chore(repo): Move documentation to the docs dir

docs/findings-per-policy-area.md

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+# Summary of study findings
+
+written into data/supplementary/findings-*.csv tables
+
+## Institutional
+
+### Labour laws / regulatory systems
+
+policies:
+
+- universal paid leave (maternal) [@Broadway2020]
+- paid leave (maternal) [@Dustmann2012]
+- paid leave (maternal) [@Mun2018]
+- contract formality regulation [@Davies2022]
+
+findings:
+
+- universal paid leave can significantly increase rtw [@Broadway2020]
+- positive rtw effects often show with medium-/long-term time-delay [@Broadway2020]
+- long-term leave periods (36months) may decrease positive wage,rtw,children's educational outcomes [@Dustmann2012]
+- paid leave does not increase female hiring pattern discrimination [@Mun2018]
+- short-term/fixed contracts can significantly decrease female rtw after maternity [@Davies2022]
+= for equality?
+
+channels:
+
+- disadvantaged mothers benefit through supplanting employer-funded leave [@Broadway2020]
+- maternal leave programs can reinforce existing household labor gender divisions [@Mun2018]
+- fixed-term contracts can have insufficient cover for otherwise applicable laws [@Davies2022]
+
+inequalities:
+
+- gender Broadway2020 Dustmann2012 Mun2018 Davies2022
+
+number:
+
+Broadway2020 + national
+Dustmann2012 - national
+Mun2018 + Japanese
+Davies2022 + UK-high ED
+
+### Protective environmental policies
+
+policies:
+
+energy sector sustainability work [@Kuriyama2021]
+
+findings:
+
+- emphasis on sustainable industries can decrease spatial inequality especially for rural regions [@Kuriyama2021]
+- targeting important to avoid reinforcing existing inequalities [@Stock2021]
+
+channels:
+
+- additional employment probability through rural energy projects [@Kuriyama2021]
+- social exclusion through elite capture of policies [@Stock2021]
+
+inequalities:
+
+- spatial Kuriyama2021
+- gender Stock2021
+
+number:
+
+Kuriyama2021 + Japanese-EnergySector
+Stock2021 - India-case-study
+
+
+### Minimum wage laws
+
+policies:
+
+- minimum wage introduction [@Chao2022] [@Gilber2001] [@SilveriaNeto2011]
+- minimum wage increase [@Alinaghi2020] [@Wong2019] [@Militaru2019] [@Sotomayor2021]
+
+findings:
+
+- short-term decreased income inequality [@Sotomayor2021]
+- long-term decreased wage inequality [@Chao2022] [@SilveriaNeto2011]
+- negligible impact on wage inequality [@Alinaghi2020] [@Gilber2001] [@Sotomayor2021]
+- larger impacts for single parents [@Alinaghi2020]
+- larger impacts for rural/disadvantaged areas [@Gilber2001] [@SilveriaNeto2011]
+- specifically targeting disadvanteged/low-earner households important for positive equality effects [@Alinaghi2020]
+- can lead to income-compression at high-earner end [@Wong2019]
+- may reinforce gender wage gap [@Wong2019]
+- may decrease gender wage gap [@Militaru2019]
+
+channels:
+
+- exit from urban manufacturing, reinforcing rural agricultural sectors [@Chao2022]
+- reaching many low-wage earners as secondary high-income household earners, but often low-wage households no wage earners at all -> bad targeting [@Alinaghi2020]
+- many women make up lower-earners, larger effect [@Militaru2019]
+- have negative effect on women's hours worked depending on household care/waged work division [@Wong2019]
+- job loss offset through higher wages [@Sotomayor2021]
+
+inequalities:
+
+- income [@Chao2022] [@Alinaghi2020] [@Gilber2001] [@SilveriaNeto2011] [@Wong2019] [@Sotomayor2021] [@Militaru2019]
+- spatial [@Chao2022] [@Gilber2001] [@SilveriaNeto2011]
+- gender [@Wong2019] [@Militaru2019]
+
+number:
+
+Chao2022 - global
+Alinaghi2020 + national
+Wong2019 + national(economicgrowth)
+Gilbert2001 + national(specific to Britain)
+SilveriaNeto2011 + national
+Militaru2019 - national
+Sotomayor2021 + national
+
+### Collective bargaining
+
+policies:
+
+- unionisation [@Alexiou2023] [@Ferguson2015]
+- collective negotiation practices [@Cardinaleschi2015]
+
+findings:
+
+- strong unionisation strongly related to decreasing income inequality [@Alexiou2023]
+- marginally positive relation to increased representation of women & minorities [@Ferguson2015]
+- marginally positive relation to closing gender pay gap [@Cardinaleschi2015]
+
+channels:
+
+- redistribution of political power through collective mobilisation [@Alexiou2023]
+- reciprocal relationship with distribution of political power [@Ahumada2023]
+- fostering more vertically equal redistributive policies [@Alexiou2023]
+- possible self-selection of minorities into more unionised enterprises [@Ferguson2015]
+- predominantly median part of wage distribution affected by collective negotiation [@Cardinaleschi2015]
+
+inequalities:
+
+- income [@Alexiou2023] [@Cardinaleschi2015] [@Ahumada2023]
+- gender [@Ferguson2015] [@Cardinaleschi2015]
+- racial [@Ferguson2015]
+
+number:
+
+Alexiou2023 - national
+Ferguson2015 - national
+Cardinaleschi2015 - national
+Ahumada2023 - national (less generalizable)
+
+### Workfare programmes
+
+policies:
+
+- workfare programme [@Whitworth2021] [@Li2022]
+
+findings:
+
+- workfare programmes can engender vertical inequality reduction while worsening spatial inequalities [@Whitworth2021]
+- higher job-provision outcomes may be achieved in contexts of lower prior material inequalities [@Li2022]
+
+channels:
+
+- job-deprived areas can experience further deprivations if not specifically targeted
+- land-ownership inequalities can increase inequality of political power, lead to political capture
+
+inequalities:
+
+- spatial [@Whitworth2021]
+- income [@Li2022]
+
+number:
+
+Whitworth2021 - subnational, rural
+Li2022 - national census
+
+### Social protection
+
+policies:
+
+- social assistance [@Wang2016]
+- conditional cash transfer [@Debowicz2014]
+- childcare subsidies [@Hardoy2015]
+- healthcare subsidy [@Carstens2018]
+
+findings:
+
+- real social income benefit levels generally rising [@Wang2016]
+- stagnating/decreasing income replacement rates may exacerbate existing inequalities [@Wang2016]
+- conditional cash transfers can produce both short-term and long-term positive income equality effects [@Debowicz2014]
+- evidence for childcare subsidies decreasing gender inequalities and increasing female labour force participation [@Hardoy2015]
+- healthcare subsidies impacts strongly dependent on correct targeting [@Carstens2018]
+
+channels:
+
+- benefit levels not being linked to wages can widen schisms between income groups [@Wang2016]
+- cash-influx lifts credit constraint effects in short-term [@Debowicz2014]
+- conditioning transfers on school attendance can generate decreased educational inequalities in long-term [@Debowicz2014]
+- childcare subsidy correct targeting can especially affect lower-income households through lifting credit constraints [@Hardoy2015]
+- subsidies which depend on not being able to participate in labour market may generate benefit trap [@Carstens2018]
+
+inequalities:
+
+- income [@Wang2016] [@Debowicz2014]
+- gender [@Hardoy2015]
+
+number:
+
+Wang2016 - regional (national census-constructed datasets)
+Debowicz2014 - national (survey)
+Hardoy2015 + (DID) national (census)
+Carstens2018
+
+
+
+
+
+policies:
+
+findings:
+
+channels:
+
+inequalities:
+
+number:
+
+###  Identified limitations/missing
+
+- regional distribution?
+- causal/correlational
+

