The role of Innovation and Technology in Economic Development: A comparative analysis in the Top 11 World Economies and Switzerland (2000-2022)

Maria Fernanda Cladera Melgar

23 December, 2023

1. Introduction

1.1 Overview and Motivation

The project “The role of Innovation and Technology in Economic Development: A comparative analysis in the 11 main economies of the world and Switzerland (2000-2022)” arises from the need to understand the contemporary phenomenon of how innovation and technology are intertwined with economic development. The selection of the most influential economies, together with Switzerland, allows us to analyze a panorama of how investment in R&D and technological progress have influenced economic growth and restructuring towards higher value-added activities.

The relevance of this study is based on the observation that the world economy is increasingly influenced by innovation and technology. The motivation to carry out this research is anchored in the curiosity to unravel the complexities of this link and offer a clear vision of its dynamics in the first two decades of the 21st century. With a look to the recent past, we seek to better understand how nations can harness innovation and technology to drive their future development.

The selected period, 2000-2022, covers some of the most rapid and disruptive changes in technology and the economy, from the rise of the Internet to the renewable energy revolution and artificial intelligence. Studying these trends is not only relevant for their past impact, but also for the light they can shed on current challenges and opportunities. Therefore, this descriptive analysis invites the reader to explore the interactions between the ease of doing business, innovation and economic growth, understanding that these elements are key for any economy that seeks to prosper in an increasingly competitive global context based on knowledge.

1.2 Objetives

  • Examine the Relationship between Innovation and Economic Growth: Investigate how different forms of innovation, such as investment in R&D and technology adoption, are related to economic growth in major global economies.

  • Evaluate the Impact of Technology on Productivity: Analyze whether the use and adoption of modern technologies have a positive effect on the productivity of a country.

  • Explore the Influence of the Educational and Business Environment on Innovation: Determine how factors such as education and ease of doing business affect a country’s ability to innovate.

  • Propose Recommendations Based on Economic Models of Innovation: Provide practical advice and policy recommendations based on innovation and technology strategies observed in successful economies.

1.4 Research questions

  • How is the GDP per capita growth related to the Research and Development investment in this major economies?

  • How have innovation and technology indicators behaved concerning the economic development of the world’s 12 main economies between 2000-2022?

  • How have technological advancements and adaptation, measured through exports of technology services, internet usage, and mobile subscriptions, behave? and how they affected productivity and economic efficiency?

  • What is the relationship between educational investment, and innovation outputs like patent applications and R&D expenditures in these economies?

  • What are the observable pattern in the ease of Doing Business Scores? and how do they correspond with innovation indicators like patent aplication and R&D expenditures?

2. Data

2.1 Data Source

The following databases were used for the project:

2.1.1. Description: World Development Indicators (WDI) Dataset

The World Development Indicators (WDI) database was retrieved from the World Bank’s Data Bank. This comprehensive dataset provides in-depth descriptions of 1,477 unique indicators across 20 variables. However, due to the focus of our analysis, 5 essential columns were selected: Series.Code, Topic, Indicator.Name, Long Definition, and Source.

Prior to analysis, we refined the data set through two main steps:

  1. Reclassification of Factors: Utilizing the fct_collapse function, we grouped two categories, Health and Trade, into a single group. This streamlined the representation of indicators within these broader categories.

  2. Creation of Macro Group Column: Employing the mutate and grepl functions, we added a new column to categorize the indicators into 11 macro groups. This additional layer of classification facilitates a clearer understanding of the overall distribution of indicators for the selection of study indicators.

Likewise, within this table, the description of the indicators for the special characters was duplicated and adjusted using str_replace_all:

Variables Description
Series.Code Indicator Code.
Topic Group of indicators into 87 categories.
Indicator.Name Indicator description.
Long.definition Long description of the indicators
Source Source of the indicator data.
Indicator.Group Group of indicators in 11 macro categories, such as: Economy, Education, Infrastructure, Private sector, Environment, Financial sector, Gender, Health, Poverty, Public Sector, Social Protection.

