Modelling broadband access impact on the economic growth at NUTS 3 level

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DESIRA carried out a quantitative analysis of the impact of broadband infrastructure on economic growth, at regional level, in order to consider the dynamics between urban and rural areas and eventual gaps between them.

At the conceptual level, a first distinction was made between digitisation and digitalisation.

The first step was to construct a Rural Digitisation Index (RDI) reflecting the digital performance of European rural areas. The aim was to use the Rural Digitisation Index to map digitalisation at the lowest territorial level covered by Eurostat (i.e. the NUTS 3 level) when classifying regions by urban-rural typology. Due to data limitations at NUTS 3 level, the RDI includes only fixed broadband coverage related indicators.

The main assumption for using only the broadband coverage out of all the Digital Economy and Society Index (DESI) indicators in the RDI is becasue  broadband coverage is a necessary condition for the development of the other dimensions targeted by the DESI. Therefore it can be partially regarded as a one-directional proxy for these policy areas. Furthermore, at NUTS 3 level, data exists only for fixed broadband coverage.

The next step was to map the evolution of broadband coverage (> 30 Mbps) in all EU Member States per NUTS 3 region between 2011 and 2018. This examination of regional broadband access between 2011 and 2018 is then analysed quantitatively in relation to its contribution to economic growth.

A quantitative model, the system generalised method of moments estimator, was used.

Regression models were run for connectivity rates of at least 30 Mbit/s and then for connectivity rates of at least 100 Mbit/s. The regression results showed that after a certain threshold speed level is reached, further increasing broadband speed does not translate into additional economic growth.

Moreover, the regression results indicated a decreased upper bound of speed-related gains for the provision of broadband access in rural Europe compared to urban areas and in the agricultural industry compared to other sectors.

In terms of connectivity rates offering at least 100 Mbit/s, this threshold level has long been attained in urban Europe, while rural areas are still falling behind significantly in 100 Mbit/s broadband infrastructure compared to their urban counterparts. At the sectoral level, the agricultural industry witnessed large gains from digitalisation between 2011 and 2018, aligning with the emergence of innovations in smart farming technologies and reassuring its importance in the future.

Relevance for monitoring and evaluation of the CAP

The system generates metric models regression, which is a dynamic linear panel model. That can account for the interplay between broadband coverage and stimulation of economic growth and account for measurement error. It further accounts for other problems that are usually well known in these growth regressions data if real GDP per capita and purchasing power parities are used. Some of the control variables include past economic growth, population growth and proxies for investments and trade orders. These data are proxied by using information on formal sector employments, usually.

Modelling impacts of digitalisation can be very useful for assessing the effects of interventions that aim to increase the digitalisation of rural areas, in particular, the effects on economic growth. The contextual background offered by DESIRA indicated the lack of data at the NUTS 3 level for measuring indicators related to human capital, use of internet, integration of digital technology or digital public services. However, it has shown that it is possible to analyse broadband access at the NUTS 3 level. This opens a new door to impact assessment at this territorial level, where many of these interventions are implemented.

Furthermore, the fixed broadband coverage at different speeds measured at the NUTS 3 level is important for monitoring and evaluation because of the Farm to Fork objective stating that ‘The Commission aims to accelerate the roll-out of fast broadband internet in rural areas to achieve the objective of 100% access by 2025’. The RDI (which includes DESI indicators on fixed broadband coverage) may be a good monitoring and reporting indicator of progress.

Knowledge of econometrics to undertand and use the 'system generalised method of moments estimator' is required to model broadband access.

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