docs/mail_2023-09-28.md

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+for me:
+
+Systematic scoping review methodology. Here we will need a description of the search protocol and inclusion criteria, outlining key concepts and definitions that relate to both forms of work and labour market outcomes (following the ILO (and others) typologies) and dimensions of inequalities. We need to include a technical justification for the adoption of a scoping systematic review. We can cite relevant references for that purpose, as done in the previous work for UN ESCAP. 
+
+- [x] description of search protocol
+- outlining key concepts and definitions from ILO (& other typologies):
+    - [x] forms of work
+    - labour market outcomes 
+    - [x] dimensions of inequality
+- description of inclusion criteria
+- [x] include technical justification for adoption of scoping syst review
+    - [x] cite relevant references
+
+- mention which observations we want to capture in matrix

docs/measures.md

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+# Measures
+
+TODO:: separation of shares, inequality indices and ratios
+
+**absolute** shows magnitude of difference between subgroups of pop -> focus on absolute welfare
+
+**relative** shows proportional difference between subgroups of pop -> focus on welfare in relation to others
+
+## Direction
+
+### Vertical
+
+Gini coeff, Theil, entropy measure, atkinson indexes
+
+### Horizontal
+
+mean differences between groups; ratios between better off/worse off
+
+GGini; GTheil; GCOV
+
+
+## All indicators
+### Absolute
+
+* quantiles (division by 5) / deciles (division by 10) / centiles (division by 100)
+
+* Absolute Gini index 
+    * ?
+
+* Standard deviation 
+    * deviations from norm in *absolute* terms
+
+* Foster-Greer-Thorbecke (FGT) poverty headcount ratio: absolute deprivation, proportion of population that falls below defined poverty line (= absolute threshold) [@Go2010]
+
+### Relative
+
+1. **Share:**
+   - A share typically refers to a unit of ownership in a company, representing a proportional claim on its assets and earnings. Shares are also known as
+ stocks or equity.
+2. **Ratio:**
+   - A ratio, on the other hand, is a comparison of two quantities. It expresses the relationship between two numbers and is often used to analyze
+ financial and economic data.
+    For example a study analyzing rural-urban income inequality using "the percentage deviation of real wages," it sounds like it might involve a ratio. The term "percentage deviation" implies a measure of how much a variable deviates from its expected or average value, expressed as a percentage. In this case, the ratio could be the percentage deviation of real wages between rural and urban areas, helping to assess the income inequality between the two.
+
+    The income ratio can be calculated as the ratio of the real wages of urban workers to rural workers. If the real wages for rural workers are decreasing ( deviating into the negative), it means the income of rural workers is declining relative to urban workers. This would likely result in an increase in the income ratio because the numerator (urban workers' wages) is relatively higher than the denominator (rual workers' wages). In summary, if the policy leads to a negative percentage deviation of real wages for rural workers, it would likely increase the income ratio between urban and rural workers.
+
+* ratios of quantiles to each other (division of various quantiles)
+
+* Gini coefficient:
+    * based Lorenz curve - population percentage versus accumulated fractions of wealth
+    * is area between lorenz curve and perfect equality
+    * needs: individual/household income/wealth; pop size
+
+* Theil index:
+    * how much income distribution (person x receives y from total income) is away from perfect uniform distribution
+    * log -based
+    * can be disaggregated into subgroups
+    * Theil L > weighting factors are population groups; Theil T > weighting factors are fraction of appropriated income
+
+* Atkinson measures:
+    * measures cumulative deviation of each income in relation to average income value in distribution
+
+* Palma ratio:
+    * dividing share of income/wealth held by top 10% by bottom 40%, thus high = bad
+
+* s80s20, s40s20 ratios:
+    * ?
+
+* Concentration index:
+    * divide whole pop by 100 to find percentiles
+    * then you know how many ppl are in each group and add their total income
+
+* Foster-Greer-Thorbecke-class distribution (@Sotomayor2021)
+
+

docs/meeting_2023-09-15.md

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+# Meeting notes 2023-09-15
+
+- what to do next week?
+- which databases to attack
+    - jstor
+    - web of science
+    - google scholar
+    - econlib
+- my plan:
+    - this week use the inequalities definitions from ILO
+    - put them into overlapping/separated term areas
+        - find alternative terms
+    - create term clusters from the individual areas
+    - do a first exploratory query for e.g. WoS with the term clusters
+- observations to capture
+    - type of work
+    - type of inequality analyzed
+    - type of indicators used (if any)
+    - types of policies
+    - selected group of people
+    - region
+    - country
+    - country classification
+    - level (national/sub-national/comparative)
+    - significance
+    - method
+-> need to really nail down the exclusion criteria as well!
+
+## Typology
+
+Pre-existing inequalities:
+
+- Gender Inequality: Gender inequality is a significant concern in the world of work. It encompasses disparities in wages, job opportunities, representation in leadership positions, and workplace discrimination based on gender.
+
+- Racial and Ethnic Inequality: This dimension focuses on disparities in employment, wages, and treatment in the workplace based on race and ethnicity. It includes issues of racial discrimination and bias.
+
+- Age Inequality: Age-related inequality considers differences in employment opportunities, job security, and treatment of workers based on their age, including issues related to youth unemployment and discrimination against older workers.
+
+- Inequality in Access to Social Protection: This pertains to variations in access to social security benefits, healthcare, and other forms of social protection based on employment status or other factors.
+
+- Educational Inequality: Educational inequalities can lead to disparities in access to quality jobs. Differences in access to education and training opportunities contribute to this dimension of inequality.
+
+- Geographical Inequality: This refers to variations in employment opportunities and working conditions based on geographic location, which can lead to rural-urban disparities.
+
+Resulting inequalities in WoW:
+
+- Occupational Inequality: Occupational inequality relates to disparities in job opportunities, career advancement, and access to certain professions or occupations. It often includes gender-based occupational segregation.
+
+- Income Inequality: This refers to differences in earnings and income among workers. It can be measured by various indicators, such as wage gaps, income quintiles, and the Gini coefficient.
+
+- Inequality in Working Conditions: This involves disparities in working conditions, including workplace safety, hours of work, and access to paid leave and benefits.
+
+- Informal Employment and Precarious Work: Inequality can arise from the prevalence of informal employment, where workers lack social protections and job security, as well as from precarious work arrangements, such as temporary contracts and gig work.