2.1.2. Description: World Development Countries (WDC) Dataset

In order to standardize the countries and carry out the necessary analyses, we decided to use the Countries database from the World Bank’s Data Bank. This database contains 265 unique observations, representing individual countries, and 31 columns, representing various variables such as country code, country name, long and short descriptions, alpha codes, regions, and income groups. For the purpose of this investigation, we have selected 3 of these columns for further analysis.

Variables Description
Country.Code Country code (3 reference letters).
Country.Name Name of the country.
Region Latin America & Caribbean, South Asia, Europe & Central Asia, Sub-Saharan Africa and others.

2.1.3. Description: Economy Dataset

The Economy data database was collected from the World Bank - Data Bank. This extensive dataset comprises 20,683 observations and 29 columns. It provides valuable information regarding the top 12 economies, various indicators categorized into 11 groups, and their associated values spanning from 1998 to 2022.

Initially, the data was structured in a broad format, with columns from 5 onwards representing consecutive years from 1998 to 2022. To start, we reshaped the data using the pivot_longer function. We then converted the data types to the appropriate formats for each variable. We convert the date values and remove non-relevant entries from the Country.Name column, such as “Blanck()”, “Database data: World Development Indicators” and “Last update: 26/ 10/2023”. Finally, we convert the columns that refer to countries into factors to ensure proper representation of the data.

Initially, the data was structured in a broad format, with columns from 5 onwards representing consecutive years from 1998 to 2022. The steps used to prepare the data were: - Reshaped the data using the pivot_longer function. - Convert the data types to the appropriate formats for each variable. In the case of the Values column, all the missing data are filled with NA, with the aim of performing the subsequent analysis. - Eliminate non-relevant entries from the Country.Name column, such as “Blanck()”, “Database data: World Development Indicators” and “Last update: 10/26/2023” , and then convert the columns into factors to guarantee an adequate representation of the data. - Include the information of the Indicator.Group and the new description of indicators from the DF_WIIIndicator table. Finally, the columns necessary for the study were selected.

Variables Description
Country.Name Description of the 12 selected countries: Brazil, Canada, China, France, Germany, India, Italy, Korea, UK, USA and Switzerland.
Country.Code Country code base on the World bank codes.
Indicator.Name Description of the 1.477 indicators of world development.
Series.Code Indicator code base on the World bank codes.
Date Date expressed in years from 1998 to 2022.
Value Value of the Indicator in USD, %, scale (1-100) and others.

2.1.4. Description: Doing Business Rank Dataset

The Doing Business Rank dataset featured in this analysis encompasses data from the World Bank’s Doing Business database for 2019 (https://archive.doingbusiness.org/en/rankings). This database provides an annual ranking of economies based on their ease of doing business, ranging from 1 to 190. A higher ranking signifies a more favorable regulatory environment for establishing and running businesses. The rankings are determined by aggregating scores across 10 key themes, each represented by several specific indicators. Each theme is assigned equal weight to ensure a balanced evaluation of the overall regulatory environment. The dataset consists of 191 observations representing countries and 13 columns representing key economic themes.

On this basis, the following steps were carried out: conversion to a long format, standardization of Country.Name and Country.Code columns and inclusion of indicator classification columns.

Variables Description
Date The year of information gathering 2019.
Country.Name Name of the country
Country.Code Country code (3 reference letters.).
Topic Group of Indicator: Private Sector & Trade: Business environment.
Indicator.Name Detailed of the 10 themes for the ranking score.
Value Value (Rank) per country.
Indicator.Group Group of indicators in macro categories: Private Sector & Trade

2.1.5. Description: Global Innovation Index Rank and score Dataset

The Global Innovation Index (GII) dataset utilized in this analysis was obtained from Mendeley Data (https://data.mendeley.com/datasets/cvkdzr8tv3/4). This dataset furnishes the GII rankings and scores from 2011 to 2022, encompassing a diverse range of 149 economies classified by income level. To seamlessly integrate the datasets, we leveraged the merge function, linking the GII_rank dataset and the GII_scores dataset based on shared country and year information. This harmonization enabled us to comprehensively assess both factors influencing innovation performance across the selected economies.The dataset consists of 1.740 observations representing countries and 27 columns representing the GII score and rank.

