-term and open-ended contracts for high-skill women working in UK public universities.
+There is both a significantly decreased return to work probability for those with fixed-term contracts,
+and most universities provide policies with more limited access to maternity payment for fixed-term contracted staff.
+The results suggest strict payment and repayment policies for early contract termination and the requirement for long-term service to qualify for enhanced maternity benefits may deter utilization under fixed-term contracts. Additionally, significant internal heterogeneity exists regarding maternity policy documents,
+with few offering favourable conditions within fixed-term contracts.
+
+@Adams2015 examine the macro-level relationships between business and credit regulations, labour laws and income inequality in developing countries from 1970 to 2012.
+In MENA, SSA, LAC and to some extent AP, they find stricter labour and business regulations actually negatively related to equitable income distribution,
+with market regulation having no significant impacts.
+They identify lacking institutional capability to accomplish regulatory policies optimized for benefits in developing countries and see the need for policies aimed at more specific targeting of inequality reduction.[^adams-targeting]
+The study also analyses the effects of FDI and school enrolment which are reviewed in their respective sections,
+though the focus remains primarily on regional trends rather than individual factors as causes for inequality.
+
+[^adams-targeting]: The authors furthermore suggest that regulatory policy in developing countries thus needs to be built specifically for their individual contexts and can not be exported in unaltered form from developed countries due to different structural make-up and institutional capabilities.
+
+### Minimum wage laws
+
+Studies focusing on minimum wage effects further delineate themselves into ones that look at the effects on a national level such as @Wong2019, @Alinaghi2020 and @Chao2022,
+and studies which specifically take sub-national spatial effects into account.
+@Wong2019 specifically focuses analysis on the impacts on income and hours worked of low-wage earners,
+finding that, generally, there was a significant positive effect on income and on waged workers' hours worked,
+which can in turn reflect positively on the country's equitable income distribution.
+At the same time, potential negative effects on the income of high earners are identified,
+suggesting an income-compression effect as employers freeze or reduce high-earners wages to offset low-earners raised floors.
+The findings hide internal heterogeneity, however:
+For hours worked there is a significant negative impact on women,
+potentially pointing to a decreased intensive margin for female workers.[^wong-limits]
+For income the effect is largest for agricultural workers,
+while for women the effect is significantly smaller than the overall sample,
+possibly also affected by the decrease in hours worked.
+Thus, while overall income inequality seems well targeted in the intervention,
+it may exacerbate the gender gap that already existed at the same time.
+
+[^wong-limits]: The study can only analyse effects during a period of economic growth for the country, which, combined with some sort-dependency in the panel data, may introduce a form of unobservable exogenous bias into this finding.
+
+@Chao2022, looking at the effects in a sample of 43 countries including LMIC and HIC,
+find strong short-term and long-term differences in outcomes:
+In the short term minimum wage introductions lead to a reduction of the skilled-unskilled wage gap,
+however an increase in unemployment and welfare,
+while in the long term the results are an overall decrease in wage inequality as well as improved social welfare.[^chao-indicator]
+It finds those results primarily stem from LMIC which experience significant effects driven by a long-term firm exit from urban manufacturing sectors,
+thereby increasing available capital for the rural agricultural sector,
+while in HIC the results largely remain insignificant.
+Some limitations of the study include the necessity to omit short-term urban firm exit for the effects to remain significant,
+as well as requiring the prior assumption of decreased inequality through increased rural agricultural capital.
+
+[^chao-indicator]: To identify the overall income inequality within the countries, the study primarily utilizes the Gini coefficient.
+
+@Alinaghi2020 conduct a microsimulation to estimate the effects of a minimum wage increase in New Zealand on overall income inequality and further disaggregate along gender and poverty lines.
+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.
+The authors caution against overestimation of the results' generalizability due to large sample weights possibly biasing results towards sole parent outcomes.
+While the effects on poverty measures overall also remain insignificant for sole parents,
+it does find significant poverty reduction for sole parents which are in employment.
+The authors suggest these findings point to bad programme targeting,
+which at best has negligible positive impact on income equality and at worst may worsen income inequality for lower income households,
+as low-wage earners are often the secondary earners in higher-income households but low-wage households often have no wage earners at all.
+
+Looking at the effect of increases in Romania,
+@Militaru2019 find that minimum wage increases generally correlate with a small wage inequality decrease,
+and also carry a larger positive impact for women.
+They identify a two-fold mechanism which increases the number of waged workers in the total number of employees and mainly concentrates benefits for workers at the minimum income level.[^militaru-limits]
+They also suggest this being the probable channel for larger impacts on female workers since they make up larger parts of low-income and minimum wage households in Romania.
+
+[^militaru-limits]: One limitation of the study may be the over-representation of employees in the sample, as well as not being able to account for tax evasion or other behavioural changes in the model.
+
+
+Turning to studies which take into account spatial effects between different regions,
+@Gilbert2001 similarly find insignificant effects on income inequality in the UK,
+agreeing with the results of @Chao2022.
+However, the effects for rural areas differ depending on their proximity to urban areas.
+While rural areas which are accessible to urban markets are less affected resulting in similarly insignificant impacts,
+more remote rural households experience almost double the reduction in inequality,
+which the authors argue points to effective targeting of the policy.
+For the results to hold, the study has to assume no significant effects on employment after the enactment of the minimum wage.
+
+Analysing both the effects of minimum wage and direct cash transfers in Brazil,
+@SilveiraNeto2011 also focus on the spatial impacts within the country.
+Incomes between regions have converged during the time frame and overall the cash transfers under the 'Bolsa Familia' programme and minimum wage are identified as 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 ostensibly 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 forming explicit part of the policies.[^silveiraneto-limits]
+
+[^silveiraneto-limits]: For the analysis, minimum wage effects had to be constructed from the effects that wages equal to the minimum wage had, and cash transfer impacts could only be estimated for the end-line analysis.
+
+On the other hand also in Brazil, @Sotomayor2021,
+looking at the poverty and inequality outcomes of subsequent minimum wage floor increases,
+finds a poverty reduction by 2.8% and income inequality reduction by 2.4% in the short term (3 months).
+In the long term the results largely agree with @SilveiraNeto2011,
+finding that minimum wage increases show diminishing returns where the legal minimum is already high in relation to median earnings.
+Overall the study finds additional unemployment costs --
+created through new job losses through the introduction --
+are offset by the increased benefits found in higher wages for workers.
+The author suggests an inelastic relationship between increases and poverty incidence,
+with the limitation that the data can only track individual dwellings (instead of household connected to their inhabitants) and thus both resembles repeat cross-sectional data more than panel data,
+and is not able to account for people or households moving to new dwellings.
+
+### Collective bargaining
+
+@Alexiou2023 take a macro-level perspective and investigate the impact of governmental party political orientation and trade unionisation levels on income inequality across countries.
+The findings indicate a negative correlation between strong unionisation and income inequality,
+attributed to enhanced political power redistribution via collective action in national contexts of powerful unions.
+Regions with weak unionisation have higher income inequality post-redistribution,
+also generally indicating a propensity towards uneven redistributive policies.[^alexious-rightwing]
+
+[^alexious-rightwing]: The study observes a positive association between right-leaning governments and income inequality, whereas centrists exhibit varied outcomes, hinting at possible inconsistencies in their redistributive strategies. However, the study can not directly identify the causal factors within these relationships.
+
+@Ahumada2023, taking the opposite approach,
+explore how imbalanced political power distributions affect the availability and strength of collective labour rights.[^ahumada-approach]
+Generally, they concur that contexts characterized by significant power disparities weaken opportunities for collective bargaining,
+primarily due to either more restricted or disregarded labour rights coupled with less deeply rooted trade unionism.
+In contrast, well establishes unionism curtails employers' lobbying efforts and make them susceptible to governments' divide-and-conquer strategies,
+being more separate and less coordinated.
+
+[^ahumada-approach]: The study employs a mix of quantitative global comparisons and qualitative analyses more specifically focused on Argentina and Chile. Thus, the strong institutional context of the two countries provides an analytical background which makes its qualitative analysis more difficult to generalize the quantitative findings.
+
+Focusing on the intersection between collective organisation and gender more specifically,
+@Dieckhoff2015 examine the influence of trade unionisation on gender inequalities within European labour markets.
+The study establishes a positive link between unionisation rates and the likelihood of standard employment contracts for both genders.
+While it finds no direct advantage for men solely through increased unionisation,
+analysis in combination with temporary contracts and family policy reforms sees men experiencing greater benefits than women.
+There is no absolute detrimental effect for either gender as women's employment in standard contracts remains stable, however,
+it may be one factor towards an increase in relative inequality for women which would agree with the findings of @Davies2022.[^dieckhoff-limit]
+
+[^dieckhoff-limit]: The study's causal explanatory power is limited somewhat by its aggregate approach across countries precluding analysis for nation-specific labour market contexts or to disaggregate the gender findings.
+
+@Cardinaleschi2019 investigate turn to collective organisation's effects on the gender wage gap in Italy.
+They identify occupational segregation as the principal cause of wage disparity as opposed to educational inequalities,
+with women predominantly working in more 'feminised' industries.
+
+While collective bargaining practices specifically targeting managerial representation and wages show some reduction in the wage gap,
+the impact is only marginally significant.[^cardinaleschi-msg]
+The authors suggest a stronger mix of policy approaches such as including human capital development through well targeted active labour market policies.
+
+[^cardinaleschi-msg]: The marginal significance primarily stems from internal heterogeneity which only significantly affects the median part of wage distributions while the rest remains insignificant.
+
+@Ferguson2015 specifically examines the relationship between unionisation and the representation of women and minority groups in management positions within U.S. companies.
+It finds that while stronger unionisation is associated with higher representation of both in management and in the overall workforce,
+the effects are only marginally significant.
+Further, the study acknowledges potential confounding factors, such as selection biases,
+should more union-friendly enterprises attract individuals who support diversity.[^ferguson-limit]
+
+[^ferguson-limit]: The study bases its analysis on union elections, and thus can not exclude self-selection effects of people joining more heavily unionised enterprises rather than unionisation increasing representation.
+
+### Workfare programmes
+
+@Whitworth2021 analyse the repercussions of a UK work programme on spatial factors of job deprivation or opportunity increases.
+Despite adopting a quasi-market model rewarding positive employment outcomes,
+the study contends that the policy's non-spatial execution inadvertently exacerbates existing spatial disparities.
+Applying concepts of "social creaming" and "parking" to spatial analysis,
+the study shows that areas already suffering from job deprivation experience further deterioration under the programme.
+Meanwhile, wealthier regions may receive beneficial impacts in an attempt to enhance programme performance metrics,
+leading to the conclusion of bad targeting through neglecting spatial components.
+
+@Li2022 conduct a study on the effects of existing inequalities on the outcomes of a work programme in India intended to provide job opportunity equality for already disadvantages population.[^li-nrega]
+Using land ownership inequality as a proxy for initial inequality levels,
+it finds a significant negative relationship to the provision of jobs through the programme.[^li-indicator]
+Primarily the authors identify resistance from landlords against programme expansion as the underlying mechanism ---
+its expansion often precedes wage hikes in the districts ---
+as they leverage their disproportionate power to influence politics or diminish collective bargaining possibilities.
+
+[^li-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.
+[^li-indicator]: The study uses the Gini coefficient as an indicator for these initial conditions of ownership inequalities and thus concludes the programme being significantly compromised through higher pre-existing capital inequality. The findings also hold true when measuring land inequality as the share of land owned by the top 10 percent of holders.
+
+### Social protection
+
+
+
+@Wang2016 conduct a comparative study on social assistance benefits and wages across 26 OECD countries from 1990 to 2009.
+The analysis finds an overall increase in real minimum income benefits in most countries, mostly excepting Eastern European countries,
+attributing these changes to deliberate policy adjustments rather than inflation.[^wang-channel]
+However, results for income replacement rates vary,
+with some countries experiencing decreases despite rising real benefits.
+The authors suggest this discrepancy is explained by the decoupling of benefit levels from wages and the absence of wage considerations in policy changes, potentially exacerbating income inequalities between income groups.
+
+[^wang-channel]: The study calculates the rise in real benefit levels by comparing the changes in benefit levels to the changes in consumer prices.