docs/meeting_2023-10-10.md

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+# Meeting notes 2023-09-15
+
+## Questions/issues
+
+Points:
+    - Ensure I am on right track with inclusion criteria at inequality-intervention-outcome nexus
+
+Q:
+    - Which format best to start producing annotated bibliography in for end of month
+    - Should we include purely qualitative reviews?
+        - Capture them (in study screening) and tag accordingly
+    - If including gray literature, I would propose specifically highlighting its status (i.e. adding it through footnotes or otherwise distinguish)
+    - Some possibilities of avoiding docx for review itself to automate some of its descriptive statistics, etc.
+
+- What is the best way to go about integrating
+    - I keep all my notes in the notes folder and zotero library
+    - I continuously update a docx file called 'Scoping_Review_WIP.docx'
+    - The individual milestones/approved and agreed upon changes are pushed to 'Scoping_Review.docx'
+
+## Updates
+
+- Natassia will work on a variety of indicators etc used
+    - e.g. brought forth in ILOStat or LSMS
+- proposing policy categorization in 3 dimensions (for extraction of intervention properties):
+    - institutional: changing the institutional *environment* of a country/context
+        - related to changes in institutions
+        - e.g. legal reforms adopted, adoption of supranational agreements (like Child Labour Convention/Recommendation), policies transforming the way of building/constructing/makeup of institutions, policies furthering institutional social protection of workers, ...
+    - structural: interventions changing the *structural* conditions for people
+        - *can* be related to institutional changes but also to others
+        - e.g. looking at the way factors of globalization shapes outcomes; technology adoption (digital transformation or green revolution); changing geographical conditions through new transportation, or access to services; or improving access to for example access to electricity or improving WASH
+    - agency/social-norms: related to lowering impact of individual characteristics of persons and empowering them individually
+        - e.g. looking at the roots of why women/lgbtq/old people have a harder time finding jobs; policies towards affirmative action
+- using all 3 indicators (yesno/boolean) in matrix also enables more intersectional look at policies, by seeing which ones attack issues from variety of angles and e.g. which ones focus on a single one
+- should allow to substantiate with solid theory and backing through existing mechanisms identified in literature
+
+## ToDo
+
+- [ ] need to identify terms for further search:
+    - [ ] 'definitions of concepts of employment creation'; employment outcomes
+        - [ ] e.g. job creation, work creation, workplace creation
+    - [ ] find the variety of interventions impacting employment
+        - [ ] e.g. infrastructure development, distributive interventions, cash transfer etc
+        - [ ] use Miguel introduced 3-dimension categorization for now
+- [ ] capture indicator: relative/absolute used in studies (extract)
+    - [ ] e.g. diff in total number of jobs per gender (absolute) vs change of job distribution in pct (relative)
+    - [ ] many will share pct looks at these
+- [ ] capture policy intervention type used (extract):
+    - [ ] see above for policy categorization, introduce in extraction list
+    - [ ] create small scale-based justification for three clusters
+- [ ] create clear inclusion/exclusion criteria on basis of above (esp LM outcomes & policy terms)
+
+- [ ] personally: find exact definitions for:
+    - indicators
+    - measures
+    - ratios
+    - and which ones are relative (ratios?) and absolute (measures?) and a comprehensive term (indic.?)