As in the Doing Business Rank data set base, the following steps were carried out: conversion to long format, standardization of the Country.Name and Country.Code columns and inclusion of indicator classification columns.

Variables Description
Date Year from 2011 to 2022
Country.Name Name of the country.
Country.Code Country code (3 reference letters).
Topic Group of Indicator: nfrastructure: Technology
Indicator.Name Detailed of the 10 themes for the ranking score.
Value Value Rank/Score per country.
Indicator.Group Group of indicators in macro categories: Infrastructure

Consideration:

Of all the bases presented, the main one for this project is Economic Data. The bases: Doing Business Rank and GII are bases to support this information.

2.2 Data Clean-Up

The cleaning process will focus on Economic Data, being the most important and most complex. The cleaning process will be divided into three stages: Missing data evaluation, filtering and information enrichment.

2.2.1. Missing Data

To begin our analysis of the data set Economic data (ED_clean), we will evaluate the missing values, also known as NA. We begin by calculating for each indicator, the total number of data and the corresponding percentage of missing data. Below is presented a histogram, with a visual representation of the distribution of missing values in the data set.

As can be seen from the 1,477 initial indicators, 554 indicators (37.5%) had a missing data rate greater than 50%. Given the substantial lack of information on these indicators, we consider it appropriate to exclude them from the analysis. This reduced the number of indicators to 923, allowing for a more focused and comprehensive evaluation. Below we present a summary of the number of indicators per area that remain after excluding those with excessive missing values.

As can be seen, the 923 indicators are found in different groups. However, the indicators that are related to the topic of study must be filtered again, which is done in the following section.

2.2.2. Data Filtering

A review of each indicator is carried out and we select 24 indicators that are related to the research topic. By limiting our focus to the 24 selected indicators, we have reduced the data set to 7,800 observations and 7 variables. To provide a clear understanding of the remaining missing values, we present a summary of the status of missing data for each indicator.

Since the first 8 indicators have a % missing data greater than 0%, we decided to graph them to understand where these information losses occur.

The three indicators with the most substantial loss of information are Technicians in R&D (per million people), and High-technology exports (% of manufactured exports). As depicted in the following graphs, these indicators exhibit significant gaps in their data coverage. The Technicians in R&D (per million people) indicator provides information from 2006 to 2020. Finally, High-technology exports (% of manufactured exports) suffers from incomplete data for some countries and only extends to 2015.

2.2.3. Data Enrichment

In this stage, 2 types of data enrichment will be carried out: - Actions for NA values. - Calculation of new columns with per capita and total indicators.

Treatment of Missing Values (NA):

For NA, the action was taken to replicate the last value from the database (considering Indicator - country). This decision was made due to the type of indicator. As seen in the graph, the 7 indicators in which said action was taken maintain the initial trends, thus not altering the information and analysis.The objective of this process is to have a better visualization of the information during the graphics.

Calculation of new indicators:

Since most of the indicators are expressed as percentages (%) of larger indicators like GDP, population, BoP services exports, and manufactured exports, we will perform 10 calculations beforehand to obtain the absolute value of each indicator. This will prove beneficial as we delve into the data spanning 1999 to 2020 and analyze changes across multiple years, enabling us to calculate CARG at regular intervals, such as every five years. Below are the 10 indicators included in the data set.

