# **V. Robustness Checks**

The main findings of this study show that the adoption of GovTech platforms that allow citizens to participate in policy decision-making and provide feedback on service delivery positively impacts citizen engagement, as determined through the entropy balancing method. In this section, additional analyses are performed to check the robustness of the main results by considering the rate of diffusion of technology, alternative data for the variable of interest, and alternative estimation strategies.

# **1. Considering the Rate of Diffusion of Technology**

In the main analysis, the study introduced a one-year lag for the variable of interest to consider the effect of the uptake and diffusion of GovTech platforms, as the effect of introducing a digital platform on citizen engagement can be expected to appear later. In the first robustness check, it was assumed that the uptake and diffusion could take time to manifest; thus, the model introduced instead a two-year lag for the citizen engagement variable to estimate the effect of the adoption of digital platforms. The results of this additional analysis are consistent with the previous findings and even more justify the adoption of digital GovTech platforms to foster citizen engagement. In all the estimations conducted, a significant and positive impact of GovTech platforms was found on citizen engagement at the 5 percent threshold at least (Table 4). The coefficients of the GovTech variable are higher compared to those obtained in the main analysis for both the baseline and multiple regressions. Accordingly, as for other forms of GovTech reforms, there is a need for a diffusion time before observing the expected positive impact of the implementation of digital platforms on citizen engagement.

**Table 4: Results of the estimations considering the rate of diffusion of technology**

||**Civil society**<br>**(20**|**participation**<br>**22)**|**Engaged so**|**ciety (2022)**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**GovTech**|**0.130****|**0.130*****|**0.539*****|**0.539*****|
||(0.0523)|(0.0343)|(0.202)|(0.141)|
|Observations|145|145|145|145|
|R-squared|0.042|0.608|0.047|0.558|
|Covariates|No|Yes|No|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **2. Using Alternative GTMI Data**

As specified in section III, the GovTech variable employed in the study as the variable of interest was constructed using 2020 GTMI data extracted from the 2022 version of the GTMI dataset. However, the 2020 data were collected remotely by the GTMI team during the COVID-19 pandemic. By contrast, the 2022 GTMI data were collected mainly based on an online survey completed by government officials. To control for the potential biases related to these aspects, this study conducts a second robustness check consisting of using the 2022 GTMI data as an alternative to the 2020 data. The study also considers the rate of diffusion of technology in this analysis.

The findings show a positive and significant impact of the adoption of GovTech platforms on citizen engagement (Table 5). Here again, the predicted coefficients for the GovTech variable are higher compared to those obtained in the main analysis. These results provide evidence that GovTech platforms 12