docs/meeting_2023-11-02.md

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+# Meeting notes 2023-09-15
+
+## Questions/issues
+
+Main points:
+    - Currently, two issues of search query:
+        - the scope of results is still very large (~5000 query results & ~1000 sources from snowballing)
+        - is overwhelmingly health-inequality outcome focused
+
+Q:
+    - One of ILO raised points was including Spanish/French studies
+        - I would keep query in English only but also try to screen Spanish/French studies that are in results
+    - I am having some trouble distinguishing between relevant / non-relevant for health outcome category
+        - e.g. policies to prevent STD transmission for sex workers -> probably in since it directly addresses an area of outcome inequality; and focuses on intersectional issues for cis men and trans workers
+        - e.g. influence of socio-economic inequalities to out of hospital medical issues -> probably out since it does not fit into world of work though still focusing on issues of inequality
+    - in reviewing methodology, ILO commented on 'LM-adjacent outcomes' not necessary for inclusion criteria;
+      I am not sure how to understand that comment - we need to measure some outcomes through our indicators and it should be LM adjacent to fix it somewhat to the WoW?
+    - I can not find the commented version of the extended outline anymore, is it still in DB?
+
+## Updates
+
+- have been executing the search protocol and started screening results
+    - identified around 30 previous reviews which focus on nexus of inequality, world of work and inequality-outcomes
+
+## Indicator
+
+[11:41 AM] Miguel Nino Zarazua
+    **PLEASE INCLUDE THE FOLLOWING INEQUALITY MEASURES IN THE DISCUSSION
+Regarding inequality measures, we need to discuss inequality indices and ratios/shares by families and then group them according to their normative theoretical principles, that is, relative, absolute, or horizontal perspectives.
+Vertical approaches
+
+1. Gini family (Gini index - a relative measure, AND Absolute Gini index-an absolute measure)
+2. Generalized Entropy family (Mean Log Deviation, Theil index, Coefficient of Variation - all relative inequality measures, AND Standard Deviation - an absolute inequality measure.) Please make sure that we when revising the literature, we do not miss any relevant measure.
+3. Atkinson Inequality Family
+4. Ratios / Top/Bot family (Palma, s80s20 ratio, s40s20 ratio)
+
+As I pointed out earlier, presenting a discussion on the properties of inequality measures is vital because we will need to discuss the implications of focusing on e.g., the Gini using wages or earnings vis-a-vis using the absolute Gini on the same outcomes. Furthermore, we also need to discuss the desirable properties that inequality measures need to satisfy (anonymity, scale independence, population independence, transfer principle, subgroup decomposability, etc.) and the implications of the limitations of some of the indices (e.g., the Gini index is not decomposable by population subgroups, which means that it cannot be used to estimate e.g. earnings inequalities by subnational levels. In this case, the MLD would be a better choice.
+Horizontal approaches
+Furthermore, we need to discuss the normative and policy implications of adopting horizontal inequality measures or ratios that compare differences in outcome between groups:
+Group Gini
+Group Theil
+Group CoV
+Ratios and shares by groups (for example looking at differences between labour force participation by men and women, or by age cohorts).
+In this sense, the discussion on inequality measures needs to go beyond relative measures and present a discussion in the context of variables and indicators related to the WoW.
+PLEASE conduct a review of the literature on inequality measures to carry out this task. I have included some papers in the shared Dropbox folder BUT please note that these are not exhausting. Please conduct a review and add ALL the consulted materials in the shared folder and in the Zotero Library.
+For the purpose of the analysis, the identification of variables in the ILO micro dataset (and ILOSTAT) that capture forms of work AND labour market outcomes is key, as we will need to outline how the ILO could use inequality measures/ratios on the existing variables/indicators to track and monitor progress in work and labour inequalities, AND also identify key informational gaps.
+Please take a look at the variables and dimensions that are available on the ILO microdata collection (ILO Microdata Explorer) and identify the following:
+
+1. Existing indicators to estimate vertical and horizontal inequality measures
+2. What other variables are currently available in main sources of information, namely: a) Population Censuses, b) Labour Force Surveys, c) Household Income and Expenditure Surveys and d) Other relevant Household Surveys could be used to conduct inequality analysis. PLEASE check questionnaires of a-d to do this.
+3. Identify key variables / indicators for inequality analysis that are currently NOT available on a-d. Please pay attention to indicators that are relevant for the Wo
+
+## ToDo
+
+- [ ] now:
+    - [x] create summary of how we are doing the review - SUN
+        - WHAT we search
+        - WHY we search it
+        - HOW we screen for/extract from it
+    - [ ] have a read through comments on outline
+    - [ ] have document outline/annotated bib for ILO ready eow at the latest
+    - [ ] look at new resolutions (Oct 2023)
+    - [x] include union/trade unionization/collective action in search terms
+    - [x] find way of capturing social dialogue in query?
+    - [-] create 'task list' for each screening/extraction step:
+        - screening:
+            - add intervention/outcome/country-region/in-out-criteria (see also screening tasks)
+            - add additional information: intersectional, review, method::qualitative
+        - extraction:
+            - build extraction grid tool (from SPPF, with above policy categorization)
+- [-] querying: create way of having one source of truth for search terms; e.g. script to transfer python lists from yaml/json files to python lists and -> output functional query (into script or through cmdline)
+- [-] screening:
+    - build tool: contains collection of all tags; kept up-to-date for any added; contains checklist for 'tag areas' (outcome; intervention; inequality; region; actor; method; ...)
+- [-] extraction:
+    - [ ] have category of 'measure' extracting type/kind of measure used (Gini/Group Gini/Palma/share/measure of means)
+    - [ ] focus on tripartism:
+        - [ ] tag studies with "actor::collective_action" (or actor::trade_union?) tag to highlight role of collective action (agency/structural)
+            - e.g. trade union talks; minimum wage; collective bargaining; ...
+        - [ ] tag studies which highlight e.g. role of individual sectors with "actor::private" to highlight role of private sector/employers (structural)
+        - [ ] tag studies which talk about concept of "social dialogue", "tripartism" or similar how employers/unions/gov solve issues to highlight soc dialog
+            - [ ] perhaps find "historical examples which highlight this process, even if we don't include it systematically"
+- [-] paper:
+    - [-] change citations of older ILO resolutions to 2023 ones in-text

docs/meeting_2023-11-29.md

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+# Meeting
+
+## Questions
+
+- screening:
+    - would sort out the multitude of 'facilitators and barriers to XY equality' in screening
+        - when they do not focus on specific interventions/policies
+        - may tag them for in-text citation as existing literature / finding channels
+    - are we sorting out/in studies which look at one *policy* but not necessarily one that is aimed at / based on strategy of inequality reduction:
+        - e.g. McLay2022, policy of stay-at-home orders during COVID effects on gender inequality 
+            -> policy but not focused on inequ. decr.
+        - or Fasang2022, looks at factors influencing intersectional inequalities in family and work outcomes by gender & race
+            -> looks at inequality factors but *not* impacts of policy dedicated to changing them
+        - or Cetin2022, looks at effect case of opening 2 food factories and empowerment through joining public space - event but not *policy*

docs/meeting_2023-12-11.md

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+# Meeting Miguel
+
+- Sync to Dropbox new versions
+    - Fix automatic synchronization
+- Restructure synthesis from inequ -> intervention to intervention -> inequ
+- Structure synthesis overall to adhere to ILO thematic areas

docs/meeting_2023-12-12.md

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+# Meeting ILO
+
+## Progress
+
+- search protocol and methodology has been finished first
+- the identification process was finished middle of November
+    - the raw study pool from querying was created and all potential studies identified
+- screening was finished 2 weeks ago
+- now I'm extracting, we are at roughly 
+    - 30 relevant studies done and by current estimates ~> 60 relevant ones are in the pool
+    - so that is what could be done end of the year, with findings extracted
+
+- but this is missing the second snowballing pass, getting relevant sources from
+    - to get links of different areas
+
+## Deadlines
+
+- december will be doable for a structural outline and preliminary findings
+- the complete pass will 
+
+
+## Outline feedback
+
+- a lot of feedback on the outline
+- issues with 
+- technical/theoretical/operational work
+    - but also highly political
+    - we need to respect the political process
+    - 
+- workers and employers side will be the 
+- final output will be pre-approved
+    - send **individual output** eoy
+        - it need to be a 'visibly' final output
+        - it does not need to be final but *complete*
+    - we will **send invoices** for our work beginning of year
+    - we will still be paid with 2023 funds
+    - work will **continue afterwards**
+
+- plan
+    - sending revised outline
+    - getting feedback
+    - have presentation
+    - sending final collated/collected output
+
+- this is planned as a 'phase1' project
+    - we are laying foundations for additional statistical work used by ILO
+    - we can bring our own own ideas
+
+- new outline 
+    - presentation of work
+    - informal workplan
+    - plans for phase2
+
+- from my side to at least minimize the issue of 'getting lost in translation' for findings or approaches
+    - I made sure to work out the rigorous / academic methodology of the review process
+    - the important/priority part now is to translate that into the findings
+
+---
+
+- "human driven exercises so there is always the possibility for errors"
+- rosalia and colleagues the reach colleagues?