2.3 Data Summary

In summary, the foundation of this analysis lies in the Economy Data (ED_wfilled) dataset, which has been meticulously refined to encompass 35 carefully chosen indicators. The other supporting datasets will facilitate regression and correlation analyses, enabling us to address the research questions effectively. A comprehensive summary of the selected indicators, along with details about the groups and periods represented by the data, is provided below.

3. Exploratory Data Analysis

We will begin with the analysis of the information in an exploratory manner; For this, we will subcategorize the indicators, aligned with the development of the research questions:

  • Innovation Indicators: All indicators related to the development of innovation and technology. As the following indicators: Global Innovation Index (GII), R&D spending, Patent applications, Scientific journal article, R&D technicians.

  • Technology adoption: Through these indicators we will know how much adaptability the country has in its industry and its population to technology and innovation; As the following indicators: High technology exports, communications and computing exports, ICT services exports, Internet use and mobile phone subscription.

  • Productivity: These indicators refer to the efficiency of the workforce in the country; As the following indicators: GDP per employed person and Unemployment with higher education.

  • Development of the business environment: This indicator presents the openness to business development in the country: Ease of doing business.

  • Advanced Education: This indicator presents the investment made in education in a country: Public spending on education.

Consideration:

Since we are working in a period of 2000 - 2022 (22 years), I will be referring to the Coumpound annual growth rate (CAGR) as growth.

3.1. Economic outlook for the world’s major economies

Being a study of the impact of innovation and technology on economic development, we will begin with a brief description of the economic growth in the study countries. This will give us a Big Picture to the economy perspective to then compare it with the innovation and technology indicators.

3.1.1. Why are these economies so relevant to the study?

With the objective of presenting the economic relevance of these countries, We present the graph of the % of the accumulated GDP of the countries with respect to the world and its trend in the years of study:

As we can see, these 12 countries have concentrated 70% of the world economy in the last 20 years. If we look at the summary table we see a growth trend for this group of countries aligned with global growth; It is worth mentioning that:

  • In the period 2000 - 2010, lower growth was experienced due to the expansion of other emerging and developing economies, particularly in Asia and Africa.

  • In the period 2010 - 2020, there is a growth greater than that of the world, this could have occurred due to: Increase in prices of raw materials in different periods, development of the service sector and above all the rise in technological innovation.

  • However, when comparing 2022, the post-covid recovery cost these large economies a little more.

3.1.2. The economic evolution of these Leading Economies

After understanding the relevance as a whole, it is advisable to present the economic participation of each of country in the world economy; this through the analysis of their indicators: GDP growth and GDP per capita.

We begin by evaluating the participation of each of the countries in the World GDP, it is inevitable to see the increase in China’s participation from the year 2000 to 2022, starting with a participation of only 3.58% and concluding with 17.86% of the world economy. . Likewise we see Japan, whose participation in 2000 was 14.68% and in 2022 it ended with only 4.21% of the world economy. The impact of development in China and India has caused (10/12 study countries) to lose participation worldwide.

Below is a graph to evaluate the annual GDP growth for each country. The heat graph gives us a first snapshot of the trends that these countries have had. As we can see, all of them present a regular growth trend (except for 2008 - Financial Crisis and 2020 - Covid 19). The three countries that stand out in terms of growth are: China, India and Korea.

With this, we have a visual of participation and trend of the economy of these powers. However, due to comparison factors, per capita values will be continually evaluated during the study. For this reason, we present the GDP per capita trends of the countries:

We can observe the following:

  • We reconfirm the positive trend of most countries (with the exception of Japan, which presents a decrease of 0.67% compared to 2000).

  • Per capita the strongest economy is Switzerland, followed by the United States, Canada and Germany.

  • The per capita growth of the countries is lower than the GDP growth. This could be due to different factors such as: Rapid Population Growth, income distribution, economic and social challenges, among others.

After analyzing these three economic indicators: GDP current US, GDP per capita US and GDP annual growth, we can identify certain similarities in the economic compartment of the countries.