+
+
+@Debowicz2014 evaluate the impact of the cash transfer programme Oportunidades on income inequality in Mexico.[^debowicz-oportunidades]
+The study reports an increase of 23 percent in the average income of the poorest households,
+attributed to both the direct cash influx and the beneficial effect for parents keeping their children in school.
+Over the long term, there is a benefit for children whose human capital increases due to the programme.
+There are also indirect benefits for children whose human capital did not increase,
+due to increased scarcity of unskilled labour as a secondary effect,
+thus suggesting a positive economic impact if correctly targeted.
+
+[^debowicz-oportunidades]: The Oportunidades programme conditions its cash benefits on the school attendance of a household's children. While this review focuses on the results for income inequality using the Gini coefficient, other indicators are also reported in the full study.
+
+
+Investigating the impact of childcare subsidies on the female labour force,
+@Hardoy2015 find an overall increase in female labour supply (roughly 5 percent),
+though without significant impact on mothers already participating in the labour market.
+Notably, the impact was greatest for low-education mothers and low-income households since their daycare expenditures constitute a larger budgetary share,
+though it may also be due to lower pre-intervention employment rates for those households.[^hardoy-limits]
+
+[^hardoy-limits]: There was a concurrent increase in childcare capacity which potentially biases the results due to the simultaneous cost reductions and capacity increases. Interestingly, significant effects were only observed among native mothers when disaggregating between migrant and native-born, though reasons for the distinction remain unspecified.
+
+
+@Carstens2018 analyse the factors affecting labour force participation rates of mentally ill individuals in the US,
+correlating it with various Medicaid programmes.
+Key motivators for participation are determined to be reduced depression and anxiety for which positive predictors are increased responsibility, problem-solving and stress management.
+Identified barriers include increased stress, discrimination based on mental state, loss of free time, tests for illegal drugs and loss of government benefits.
+Medicaid ABD (Aged, Blind, Disabled) additionally had a strong negative correlation with participation,
+due to its requirement of demonstrating inability to work creating a negative dependency loop.[^carstens-limits]
+
+[^carstens-limits]: This loop may create a benefit trap with disability determined through abstention from the labour market and benefits disappearing when entering the labour force. The overall identified barriers may, however, be skewed upwards due to an over-representation of racial minorities, women and older individuals in the study's relatively small sample size.
+
+
+
+@Cieplinski2021 conducted a simulation study on a working hours reduction and introduction of UBI in Italy,
+finding that both decreased overall income inequality through different mechanisms.
+UBI sustains aggregate demand, promoting more equitable income distribution,
+while working time reductions significantly decrease aggregate demand through lower individual incomes,
+but in turn increases overall labour force participation and employment.[^cieplinski-notes]
+
+[^cieplinski-notes]: Through these mechanisms environmental outcomes are also oppositional, with work time reduction decreasing the ecological footprint while UBI increases it. However, these results only hold for the assumption that workers will have to accept both lower income and consumption levels (through stable labour market entry) under reduction of working time.
+
+## Structural factors
+
+{{< portrait >}}
+
+::: {#tbl-findings-structural}
+
+```{python}
+# | label: tbl-findings-structural
+from src.model import validity
+from src.model.validity import strength_for # Careful: ruff org imports will remove
+
+findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-structural.csv")
+fd_df = validity.add_to_findings(findings_structural, df_by_intervention)
+
+outp = Markdown(
+ tabulate(
+ fd_df[
+ [
+ "area of policy",
+ "internal_validity",
+ "external_validity",
+ "findings",
+ "channels",
+ ]
+ ].fillna(""),
+ showindex=False,
+ headers=[
+ "area of policy",
+ "internal strength",
+ "external strength",
+ "main findings",
+ "channels",
+ ],
+ tablefmt="grid",
+ )
+)
+del findings_structural, fd_df
+outp # type: ignore[ReportUnusedExpression]
+```
+
+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.
+Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
+evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
+
+Summary of main findings for structural policies
+
+:::
+
+{{< landscape >}}
+
+### Fiscal growth and trade liberalisation
+
+Complementing their research on institutional labour regulation,
+@Adams2015 explore how business regulation, credit regulation and FDI impact long-term income inequality in developing countries.
+They find a positive correlation between FDI and income inequality,
+arguing for it to be unlikely to generate positive welfare effects in developing countries primarily through bad targeting,
+while business regulations show more mixed outcomes.[^adams-targeting-fdi]
+However, as with their results for labour regulation,
+the regional focus of the results makes it hard to disaggregate individual causes for individual national contexts.
+
+[^adams-targeting-fdi]: The primary channel, they argue, is FDI often operating on the wrong targeting incentive structure. It would only be able to generate more equity when correctly targeting the creation of rooted connections from the local economy to surrounding economies instead of moving capital directly to surrounding economies.
+
+@Liyanaarachchi2016 investigate the effects of trade liberalisation on income inequality and absolute poverty in Sri Lanka, using a simulation model.
+They find complete elimination of tariffs resulting in absolute poverty reduction,
+with tariff elimination combined with resulting fiscal policy responses for budget balancing having more mixed effects but still an absolute reduction in poverty.
+Income inequality, however, sees short-term increases in most sectors and in all sectors over the long term.
+The study identifies rising wage differences,
+especially increases for managers, professionals and technicians,
+as well as widening gaps between urban workers,
+while low-income households become more dependent on private or government transfers that do not increase with the liberalisation.
+
+@Xu2021 look at both trade liberalisation and FDI effects in 38 sub-Saharan countries,
+finding a negative correlation between FDI and income inequality,
+with positive relationships instead found with trade liberalisation, corruption level, political stability and rule of law and education.
+The contradicting findings to @Adams2015 and @Liyanaarachchi2016 might arise due to differences in sample, variables used or the different periods under study.[^xu-adams-contradictions]
+They identify income inequality reductions through FDI benefiting the agricultural sector,
+primarily employing low-skilled labour,
+while trade liberalisation instead creates jobs in other countries through higher import than export rates.
+
+[^xu-adams-contradictions]: While both use a generalized method of moments approach utilizing the Gini indicator and an analysis on the regional level, the latter includes a broader range of independent domestic structural variables.
+Additionally, while @Adams2015 study long-term processes from 1970 to 2012, @Xu2021 use panel data from 2000 to 2015 restricted to sub-Saharan countries, which may account for some of the differences.
+
+@Khan2021 paint a more nuanced picture for trade liberalisation effects in a simulation study on free trade agreements (FTAs) in Pakistan,
+with some trade agreements related negatively to income inequality and others positively,
+for large-scale, regional and bilateral agreements.
+Impacts are more dependent on micro-economic factors:
+The authors identify grain and livestock as the primary channels through which trade agreements affect income inequality,
+as these are the predominant sources of income for poor rural households.
+Farm-worker incomes generally increase if trade agreements increase grain or livestock exports,
+leading to more equitable distribution,
+while inequality increases instead if this does not happen.[^khan-notes]
+While most of these changes are obscured short-term by greater mobility with more even distribution across regions and households,
+in the long term the study finds more equitable distribution if agricultural growth can be sustained.
+
+[^khan-notes]: Additionally, the authors identify some wage compression effects between urban and rural households, with richer urban households often decreasing service and processed food production in this case. Generalizability may also be limited due to this strong focus on production factor reallocations within the context of rural Pakistan.
+
+@Rendall2013 focus on the impacts of structural changes in Brazil, Mexico, Thailand and India 1987 to 2008,
+especially on female labour market participation and the gender wage gap.
+The results are quite divergent, with India having largely stable participation inequality,
+mixed results in Mexico and Thailand and Brazil a stronger change towards equity similar to that of the US,
+identifying the move from mostly physical labour to more service-oriented economies as primary driver.[^rendall-brain-brawn]
+Female wage gap fell most rapidly in Brazil, though widening again since 2005,
+widening in Mexico during the 1990s, closing again more recently,
+and produced more mixed results in Thailand and India.
+The authors argue this disconnect between participation and wage shows the impacts of contextually different structural labour market changes:
+a quick rise in less physically-oriented occupations in Brazil,
+the introduction of a feminised manufacturing sector Mexico in the 1990s,
+and more subsistence-oriented labour markets with diverging skill structures in Thailand and India.
+
+[^rendall-brain-brawn]: The study uses a framework termed 'brawn' (physical labour) to 'brain' (less physically demanding labour, such as office work and service economy). The concept sees capital displacing brawn in production for transition economies which they find confirmed in all countries, though to different extents.
+As with the study's results for wage gap fluctuations, there are a variety of mediating factors at play in each context, some of which may be unidentified.
+
+@Wang2020 in turn use a simulation to focus on the spatial income inequality effects of terminating subsidies for the agricultural grain sectors in China.
+They see the removal leading to gradual improvements in the industrial economic infrastructure,
+though a short-term rural-urban income inequality increase is observed.
+Long-term the decreases in real wages for rural workers would alleviate,
+increasing the rural income ratio, though with a gap that would remain incompletely closed.[^wang-channels]
+Thus, the authors identify a trade-off between improvements to the national economic output,
+benefited by the removal,
+and rural-urban income disparity,
+benefiting instead more from the subsidies, especially short-term.
+
+[^wang-channels]: Especially for the short-term increase, but also the remaining inequality gap, the study sees the displacement of rural unskilled labour resulting in increased supply of unskilled labour which is challenging to absorb into manufacturing or service sectors as slowing down the process,
+in addition to low price elasticity of agricultural products contributing to an overall decline of rural incomes.
+Thereby the study strongly binds its findings to the structural economic characteristics of the Chinese labour market and its results only hold for the assumption of static national employment.
+
+Finally, @Go2010 model the effects of a wage subsidy targeted at employers of low- and medium-skilled workers on poverty and income inequality in South Africa.
+They find overall income inequality significantly reduced by 0.5 percentage points through an income redistribution due to an increase in formal employment for low- and medium-skilled workers,
+as the subsidy works as an incentive for new job creations.
+An equally significant 1.6 percent of households move out of poverty using an absolute poverty headcount ratio,
+which equally holds for both urban and rural spaces,
+through the targeting being most beneficial for the poorest households through greater income gains and more households being affected.[^go-limits]
+
+[^go-limits]: This approach also restricts the study somewhat by being unable to account for exogenous shocks or remaining unobservables and prior assumptions binding it more closely to the structure of the South African labour market.
+
+The prevalence of equilibrium modelling simulations may exacerbate several limitations:
+A heavy reliance on initial assumptions for results to hold makes these studies susceptible to overlooking exogenous factors or shocks to the system and struggle to accurately represent long-term dynamics.
+Additionally, they often fail to account for the practical challenges to policy implementations through institutional frameworks or political contexts,
+Recognizing such shortcomings is crucial when assessing the reliability and applicability of their findings,
+as will be discussed in considering study robustness.
+
+
+
+### Infrastructure and technological change
+
+@Kuriyama2021 analyse the effects of Japan's energy sector decarbonisation efforts on spatial inequalities.
+While employment in general is positively affected, especially rural sectors benefit from increased employment probability.
+They identify the renewable energy sector's strong entrenchment in rural areas for large-scale projects such as wind, geothermal or large-scale solar power generation.
+At the same time, for the context of Japan some new inequalities may be generated between different regions due to new barriers such as limited energy transmission line capacities or potentially mismatched locations of demand and supply.[^Kuriyama-limits]
+
+[^Kuriyama-limits]: The strong connection to Japanese context also provides one limitation to the study's generalizability, along with carrying strong assumptions about initial and future employment numbers and power generation amounts.
+
+@Stock2021 explores both inclusive and exclusionary effects of infrastructure development,
+including training and temporary employment for local semi-/unskilled labour under the 'gender inclusive' development of a solar park in India,
+in an observational study on achieving micro-level equality through regional uplifting.
+They primarily conclude an increase in inequality through socio-economic exclusion and especially exacerbated impacts for women of lower castes.
+Redistributive potential was stymied through capture by (female) village elites,
+while only an insignificant amount of women from local villages found employment at the park,
+predominantly from the dominant caste.[^stock-notes]
+Though not able to make causal inferences because of the study design,
+the author suggests this may be an example of institutional design neglecting structural power relations and individual agency, especially intersectional between gender and caste.
+
+[^stock-notes]: While acquiring basic additional skills, none of the women participating in training remained connected to the operators of the solar park and none were hired, which disconnects the targeting of training from that of the eventual labour force participants.
+
+
+Focusing on transport infrastructure investments, @Adam2018 model the effects on household income inequalities in rural Tanzania.
