# **4. Ordinary Least Squares (OLS) Regressions on the Matching Sample**

This study also conducts subsequent OLS regressions on the matching sample as another step of PSM (as the use of WLS for entropy balancing). This method, which is less common in the literature (DuGoff et al. 2014; Ho et al. 2007), allows conducting both baseline and multiple regressions on the matching sample as an alternative to entropy balancing. The PSM sample was generated using the nearest neighbor matching technique and a Logit model. The regression coefficients presented for the OLS models estimated using the PSM sample indicate a significant and positive impact of GovTech platforms on citizen engagement, irrespective of the form of the regression (Table 7). As for the main analysis through the entropy balancing method (with the weighted least squares estimator), the results reveal a higher impact of GovTech platforms on citizen engagement measured through the engaged society variable than that measured through the civil society participation index. The significance of the coefficients of the GovTech variable, irrespective of the method employed, shows that the findings are not sensitive to alternative identification strategies.

**Table 7: Results of the estimations conducted through OLS regressions on the matching sample**

||**Civil**<br>**CSPI (**|**society par**<br>**2021)**|**ticipation (C**<br>**CSPI (**|**SPI)**<br>**2022)**|**ES (2**|**Engaged s**<br>**021)**|**ociety (ES)**<br>**ES (**|**2022)**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**GovTech**|**0.178*****<br>**(0.0475)**|**0.0870****<br>*****<br>**(0.0292)**|**0.247*****<br>**(0.0552)**|**0.130*****<br>**(0.0326)**|**0.725*****<br>**(0.204)**|**0.321****<br>**(0.137)**|**0.850*****<br>**(0.196)**|**0.460*****<br>**(0.132)**|
|Observations|160|160|160|160|160|160|160|160|
|R-squared|0.082|0.689|0.113|0.722|0.074|0.625|0.106|0.635|
|Covariates|No|Yes|No|Yes|No|Yes|No|Yes|
|||Stan<br>***|dard errors i<br>p<0.01, ** p|n parenthes<br><0.05, * p<|es<br>0.1||||

Overall, the results of the robustness checks indicate that the adoption of GovTech platforms positively affects citizen engagement, regardless of the rate of diffusion of technology, the version of GTMI data used, and the estimation strategy employed.

# **VI. Sensitivity/Heterogeneity Analysis**

The present study also includes a sensitivity analysis focused on the civil society participation variable to consider the role of factors needed to ensure that the implementation of digital platforms translates into improved citizen engagement, as highlighted by World Bank (2022), Peixoto and Steinberg (2019), and Gulati et al. (2014). The idea is that factors such as government effectiveness, conflict and fragility, and the level of development can magnify or alleviate the effect of GovTech platforms on citizen engagement. The study also considers the level of development as a potential source of heterogeneity to test if it plays a role in the success of GovTech platforms in fostering citizen participation. Following Lin and Ye (2009) and Sawadogo (2020), the study uses a control function regression methodology for the heterogeneity analysis developed by Wooldridge (2002). The method involves including the propensity scores firstly estimated as a control function in an OLS regression. The statistical significance of the propensity scores would reflect the presence of self-selection in the models.

The results show that GovTech platforms are more effective in fostering citizen engagement in developed (high-income) economies, and in countries where the government is efficient; institutional and social fragility is low; and there is no conflict or only low-intensity conflict (Table 8). More specifically, the 15