3.1.3. Classifying countries according to their per capita growth

With the objective of evaluating similar patterns in these countries regarding economic growth, the k-meas calculation was developed. The calculation of the optimal number of clusters was carried out, which resulted in 2.

However, it was considered convenient to work with 3 clusters; This is because through the Clustering graph we identify the separation of 3 clusters:

  • Cluster 1 - Balanced economic Powerhouses: Korea, Japan, Canada, UK, Italy, France and Germany.

  • Cluster 2 - Emerging Market Giants: Brazil, China and India.

  • Cluster 3 - High-Income: Switzerland and the United States.

As first conclusions from the economic analysis we can understand that:

  • The 12 economies studied are economically relevant since they represent 70% of the world’s GDP.

  • In % GDP growth (CAGR 2000-2022) it presents a growing trend (except for Japan). However, only 2 countries present a growth greater than world growth (5.07%), China (13.03%) and India (9.41%). On the other hand, we have countries like Italy (2.59%) and the UK (2.82%) that have growth almost half of the world’s growth.

  • If we evaluate the GDP per capita, we find Switzerland leading, having a GDP per capita approximately 7 times more than the world GDP per capita. Regarding the growth in this indicator, we see that there are 5 countries which have a greater growth than the world (3.85%): China (12.47%), India (7.97%), Korea (4.5%), Brazil (4.05%) and Switzerland (4%).

  • Likewise, according to the economic indicators we can classify the study countries into 3: High-income, Balanced Economic Powerhouses and Emerging Market Giants.

After what has been presented, we know that the majority of countries present economic growth, now we have to evaluate if this is due to innovation and technology.

3.2. How are this economies looking the Global Innovation Index (GII)?

To begin the innovation analysis we will be considering the Global Innovation Index (GII), this is an indicator measured annually by the World Intellectual Property Organization (WIPO) with the objective of measuring the capacity of a country in terms of innovation. This index evaluates different categories such as: Innovation in institutions, Human capital and research, Infrastructure, Market sophistication, among others. Specifically for this analysis we will be considering the general index.

Starting with the analysis of Innovation indicators, we find a new ranking of countries. We have Switzerland and USA leading the ranking for the last years. Also within the ranking are new countries such as Sweden, Holland, Denmark, Singapore, among others.

3.2.1. How the level of innovation has evolved in these 12 leading Economies?

We will evaluate according to our 3 clusters of countries:

High-Income (Switzerland and the United States): Switzerland has remained a leader in innovation for the last 12 years. This is because it surpasses the rest of the countries in terms of first-class patents and intellectual property regulations, and occupies one of the first positions in research cooperation between industry and universities. On the other hand, in the United States we see that it went from being the 7th most innovative (2011) to 2nd (2022). This is interesting given that the growth of their GDP during 2010 to 2022 was also higher than the decade of 2000 - 2010. We can first conclude that these economies are not only High-Income, but also High-Innovation (High-Income-Innovation).

The Balanced Economic Powerhouses countries: In innovation this group of countries is led by the UK (position 4) and these countries have also remained in the top 20 in the last 5 years.

The Emerging Market Giants: These countries started in the top +25 for innovation, however the only one that has achieved a position of 11 is China. India went from position 62 to position 40; On the other hand, Brazil started in position 47 and finished in position 54.

Preliminarily from this data we are suggesting a certain correlation between economic impulse and innovation. Next we will continue to address the indicators to evidence or not the hypothesis.

4. Analysis

4.2. How have technological advancements and adaptation, measured through exports of technology services, internet usage, and mobile subscriptions, behave? and how they affected productivity and economic efficiency?

To answer this question we will analyze 5 indicators: Communications, computer, etc. per capita, High-technology, exports per capita, ICT service exports per capita, Individuals using the Internet % of population and Mobile cellular subscriptions per 100 people.

  • The first three indicators will be use to assessing the technological advancement and economic adaptability of a country. Analyzing this indicators is a way to measure the extent to which a country is integrated into the global knowledge and technology economy. These indicators can reflect a country’s capacity to produce and export goods and services with high added value and its integration into international trade and production networks.