docs/meeting_2023-12-23.md

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+# Meeting Miguel 2023-12-23
+
+achieved:
+
+- [x] fleshed out sections:
+    - education
+    - infrastructural change
+- [ ] discussion filled out
+    - [ ] seen through inequalities, what works what does not
+    - [ ] move existing reviews into discussion
+- more synthesized findings
+- introduction better prepared
+- conclusion?
+
+- we have section on unionisation now
+
+# Remaining issues
+
+- synthesis writing - remains very descriptive for the time being
+- discussion -
+    - in-text breakdowns exists but missing in tabular representation
+    - still separated from qualitative grouping into more/less effective channels
+- appendix extraction - matrix structure?
+
+# I would aim to do next:
+
+- breakdowns by country income groups
+- create tabular information containing:
+    - indicator (relative/absolute)
+    - horizontal/vertical
+    - causal/correlational
+    - study design
+- move matrices into appendix
+- keep on extracting rest of snowball pool
+
+# questions
+
+- how much to focus on indicators in my analysis
+    - for now mentioning them and have in tabular breakdown
+- the plan is that we hand in a draft report end of year now
+    - but I still hand in my side as scoping review separately?

docs/meeting_2024-01-26.md

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+# Meeting Miguel 2024-01-26
+achieved:
+
+- [x] before end of year:
+    - hammered out a preliminary state of the art
+    - we have a synthesis that roughly follows our framework
+
+    - 800 remaining to be screened/extracted
+
+- [ ] focus now:
+    - table reviewing main findings and strength of evidence
+    - screen the remaining 200 studies
+    - improve the writing on the synthesis itself
+        - to adhere a little closer to framework categorization
+- [ ] questions:
+    - large segment of 'model-only' studies, mostly from macroeconomics
+        - they do not form part of our main extracted sample pool
+          but I just refer to them
+    - logistical:
+        - create extraction matrices with main/statistical findings
+            - are we going to put those in the main Appendix?
+              or do we just pass on the CSV extraction as the data?
+        - is there a specific length I am going for for the end result?
+        - is it fine if for now I leave intro/conclusion more vague to fit in with rest of the document
+    - what we do not have anything on yet:
+        - ILO Convention recommendations
+        - Transition to the formal economy
+        - fair trade
+
+findings - channels - policy recc
+gender inequality
+- supply-side effects, esp maternal (family planning incentives; job protection; equal opportunity household carework)
+- persistent discrimination and cultural views (strenghtening female agency, vicious circle of low FLFP and education)
+- organisational disadvantagement in new economy (promoting female networking needs, self-promotion, managerial discretions)
+
+spatial inequality
+- spatial component of non-spatial policies ()
+
+## TODO
+
+- [x] sort synthesis closer to framework
+- [ ] create main findings table
+- [ ] extract as much as possible
+- create external (representativeness) / internal (strength of method) validity viz
+
+### Synthesis restructuring
+
+- employment creation
+    Whitworth2021
+- equal access to quality education & training
+- worker protection
+    paid leave
+        Broadway2020
+        Dustmann2012
+        Davies2022
+    unioniz
+        Cardinaleschi2019
+        Dieckhoff2015
+- formalization
+    minimum wage laws
+        Chao2022
+        Alinaghi2020
+        Wong2019
+        Gilbert2001
+        Militaru2019
+- gender equality / non-discrimination
+    Clark2019
+- fair trade and development
+    Alexiou2023
+    Ferguson2015
+- adequate social protection
+    Carstens2018
+    SilveiraNeto2011
+    Hardoy2015
+    Mun2018
+    Hojman2019
+
+institutional
+    labour laws / regulatory systems
+        Broadway2020
+        Dustmann2012
+        Mun2018
+    protective environmental policies
+        Davies2022
+        Kuriyama2021
+        Stock2021
+    minimum wage laws
+        Chao2022
+        Alinaghi2020
+        Wong2019
+        Gilbert2001
+        SilveiraNeto2011
+        Militaru2019
+    collective bargaining
+        Alexiou2023
+        Ferguson2015
+        Cardinaleschi2019
+        Dieckhoff2015
+        Ahumada2023
+    active labour market policies
+        Whitworth2021
+    social protection laws/systems
+        Carstens2018
+        Hardoy2015
+        Clark2019
+        Hojman2019
+structural
+    economic growth
+        Adams2015
+        Xu2021
+        Khan2021
+        Liyanaarachchi2016
+        Rendall2013
+        Cieplinski2021
+    fiscal policies
+        Shin2006
+        Wang2020
+    automation/technology
+    transport infrastructure
+        Blumenberg2014
+        Adam2018
+    informal economy
+    education access
+        Delesalle2021
+        Pi2016
+        Suh2017
+        Coutinho2006
+        Mukhopadhaya2003
+    training?
+        Shepherd-Banigan2021
+        Rosen2014
+        Gates2000
+        Poppen2017
+        Thoresen2021
+agency
+    occupational segregation and social exclusion
+        Bailey2012
+        Wang2016
+        Standing2015
+        Al-Mamun2014
+        Emigh2018
+    unconscious bias and discriminatory norms
+        Field2019

docs/meeting_2024-02-09.md

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+# Meeting Miguel 2024-02-09
+
+achieved:
+
+- [ ] remaining study batch screened
+    - added active labour market policy studies
+    - starting to wrangle synthesis:
+        - better categorization for some and did dual categorizations, e.g. job search assistance or subsidized employment/wages partly into ALMP
+        - Moved away again a little from strict framework adherence to make more sense for the synthesis -> workfware programmes
+
+- [ ] focus now:
+    - table reviewing main findings
+    - screen the remaining 200 studies
+    - improve the writing on the synthesis itself
+        - to adhere a little closer to
+
+- [ ] questions:
+    - logistical:
+        - create extraction matrices with main/statistical findings
+            - are we going to put those in the main Appendix?
+              or do we just pass on the CSV extraction as the data?
+        - is there a specific length I am going for for the end result?
+        - is it fine if for now I leave intro/conclusion more vague to fit in with rest of the document
+    - writing:
+        - categories overlap sometimes, of course
+            - a study on NREGS in India which has minimum wage effects as primary channel
+            - a study on vocational training which fuses it with workfare program (ALMP)
+        - I would put the focus on 'per-area' conclusions, not the 'per-study' 
+    - validity visualizations
+        - built a simple scoring rubric of 0-not fulfilled, 1-implicitly fulfilled, 2-explicitly fulfilled
+        - internal: (design + method) - (selection bias + measurement bias + confounding bias + performance bias)
+        - external: (representativeness) - (generalizability concerns + population invalidity + ecological invalidity)
+        - internal:
+            - design (observational/qualitative, correlational, causal(quasi-experimental/experimental))
+            - confounding variable control (no mention, limits mentioned, limits mentioned and explicitly discussed)
+            - data collection (not mentioned, sources mentioned, sources/sample size/observation-length given)
+            - statistical control (no robustness checks, data robustness mentioned, explicit robustness checks)
+            - biases (no mention, possible biases mentioned, explicitly controlled for/influcence explained)
+        - external:
+            - generalizability (no mention, implicitly generalizable, explicitly generalizable)
+            - representativeness of sample (no mention/sub-national, national, regional/global)
+            - ecological validity (no mention, implicit policy implication, explicit policy implication)
+
+- [ ] TODO
+    - main findings table still remaining
+    - write it out 
+    - shorten?