+The effectiveness hinges significantly on the financing scheme employed:
+Rural households are generally worse off when developments are deficit-financed or paid through tariff revenues,
+while benefiting from financing through consumption taxes or by external aid.
+The study can not find a Pareto optimum for any of the investment measures for rural and urban locations simultaneously,
+with some worse off in each scheme as much of the increased equity stems from rural workers' movement out of quasi-subsistence agriculture to other locations or other sectors.[^adam-limits]
+
+[^adam-limits]: While the study does create causal inferences its modelling approach limits it to representing the limited subset of these financing schemes,
+as well as to the assumption of no additional population growth for the results to hold.
+
+@Blumenberg2014 examines the spatially motivated 'Moving to Opportunity' programme's impacts on employment for disadvantaged US households,
+comparing it to those of owning an automobile.
+No increase in employment probability was found for participants,
+while vehicle ownership correlated with improved employment outcomes for low-income households and in 'transit-rich' areas.[^blumenberg2014]
+While access to improved transit is related to employment probability, the move to a transit-rich area alone does not increase it significantly,
+reflecting potentially a certain required threshold before it facilitates employment,
+or individuals' strategic relocation to make better use of public transport.
+Ultimately, the findings signal the need for improved individual access to automobiles in disadvantaged households or extensive transit network upgrade which have to cross an efficiency threshold.
+
+[^blumenberg2014]: The programme provided vouchers to randomized households for relocation to a geographically unrestricted area,
+or specifically to a low-poverty area (treatment group),
+some of which were also located in 'transit-rich' areas well-connected to public transport systems.
+The study's strong oversampling of female participants (98%) may obscure intersectionality between spatial and gender disadvantages,
+as well as retaining some possibility for endogeneity bias through unobserved factors such as individual motivation or ability.
+
+
+Looking at the effects of technological change such as the introduction of legal access to contraceptive measures for women in the US instead,
+@Bailey2012 evaluate the effects on the gendered hourly working wage distribution.
+Of the overall closing of the pay gap between 1980 and 2000, this access from an early age contributed nearly 10 percent in the 1980s and over 30 percent in the 1990s,
+thus contributing an estimated third of total female wage during throughout.
+Primarily, the study identifies greater educational attainment, occupational upgrading, increased individual agency over human capital and career investments and increased labour market experience due to fewer early exits as the underlying mechanisms.[^Bailey-limits]
+
+[^Bailey-limits]: With the structure of the data, the study cannot capture access to contraception beyond age 20, restricting the window of analysis especially on women under 21, as well as not being able to control for exogenous social multiplier effects such as changed employer hiring or promotion patterns, changed marriage and childbearing expectations or overarching paradigmatic norms concerning women's work.
+
+### Education access
+
+@Adams2015 also analyse the effects of school enrolment in developing countries between 1970 and 2012,
+finding it positively related with equitable income distribution and thus argue for the effectiveness of well-targeted education policies.
+They especially identify additional enrolment as increasing the capacity of public administration practitioners,
+in turn leading to the creating of effective policies which are more adapted to the developing countries' institutional contexts.
+Thus, education-oriented policies here are seen as two-fold improvements:
+as short- and medium-term increase of human capital, but also as long-term capacity-building measures.
+
+
+
+@Mukhopadhaya2003 turn to Singapore's educational schemes awarding scholarships and monetary benefits for higher educational achievements,
+finding that, due to non-optimal targeting, they may in fact exacerbate existing inequalities for migrants in the country.[^singapore-migrants]
+High-income households (predominantly non-migration households) experience over-representation in high-achievement education brackets,
+and thus create a policy of bad targeting when those in turn receive income inequality exacerbating monetary benefits.
+They thus argue that the Singaporean policies, aimed at providing equal educational opportunity for all,
+may in fact further disadvantage lower-income households with low-education parental backgrounds, thereby increasing inequality.
+
+[^singapore-migrants]: Inequality for migrants in Singapore is relatively high, due to existing income inequalities between the predominant occupations, as well as national migration policies which further stimulate occupational segregation. The study specifically examines two programmes, the 'Edusave Entrance Scholarship for Independent Schools' (EESIS), awarding the top 25% students full secondary education scholarships, and the 'Yearly Awards' scheme awarding cash benefits to the top 5% students that are not EESIS participants each year. The 'Yearly Awards', through prior EESIS exclusion has less of a vicious circle targeting effect than EESIS itself.
+
+
+@Delesalle2021 examines the effects of a universal primary education policy on labour market outcomes in rural Tanzania,
+finding generally positive impacts which also differ along both spatial and gender lines:
+the greatest positive effects are seen for non-agricultural, self-employed or wage work,
+while men tend to move from agricultural to non-farm wage work and rural women experience an increased probability to work in agriculture and to work formally.[^delesalle-indicators]
+
+
+[^delesalle-indicators]: The study uses consumption of households as its only indicator for the policy returns, which, along with its inability to directly identify those who comply with the intervention due to having to construct returns for household heads only, should be seen as its primary limitations. Additionally, there may be a 'villagization' effect by bringing people together in the community villages for the intervention which may bias results.
+
+
+@Pi2016 conduct a study on the impacts of allowing increased access to social welfare provisions and education to urban migrants in China,
+looking at the effects on wage inequality between skilled and unskilled sectors and workers.[^pi-skilled-unskilled]
+Reforms to increase social security access and education for urban migrants decreases sectoral wage inequality only if the skilled sector is more capital intensive than the unskilled sector.
+There are several limitations to the study such as no disaggregation between the private and the (important for the Chinese economy) public sector,
+job searching not being part of the model, and, most importantly,
+a severely restricted generalizability due to the reform characteristics being strongly bound to the institutional contexts of Chinese *hukou*[^hukou] systems.
+
+[^pi-skilled-unskilled]: It uses skilled-unskilled inequality instead of rural-urban inequalities since the real wages of the rural sector are already much lower in China,
+making comparisons along the 9th to 10th decile ratios more difficult.
+
+[^hukou]: The hukou system generally denotes a permission towards either rural land-ownership and agricultural subsidies for the rural hukou or social welfare benefits and employment possibilities for the urban hukou, and children of migrants often have to go back to their place of registered residence for their college entrance examination. This study looks at reforms undoing some of the restrictions under the system.
+
+@Suh2017 examines the effects of education-focused policies specifically on married women's employment in South Korea,
+finding that they significantly increase their employment probability, as well reiterating an overall negative correlation between female labour force participation with income inequality.
+At the same time, policies focused on education alone, while a necessary condition, are not yet sufficient conditions,
+with both married women's family size and structure acting as mediating variables.
+Finally, the study identifies intergenerational impacts as important dimension, with the policies also positively correlating to daughters' education levels.
+
+
+
+
+@Coutinho2006 focus on the impacts of special education for young men and women with disabilities in the US,
+finding that the intervention benefits men significantly more than women and those differences being greatest in more disadvantaged groups.
+Young women with disabilities were significantly less likely to be employed,
+earned less than male groups,
+had lower likelihood of obtaining high school diplomas and were more likely to be biological parents.[^coutinho-notes]
+The study highlights the need of integrating efforts to strengthen personal agency to remain in education longer and delay biological parenthood,
+with transition services focused on self-advocacy and self-determination for young women to supplement structural education preconditions.
+
+[^coutinho-notes]: While these differences were marginal for high-achiever education, they were became significant for lower-achieving and special needs subgroups. Additionally, more women were employed in clerical positions and significantly more men in technical or skilled positions, both for the special education and control groups. The study could not include students with severe impairments due to using self-reporting for sampling, which may also have introduced some selection bias into the results.
+
+On the other hand, @Shepherd-Banigan2021 qualitatively evaluate the effects of vocational and educational training being provided to disabled veterans in the US
+and find the reverse situation of the interventions helping to strengthen individual agency, autonomy and motivation,
+but skill development efforts being impeded if there is the possibility of disability payment loss when the conditions for potential job acquisition are created.[^shepherd-notes]
+The study thus sheds light on the intersection between structural efforts to provide a facilitating environment,
+such as investigated by @Coutinho2006 and @Suh2017,
+but also specifically highlights the necessity of a well-targeted structural environment,
+concurring with the findings by @Carstens2018.
+
+[^shepherd-notes]: The primary identified barriers to return to work efforts are an individual's health problems as well as various programmes not accommodating the needs of disabled veteran students,
+while the primary facilitators are financial assistance provided for education as well as strengthened individual agency through motivation. Participants being restricted to veterans with a caregiver may oversample more substantial impairments.
+
+With a similar focus, @Poppen2017 look at the specific factors influencing employment probability for disabled people in the US,
+finding that the primary facilitators of successful vocational rehabilitation programmes are the participation in a youth-transition programme and having made use of additional optional rehabilitation services,
+while barriers were larger for people receiving social security benefits, as well as female participants.[^poppen-notes]
+Thus, the study reiterates the need for multi-dimensional programmes and especially highlights the gender dimension within educational efforts.
+
+[^poppen-notes]: Additionally, having a mental illness or traumatic brain injury as well as having multiple disabilities or an interpersonal or self-care impediment were significantly negatively correlated with employment probability. The study has limits to its generalizability, sampling data from a single US state.
+
+@Gates2000 also conducted a qualitative study on the specific mechanisms of workplace accommodation increasing successful return to work programmes,
+especially the disaggregation into social and technical components, as well as including a disclosure and psycho-educational plan.
+The findings highlight the importance of considering the social component of return to work efforts,
+with programme failure often being correlated to sole reliance on the functional aspect.[^gates-notes]
+
+[^gates-notes]: The primary barrier identified is relationship issues, not functional ones, with supervisors playing a key role for the success of accommodation programmes, while agency-strengthening measures such as a concrete training plan involving the worker but also other key workplace players become essential drivers, and a major channel becomes 'who' is involved, not just 'what' is involved. The generalizability of the findings may be limited due to its small non-randomized sample.
+
+@Rosen2014, in an experimental study on benefits and vocational training counselling for disabled veterans in the US,
+find a significantly positive correlation between participation and return to work through the average hours worked,
+though the study is not able to clearly identify the exact mediating variables.[^rosen-notes]
+@Thoresen2021 agree with these findings in a mixed-methods study to investigate the effects of vocational training programmes on income inequality and hours worked for participants in Australia,
+finding that the intervention is significantly correlated with reducing inequality in both dimensions, though not fully closing the gap.
+Especially the income distribution is significantly positively affected, more so for the incomes of female participants and participants which received a disability pension.[^thoresen-limits]
+
+[^rosen-notes]: The change in hours worked is measured through a follow-back calendar which compares previous time worked to that in the 28 days preceding the study's final measurement. And while the intervention clearly targets both environmental factors and personal agency, neither beliefs about work, beliefs about benefits, nor services for mental health or substance abuse significantly impacted the outcomes.
+
+[^thoresen-limits]: Generalizability of the study may be impacted by its small control sample and non-representative sample on a national level, and should thus be taken care of to not overestimate the its explanatory power.
+
+
+The studies thus not only reinforce recommendations for strength-based approaches, emphasising the benefits of work,
+but also highlight the targeting importance of subsidy programmes in general on the one hand,
+in the worst case reducing equity through bad targeting mechanisms,
+and their negative reinforcement effects widening existing inequalities of gender, age and racial discrimination through such targeting on the other.
+
+## Agency factors
+
+{{< portrait >}}
+
+::: {#tbl-findings-agency}
+
+```{python}
+# | label: tbl-findings-agency
+from src.model import validity
+from src.model.validity import strength_for # Careful: ruff org imports will remove
+
+findings_agency = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-agency.csv")
+fd_df = validity.add_to_findings(findings_agency, df_by_intervention)
+
+outp = Markdown(
+ tabulate(
+ fd_df[
+ [
+ "area of policy",
+ "internal_validity",
+ "external_validity",
+ "findings",
+ "channels",
+ ]
+ ].fillna(""),
+ showindex=False,
+ headers=[
+ "area of policy",
+ "internal strength",
+ "external strength",
+ "main findings",
+ "channels",
+ ],
+ tablefmt="grid",
+ )
+)
+del findings_agency, fd_df
+outp # type: ignore[ReportUnusedExpression]
+```
+
+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.
+Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
+evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
+
+Summary of main findings for agency-based policies
+
+:::
+
+{{< landscape >}}
+
+### Occupational segregation and social exclusion
+
+@Emigh2018 evaluate the effects of direct state transfers to people in poverty in the post-socialist market transition economies of Hungary, Romania and Bulgaria,
+finding that overall direct transfer levels on their own are, while beneficial against absolute poverty in the short-term,
+often too small to eliminate long-term adverse effects of market transitions.
+The study identified short-term poverty-eliminating effects, consistent with an institutionalist perspective,
+though, also consistent with what the study terms the 'underclass' perspective,
+these benefits providing having no overarching impacts on the deprivations members of disadvantaged groups face.[^emigh-notes]
+While it is missing matching long-term panel data for a solid conclusion the results also suggest little evidence for the neoclassical proposition of welfare dependency being generated.
+
+[^emigh-notes]: This is especially noteworthy in this context since the study also finds a strong correlation between poverty and socio-demographic characteristics such as low education, larger households and Roma households.
+
+@Bartha2020 focus on gendered employment inequalities in an observational study on the long-term care policy trajectories of European countries,
+finding that few still fit one of the ideal-type households of male bread-winner, unsupported double-earner, or supported double-earner households.
+No countries fall into the male bread-winner category,
+and only half the countries into either supported double-earner, mostly prevalent in Western Europe and Scandinavia,
+or unsupported double-earner, more prevalent in Southern and Eastern Europe.
+Especially in the latter model women take on more unpaid care work,
+though the prevalence is visible in all models which, the authors' suggest,
+plays an important role in explaining the persistent employment rate gap.[^bartha-notes]
+
+[^bartha-notes]: With the policies pursued such as pensions, provision of residential/at-home care facilities, regulation and social protection, where female unpaid care work is reducing, the 'familialization' of care work is taken on as cash-for-care work by migrants. While possibly increasing female labour force participation, the study does not find the work sustainable or providing decent work, often remaining poorly regulated, low-paid and reinforcing gender dynamics in migrant communities.
+
+@Shin2006 specifically focus on the effects of wage-setting or fertility differences for teachers,
+finding that providing relatively higher wages compared to non-teaching processions significantly increases female labour force participation,
+though the strongest predictor remains the possession of an education-focused higher education and higher education in general.
+The presence of a new-born baby also significantly decreases labour force participation, having almost double the effect in teaching professions,
+identifying the low wages minimizing the exit costs as those leaving the labour market experience relatively lower temporary wage losses.[^shin-notes]
+
+[^shin-notes]: The presence of a new-born baby is not found to have an effect on job choices between teaching and non-teaching, however. The study can not make statements on male teachers due to the data being restricted on female teachers' panel data.
+
+In a mixed-methods study, @Standing2015 look at the effects of providing UBI for villages in India,
+finding that they generally agree that the intervention significantly reduces household debts,
+but that beyond the monetary benefits it carries 'emancipatory value' increasing economic security and empowerment.
+In a qualitative evaluation the study identifies reduced dependency risks,
+allowing long-term saving, avoidance of new debts, or, if debts have to be incurred, less exploitative forms of borrowing,
+and increasing collective forms of risk taking through reducing the local scarcity of money by infusing it into the community.[^standing-notes]
+
+[^standing-notes]: The UBI reduces dependency risk primarily to lenders with high associated fees (through allowing repayment of existing debts) and not having to work for the lender directly (or provide part of wages). The decreased scarcity also allows a shift to institutionalized saving and thus increased shock event resilience.
+
+
+@Clark2019, in an experimental study providing vouchers for childcare to poor women in urban Kenya,
+find similar positive effects on economic empowerment for married mothers through increased employment probability and hours worked.
+For single mothers, while the study sees a negative effect on hours worked, the incomes remain stable,
+suggesting an pre-intervention increased workload compared to married mothers,
+with the intervention providing the ability to shift to jobs with more regular hours instead.[^clark-notes]
+
+[^clark-notes]: The shift to more regular hours is made possible through childcare provisions where before they may have been incompatible with personal childcare, though the study is restricted to effects within a period of one year, limiting its long-term conclusions.
+
+@Hojman2019 see similar results in urban Nicaragua,
+where the provision of free childcare significantly increased employment probability of the mothers,
+reflecting the increased parental agency through reducing care work requirements.
+While these results hold regardless of childcare quality,
+for high-quality care there are also significant positive effects on the human capital of children.[^hojman-note]
+
+[^hojman-note]: These results reflect the same effects on children's human capital as providing external child-care stimuli in @Dustmann2012.
+
+### Unconscious bias and discriminatory norms
+
+
+
+@Al-Mamun2014 examine the impacts of micro-finance programme in urban Malaysia on women's economic empowerment,
+finding that the ability to receive collateral-free credit increased female household decision-making and personal economic security.[^almamun-notes]
+The study identifies the increased access to finance but also the establishment of an increased collective agency for the women in organised meetings and trainings
+though the restriction to group loans can negatively affect outcomes through existing obstacles to collective organisation such as racial and socio-demographic barriers.
+
+[^almamun-notes]: The intervention's collateral-free disbursements are restricted to low-income urban individuals, though not specifically gendered.
+
+@Field2019 evaluate a more explicitly gendered experimental intervention granting women increased access to their own financial accounts and training,
+finding that short-term the combination increased female labour force participation and long-term increased the acceptance of women working in affected households and significantly increased female hours worked.[^field-notes]
+The intervention took place on the background of the Indian MGNREGS programme which, though ostensibly mandating gender wage parity,
+often risks discouraging female workers or restricting their agency since earned wages are deposited into a single household account ---
+predominantly owned by the male head of household.[^field-mgnregs]
+The study argues for a newly increased bargaining power through having greater control over one's income ultimately reflecting onto local gender norms themselves.
+
+[^field-mgnregs]: The Mahatma Gandhi National Rural Employment Guarantee Scheme, one of the largest redistribution programmes on the household level in the world, entitling each household to up to 100 days of work per year.
+
+[^field-notes]: The impacts on increased hours worked were concentrated on households where previously women worked relatively lower amounts and with stronger norms against female work, while less constrained households' impacts dissipated over time.
+
+# 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 increased throughout this period.
+
+```{python}
+#| label: fig-publications-per-year
+#| fig-cap: Publications per year
+from typing import cast
+
+df_study_years = (
+ df.groupby(["author", "year", "title"])
+ .first()
+ .reset_index()
+ .drop_duplicates()
+ ["year"].value_counts()
+ .sort_index()
+)
+df_study_years = cast(pd.DataFrame, df_study_years) # fix potential type errors
+
+# 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
+
+```{python}
+#| label: discussion-prep-inequality-df
+#| echo: false
+# dataframe containing each intervention inequality pair
+df_inequality = (
+ df[["region", "intervention", "inequality"]]
+ .assign(
+ Intervention = lambda _df: (_df["intervention"]
+ .str.replace(r"\(.+\)", "", regex=True)
+ .str.replace(r" ?; ?", ";", regex=True)
+ .str.strip()
+ .str.split(";")
+ ),
+ inequality = lambda _df: (_df["inequality"]
+ .str.replace(r"\(.+\)", "", regex=True)
+ .str.replace(r" ?; ?", ";", regex=True)
+ .str.strip()
+ .str.split(";")
+ )
+ )
+ .explode("Intervention")
+ .explode("inequality")
+ .reset_index(drop=True)
+)
+```
+
+Turning to the available studies from a perspective of inequalities,
+@tbl-inequality-crosstab breaks down the individually targeted inequalities per intervention type.
+
+```{python}
+#| label: tbl-inequality-crosstab
+#| tbl-cap: Intervention types by the inequalities targeted
+
+df_temp = df_inequality.loc[
+ (df_inequality["inequality"] == "income")
+ | (df_inequality["inequality"] == "gender")
+ | (df_inequality["inequality"] == "spatial")
+ | (df_inequality["inequality"] == "disability")
+ | (df_inequality["inequality"] == "ethnicity")
+ ]
+df_temp = df_temp.rename(columns={"inequality": "Inequality"})
+tab = pd.crosstab(df_temp["Intervention"], df_temp["Inequality"],
+ margins=True).reindex(["income", "gender", "spatial", "ethnicity", "disability"], axis="columns").sort_values("income", ascending=False)
+del df_temp
+tab
+```
+
+Most studies focus on some indicator of income inequality within national or regional contexts.
+The second most analysed inequality is that of gender,
+followed by spatial inequalities, disabilities, ethnicities, age, inequalities of migration, education and intergenerational issues.
+
+Only a small amount of studies carry analysis of inequalities in the world of work surrounding migration,
+generational connections, age and education into the world of work.
+Age-related inequalities prominently factor into studies as an intersection with disability,
+in focusing on the effects of older people with disabilities on the labour market [@Kirsh2016].
+Studies that solely or mainly target age-related inequalities themselves often do so with a stronger focus on the effects on seniors' health outcomes and long-term activation measures,
+with some extending into the effects of differentiated pension systems.[^pension-studies]
+Equally, for migration few studies can strictly delineate it from racial inequalities or considerations of ethnicity.
+
+[^pension-studies]: Studies focusing on the effects of pensions themselves often do not intersect back into outcomes within the world of work. For an overview on pensions and health effects, see @VanDerHeide2013; for pensions and other intersectional inequalities, see for example @Zantinge2014.
+
+Thus, for the current state of the literature on analyses of policy interventions through the lens of inequality reduction within the world of work,
+there are remaining gaps of academic lenses for generational inequalities, age-related inequalities, educational inequalities and inequalities of migration processes when looking at the quantity of output.
+Care should be taken not to overestimate the decisiveness of merely quantified outputs ---
+multiple studies with strong risk of bias may produce less reliable outcomes than fewer studies with stronger evidence bases ---
+however, it does provide an overview of the size of evidence base in the first place.
+
+
+
+Looking into the prevalence of individual interventions from the gender inequality dimension,
+the crosstab shows that interventions on paid leave, subsidies, collective bargaining, and education received the most attention.
+Thus a slight preference towards institutional and structural interventions is visible,
+though the dimension seems to be viewed from angles of strengthening individual agency just as well,
+with subsidies often seeking to nourish this approach,
+and training, and interventions towards financial agency also being represented in the interventions.
+
+
+Interventions affecting spatial inequalities are often also primarily viewed through indicators of income.
+Interventions aiming at reducing spatial inequalities primarily base themselves on infrastructural changes,
+which aligns with expectations of the infrastructural schism between urban and rural regions.
+Additionally, education interventions target spatial inequalities,
+with the effects of minimum wage, work programmes, interventions strengthening financial agency, trade liberalization and training playing a reduced role.
+
+This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects which should be taken into account to avoid worsening issues:
+such as was seen in the further exclusion of already disadvantaged women from employment, infrastructure and training opportunities in India under bad targeting and elite capture [@Stock2021],
+or further deprivation of already disadvantaged regions in the UK work programme [@Whitworth2021].
+Rural communities relying on agricultural economies in particular may be vulnerable to exogenous structural shock events such as climate change, which may thus need to be a focal point for future structural interventions [@Salvati2014].
+
+The results agree with the systematic review of income, employment and poverty correlations by [@Perez2022],
+in that employment plays a significant in spatial disadvantages,
+however with different primary barriers for different spatial contexts.[^perez-interventions]
+On the other hand, as the results by @Hunt2004 have also shown,
+individual measures on their own such as commuting subsidies in this case, while having positive results,
+may not provide enough lasting impact over the long term and may need embedding in a more holistic approach,
+combining multiple policy packages.
+
+[^perez-interventions]: The identified interventions largely overlap with the identified pertinent interventions in this review: credit programmes, institutional support for childcare, guaranteed minimum income/universal basic income or the provision of living wages, commuting subsidies, and housing mobility programs. However, due to their focus on urban contexts, the identified barriers differ.
+
+
+Few studies approach disability inequalities primarily through the prism of income inequality,
+preferring return to work, employment rates or amount of hours worked as indicators.
+Only when looking at the intersection of disability and gender is income the more utilized indicator,
+through measuring female income ratios compared to those of males.
+
+Here, a split between frameworks that favour agency-based approaches,
+putting organisational barriers and environmental activation, as well as individual (re-)integration within the world of work into focus,
+and frameworks which focus on the institutional-structural component,
+with a focus on educational inclusion, and selection and eligibility criteria for benefit or vocational programmes.[^disability-approaches]
+In addition to employment or return to work based indicators it might thus be pertinent to include a focus on decent work and meaningful achievement as additional indications of successful programmes.