  • The last two indicators are used to evaluate the technological adaptability of the countries’ population. Since this gives us a clear idea of how people in a country interact with technology in their daily lives, and are crucial to understanding the level of technological development of a society and its ability to keep pace with global technological evolution. High penetration of both the Internet and mobile telephony is indicative of a society that is better equipped to take advantage of the opportunities offered by ICT, which is an important component of economic and social adaptability and resilience.

4.2.1. How have technology export levels evolved?

We will begin with the evaluation of the incidence of technology exports as a % of services exports and a % of manufacturing exports. Below it will be presented by indicator and cluster of countries.

Main points:

  • Balanced Economic have a broader distribution with a larger range extending from low to high values, indicating variability in the contribution of communications and computer exports within this group.
  • Emerging Market group present a narrower and more focused distribution than the Balanced Economic cluster, suggesting that they have similar levels of communications and computer exports compared to their total services exports.
  • High Income-Innovation cluster shows a centered distribution with less dispersion than the Balanced Economic cluster, suggesting greater consistency in the proportion of communications and computer exports between these countries. This could reflect a consolidated and possibly more advanced approach in the ICT sector.

Main points:

  • Balanced Economic: exhibit a broad distribution with two distinct peaks, indicating two subgroups within this cluster: one with a lower and another with a higher proportion of high-tech exports.

  • Emerging Markets have a broader distribution than High Income-Innovation, which may reflect different levels of specialization in high-tech products within this group.

  • High Income-Innovation have a relatively symmetrical distribution, although less concentrated than in the communications and computers graph, indicating a significant proportion of high-tech exports, consistent with economies that specialize in high value-added goods.

Main points:

  • Balanced Economics show a distribution with a tail that extends toward negative values, which could be an error in the data or a representation of outliers.

  • Emerging Market group shows considerable variation, with a distribution extending towards very high values. This could indicate that some developing countries are gaining significant market share in ICT services exports.

  • High Income-Innovation show a more compact and focused distribution, suggesting a consistent and moderate participation of ICT services exports in total services exports.

Preliminary comparative Analysis:

  • The High Income-Innovation tend to show greater consistency in their export percentages in the communication, computers and ICT sectors, which is expected given their level of economic and technological development.

  • Emerging Markets exhibit greater variability across all three charts, suggesting there is significant diversity in how different countries in this group are integrating into the global technology economy.

  • The Balanced Economics appear to have significant variability in high-tech exports, indicating different degrees of industrial specialization and technological capacity.

With this presentation of the incidence of the export of technology within the export of services and manufacturing, we will present the evolution of these indicators in each country over time.

  • Communications, computers, etc. per capita:

A clear leader is observed (Switzerland) showing significant growth over time, its upward trajectory indicates an increase in per capita exports in this sector, suggesting sustained development and possibly strong global demand for these goods and services. The other countries and the world average show more moderate and consistent growth, with some convergence in the most recent values.

  • High technology exports per capita:

This graph shows considerable variability between countries, with some experiencing significant spikes that could represent specific events such as large export contracts, changes in trade policy, or fluctuations in global demand. Some countries present an upward trend, although not as marked as in the first graph, indicating a growth in high technology export capacity at a per capita level.

  • ICT service exports per capita:

Similar to the first graph, Switzerland stands out significantly above the others, showing a notable increase in ICT services exports per capita. This could reflect a strong national focus on exporting ICT services or a competitive advantage in this sector. The other countries and the world average have a more gradual upward trend, suggesting a steady growth in the importance of ICT services in the global economy.

The countries represented vary in their performance and growth in the ICT and high technology sectors, reflecting the diversity of economic strategies and levels of technological development. The clear leaders in these charts can be considered centers of technological innovation and are likely to invest significantly in R&D. Countries showing slower growth may be developing in these sectors or may have economies focused on other types of industries.