docs/review-pool-years-done.md

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+# Years done of overall sample pool
+
+- [x] 2000
+- [x] 2001
+- [ ] 2002
+- [ ] 2003
+- [x] 2004
+- [ ] 2005
+- [ ] 2006
+- [ ] 2007
+- [ ] 2008
+- [ ] 2009
+
+- [ ] 2010
+- [ ] 2011
+- [x] 2012
+- [ ] 2013
+- [x] 2014
+- [x] 2015
+
+- [x] 2016
+- [x] 2017
+- [x] 2018
+- [x] 2019
+- [x] 2020
+- [x] 2021
+- [x] 2022
+- [x] 2023

docs/screening-tool.md

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+# Scoping tool
+
+- after identification a large pool of potential studies will be in the zotero index (usually > 1000)
+- to make scoping (somewhat) rapid, we employ multi-step exclusions
+    - rapid scoping reviews sometimes only make use of single steps, e.g. only title-screening, as inclusion criteria
+- title screening
+    - exclude all studies that do not match with `out::title`
+    - move all studies that do match into next step with `TODO::abstract`
+- abstract screening
+    - exclude all studies that do not match with `out::abstract`
+    - move all studies that do match into next step with `TODO::full-text`
+    - studies which seem reasonably sure to be of vale can be tagged with `relevant` to be easier to spot later on
+- fulltext screening
+    - exclude all studies that do not match with `out::full-text`
+    - move all studies that do match into next step with `TODO::extract` 
+- for any of the above steps:
+    - if the study is a potentially relevant review, mark with `TODO::review` for later snowballing
+    - if you are unsure and require outside input mark with `TODO::QUESTION`
+- extraction
+    - mark successful extraction with `done::extracted`
+    - often done as full-text screening and extraction in single step
+    - add any relevant tags, but at least `type::`, `inequality::`, `country::` and possibly `region::` tags
+
+# Category reference
+
+Tracks the categories and possible values of tags (or 'keywords') for the screening process step of scoping.
+
+## TODO
+
+A work-in-progress tag which simply states the step of the scoping review 
+process the respective source is currently at.
+
+- TODO::QUESTION
+- TODO::SPANISH
+- TODO::abstract
+- TODO::full-text
+- TODO::review
+- TODO::title
+- done::extracted
+- done::preliminary
+
+## Cite
+
+Even if a study is not directly relevant for the final extraction pool,
+it might contain valuable information. These tags give the reason they may
+prove interesting yet when considering the inequalities analyzed by them.
+
+- cite::channels
+- cite::framework
+- cite::further_reading
+
+## Country
+
+The main country of interest for the study. Multiple possible for comparative/multiple case studies. 
+Global studies, or ones using dozens of countries will not be tagged.
+
+- country::Argentina
+- country::Armenia
+- country::Australia
+- country::Austria
+- country::Bangladesh
+- country::Belgium
+- country::Bengal
+- country::Bolivia
+- country::Bosnia_Herzegovina
+- country::Botswana
+- country::Brazil
+- country::Britain
+- country::Burundi
+- country::Cambodia
+- country::Cameroon
+- country::Canada
+- country::Chile
+- country::China
+- country::Colombia
+- country::Croatia
+- country::Cyprus
+- country::Czech_Republic
+- country::Denmark
+- country::Ecuador
+- country::Eswatini
+- country::Ethiopia
+- country::Finland
+- country::France
+- country::Germany
+- country::Ghana
+- country::Greece
+- country::Guatemala
+- country::Honduras
+- country::HongKong
+- country::Hungary
+- country::India
+- country::Indonesia
+- country::Iran
+- country::Ireland
+- country::Israel
+- country::Italy
+- country::Japan
+- country::Jordan
+- country::Kazakhstan
+- country::Kenya
+- country::Korea
+- country::Liberia
+- country::Lithuania
+- country::Madagascar
+- country::Malawi
+- country::Malawy
+- country::Malaysia
+- country::Malta
+- country::Mexico
+- country::Mongolia
+- country::Morocco
+- country::Nepal
+- country::Netherlands
+- country::New_Zealand
+- country::Nicaragua
+- country::Nigeria
+- country::Norway
+- country::Pakistan
+- country::Paraguay
+- country::Philippines
+- country::Poland
+- country::Portugal
+- country::Romania
+- country::Russia
+- country::Rwanda
+- country::Saudi_Arabia
+- country::Senegal
+- country::Slovakia
+- country::South_Africa
+- country::Spain
+- country::Sri_Lanka
+- country::Sweden
+- country::Switzerland
+- country::Taiwan
+- country::Tanzania
+- country::Thailand
+- country::Timor-Leste
+- country::Turkey
+- country::US
+- country::Ukraine
+- country::Uruguay
+- country::Vietnam
+- country::West_Bank
+- country::Zambia
+
+## Inequality
+
+The type of inequality illuminated by a study.
+Will often be multiple, a strong sign for a study's intersectional approach.
+
+- inequality::age
+- inequality::consumption
+- inequality::disability
+- inequality::education
+- inequality::ethnicity
+- inequality::gender
+- inequality::generational
+- inequality::health
+- inequality::income
+- inequality::language
+- inequality::lgbt
+- inequality::migration
+- inequality::poverty
+- inequality::racial
+- inequality::socio-demographic
+- inequality::spatial
+
+## Issue
+
+If there is an issue with a study which may impede further 
+progress, or any progress at all - even if it may not be sorted
+out from relevance yet, or if it has.
+
+- issue::age
+- issue::language
+- issue::no-access
+
+## Method
+
+If one method is clearly marked as primary, or surprisingly is not 
+the primary method within title and or abstract, it will be noted 
+here.
+
+- method::qualitative
+- method::quantitative
+
+## Out
+
+The reason, or step of the screening process for the study falling 
+out of relevance. Will be used later to generate accurate numbers 
+per screening step.
+
+- out::abstract
+- out::full-text
+- out::language
+- out::review
+- out::title
+- out::year
+
+## Region
+
+The general world region a study is working in.
+May align to the individual country (or countries) it is analyzing,
+but may also stand on its own.
+
+- region::global
+- region::AP
+- region::EU
+- region::LAC
+- region::MENA
+- region::NA
+- region::SSA
+
+## Review
+
+If a study is a review, it will be captured under one of these tags.
+
+- review::?
+- review::critical
+- review::integrative
+- review::meta
+- review::narrative
+- review::scoping
+- review::systematic
+- review::umbrella
+
+## Type
+
+The primary type of intervention, circumstance or shock the study is interested in.
+
+- type::automation
+- type::cash_transfer
+- type::child_labor
+- type::collective_action
+- type::cooperative_entrepreneurship
+- type::counseling
+- type::csr
+- type::electrification
+- type::experimental
+- type::infrastructure
+- type::institutional
+- type::marketization
+- type::maternity_benefit
+- type::microcredit
+- type::minimum_wage
+- type::pension
+- type::regulation
+- type::rtw
+- type::structural
+- type::subsidy
+- type::taxation
+- type::trade_liberalization
+- type::training
+- type::ubi
+- type::universal_licensing
+- type::volunteering
+- type::welfare
+- type::work_programme
+
+## Misc
+
+- ⚠️ Invalid DOI
+- ⛔ No DOI found
+- favorite
+- relevant
+- integrated
+- intersectional
+- definition

