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+# By age
+
+## Overview
+
+The deep poverty by age group metrics provide insights into how a policy reform affects deep poverty rates for different age categories. These metrics are part of the `EconomicImpact` class and can be calculated for children, adults, seniors, and the overall population.
+
+## Available Metrics
+
+1. `poverty/deep/child`: Calculates the deep poverty rate for children.
+2. `poverty/deep/adult`: Calculates the deep poverty rate for adults.
+3. `poverty/deep/senior`: Calculates the deep poverty rate for seniors.
+4. `poverty/deep/age/all`: Calculates the overall deep poverty rate.
+
+## Metric Structure
+
+Each metric returns a dictionary with the following keys:
+- `baseline`: The deep poverty rate before the reform.
+- `reform`: The deep poverty rate after the reform.
+- `change`: The percentage change in the deep poverty rate due to the reform.
+
+All values are rounded to 1 decimal place for the baseline, reform, and change.
+
+## Example Usage
+
+```python
+from policyengine import EconomicImpact
+
+# Initialize the EconomicImpact class with a reform
+impact = EconomicImpact(reform={
+  "gov.hmrc.income_tax.rates.uk[0].rate": {
+    "2024-01-01.2100-12-31": 0.55
+  }
+}, country="uk")
+
+# Calculate deep poverty rates by age group
+child_deep_poverty = impact.calculate("poverty/deep/child")
+adult_deep_poverty = impact.calculate("poverty/deep/adult")
+senior_deep_poverty = impact.calculate("poverty/deep/senior")
+overall_deep_poverty = impact.calculate("poverty/deep/age/all")
+
+# Print the results
+print(f"Child deep poverty: {child_deep_poverty}")
+print(f"Adult deep poverty: {adult_deep_poverty}")
+print(f"Senior deep poverty: {senior_deep_poverty}")
+print(f"Overall deep poverty: {overall_deep_poverty}")
+
+```
+
+## output
+
+```
+Child deep poverty: {'baseline': 2.4, 'reform': 2.5, 'change': 0.7}
+Adult deep poverty: {'baseline': 2.6, 'reform': 2.7, 'change': 3.9}
+Senior deep poverty: {'baseline': 1.8, 'reform': 1.8, 'change': 0.5}
+Overall deep poverty: {'baseline': 2.4, 'reform': 2.5, 'change': 2.7}
+
+```
+
+## Interpretation
+
+In this example:
+
+- The child deep poverty rate increases slightly from 2.4% to 2.5%, a 0.7% increase.
+- The adult deep poverty rate increases from 2.6% to 2.7%, a 3.9% increase.
+- The senior deep poverty rate remains stable at 1.8%, with a minimal 0.5% increase.
+- The overall deep poverty rate increases from 2.4% to 2.5%, a 2.7% increase.
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diff --git a/docs/Poverty/deep/bygender.md b/docs/Poverty/deep/bygender.md
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+# By gender
+
+## Overview
+
+The deep poverty by gender metrics provide insights into how a policy reform affects deep poverty rates for different genders. These metrics are part of the `EconomicImpact` class and can be calculated for males, females, and the overall population.
+
+## Available Metrics
+
+1. `poverty/deep/male`: Calculates the deep poverty rate for males.
+2. `poverty/deep/female`: Calculates the deep poverty rate for females.
+3. `poverty/deep/gender/all`: Calculates the overall deep poverty rate.
+
+## Metric Structure
+
+Each metric returns a dictionary with the following keys:
+- `baseline`: The deep poverty rate before the reform.
+- `reform`: The deep poverty rate after the reform.
+- `change`: The percentage change in the deep poverty rate due to the reform.
+
+All values are rounded to 2 decimal places for the baseline and reform, and to 1 decimal place for the change.
+
+## Example Usage
+
+```python
+from policyengine import EconomicImpact
+
+# Initialize the EconomicImpact class with a reform
+impact = EconomicImpact(reform={
+  "gov.hmrc.income_tax.rates.uk[0].rate": {
+    "2024-01-01.2100-12-31": 0.55
+  }
+}, country="uk")
+
+# Calculate deep poverty rates by gender
+male_deep_poverty = impact.calculate("poverty/deep/male")
+female_deep_poverty = impact.calculate("poverty/deep/female")
+overall_deep_poverty = impact.calculate("poverty/deep/gender/all")
+
+# Print the results
+print(f"Male deep poverty: {male_deep_poverty}")
+print(f"Female deep poverty: {female_deep_poverty}")
+print(f"Overall deep poverty: {overall_deep_poverty}")
+
+```
+
+## output 
+
+```
+Male deep poverty: {'baseline': 2.66, 'reform': 2.73, 'change': 2.5}
+Female deep poverty: {'baseline': 2.16, 'reform': 2.23, 'change': 2.9}
+Overall deep poverty: {'baseline': 2.41, 'reform': 2.47, 'change': 2.7}
+
+```
+
+## Interpretation
+
+In this example:
+
+- The baseline male deep poverty rate is 2.66%, which increases to 2.73% after the reform, representing a 2.5% increase.
+- The baseline female deep poverty rate is 2.16%, which increases to 2.23% after the reform, representing a 2.9% increase.
+- The overall deep poverty rate increases from 2.41% to 2.47%, a change of 2.7%.
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diff --git a/docs/Poverty/regular/byage.md b/docs/Poverty/regular/byage.md
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+# By age
+
+## Overview
+
+The regular poverty by age group metrics provide insights into how a policy reform affects poverty rates for different age categories. These metrics are part of the `EconomicImpact` class and can be calculated for children, adults, seniors, and the overall population.
+
+## Available Metrics
+
+1. `poverty/regular/child`: Calculates the poverty rate for children.
