Panel Data Analysis
Context
Objective: Analyze the impact of devolution on service delivery (e.g., healthcare access) across counties in Kenya over time.
Data: A panel dataset containing data for 47 counties in Kenya from 2010 to 2020. Key variables include:
- Dependent Variable: Healthcare Access Index (a composite measure of healthcare quality and availability in each county).
- Independent Variables:
- Devolution Period Indicator (1 for years after 2013 when devolution started, 0 otherwise).
- County-level government expenditure on healthcare.
- Socioeconomic factors (e.g., literacy rates, poverty rates).
- Fixed Effects/Random Effects Variables: Unobserved county characteristics (e.g., geography, cultural practices).
Model Specification
Fixed Effects Model
The fixed effects model is specified as:
\[ \text{HealthcareAccess}_{it} = \beta_0 + \beta_1 \text{DevolutionPeriod}_{t} + \beta_2 \text{GovHealthcareExp}_{it} + \beta_3 \text{LiteracyRate}_{it} + \beta_4 \text{PovertyRate}_{it} + \mu_i + \epsilon_{it} \]
- \( i \): County index.
- \( t \): Year index.
- \( \mu_i \): County-specific time-invariant characteristics (e.g., geography).
- \( \epsilon_{it} \): Error term.
Random Effects Model
The random effects model is specified as:
\[ \text{HealthcareAccess}_{it} = \beta_0 + \beta_1 \text{DevolutionPeriod}_{t} + \beta_2 \text{GovHealthcareExp}_{it} + \beta_3 \text{LiteracyRate}_{it} + \beta_4 \text{PovertyRate}_{it} + u_i + \epsilon_{it} \]
- \( u_i \): Random county-specific effect uncorrelated with independent variables.
Estimation and Analysis
Model Results (Hypothetical)
| Variable | Coefficient (\(\beta\)) | p-value |
|---|---|---|
| DevolutionPeriod | 1.25 | 0.001 |
| GovHealthcareExp | 0.45 | 0.002 |
| LiteracyRate | 0.15 | 0.050 |
| PovertyRate | -0.30 | 0.004 |
| Variable | Coefficient (\(\beta\)) | p-value |
|---|---|---|
| DevolutionPeriod | 1.20 | 0.001 |
| GovHealthcareExp | 0.42 | 0.003 |
| LiteracyRate | 0.14 | 0.060 |
| PovertyRate | -0.28 | 0.005 |
Hausman Test
Result: p-value < 0.05, indicating that the fixed effects model is more appropriate as it controls for unobserved heterogeneity that might correlate with independent variables.
Interpretation of Results
- Devolution Period: The positive and significant coefficient suggests that healthcare access improved significantly after the introduction of devolution.
- Government Healthcare Expenditure: Higher healthcare spending is strongly associated with improved access, emphasizing the importance of resource allocation.
- Literacy Rate: A positive but smaller coefficient suggests that education levels moderately influence healthcare access.
- Poverty Rate: A significant negative relationship indicates that higher poverty rates reduce healthcare access.
Conclusion
The analysis provides robust evidence that devolution has positively impacted healthcare service delivery in Kenya. However, the effectiveness varies across counties, potentially due to differences in governance capacity and socioeconomic factors. Policies should focus on equitable resource allocation and capacity building in underperforming counties.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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