primer domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/ikq167bdy5z8/public_html/propertyresourceholdingsgroup.com/wp-includes/functions.php on line 6114For the first time, new research has revealed a significant and robust correlation between the speed of mobile broadband and labour productivity measured over a one-year period.
Today’s significant slowdown in global economic growth serves as a reminder not to lose sight of the importance of productivity. While rising economic hardships affect everyone, they disproportionately affect vulnerable populations in low-income countries. According to the IMF, current supply chain constraints and rising commodity prices are contributing to 2022 inflation projections of 5.7 percent in advanced economies and 8.7 percent in emerging market and developing economies.
A recent Ericsson-sponsored research project looked at the historical relationship between labour productivity in 116 countries and mobile broadband speed in each of those markets.
The findings, published in the research paper “The Economic Impact of Mobile Broadband Speed,” imply a significant increase in national economic productivity one year after the implementation of faster mobile broadband networks. A 10% increase in mobile broadband speed in period t-1 corresponds to a 0.2 percent increase in labour productivity. Thus, the findings indicate that investments in mobile broadband speed pay off after one year.
Rapid economic growth is facilitated by faster mobile networks.
To investigate the relationship between mobile broadband speed and labour productivity, researchers used statistical tools and economic theory (i.e. econometric methods). The study includes and is based on Ookla Speedtest Intelligence® data processed by Ericsson. Speedtest is an app service that measures the speed of a mobile device. The Speedtest Intelligence® database contains information from millions of tests and readings taken with the app.
In terms of economic development, the countries included in the analysis differ. It could be argued that countries at similar economic stages should be compared. As a result, I divided the sample into high- and low-income countries based on World Bank classifications. The countries are classified according to their gross national income (GNI). A low-income country has a GNP of $4095 or less, while a high-income country has a GNP of $4096 or more. Furthermore, the countries are classified as OECD and non-OECD. The division revealed an intriguing fact: results are only significant for non-OECD and low-income countries.
