• Independent Variable (Treatment): 大数据管理局
• Dependent Variable (Outcome): 区域协调发展

Steps to Perform DID Analysis:

1. Data Preparation:
   • Ensure the dataset is clean and contains the necessary variables.
   • Create a binary treatment indicator variable for the presence of 大数据管理局.
   • Define pre-treatment and post-treatment periods.
2. Model Specification:
   • Use a regression model to estimate the DID effect.
   • The model will include interaction terms between the treatment indicator and time.
3. Estimation and Interpretation:
   • Estimate the DID model and interpret the coefficients, especially the interaction term, which represents the DID estimate.

DID Model Specification:

• $\text{Outcome}_{it}$ is the 区域协调发展 for unit $i$ at time $t$.
• $\text{treatment}_i$ is the treatment indicator.
• $\text{post\_treatment}_t$ is the post-treatment period indicator.
• $\text{treatment}_i \times \text{post\_treatment}_t$ is the interaction term capturing the DID effect.

• R-squared: 0.068, indicating that about 6.8% of the variability in 区域协调发展 is explained by the model.
• Treatment Effect (Interaction Term): The coefficient for the interaction term treatment:post_treatment is not statistically significant (p-value = 0.421), suggesting that the establishment of 大数据管理局 did not have a significant impact on 区域协调发展 in the post-treatment period.

Interpretation:

• The lack of statistical significance in the interaction term implies that there is no strong evidence to suggest a causal effect of 大数据管理局 on 区域协调发展 based on the available data.