+Taken together, these results especially reinforce the results of @Poppen2017 and @Thoresen2021,
+for the importance of correct targeting to avoid unintended negative outcomes,
+while the evidence base also highlights research gaps in contexts and regions other than developed high-income countries.
+
+[^disability-approaches]: For exemplary frameworks in the agency perspective, see @Martin2020 and @Lindsay2015; for the latter see @Lindsay2015a and @Gruber2014.
+
+
+Studies on migration- or ethnicity-based inequalities predominantly focus on structural interventions such as education, fiscal policies or infrastructure,
+or the effects of institutional contexts such as collective bargaining, unionisation or universal incomes.
+The primary indicators are mixed between indicators of income inequality and others such as employment probability,
+though with a focus on absolute measures such as poverty, hours worked or debt.
+While some frameworks do put agency-driven necessities to the foreground,
+there is a consensus for structural approaches required to enable this agency.[^migration-frameworks]
+
+[^migration-frameworks]: For an agency-focused approach, see @Siebers2015; for an example of structural requirements, see @Goodburn2020 or @Samaluk2014 for a discussion of structural power dynamics; for an institutional focus, see @Clibborn2022.
+
+# Conclusions
+
+The preceding study undertook a systematic scoping review of the literature on inequalities in the world of work.
+It focused on the variety of approaches to policy interventions, from institutional to structural to more agency-driven programmes,
+and highlighted the inequalities targeted, analysed in subsequent study, their methods and limitations,
+to arrive at a picture of which lays out the strengths and weaknesses of current approaches.
+
+Wide gaps exist between the research on existing topics within the areas and intersections of inequalities in the world of work.
+First, while regionally research on such inequalities seems relatively evenly distributed,
+focus prevalence on individual inequalities varies widely.
+
+Research into interventions preventing income inequality are still the dominant form of measured outcomes,
+which makes sense for its prevailing usefulness through a variety of indicators and its use to investigate both vertical and horizontal inequalities.
+However, care should be taken not to over-emphasize the reliance on income inequality outcomes:
+they can obscure intersections with other inequalities,
+or diminish the perceived importance of tackling other inequalities themselves, if not directly measurable through income.
+Thus, while interventions attempt to approach the inequality from a variety of institutional, structural and agency-oriented approaches already,
+this could be further enhanced by putting a continuous focus on the closely intertwined intersectional nature of the issue.
+
+Gender inequality is an almost equally considered dimension in the interventions,
+a reasonable conclusion due to the inequality's global ubiquity and persistence.
+Most gender-oriented policy approaches tackle it directly alongside income inequality outcomes,
+especially viewed through gender pay gaps and economic (dis-)empowerment,
+approaching it from backgrounds of structural or agency-driven interventions.
+While both approaches seem fruitful in different contexts, few interventions strive to provide a holistic approach which combines the individual-level with macro-impacts,
+tackling both institutional-structural issues while driving concerns of agency simultaneously.
+
+Spatial inequalities are primarily viewed through rural-urban divides,
+concerning welfare, opportunities and employment probabilities.
+Spatially focused interventions primarily tackle infrastructural issues which should be an effective avenue since most positive interventions are focused on the structural dimension of the inequality.
+However, too many interventions, especially focused on reducing income inequalities,
+still do not take spatial components fully into view,
+potentially leading to worse outcomes for inequalities along the spatial dimension.
+
+Disabilities are rarely viewed through lenses other than employment opportunities.
+While most interventions already focus on dimensions of strengthening agency and improved integration or reintegration of individuals with disabilities into the world of work,
+a wider net needs to be cast with future research focusing on developing regions and the effects of more institutional-structural approaches before clearer recommendations can be given based on existing evidence.
+
+Ethnicity and migration provide dimensions of inequalities which are, while more evenly distributed regionally,
+still equally underdeveloped in research on evidence-based intervention impacts.
+Currently, there is a strong focus on institutional-structural approaches,
+which seems to follow the literature in what is required for effective interventions.
+However, similarly to research on inequalities based on disability, there are clear gaps in research
+on ethnicity and especially migration, before clearer pictures of what works can develop.
+
+The intertwined nature of inequalities, once recognized, requires intervention approaches which heed multi-dimensional issues and can flexibly intervene and subsequently correctly measure their relative effectiveness.
+To do so, perspectives need to shift and align towards a new, more intersectional approach which can incorporate both a wider array of methodological approaches between purely quantitative and qualitative research,
+while relying on indicators for measurement which are flexible yet overlapping enough to encompass such a broadened perspective.
+
+# Bibliography
+
+::: {#refs}
+
+:::
+
+# Appendices {.appendix .unnumbered}
+
+## Appendix A - Term clusters {#sec-search-protocol .unnumbered}
+
+The search protocol followed a three-staged process of execution: identification, screening and extraction.
+First, in identification, the relevant policy, inequality and world of work related dimensions were 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.
+While the resulting study pools could be screened for in multiple languages, the search queries themselves were passed to the databases in English-language only.
+Relevant results were then complemented through the adoption of a 'snowballing' technique,
+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.
+
+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]
+
+[^existingreviews]: TODO: citation of existing reviews used; ILO definitions if mentioned
+
+Identified terms comprising the world of work can be found in the Appendix tables @appatbl-wow-terms, @appatbl-intervention-terms, and @appatbl-inequality-terms,
+with the search query requiring a term from the general column and one other column of each table respectively.
+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,
+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).
+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.
+
+Second, in screening, duplicate results were removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
+language, title, abstract, full text and literature superseded through newer publications.
+Properties in these characteristics were used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
+
+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,
+such as 'excluded::language', 'excluded::title', 'excluded::abstract', and 'excluded::superseded'.
+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.
+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'.
+These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings [@ILO2022].
+
+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').
+The complete process of identification and screening is undertaken with the help of the Zotero reference manager.
+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.
+
+::: {#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}
+""")
+```
diff --git a/meeting_eoy.qmd b/manuscript/meeting_eoy.qmd
similarity index 93%
rename from meeting_eoy.qmd
rename to manuscript/meeting_eoy.qmd
index fb6a057..1cb0b1b 100644
--- a/meeting_eoy.qmd
+++ b/manuscript/meeting_eoy.qmd
@@ -1,5 +1,5 @@
---
-bibliography: 02-data/intermediate/zotero-library.bib
+bibliography: ../data/references/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@@ -30,26 +30,12 @@ import bibtexparser
sns.set_style("whitegrid")
-DATA_DIR=Path("./02-data")
-RAW_DATA=DATA_DIR.joinpath("raw")
-WORKING_DATA=DATA_DIR.joinpath("intermediate")
-PROCESSED_DATA=DATA_DIR.joinpath("processed")
-SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
+from src import globals as g
-bib_string=""
-for partial_bib in 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 WORKING_DATA.glob("**/*.bib"):
- with open(partial_bib) as f:
- bib_string+="\n".join(f.readlines())
-bib_sample = bibtexparser.parse_string(bib_string)
+from src import bib_sample
# load relevant studies
-from src import load_data
+from src.extract import load_data
# load zotero-based metadata: citations and uses
zot_df = pd.DataFrame([
@@ -63,10 +49,10 @@ zot_df = pd.DataFrame([
], 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"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
+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"{PROCESSED_DATA}/relevant")
+bib_df = (load_data.from_yml(f"{g.EXTRACTED_DATA}")
.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"]),
@@ -102,7 +88,7 @@ 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 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
+# 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}}
diff --git a/notes.qmd b/manuscript/notes.qmd
similarity index 99%
rename from notes.qmd
rename to manuscript/notes.qmd
index 987702a..8a43173 100644
--- a/notes.qmd
+++ b/manuscript/notes.qmd
@@ -1,5 +1,5 @@
---
-bibliography: 02-data/intermediate/zotero-library.bib
+bibliography: ../data/references/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@@ -21,12 +21,7 @@ subtitle: Conceptual Definitions and Key Terms
```{python}
#| echo: false
-from pathlib import Path
-DATA_DIR=Path("./02-data")
-RAW_DATA=DATA_DIR.joinpath("raw")
-WORKING_DATA=DATA_DIR.joinpath("intermediate")
-PROCESSED_DATA=DATA_DIR.joinpath("processed")
-SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
+from src import globals as g
## standard imports
from IPython.core.display import Markdown as md
@@ -396,7 +391,7 @@ Policy *areas*, identified by @ILO2022b:
#| label: tbl-inclusion-criteria
#| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}
-inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
+inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
@@ -861,7 +856,7 @@ from @Pinto2021:
```{python}
#| echo: false
#| output: asis
-with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
+with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
query = f.read()
t3 = "`" * 3
diff --git a/presentation_summary.md b/manuscript/presentation_summary.md
similarity index 100%
rename from presentation_summary.md
rename to manuscript/presentation_summary.md
diff --git a/scoping_review.qmd b/manuscript/scoping_review.qmd
similarity index 94%
rename from scoping_review.qmd
rename to manuscript/scoping_review.qmd
index 28212d4..b7685d1 100644
--- a/scoping_review.qmd
+++ b/manuscript/scoping_review.qmd
@@ -22,45 +22,25 @@ crossref:
---
```{python}
-#| label: load-data
+#| label: standard-imports
#| echo: false
-from pathlib import Path
+#| output: false
+import src.globals as g
import re
-## standard imports
-from IPython.core.display import Markdown as md
+from IPython.display import display, Markdown, HTML
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
-import seaborn as sns
from tabulate import tabulate
-import bibtexparser
-
+import seaborn as sns
sns.set_style("whitegrid")
+```
-DATA_DIR=Path("./02-data")
-RAW_DATA=DATA_DIR.joinpath("raw")
-WORKING_DATA=DATA_DIR.joinpath("intermediate")
-PROCESSED_DATA=DATA_DIR.joinpath("processed")
-SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
-
-from src import prep_data
-
-# raw database-search results
-bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
-# the complete library of sampled (and working) literature
-bib_sample = prep_data.bib_library_from_dir(WORKING_DATA)
-
-# load relevant studies
-from src import load_data
-
-bib_df = prep_data.observations_with_metadata_df(
- raw_observations = load_data.from_yml(PROCESSED_DATA),
- study_metadata = prep_data.bib_metadata_df(bib_sample),
- country_groups = prep_data.country_groups_df(Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
-)
-raw_observations = None
-zot_df = None
-df_country_groups = None
+```{python}
+#| label: load-dataframes
+#| echo: false
+#| output: false
+from src import df, df_by_intervention, validities
```
@@ -247,7 +227,7 @@ The identified channels here are different social supports, gender role expectat
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.
-
+
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.
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.
@@ -290,7 +270,8 @@ There is also low evidence for return to work being increased by education, work
# Methodology and data
-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.
+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,
+as well as give an overview of the collected data.
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.
It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research,
while limiting "bias by the systematic assembly, critical appraisal and synthesis" through applying scientific strategies to the review itself [@Cook1995].
@@ -306,14 +287,12 @@ With an increasingly adopted approach in recent years, with rigorous dichotomy o
## The search protocol
-
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.
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.
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.
-
The search protocol then follows a three-staged process of execution: identification, screening and extraction.
-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.
+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.
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.
Relevant results are then complemented through the adoption of a 'snowballing' technique,
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.
@@ -322,51 +301,39 @@ To identify potential studies and create an initial sample, relevant terms for t
Identified terms comprising the world of work can be found in @tbl-wow-terms,
with the search query requiring a term from the general column and one other column.
-::: {#tbl-wow-terms}
-
```{python}
#| label: tbl-wow-terms
#| tbl-cap: World of work term cluster
-terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
-md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
+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
-
-:::
-
The world of work cluster, like the inequality and policy intervention clusters below, 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,
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).
At least one general term and at least one additional term have to be mentioned by a study to be identified for the initial sample pool.
For the policy intervention cluster, a variety of terms have been identified both from the ILO policy areas and guidelines as well as existing reviews, as can be seen in @tbl-intervention-terms.
Where terms have been identified from previous reviews outside the introduced ILO policy guidelines,
-there source has been included in the table.
+their sources have been included in the table.
For the database query, a single term from the general category is required to be included in addition to one term from *any* of the remaining categories.