The following tables present a summary of the information and comparison with the GDP per capita by indicator.

4.2.3. What are the observable pattern in the country productivity?

To evaluate the country’s productivity we will be evaluating the GDP per capita and the GDP per person employed. This last indicator is a more direct measure of labor productivity. It shows the value of GDP produced by each person employed, which gives an idea of how efficiently labor resources are being used in economic production.

Comparing both graphs, it can be seen that the growth of GDP per capita does not always translate into a proportional increase in GDP per employed person. This may be due to factors such as increased labor participation or the contribution of other sectors that do not strictly depend on employment, such as investment income or natural resources. The world average in GDP per employed person does not show as marked an increase as in GDP per capita, suggesting that global economic growth is not equally reflected in labor productivity. This may be due to an increase in the number of people employed that is not accompanied by a proportional growth in economic output. Productivity, measured by GDP per person employed, is a critical indicator for understanding how economies can sustain and improve living standards. Countries that achieve sustained increases in this indicator are likely investing in technology, education, and efficiencies in the production process.

To conclude the analysis of patterns in productivity, we will perform a multiple correlation analysis to reveal whether factors such as the use of technology and investment in R&D are associated with higher levels of productivity in countries.

Main points:

  • RDpc (Research & Development per capita) and GDPppe (Gross Domestic Product per person employed) have a correlation coefficient of 0.728, indicating a strong positive relationship. This suggests that higher R&D investment per capita is associated with higher productivity per employee.

  • ITC (Information and Communications Technology exports) and GDPppe have a correlation of 0.494, which is moderate and positive, indicating a decent level of association between ITC and productivity per employee.

  • CCS (Communication, Computer, etc., services exports) and GDPppe show a very strong positive correlation of 0.977. If CCS stands for exports of computer and communication services, this high correlation would imply that countries exporting more of these services have higher GDP per person employed.

  • HT (High Technology exports) and GDPppe have a correlation coefficient of 0.550, which is a moderate positive correlation.

  • UseInt (Internet Usage) and GDPppe have a correlation of 0.689, which is strong and suggests that higher internet usage in the population is associated with higher productivity per employee.

  • MobSus (Mobile Subscriptions) and GDPppe have a correlation of 0.458, which is a moderate positive correlation.

Conclusion: Based on the provided correlation coefficients, there is evidence of positive associations between technology-related variables (such as R&D investment, technology exports, internet usage, and mobile subscriptions) and the productivity of a country as measured by GDP per person employed. The strongest correlations are with CCS and RDpc, suggesting that these areas might be key drivers of productivity. However, caution should be exercised in interpreting these results, as correlation does not imply causation, and other underlying factors might be influencing these relationships.

4.3. What is the relationship between educational investment, and innovation outputs like patent applications and R&D expenditures in these economies?

Research on the influence of education on innovation is essential when examining the economic development of leading economies and Switzerland between 2000 and 2022. This research explores how investment in education correlates with innovation indicators, such as patents and spending on innovation. R&D.

4.3.1. How has Goverment expenditure on Education (% of GDP) change over time, and what is its relationship with the countries innovation outputs?

Considering the following as indicators: input (Expenditure on education per capita) and production (Patent Applications and Articles from scientific and technical journals), we present the trend graphs over the time.

Main points:

  • Government Spending on Education:

On the per capita spending graph, it appears that there is broad-based growth over time in most countries. Some countries show a steeper increase than others, which may reflect a political focus on education or changes in demographics.

Regarding the percentage of GDP allocated to education, the variability is more notable between countries. Some are trending upwards, others are relatively stable, and some even show occasional declines. This could be influenced by changes in political priorities, fluctuations in GDP or variations in educational investment.

  • Innovation Results:

Patent applications show a clearly upward trend in countries such as China, indicating significant growth in the production of patentable innovations. Other countries maintain a more moderate growth rate. In terms of scientific and technical publications, a constant increase is observed worldwide, reflecting an increase in research and knowledge development. The graph shows that the production of scientific knowledge is growing, with some economies, such as China, exhibiting very rapid growth.

The following tables present a summary of the information and comparison by indicator.

The analysis suggests that although education spending has increased over time in many major economies, the relationship with innovation outcomes is less clear and merits further study. Therefore we perform a correlation analysis for these variables:

** Main points**:

  • RDpc (R&D Expenditure per capita) and GovEdEx (Government Expenditure on Education): The correlation between R&D spending per capita and government spending on education is very high (0.867), suggesting that countries that invest more in education also tend to spend more on R&D. This relationship underlines the importance of education as a basis for research and development.

  • Patent and GovEdEx: There is a weak negative correlation between government spending on education and patents (-0.195), which is contrary to the common expectation that more investment in education would lead to more patents. This could indicate that the ability to generate patents does not depend solely on spending on education, or that there is a time lag between investment and the generation of patents.

  • ScArt (Scientific and Technical Articles) and GovEdEx: The correlation between education spending and scientific and technical articles is positive but low (0.049), suggesting that there is not a strong relationship or that other factors could be influencing the production of scientific publications.

  • TchRD (Technology Spending on R&D) and GovEdEx: There is a strong positive correlation between technology R&D spending and education spending (0.879), indicating that investments in education can be closely linked to specific R&D activities in the technology sector.

  • GDPpc (GDP per capita) and GovEdEx: A very high correlation (0.967) between GDP per capita and education spending suggests that countries with higher levels of educational spending also tend to have higher GDP per capita, which could be interpreted as an indirect measure of the return on education. investment in education.

Conclusions: - The strong link between government spending on education and spending on R&D reinforces the idea that education is a fundamental pillar for promoting research and innovation in an economy.

  • The negative relationship between patents and education spending is unexpected and suggests that a country’s ability to generate patents may depend on factors other than education, such as patent policy, innovation infrastructure, or business culture. Investments in education seem to be more directly related to technological R&D activities than to the production of scientific articles.

  • GDP per capita, as a measure of overall economic prosperity, is strongly associated with education spending, suggesting that investment in human capital is a sound economic strategy.

Consideration: Weak negative correlations or no correlation (as in the case of patents and scientific articles) may require further analysis, possibly considering time lags or the role of additional intermediaries and factors not included in this analysis.

4.4. What are the observable pattern in the ease of Doing Business Scores? and how do they correspond with innovation indicators like patent aplication and R&D expenditures?

Analyzing the ease of doing business in relation to innovation indicators such as patent applications and R&D expenditures can reveal how regulatory environments impact economic innovation. Observable patterns may show that countries with business-friendly policies tend to have higher innovation outputs.

4.1.1. What are the observable pattern in the ease of Doing Business Scores?

Below, we present charts to give a comparative view of the study countries’ ease of doing business rankings, along with specific scores in the categories of “starting a business,” “getting credit,” and “paying taxes.”

Observable Patterns: Ease of Doing Business Scores:

  • Asia’s Leadership: South Korea consistently stands out in the rankings, ranking high in overall ease of doing business as well as in specific subcategories. This suggests that the country has created an efficient and favorable business environment.

  • Mixed Performance: While some countries such as the United Kingdom and the United States maintain high ranks in overall ease of doing business, their rankings vary across subcategories. For example, the United States ranks lower in the “starting a business” category, which could indicate the presence of more significant barriers or paperwork for new entrepreneurs.

  • Contrasts in Taxes and Credits: Countries vary considerably in their rankings of “getting credit” and “paying taxes.” For example, China ranks relatively low in “paying taxes” but better in “getting credit.” This may reflect differences in tax policy and the availability of credit to businesses.

  • Challenges in Emerging Economies: Brazil and India, despite being large and growing economies, show lower ranks in the ease of doing business, which could point to areas of improvement in their business climate.

  • