diff --git a/scoping_review.qmd b/scoping_review.qmd
index e66ec76..c770a99 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -611,6 +611,62 @@ Since policies employed in the pursuit of increased equality can take a wide for
 the following synthesis will first categorize between the main thematic area and its associated interventions,
 which are then distinguished between for their primary outcome inequalities.
 
+Findings:
+
+| area of policy        | findings                                                                             | channels                                                                                                       |
+| ---                   | ----                                                                                 | ----                                                                                                           |
+| minimum wage          | mixed evidence for short-/medium-term income inequality impacts                      | can lead to income compression at higher-earner ends                                                           |
+|                       | some evidence for long-term inequality decrease                                      | job loss offsets through higher wages                                                                          |
+|                       |                                                                                      | some spatial transfer from urban manufacturing sectors to rural agricultural sectors                           |
+|                       | bad targeting can exacerbate existing inequalities                                   | negative effect on women's hours worked if strong household labour divisions                                   |
+|                       |                                                                                      | low-earners sometimes secondary high-income household earners while low-wage households have no earners at all |
+|                       | potential impact larger for single parents, rural/disadvantaged locations            | women more affected if they make up large share of low-wage earners                                            |
+|                       |                                                                                      |                                                                                                                |
+| paid leave            | evidence for significant increase in rtw after childbirth                            | esp. disadvantaged women benefit due to no prior employer-funded leave                                         |
+|                       | some evidence for positive rtw effects to occur with medium-/long-term time delay    | short-term exit but no long-term increase to hiring pattern discrimination                                     |
+|                       |                                                                                      | can exacerbate existing household labour division                                                              |
+|                       | mixed evidence for fixed-/short-term contracts counter-acting effect on rtw          | fixed-term contracts often insufficiently covered by otherwise applicable labour regulation                    |
+|                       |                                                                                      |                                                                                                                |
+| collective bargaining | evidence for decreased income inequality with strong unionisation                    | stronger collective political power vector enables more equal redistributive policies                          |
+|                       |                                                                                      | increased probability for employment on formal, standard employment contract                                                                                                               |
+|                       | marginal evidence for increased income/representation of women/minorities in workforce/management | internal heterogeneity due to predominantly affecting median part of wage distribution |
+|                       |                                                                                      |   self-selection of people joining more unionised enterprises/organisations/sectors                                                                                                                                                                                                                           |
+|                       |                                                                                      |   depending on targeting of concurrent policies can bestow more benefits on men, increasing horizontal inequalities                                                                                                             |
+
+Strength of Evidence
+
+```{python}
+# Create a dictionary with the data for the dataframe
+data = {
+    'strong_internal_validity': [
+        'weak', 'strong', 'strong', 'strong', 'strong', 'weak', 'strong', #minimum wage
+        'strong', 'weak', 'strong', 'strong', # paid leave
+        'strong', 'weak' # protective env policies
+    ],
+    'strong_external_validity': [
+        'strong', 'strong', 'weak', 'weak', 'strong', 'strong', 'strong', #minimum wage
+        'strong', 'strong', 'weak', 'weak', # paid leave
+        'strong', 'weak' # prot env policies
+    ],
+    'policy': [
+        'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage', 'minimum wage',
+        'paid leave', 'paid leave', 'paid leave', 'paid leave',
+        'environmental infrastructure','environmental infrastructure'
+    ]
+}
+
+# Create the dataframe
+test_df = pd.DataFrame(data)
+
+# Assuming df is your dataframe
+# Melt the dataframe to long format for plotting
+melted_df = test_df.melt(value_vars=['strong_internal_validity', 'strong_external_validity'], id_vars
+='policy', var_name='Validity')
+
+# Create a stacked histplot using Seaborn
+sns.histplot(data=melted_df, y='policy', hue='Validity', multiple='stack')
+```
+
 ## Institutional
 
 ### Labour laws and regulatory systems