docs/terms_of_reference-key_terms.md

@@ -0,0 +1,261 @@
+# Terms of Reference
+
+- attention to root causes of inequalities of work
+    - drivers and determinants across all dimensions
+- addressing both distribution and redistribution
+    - seen as the most innovative one (?)
+- fundamental principles and rights in intl labor standards
+- social dialogue and tripartism
+- interconnectedness, integration and monitoring
+- country-specific approaches
+
+the assignment will:
+- explain what inequalities in the world of work are
+- why they should be addressed
+- what is added value of ILO doing so
+
+with target groups:
+- ILO staff & constitutents
+- external stakeholders & partners
+
+objectives:
+- general: support position of ILO as key actor in ongoing debates and initiatives of multilateral system on poverty and inequalities
+- 1: improve understanding of what inequalities in the world of work are
+    - root causes
+    - their linkages
+    - how they feed into outcomes
+- 2: identify evidence-based policy responses to prevent and reduce inequalities in the world of work
+    - minimizing of inequalities
+    - reducing the outcomes of inequalities
+
+dates:
+- Sep 30: 
+    - draft detailed outline for conceptual framework
+    - chapter indication
+    - definition of inequalities in world of work, drivers and determinants
+- Oct 30:
+    - annotated bibliography identifying effective and evidence-based policy responses to adress inequality in world of work
+    - annotated bibl will inform dev of conceptual framework
+    - presentation to task force to gather inputs and views from diff departments
+
+## Working Strategy
+
+### Identifying root causes of LM inequalities
+
+- a fundamental typology of inequalities within the LM *and* beyond the labor market is necessary
+    - as ILO recognizes inequalities generate feedback cycles
+        - within a life cycle and inter-generationally (through inequalities of outcomes)
+    - to understand inequalities in the world of work, those beyond should not be a black box
+        - gender/socio-demographic/pre-existing inequalities
+        - put very simply: 
+            - it might make sense for a mother to move away from a job to a space with better educational access for her child and the resulting issue should concern *both*
+                - access to better education in the original region
+                - the impact of better job availability or active labour market policies in the new region
+- for each form of work to understand the primary inequalities in the LM
+    - we need to understand how pre-existing inequalities feed into them as independent variables
+    - often this will take the form of e.g. socio-demographic inequalities reflecting income inequalities
+    - but not always, and that is the locating of the root causes I would see as primary goal for the first part of the review
+
+### Identifying evidence-based policy responses which address these inequalities
+
+- if we manage to break open the black box of root causes in this way
+    - for vertical but especially for horizontal inequalities
+- it should make it easier to analyze the impacts of policies removing inequalities for
+    - the causes they help reduce the effect of
+    - the impact on labour market inequalities itself
+    - and the effects on resulting equality/inequalities of opportunity 
+
+# Conceptual definitions
+
+labour
+
+'nature of work'
+
+work
+
+worlds of work
+
+inequality in work
+
+## forms of work
+
+paid employment work
+own-use production work
+unpaid work
+care work 
+volunteer work
+unpaid trainee work
+
+## labor market outcomes
+
+employment
+unemployment
+underemployment
+labour force participation
+self-employment/informality
+labour productivity
+skills
+wages/earnings
+hours worked
+job security
+social protection coverage
+labour mobility
+
+## socio-demographic categories
+
+gender
+ethnicity
+race
+age
+disability
+
+## types of inequalities (in worlds of work)
+
+general characteristics of inequality:
+
+- 'pre-world of work' inequalities; starting well before individuals enter world of work
+    - unequal opportunities to healthcare, literacy, quality education
+    - often borne from poverty, gender, family background, lack of legal status/identity
+    - others often born with many advantages that give easy opportunistic access to build human capital
+- inequality of opportunities !== inequality of outcome
+    - often, today's inequalities affect future (generations') opportunities
+    - high levels of current inequality = reduction in future social mobility
+    - conversely, (some?) focus should be put on equality of outcome today to ensure equality of opportunity tomorrow
+- while focusing on these intra-country horizontal inequalities, inter-country inequalities (esp income inequality, but predisposing other as well) should not be neglected
+- distinction between:
+    - vertical inequality
+        - between all households in a country
+    - horizontal inequality
+        - betweek different groups
+        - disparities in employment outcomes, labour rights, opportunities between groups depending on gender, age, nationality, ethnicity, health status, disability or other characteristics
+
+forms of inequality:
+
+- access to essential services (health, education, housing)
+- income inequality
+- access to means of subsistence (esp related to employment)
+- gender inequality (part of horizontal, being one of the 'greatest forms of inequality today')
+    - results in gender-based violence, harassment, domestic violence, unpaid care work
+    - especially girls often facing unequal opportunities and 'persistent gender stereotypes in their access to education and health services and in other aspects of life' [13, ILC]
+    - in most regions wome over-represented among poorest and under-represented among richest people (esp in SoutAs;EastAs;Pacific regions)
+        - born primarily from unequal access to quality education, inequalities and injustices in labour market participation, gender earnings gaps
+        - often also results in children being exposed to severe health and food deprivation and differences in control over assets (capital/land)
+        - recently exacerbated by COVID-19 reversing equality progress through increased women's/men's paid/unpaid/care work inequalities 
+- girls/boys from ethnic minorities, indigenous, tribal populations, remote rural areas often facing barriers accessing quality education & essential services
+    - indigenous people account for 6% of world pop but 19% of extreme poor
+    - stark contrast between migrant workers' high labour force participation rates and large proportion of low-income households
+- spatial inequalities (rural/urban; small/large cities; richt/poor regions)
+    - contributes to overall more fractures/unequal societies
+- intersectional inequalities (made possible to highlight by vert/horiz inequality split)