-::: {#tbl-intervention-terms}
-
```{python}
#| label: tbl-intervention-terms
-#| tbl-cap: Intervention term cluster
-terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
+#| tbl-cap: Policy intervention term cluster
+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"]
-md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
+Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
```
-Policy intervention term cluster
-
-:::
-
Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality,
as seen in @tbl-inequality-terms.
```{python}
#| label: tbl-inequality-terms
#| tbl-cap: Inequality term cluster
-terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
-md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
+terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
+Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean 'AND'),
@@ -387,10 +354,10 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
::: {#tbl-inclusion-criteria}
```{python}
-#| label: tbl-inclusion-criteria
+#| label: inclusion-criteria
-inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
-md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
+inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
+Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
Source: Author's elaboration
@@ -411,57 +378,12 @@ Last, for extraction, studies are screened for their full-texts, irrelevant stud
Should any literature reviews be identified as relevant during this screening process,
they will in turn be crawled for cited sources in a 'snowballing' process,
and the sources will be added to the sample to undergo the same screening process explained above.
+The resulting process can be seen in @fig-prisma.
-```{python}
-#| label: calculate-scoping-flowchart
-#| echo: false
-#| output: asis
-
-nr_database_query_raw = len(bib_sample_raw_db.entries)
-nr_snowballing_raw = 2240
-
-all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()]
-nr_database_deduplicated = len([1 for kw in all_keywords if "sample::database" in kw])
-nr_snowballing_deduplicated = len([1 for kw in all_keywords if "sample::snowballing" in kw])
-nr_out_superseded = len([1 for kw in all_keywords if "out::superseded" in kw])
-
-FULL_RAW_SAMPLE_NOTHING_REMOVED = nr_database_query_raw + nr_snowballing_raw
-FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded
-
-NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
-NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
-NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
-
-nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - FULL_SAMPLE_DUPLICATES_REMOVED
-nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + NON_ZOTERO_CAPTURE_TITLE_REMOVAL
-nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
-nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
-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 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
-# from: https://github.com/quarto-dev/quarto-cli/discussions/6508
-print(f"""
-```{{mermaid}}
+```{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_snowballing_raw})"] --> starting_sample["Starting sample (n={FULL_RAW_SAMPLE_NOTHING_REMOVED})"];
-
- starting_sample -- "Duplicate removal ({nr_out_duplicates+nr_out_superseded} removed) "--> dedup["Records after duplicates removed (n={FULL_SAMPLE_DUPLICATES_REMOVED})"];
-
- dedup -- "Title screening ({nr_out_title} excluded)" --> title_screened["Records after titles screened (n={FULL_SAMPLE_DUPLICATES_REMOVED - nr_out_title})"];
-
- title_screened -- "Abstract screening ({nr_out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract})"];
-
- abstract_screened -- " Language screening ({nr_out_language} excluded) "--> language_screened["Records after language screened (n={FULL_SAMPLE_DUPLICATES_REMOVED-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})"];
-{t3}
-""")
+%%| fig-cap: PRISMA flowchart for scoping process
+%%| file: ../data/processed/prisma.mmd
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
@@ -480,68 +402,81 @@ For a full list of validity ranks, see @apptbl-validity-external and @apptbl-val
## Data
```{python}
-#| label: calculate-relevant-studies
-#| echo: false
-# TODO Remove redundant 'relevant' studies observation below once all studies are extracted.
-nr_relevant = len([1 for kw in all_keywords if "relevant" in kw])
+from src.model import prisma
+p = prisma.PrismaNumbers()
```
-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`.
+The query execution results in an initial sample of
+`{python} p.raw_db`
+potential studies identified from the database search as well as
+`{python} p.raw_snowball`
+potential studies from other sources,
+leading to a total initial number of
+`{python} p.raw_full`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
-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.
-
-
+Of these,
+`{python} p.dedup_full - p.out_title - p.out_abstract - p.out_language`
+have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
+from which in turn
+`{python} p.final_extracted`
+have ultimately been extracted.
The currently identified literature rises somewhat in volume over time,
with first larger outputs identified from 2014,
as can be seen in @fig-publications-per-year.
-
-
-
```{python}
#| label: fig-publications-per-year
#| fig-cap: Publications per year
df_study_years = (
- bib_df.groupby(["author", "year", "title"])
+ df.groupby(["author", "year", "title"])
.first()
.reset_index()
.drop_duplicates()
+ ["year"].value_counts()
+ .sort_index()
)
-# plot by year TODO decide if we want to distinguish by literature type/region/etc as hue
-# FIXME should be timeseries plot so no years are missing
-ax = sns.countplot(df_study_years, x="year")
-ax.tick_params(axis='x', rotation=45)
-ax.set_xlabel("")
+# 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.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # ensure rotated right-anchor
plt.tight_layout()
plt.show()
df_study_years = None
```
-Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.
-While this can mean a decreased interest or different focus points within academia during those time spans,
-it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions,
-and should thus be kept in mind for future scoping efforts.
+Anomalies such as the relatively significant dips in output in 2016 and 2017 become especially interesting against the strong overall increase of output.
+While this can mean a decreased interest or different focus points within academia during the time span,
+it may also point towards newly arising alternative term clusters, or a re-focus towards different interventions,
+either of which may not be completely captured in the data and should thus be kept in mind for future scoping efforts.
-The predominant amount of literature is based on white literature, with only a marginal amount solely published as gray literature.
+The predominant amount of literature is based on white literature, with only a marginal amount solely published as grey literature.
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.
Such a stark gap speaks to a well targeted identifaction procedure, with more up-to-date white literature correctly superseding potential previous publications.
@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
From the literature sample, several patterns emerge:
-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.
+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.
```{python}
#| label: fig-citations-per-year-avg
#| fig-cap: Average citations per year
-bib_df["zot_cited"] = bib_df["zot_cited"].dropna().astype("int")
-grpd = bib_df.groupby(["year"], as_index=False)["zot_cited"].mean()
+df["zot_cited"] = df["zot_cited"].dropna().astype("int")
+grpd = df.groupby(["year"], as_index=False)["zot_cited"].mean()
fig, ax = plt.subplots()
ax.bar(grpd["year"], grpd["zot_cited"])
sns.regplot(x=grpd["year"], y=grpd["zot_cited"], ax=ax)
#ax = sns.lmplot(data=grpd, x="year", y="zot_cited", fit_reg=True)
+
+years_list = np.arange(2000, 2024).tolist()
+ax.set_xticks(years_list)
ax.tick_params(axis='x', rotation=45)
+ax.set_ylabel("Citations")
+ax.set_xlabel("Year")
plt.tight_layout()
plt.show()
```
@@ -551,8 +486,10 @@ This suggests, first, no overall decrease in academic interest in the topic over
and second, no linearly developing concentration or centralization of knowledge output and dissemination,
though it also throws into question a clear-cut increase of *relevant* output over time.
-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.
-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.
+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 yet approach from a single coherent perspective.
+It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the literature review process,
+a possible sign of more focused knowledge production and thus valuable to more closely review during screening.
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).
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.
@@ -560,10 +497,6 @@ This is because, as @fig-publications-per-year showed, the overall output was no
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.
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.
-
-
-
-
# Synthesis: A multitude of lenses
This section will present a synthesis of evidence from the scoping review,
@@ -574,7 +507,7 @@ analysing the main findings per policy area, as well as underscore individual st
#| fig-cap: Available studies by primary type of intervention
by_intervention = (
- bib_df
+ df
.fillna("")
.groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
.agg(
@@ -637,10 +570,10 @@ study_strength_bins = {
def strength_for(val):
return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
-findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
+findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
-md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
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.
@@ -814,7 +747,7 @@ The results show the same trends for measurement of land inequality using the sh
### Social protection
-
+
@Wang2016 undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries.
@@ -851,8 +784,7 @@ The authors suggest this shows the primary channel of the programme becoming a b
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.
-
-
+
@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,
@@ -870,10 +802,10 @@ One limitation of the study is the modelling assumption that workers will have t
#| label: tbl-findings-structural
from src.model import validity
-findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
+findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
-md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
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.
@@ -1107,10 +1039,10 @@ Though the intervention clearly aims at strengthening some aspect of individual
#| label: tbl-findings-agency
from src.model import validity
-findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
+findings_agency = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
-md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
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.
@@ -1175,8 +1107,7 @@ Some limitations to the study include a relatively small overall sample size, as
### Unconscious bias and discriminatory norms
-
-
+
@Al-Mamun2014 conduct a study on the impacts of an urban micro-finance programme in Malaysia on the economic empowerment of women.
The programme introduced the ability for low-income urban individuals to receive collateral-free credit.
@@ -1204,7 +1135,7 @@ The authors suggest the primary channel is the newly increased bargaining power
#| label: prep-inequalities-crosstabs
# dataframe containing each intervention inequality pair
df_inequality = (
- bib_df[["region", "intervention", "inequality"]]
+ df[["region", "intervention", "inequality"]]
.assign(
Intervention = lambda _df: (_df["intervention"]
.str.replace(r"\(.+\)", "", regex=True)
@@ -1241,7 +1172,7 @@ in which fewer studies have been identified.
#| fig-cap: Studies by regions analysed
by_region = (
- bib_df[["region"]]
+ df[["region"]]
.assign(
region = lambda _df: (_df["region"]
.str.replace(r" ?; ?", ";", regex=True)
@@ -1275,7 +1206,7 @@ with each region often having both a specific focus in policy-orientations and a
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 gray literature captured compared to other regions,
+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.
@@ -1301,17 +1232,19 @@ validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=T
validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
#validities["external_validity"] = validities["external_validity"].astype('category')
validities["internal_validity"] = validities["internal_validity"].astype('category')
+validities["External Validity"] = validities["external_validity"]
+validities["Internal Validity"] = validities["internal_validity"]
plt.figure().set_figheight(5)
sns.violinplot(
data=validities,
- x="internal_validity", y="external_validity", hue="design",
+ 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,
+ x="Internal Validity", y="External Validity", legend=False,
color="darkmagenta",
s=4
)
@@ -1339,7 +1272,7 @@ again with the exception of @Thoresen2021 already mentioned.
sns.displot(
data=validities,
- x="external_validity", hue="internal_validity",
+ x="External Validity", hue="Internal Validity",
kind="kde",
multiple="fill", clip=(0, None),
palette="ch:rot=-0.5,hue=1.5,light=0.9",
@@ -1368,8 +1301,8 @@ many studies use income measurements and changes in income or income inequality
```{python}
#| label: inequality-targeting-implicit-explicit
-targeting_majority = bib_df["targeting"].value_counts().index.tolist()[0]
-targeting_minority = bib_df["targeting"].value_counts().index.tolist()[-1]
+targeting_majority = df["targeting"].value_counts().index.tolist()[0]
+targeting_minority = df["targeting"].value_counts().index.tolist()[-1]
```
Often, however, income inequality is not the primary inequality being targeted, but used to measure the effects on other inequalities by seeing how the effects of respective inequality and income intersect, as will be discussed in the following section.
@@ -1381,7 +1314,7 @@ with only a minority of studies looking at policies with an `{python} targeting_
#| fig-cap: Types of inequality analysed
by_inequality = (
- bib_df[["inequality"]]
+ df[["inequality"]]
.assign(
inequality = lambda _df: (_df["inequality"]
.str.replace(r"\(.+\)", "", regex=True)
@@ -1436,7 +1369,6 @@ Similarly, rarely do studies delineate generational outcomes from income, gender
[^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.
-
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.
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.
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.
@@ -1463,7 +1395,7 @@ findings - channels - policy recc
- supply-side effects, esp maternal (family planning; care work)
- organisational disadvantagement in new economy (networking needs, self-promotion, managerial discretions)
-TODO include unionisation effects on gender
+TODO perhaps add specific section for unionisation effects on gender?
-->
Due to its persistent characteristics, gender inequality is an often analysed horizontal dimension of workplace inequality in the study sample,
@@ -1476,7 +1408,7 @@ with the other regions trailing further behind in output.
#| fig-cap: Regional distribution of studies analysing gender inequalities
by_region_and_inequality = (
- bib_df[["inequality", "region"]]
+ df[["inequality", "region"]]
.assign(
region = lambda _df: (_df["region"]
.str.replace(r" ?; ?", ";", regex=True)
@@ -1798,7 +1730,7 @@ while relying on indicators for measurement which are flexible yet overlapping e
#| label: full-search-query
#| echo: false
#| output: asis
-with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
+with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
query = f.read()
t3 = "`" * 3
@@ -1846,7 +1778,7 @@ Internal validity ranking. Adapted from @Maitrot2017.
