Innovative Capacity and Participatory Policymaking in Armenia

The data of the Innovative Capacity survey has enabled the exploration of how public governance factors affect innovation outputs. By evaluating both perceptions of formal supports for public sector innovation and demographic data of the respondents, a logit regression model was constructed. This model provides insights into the main determinants that influence the probability of participating in innovation within the public sector. It is important to recognise that some intrinsic characteristics of specific organisations within the civil service and policy sectors may naturally predispose them to innovate. Therefore, the model controlled for these confounding factors to isolate the impact of each determinant on the likelihood of innovation.

Furthermore, it is essential to consider that the factors influencing innovation may interact bidirectionally. For example, wider sets of skills or motivations can initially stimulate innovation, while engaging in innovation activities can further enhance these drivers. Additionally, there may be underlying correlations and interactive effects among various factors that might make the isolation of a singular determinant of innovation within the public sector challenging.

Given these considerations, the results from the analysis should not be interpreted as definitive causal factors of innovation. However, they provide a comprehensive image of the main determinants for innovation and highlight the most pertinent factors the Armenian government should address to bolster its innovative capacity.

By understanding these dynamics, the Armenian government can strategically focus its efforts on overcoming existing challenges and nurturing an environment conducive to sustained innovation, ultimately leading to more effective and responsive public services.

Using this data, the use of a logit regression can provide relevant information for understanding what fosters innovation in the Armenian public sector context. This econometric model helps us to understand the relationship between participation in innovation - the dependent variable - and the strength and nature (positive or negative) of a number of independent variables1. The effect of each independent variable alludes to how they affect the odds of participating in innovation.

The regression results in Table 5.1 illustrate the statistical significance of the relationship between the probability of having participated in innovation – the dependent variable – in the Armenia public sector, and various potential determinants of innovation – the independent variables – in the Innovation Capacity Survey dataset. The analysis includes responses from 2,500 public servants across nine policy sectors and two types of government organisations, including Ministries and State Agencies.

Participation in innovation, here the dependent variable, is a binary variable. The survey question is phrased as follows: “The OECD defines innovation as something new or novel to context, implemented, and aimed at achieving impact. The focus of innovations could include services, products, processes, working methods and operating procedures, policy development, communication. Based on this definition, have you been involved in designing and/or implementing an innovation in your workplace during the last two-year period?”. The variable takes value 1 for “Yes” responses and takes value 0 for “No” responses.

Table 5.2 presents the average odds ratios, which indicate the multiplicative change in the odds of innovation occurring associated with a one-standard-deviation change in the predictor variable. It is worth noting that socioeconomic controls (not shown) do not exhibit relevant patterns to innovation. For example, Gender, Age, and Tenure do not show a strong correlation with the dependent variable. This would suggest that they do not have a measurable impact on the likelihood of innovation, as their influence cannot be distinguished from having no effect at all. Hierarchical role, in contrast, suggests that higher ranks in the civil service – such as Executives, Senior and Middle manager level staff – have a positive and strong correlation with participating in innovation compared to Secretarial staff.