+2. `poverty/regular/adult`: Calculates the poverty rate for adults.
+3. `poverty/regular/senior`: Calculates the poverty rate for seniors.
+4. `poverty/regular/all`: Calculates the overall poverty rate.
+
+## Metric Structure
+
+Each metric returns a dictionary with the following keys:
+- `baseline`: The poverty rate before the reform.
+- `reform`: The poverty rate after the reform.
+- `change`: The percentage change in the poverty rate due to the reform.
+
+All values are rounded to 2 decimal places for the baseline and reform, and to 1 decimal place for the change.
+
+## Example Usage
+
+```python
+from policyengine import EconomicImpact
+
+# Initialize the EconomicImpact class with a reform
+impact = EconomicImpact(reform={
+  "gov.hmrc.income_tax.rates.uk[0].rate": {
+    "2024-01-01.2100-12-31": 0.55
+  }
+}, country="uk")
+
+# Calculate poverty rates by age group
+child_poverty = impact.calculate("poverty/regular/child")
+adult_poverty = impact.calculate("poverty/regular/adult")
+senior_poverty = impact.calculate("poverty/regular/senior")
+overall_poverty = impact.calculate("poverty/regular/all")
+
+# Print the results
+print(f"Child poverty: {child_poverty}")
+print(f"Adult poverty: {adult_poverty}")
+print(f"Senior poverty: {senior_poverty}")
+print(f"Overall poverty: {overall_poverty}")
+
+```
+## Example Output
+
+```
+Child poverty: {'baseline': 0.32, 'reform': 0.36, 'change': 10.1}
+Adult poverty: {'baseline': 0.17, 'reform': 0.19, 'change': 8.4}
+Senior poverty: {'baseline': 0.13, 'reform': 0.17, 'change': 30.5}
+Overall poverty: {'baseline': 0.20, 'reform': 0.22, 'change': 11.7}
+```
+## Interpretation
+
+In this example:
+
+- The child poverty rate increases from 32% to 36%, a 10.1% increase.
+- The adult poverty rate increases from 17% to 19%, an 8.4% increase.
+- The senior poverty rate increases from 13% to 17%, a significant 30.5% increase.
+- The overall poverty rate increases from 20% to 22%, an 11.7% increase.
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diff --git a/docs/Poverty/regular/bygender.md b/docs/Poverty/regular/bygender.md
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+# By gender
+
+## Overview
+The regular poverty by gender metrics provide insights into how a policy reform affects poverty rates for different genders. These metrics are part of the EconomicImpact class and can be calculated for males, females, and the overall population.
+
+## Available Metrics
+- `poverty/regular/male`: Calculates the poverty rate for males.
+- `poverty/regular/female`: Calculates the poverty rate for females.
+- `poverty/regular/gender/all`: Calculates the overall poverty rate.
+
+## Metric Structure
+Each metric returns a dictionary with the following keys:
+- `baseline`: The poverty rate before the reform.
+- `reform`: The poverty rate after the reform.
+- `change`: The percentage change in the poverty rate due to the reform.
+
+All values are rounded to 2 decimal places for the baseline and reform, and to 1 decimal place for the change.
+
+## Example Usage
+```python
+from policyengine import EconomicImpact
+
+# Initialize the EconomicImpact class with a reform
+impact = EconomicImpact(reform={
+  "gov.hmrc.income_tax.rates.uk[0].rate": {
+    "2024-01-01.2100-12-31": 0.55
+  }
+}, country="uk")
+
+# Calculate poverty rates by gender
+male_poverty = impact.calculate("poverty/regular/male")
+female_poverty = impact.calculate("poverty/regular/female")
+overall_poverty = impact.calculate("poverty/regular/gender/all")
+
+# Print the results
+print(f"Male poverty: {male_poverty}")
+print(f"Female poverty: {female_poverty}")
+print(f"Overall poverty: {overall_poverty}")
+```
+
+## Example output 
+
+```
+Male poverty: {'baseline': 0.18, 'reform': 0.21, 'change': 12.9}
+Female poverty: {'baseline': 0.21, 'reform': 0.23, 'change': 10.7}
+Overall poverty: {'baseline': 0.20, 'reform': 0.22, 'change': 11.7}
+
+```
+
+## Interpretation
+
+In this example:
+
+- The baseline male poverty rate is 18%, which increases to 21% after the reform, representing a 12.9% increase.
+- The baseline female poverty rate is 21%, which increases to 23% after the reform, representing a 10.7% increase.
+- The overall poverty rate increases from 20% to 22%, a change of 11.7%.
+
+These metrics can help policymakers understand the differential impact of a reform on poverty rates across genders.
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diff --git a/docs/Poverty/regular/index.md b/docs/Poverty/regular/index.md
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+# Regular poverty
+
+## Overview
+
+Regular poverty metrics measure the proportion of individuals living below the standard poverty line. These metrics provide crucial insights into the effectiveness of economic policies and their impact on different demographic groups.
+
+## Available Metrics
+
+Our regular poverty analysis includes the following categories:
+
+1. **Poverty by Age Group**
+   - Child Poverty
+   - Adult Poverty
+   - Senior Poverty
+   - Overall Poverty
+
+2. **Poverty by Gender**
+   - Male Poverty
+   - Female Poverty
+   - Overall Poverty
+
+## Importance
+
+Regular poverty metrics are essential for:
+- Assessing the overall economic well-being of a population
+- Identifying vulnerable groups most affected by poverty
+- Evaluating the effectiveness of poverty reduction policies
+- Comparing poverty rates across different demographics
+
+## Usage
+
+These metrics can be accessed using the `EconomicImpact` class. For detailed information on each metric, please refer to the respective documentation pages.
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