```{python}
#| label: apptbl-extraction-matrix
#| tbl-cap: Extraction matrix
-bib_df
+df
```
{{< pagebreak >}}
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 0000000..82cdce4
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,18 @@
+# Notebooks
+
+The notebooks in this directory undertook a variety of exploratory data analysis.
+They are possibly outdated and not guaranteed to work as the project around them continues changing.
+However, they are kept here for the moment to serve as reminders and inspiration for further data analysis.
+
+Each notebook looked into a different issue:
+
+explore.qmd is a fundamental data exploration of the extracted data-set.
+
+test-magma.qmd explores the interaction with ipynb notebooks through the neovim plugin magma-nvim.
+
+bibmanip.qmd explores the parsing and analysis of .bib bibtex files through python.
+
+main-findings.qmd is an attempt to distill the variety of findings into a short-form table format.
+
+rank_validities.qmd attempts to create a map of the validity of each study utilizing the
+ internal/external validity method used in the main paper.
diff --git a/00-notebooks/bibmanip.qmd b/notebooks/bibmanip.qmd
similarity index 90%
rename from 00-notebooks/bibmanip.qmd
rename to notebooks/bibmanip.qmd
index e56f2b9..633be95 100644
--- a/00-notebooks/bibmanip.qmd
+++ b/notebooks/bibmanip.qmd
@@ -1,5 +1,5 @@
---
-bibliography: ../02-data/intermediate/zotero-library.bib
+bibliography: ../data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@@ -22,7 +22,8 @@ subtitle: Addressing inequalities in the World of Work
```{python}
#| echo: false
from pathlib import Path
-data_dir=Path("../02-data")
+import src.globals as g
+data_dir = g.DATA_DIR
## standard imports
from IPython.core.display import Markdown as md
@@ -43,8 +44,9 @@ sns.set_style("whitegrid")
import bibtexparser
bib_string=""
-print(f"path: {data_dir.joinpath('raw/01_wos-sample_2023-11-02').absolute()}")
-for partial_bib in data_dir.joinpath("raw/01_wos-sample_2023-11-02").glob("*.bib"):
+sample_dir = g.REFERENCE_DATA.joinpath("01_wos-sample_2023-11-02")
+print(f"path: {sample_dir.absolute()}")
+for partial_bib in sample_dir.glob("*.bib"):
with open(partial_bib) as f:
bib_string+="\n".join(f.readlines())
sample = bibtexparser.parse_string(bib_string)
diff --git a/00-notebooks/explore.qmd b/notebooks/explore.qmd
similarity index 94%
rename from 00-notebooks/explore.qmd
rename to notebooks/explore.qmd
index 2ae6f92..4eb26f5 100644
--- a/00-notebooks/explore.qmd
+++ b/notebooks/explore.qmd
@@ -1,5 +1,5 @@
---
-bibliography: 02-data/intermediate/zotero-library.bib
+bibliography: data/intermediate/zotero-library.bib
title: Grab yml
---
@@ -184,23 +184,18 @@ from matplotlib import pyplot as plt
import seaborn as sns
from tabulate import tabulate
import bibtexparser
+import src.globals as g
sns.set_style("whitegrid")
-DATA_DIR=Path("./02-data")
-RAW_DATA=DATA_DIR.joinpath("raw")
-WORKING_DATA=DATA_DIR.joinpath("intermediate")
-PROCESSED_DATA=DATA_DIR.joinpath("processed")
-SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
-
bib_string=""
-for partial_bib in RAW_DATA.glob("**/*.bib"):
+for partial_bib in g.REFERENCE_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 WORKING_DATA.glob("**/*.bib"):
+for partial_bib in g.REFERENCE_DATA.joinpath("zotero-library.bib"):
with open(partial_bib) as f:
bib_string+="\n".join(f.readlines())
bib_sample = bibtexparser.parse_string(bib_string)
@@ -222,10 +217,10 @@ zot_df = pd.DataFrame([
], 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"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
+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"{PROCESSED_DATA}")
+bib_df = (load_data.from_yml(f"{g.PROCESSED_DATA}")
.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"]),
diff --git a/00-notebooks/main-findings.qmd b/notebooks/main-findings.qmd
similarity index 97%
rename from 00-notebooks/main-findings.qmd
rename to notebooks/main-findings.qmd
index 663c2b2..0996e55 100644
--- a/00-notebooks/main-findings.qmd
+++ b/notebooks/main-findings.qmd
@@ -15,7 +15,7 @@ import bibtexparser
sns.set_style("whitegrid")
-DATA_DIR=Path("./02-data")
+DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
@@ -120,7 +120,7 @@ datavis:
```{python}
-findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
+findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
findings_institutional
from src.model import validity
import math
diff --git a/00-notebooks/rank_validities.qmd b/notebooks/rank_validities.qmd
similarity index 99%
rename from 00-notebooks/rank_validities.qmd
rename to notebooks/rank_validities.qmd
index 2544a94..10d5907 100644
--- a/00-notebooks/rank_validities.qmd
+++ b/notebooks/rank_validities.qmd
@@ -16,7 +16,7 @@ import bibtexparser
sns.set_style("whitegrid")
-DATA_DIR=Path("./02-data")
+DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
diff --git a/00-notebooks/test-magma.qmd b/notebooks/test-magma.qmd
similarity index 84%
rename from 00-notebooks/test-magma.qmd
rename to notebooks/test-magma.qmd
index 4a8d8e4..5213452 100644
--- a/00-notebooks/test-magma.qmd
+++ b/notebooks/test-magma.qmd
@@ -1,5 +1,5 @@
---
-bibliography: 02-data/intermediate/zotero-library.bib
+bibliography: data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@@ -28,8 +28,10 @@ zotero:
```{python}
#| echo: false
from pathlib import Path
-DATA_DIR=Path("./02-data")
-BIB_PATH = DATA_DIR.joinpath("raw/01_wos-sample_2023-11-02")
+import src.globals as g
+DATA_DIR = g.DATA_DIR
+RAW_DATA = g.RAW_DATA
+BIB_PATH = g.REFERENCE_DATA.joinpath("01_wos-sample_2023-11-02")
## standard imports
from IPython.core.display import Markdown as md
diff --git a/poetry.lock b/poetry.lock
index 20d8100..5d320bb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
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+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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lxml = ["lxml"]
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[[package]]
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test = ["objgraph", "psutil"]
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+
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name = "idna"
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description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.5"
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name = "importlib-metadata"
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description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
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perf = ["ipython"]
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[[package]]
name = "ipykernel"
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description = "IPython Kernel for Jupyter"
optional = false
python-versions = ">=3.8"
files = [
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@@ -806,7 +868,7 @@ matplotlib-inline = ">=0.1"
nest-asyncio = "*"
packaging = "*"
psutil = "*"
-pyzmq = ">=20"
+pyzmq = ">=24"
tornado = ">=6.1"
traitlets = ">=5.4.0"
@@ -815,61 +877,62 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
pyqt5 = ["pyqt5"]
pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
[[package]]
name = "ipython"
-version = "8.17.2"
+version = "8.25.0"
description = "IPython: Productive Interactive Computing"
optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
files = [
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colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
jedi = ">=0.16"
matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
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+prompt-toolkit = ">=3.0.41,<3.1.0"
pygments = ">=2.4.0"
stack-data = "*"
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kernel = ["ipykernel"]
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nbconvert = ["nbconvert"]
nbformat = ["nbformat"]
notebook = ["ipywidgets", "notebook"]
parallel = ["ipyparallel"]
qtconsole = ["qtconsole"]
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python-versions = ">=3.7"
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comm = ">=0.1.3"
ipython = ">=6.1.0"
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traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.9,<4.1.0"
+widgetsnbextension = ">=4.0.11,<4.1.0"
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test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
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[[package]]
name = "jinja2"
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+version = "3.1.4"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
files = [
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@@ -926,38 +989,35 @@ i18n = ["Babel (>=2.7)"]
[[package]]
name = "json5"
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+version = "0.9.25"
description = "A Python implementation of the JSON5 data format."
optional = false
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+python-versions = ">=3.8"
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-dev = ["hypothesis"]
-
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name = "jsonpointer"
-version = "2.4"
+version = "3.0.0"
description = "Identify specific nodes in a JSON document (RFC 6901)"
optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
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name = "jsonschema"
-version = "4.19.2"
+version = "4.22.0"
description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.8"
files = [
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@@ -980,17 +1040,17 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
[[package]]
name = "jsonschema-specifications"
-version = "2023.7.1"
+version = "2023.12.1"
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false
python-versions = ">=3.8"
files = [
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-referencing = ">=0.28.0"
+referencing = ">=0.31.0"
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name = "jupyter"
@@ -1041,13 +1101,13 @@ testing = ["coverage", "ipykernel", "jupytext", "matplotlib", "nbdime", "nbforma
[[package]]
name = "jupyter-client"
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+version = "8.6.2"
description = "Jupyter protocol implementation and client libraries"
optional = false
python-versions = ">=3.8"
files = [
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@@ -1087,13 +1147,13 @@ test = ["flaky", "pexpect", "pytest"]
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name = "jupyter-core"
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mysql = ["mysqlclient (>=1.4.0)"]
mysql-connector = ["mysql-connector-python"]
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-secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "wcwidth"
-version = "0.2.9"
+version = "0.2.13"
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
files = [
- {file = "wcwidth-0.2.9-py2.py3-none-any.whl", hash = "sha256:9a929bd8380f6cd9571a968a9c8f4353ca58d7cd812a4822bba831f8d685b223"},
- {file = "wcwidth-0.2.9.tar.gz", hash = "sha256:a675d1a4a2d24ef67096a04b85b02deeecd8e226f57b5e3a72dbb9ed99d27da8"},
+ {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+ {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
]
[[package]]
name = "webcolors"
-version = "1.13"
+version = "24.6.0"
description = "A library for working with the color formats defined by HTML and CSS."
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"},
- {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"},
+ {file = "webcolors-24.6.0-py3-none-any.whl", hash = "sha256:8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1"},
+ {file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"},
]
[package.extras]
docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["pytest", "pytest-cov"]
+tests = ["coverage[toml]"]
[[package]]
name = "webencodings"
@@ -3003,47 +3114,47 @@ files = [
[[package]]
name = "websocket-client"
-version = "1.6.4"
+version = "1.8.0"
description = "WebSocket client for Python with low level API options"
optional = false
python-versions = ">=3.8"
files = [
- {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
- {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
+ {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+ {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
]
[package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
[[package]]
name = "widgetsnbextension"
-version = "4.0.9"
+version = "4.0.11"
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
files = [
- {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
- {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
+ {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
+ {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
]
[[package]]
name = "zipp"
-version = "3.17.0"
+version = "3.19.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
- {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+ {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+ {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[metadata]
lock-version = "2.0"
python-versions = "<3.13,>=3.11"
-content-hash = "f6f60ec28f3f1e61377114f1b58e6117b45fb290f362ad471790611505e95dfc"
+content-hash = "44bb19f7379cc5c7e91f92c15198c3277c70233d78f6a02f65671fc947429389"
diff --git a/pyproject.toml b/pyproject.toml
index ec8b03f..64fc994 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,20 +3,20 @@ name = "scoping-review"
version = "0.5.1"
description = ""
authors = ["Marty Oehme "]
-readme = "README.md"
+packages = [{ include = "src" }]
[tool.poetry.dependencies]
python = "<3.13,>=3.11"
pandas = "^2.1.1"
seaborn = "^0.13.0"
-bibtexparser = {version = ">=2.0.0b1", allow-prereleases = true}
+bibtexparser = { version = ">=2.0.0b1", allow-prereleases = true }
jupyter = "^1.0.0"
jupyter-cache = "^0.6.1"
tabulate = "^0.9.0"
openpyxl = "^3.1.2"
[tool.poetry.group.dev.dependencies]
-pynvim = "^0.4.3"
+pynvim = "^0.5.0"
pyperclip = "^1.8.2"
poethepoet = "^0.24.4"
@@ -26,12 +26,14 @@ build-backend = "poetry.core.masonry.api"
[tool.ruff]
ignore = ["E402"]
+[tool.pyright]
+typeCheckingMode="basic"
[tool.poe]
poetry_command = ""
[tool.poe.tasks.preview]
help = "Preview the main review in the browser"
-cmd = "quarto preview scoping_review.qmd --to=html"
+cmd = "quarto preview manuscript/article.qmd --to=html"
[tool.poe.tasks.render]
help = "Render all project files"
cmd = "quarto render"
@@ -41,15 +43,19 @@ cmd = "nvim"
[tool.poe.tasks.extract]
help = "Extract the csv data from raw yaml files"
shell = """
-python src/matrix.py > 02-data/processed/extracted.csv
+python src/extract/raw_to_extracted_csv.py > data/processed/extracted.csv
+"""
+[tool.poe.tasks.prisma]
+help = "Update PRISMA flowchart numbers"
+shell = """
+python src/model/prisma.py > data/processed/prisma.mmd
"""
[tool.poe.tasks.milestone]
help = "Extract, render, commit and version a finished artifact"
shell = """
quarto render --output-dir 05-final_paper
-poe extract
VERSION="$(poetry version -s minor)"
-git add pyproject.toml 02-data 05-final_paper
+git add pyproject.toml data 05-final_paper
git commit -m "Publish version $VERSION" --no-gpg-sign
git tag -a -m "new bundle for $(date -Isecond)" "$VERSION"
"""
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..b1857ec
--- /dev/null
+++ b/src/__init__.py
@@ -0,0 +1,13 @@
+from src.process.generate_dataframes import bib_sample, bib_sample_db_raw, df_by_intervention, df_main, df_validities
+
+# each observation in a single dataframe
+df = df_main()
+
+# all observations but split per individual intervention
+df_by_intervention = df_by_intervention()
+
+# Calc study validities (internal & external separated)
+validities = df_validities()
+
+bib_sample = bib_sample()
+bib_sample_db_raw = bib_sample_db_raw()
diff --git a/src/load_data.py b/src/extract/load_data.py
similarity index 86%
rename from src/load_data.py
rename to src/extract/load_data.py
index 7cb5d8f..bf5a0f4 100644
--- a/src/load_data.py
+++ b/src/extract/load_data.py
@@ -5,11 +5,15 @@ from pathlib import Path
from pandas import DataFrame, read_csv
try:
- import src.yml as yaml # for quarto document scripts
+ import src.extract.yml as yaml # for quarto document scripts
except ModuleNotFoundError:
import yml as yaml # for directly running the package
-DEFAULT_YAML_PATH = Path("02-data/processed")
+try:
+ import src.globals as g
+ DEFAULT_YAML_PATH = g.EXTRACTED_DATA
+except ModuleNotFoundError:
+ DEFAULT_YAML_PATH = Path("data")
def to_tsv(studies: list[dict]) -> str:
diff --git a/src/matrix.py b/src/extract/raw_to_extracted_csv.py
similarity index 81%
rename from src/matrix.py
rename to src/extract/raw_to_extracted_csv.py
index d4072d5..d71f1e5 100644
--- a/src/matrix.py
+++ b/src/extract/raw_to_extracted_csv.py
@@ -8,6 +8,10 @@ try:
except ModuleNotFoundError:
from model import validity # for directly running the package
+try:
+ import src.process.load_data as load
+except ModuleNotFoundError:
+ import load_data as load
def extract(df: DataFrame, file: Path | StringIO) -> None:
(
@@ -21,12 +25,11 @@ if __name__ == "__main__":
import os
import sys
- import load_data
if len(sys.argv) == 2:
- df = load_data.from_yml(Path(sys.argv[1]))
+ df = load.from_yml(Path(sys.argv[1]))
else:
- df = load_data.from_yml()
+ df = load.from_yml()
output = StringIO()
extract(df, output)
diff --git a/src/yml.py b/src/extract/yml.py
similarity index 100%
rename from src/yml.py
rename to src/extract/yml.py
diff --git a/src/globals.py b/src/globals.py
new file mode 100644
index 0000000..d56a9ea
--- /dev/null
+++ b/src/globals.py
@@ -0,0 +1,11 @@
+import os
+from pathlib import Path
+
+PROJECT_DIR = Path(os.getenv("QUARTO_PROJECT_DIR", "."))
+
+DATA_DIR = PROJECT_DIR.joinpath("data")
+
+EXTRACTED_DATA = DATA_DIR.joinpath("extracted")
+REFERENCE_DATA = DATA_DIR.joinpath("references")
+SUPPLEMENTARY_DATA = DATA_DIR.joinpath("supplementary")
+PROCESSED_DATA = DATA_DIR.joinpath("processed")
diff --git a/src/model/prisma.py b/src/model/prisma.py
new file mode 100644
index 0000000..576b14c
--- /dev/null
+++ b/src/model/prisma.py
@@ -0,0 +1,67 @@
+from src import bib_sample, bib_sample_db_raw
+
+
+class PrismaNumbers:
+ raw_db = len(bib_sample_db_raw.entries)
+ raw_snowball = 2240
+
+ # list of all keywords (semicolon-delimited string) for each entry in sample
+ all_kw = [
+ entry["keywords"]
+ for entry in bib_sample.entries
+ if "keywords" in entry.fields_dict.keys()
+ ]
+
+ # calculate deduplicated and superseded amounts
+ dedup_db = len([1 for kw in all_kw if "sample::database" in kw])
+ dedup_snowball = len([1 for kw in all_kw if "sample::snowballing" in kw])
+ out_superseded = len([1 for kw in all_kw if "out::superseded" in kw])
+
+ raw_full = raw_db + raw_snowball
+ dedup_full = dedup_db + dedup_snowball + out_superseded
+
+ # additional non-captured numbers
+ NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
+ NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
+ NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
+
+ out_duplicates = raw_full - dedup_full
+ out_title = (
+ len([1 for kw in all_kw if "out::title" in kw])
+ + NON_ZOTERO_CAPTURE_TITLE_REMOVAL
+ )
+ out_abstract = (
+ len([1 for kw in all_kw if "out::abstract" in kw])
+ + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
+ )
+ out_fulltext = (
+ len([1 for kw in all_kw if "out::full-text" in kw])
+ + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
+ )
+ out_language = len([1 for kw in all_kw if "out::language" in kw])
+
+ final_extracted = len([1 for kw in all_kw if "done::extracted" in kw])
+
+
+del bib_sample, bib_sample_db_raw
+
+if __name__ == "__main__":
+ prisma = PrismaNumbers()
+
+ # FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
+ outp = f"""
+ flowchart TD;
+ search_db["Records identified through database searching (n={prisma.raw_db})"] --> starting_sample;
+ search_prev["Records identified through other sources (n={prisma.raw_snowball})"] --> starting_sample["Starting sample (n={prisma.raw_full})"];
+
+ starting_sample -- "Duplicate removal ({prisma.out_duplicates+prisma.out_superseded} removed) "--> dedup["Records after duplicates removed (n={prisma.dedup_full})"];
+
+ dedup -- "Title screening ({prisma.out_title} excluded)" --> title_screened["Records after titles screened (n={prisma.dedup_full - prisma.out_title})"];
+
+ title_screened -- "Abstract screening ({prisma.out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={prisma.dedup_full-prisma.out_title-prisma.out_abstract})"];
+
+ abstract_screened -- " Language screening ({prisma.out_language} excluded) "--> language_screened["Records after language screened (n={prisma.dedup_full-prisma.out_title-prisma.out_abstract-prisma.out_language})"];
+
+ language_screened -- " Full-text screening ({prisma.out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={prisma.final_extracted})"];
+ """
+ print(outp)
diff --git a/src/model/validity.py b/src/model/validity.py
index 401ed29..30a7343 100644
--- a/src/model/validity.py
+++ b/src/model/validity.py
@@ -26,6 +26,21 @@ METHOD_RANKINGS = {
5.0: ["RCT", "randomi(?:s|z)ed.control.trial"],
}
+STRENGTH_BINS: dict[float, str] = {
+ 0.0: r"\-",
+ 5.0: r"\+",
+ 10.0: r"\++",
+}
+
+
+def strength_for(val: str):
+ """Returns the numeric value for a specific strength bin str representation.
+
+ Does a reverse lookup of a strength bin for its representation as a string,
+ such as looking for '++' and finding 10.0 as its required validity strength.
+ """
+ return list(STRENGTH_BINS.keys())[list(STRENGTH_BINS.values()).index(val)]
+
def calculate(
df: DataFrame,
@@ -88,7 +103,7 @@ def calculate(
def add_to_findings(
findings_df: DataFrame,
studies_by_intervention: DataFrame,
- strength_bins: dict[float, str] | None = None,
+ strength_bins: dict[float, str] | None = STRENGTH_BINS,
) -> DataFrame:
"""Returns summary of findings with validities added.
diff --git a/src/prep_data.py b/src/process/add_metadata.py
similarity index 94%
rename from src/prep_data.py
rename to src/process/add_metadata.py
index c8e1c9e..c328706 100644
--- a/src/prep_data.py
+++ b/src/process/add_metadata.py
@@ -31,6 +31,19 @@ def bib_library_from_dir(
return bibtexparser.parse_string(bib_string)
+def bib_library_from_file(
+ file: Path, disable_warnings: bool = True
+) -> bibtexparser.Library:
+ with open(file) as f:
+ bib_string = f.read()
+ if disable_warnings:
+ cm = all_logging_disabled
+ else:
+ cm = nullcontext
+ with cm():
+ return bibtexparser.parse_string(bib_string)
+
+
def bib_metadata_df(sample: bibtexparser.Library) -> pd.DataFrame:
"""Returns dataframe with relevant metadata extracted from a bibtex library.
diff --git a/src/process/generate_dataframes.py b/src/process/generate_dataframes.py
new file mode 100644
index 0000000..72f6950
--- /dev/null
+++ b/src/process/generate_dataframes.py
@@ -0,0 +1,90 @@
+import re
+
+import pandas as pd
+from bibtexparser import Library
+
+import src.globals as g
+from src.extract import load_data as load
+from src.model import validity
+
+## Creates 3 important data structures:
+# df: The main dataframe containing all final sample studies
+# df_by_intervention: The same dataframe but split up by individual interventions per study
+# validities: The studies with their validities, containing only quasi-/experimental studies
+
+
+# the complete library of sampled (and working) literature
+def bib_sample() -> Library:
+ return meta.bib_library_from_file(g.REFERENCE_DATA.joinpath("zotero-library.bib"))
+
+
+# raw database-search results
+def bib_sample_db_raw() -> Library:
+ return meta.bib_library_from_dir(g.REFERENCE_DATA.joinpath("db"))
+
+
+def df_main() -> pd.DataFrame:
+ df = meta.observations_with_metadata_df(
+ raw_observations=load.from_yml(g.EXTRACTED_DATA),
+ study_metadata=meta.bib_metadata_df(bib_sample()),
+ country_groups=meta.country_groups_df(
+ g.SUPPLEMENTARY_DATA.joinpath("wb-country-groupings.xlsx")
+ ),
+ )
+ return df
+
+
+from src.process import add_metadata as meta
+
+
+def df_by_intervention() -> pd.DataFrame:
+ df_by_intervention = (
+ df_main()
+ .fillna("")
+ .groupby(
+ [
+ "author",
+ "year",
+ "title",
+ "design",
+ "method",
+ "representativeness",
+ "citation",
+ ]
+ )
+ .agg(
+ {
+ "intervention": lambda _col: "; ".join(_col),
+ }
+ )
+ .reset_index()
+ .drop_duplicates()
+ .assign(
+ intervention=lambda _df: _df["intervention"].apply(
+ lambda _cell: set(
+ [x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")]
+ )
+ ),
+ )
+ .explode("intervention")
+ )
+ return df_by_intervention
+
+
+def df_validities() -> pd.DataFrame:
+ validities = validity.calculate(df_by_intervention())
+ validities["identifier"] = (
+ validities["author"].str.replace(r",.*$", "", regex=True)
+ + " ("
+ + validities["year"].astype(str)
+ + ")"
+ )
+ validities = validities.loc[
+ (validities["design"] == "quasi-experimental")
+ | (validities["design"] == "experimental")
+ ]
+ # validities["external_validity"] = validities["external_validity"].astype('category')
+ validities["internal_validity"] = validities["internal_validity"].astype("category")
+ validities["External Validity"] = validities["external_validity"]
+ validities["Internal Validity"] = validities["internal_validity"]
+ return validities