+- unequal distribution of work & labour income
+    - among workforce among most important determinants of inequality
+    - unemployment: forecloses income prospects; highest rate in young people
+    - underemployment:
+        - low wages make meeting basic needs impossible (esp. food, healthcare, education, decent housing)
+        - including differentiation time-related underemployment (would like to work more paid hours); potential labour force (would like to work but not actively searching or not available for work); creates (way) higher numbers than purely unemployment numbers - especially in LIC
+    - recently, (COVID-19) women, young people, less educated, low earners less likely to keep their jobs
+
+## types of policies
+
+- income:
+    - difference between primary distribution ('market income', through property and employment) and secondary distribution ('disposable income', through taxes and transfers), and tertiary distribution (public services)
+    - tax and transfers redistributing incomes towards greater equality in disposable income
+        - extent of redistribution limited by small fiscal resources (e.g. through informal labour predominance)
+    - policies to reduce income difference between urban/rural, ethnic minorities/majority one of main drivers of reduced income inequality (LAC region)
+
+# Summary draft
+
+- pre-world of work characteristics already taking huge influence on labour market and related equality of outcomes
+    - inequality in access to essential services (health, education, housing)
+    - inequality in access to means of subsistence (esp related to employment)
+- income inequality huge driver of resulting inequalities, in turn already influenced by characteristics, additionally:
+    - spatial inequality
+    - gender inequality
+    - employment inequality:
+        - unemployment: forecloses income prospects; highest rate in young people
+        - underemployment: low wages make meeting basic needs impossible (esp. food, healthcare, education, decent housing)
+            - split into time-related underemployment and potential labour force
+
+# Additional Terms
+
+## Methods
+
+### Survey-based
+
+- Likert scale (1-4/1-5 scale questionnaire)
+- Cronbach's alpha test score (reports coherence of set of items in a group)
+- Binary answer (yes/no)
+
+## Representativeness
+
+In academic studies, representativeness can be assessed at various levels,
+ depending on the scope and objectives of the research. Here are the different
+ levels of representativeness commonly considered in academic studies:
+
+1. National Representativeness: This level of representativeness indicates that the
+ sample used in the study is reflective of the entire population of a specific
+ country. The findings are intended to be generalizable to the entire nation.
+
+2. Subnational Representativeness: At this level, the study aims to be
+ representative of a specific subnational region within a country, such as a state,
+ province, or city. The findings are intended to be applicable to the population
+ within that specific geographic area.
+
+3. Regional Representativeness: Some studies may focus on representing a broader
+ region, such as a group of countries within a certain geographical area. The
+ findings are intended to be generalizable to the population within that regional
+ context.
+
+4. Organizational or Institutional Representativeness: In some cases, studies may
+ aim to be representative of specific organizations, institutions, or industries.
+ The findings are intended to be applicable to similar entities within the same
+ category.
+
+5. Demographic Representativeness: This level of representativeness focuses on
+ ensuring that the sample used in the study is representative of specific
+ demographic characteristics, such as age, gender, ethnicity, income level, or
+ education level.
+
+6. Sectoral Representativeness: Some studies may aim to be representative of
+ specific sectors or industries, such as healthcare, education, finance, or
+ technology. The findings are intended to be applicable to similar sectors or
+ industries.
+
+These different levels of representativeness help researchers and readers
+ understand the extent to which the findings of a study can be generalized to
+ different populations, regions, or contexts. It is important for researchers to
+ clearly define the level of representativeness they are aiming for and to use
+ appropriate methods to achieve it.
+
+## Validity
+
+Internal validity and external validity are both important concepts in research
+ design and are used to assess the quality and generalizability of study findings.
+ Here's a brief explanation of the differences between the two:
+
+Internal Validity:
+- Internal validity refers to the extent to which a study accurately measures the
+  relationship between the variables it is investigating, without the influence of
+  confounding factors.
+- It assesses whether the observed effects or outcomes in a study can be attributed
+  to the manipulation of the independent variable, rather than to other factors.
+- Factors that can impact internal validity include experimental design, control of
+  extraneous variables, and the accuracy of measurements and data collection methods.
+
+External Validity:
+- External validity refers to the extent to which the findings of a study can be
+  generalized to other populations, settings, or conditions beyond the specific
+  sample and context studied.
+- It assesses the degree to which the results of a study can be applied to
+  different individuals, groups, or situations.
+- Factors that can impact external validity include the representativeness of the
+  sample, the ecological validity of the study conditions, and the relevance of the
+  findings to real-world settings.
+
+good link: https://learning.edanz.com/validity-systematic-review/
+
+In summary, internal validity focuses on the accuracy and reliability of the study's
+ findings within the specific context of the research, while external validity
+ focuses on the generalizability and applicability of the findings to broader
+ populations or settings. Both types of validity are important considerations in
+ research design and interpretation of study results.

docs/validity_tool.md

@@ -0,0 +1,28 @@
+# Validity estimators
+
+For a general concept description see ../docs/terms_of_reference-key_terms.md#validity
+
+From Maitrot2017 -> Section 4, Figure 3 and Appendix table notes
+They rank *only* quasi-experimental/experimental
+
+Do we too?
+
+## internal
+
+
+0-5 ranking using study design/method:
+
+OLS=2 (ordinary least squares)
+DID=3 (difference-in-difference)/(-in-difference)
+DM=3 ()
+PSM=3.5 (propensity score matching)
+IV=4 (instrumental variable)
+RD=4.5 (regression discontinuity)
+RCT=5
+
+## external
+
+non-representative surveys=2
+sub-nationally representative surveys=3
+nationally representative surveys=4
+surveys based on census data=5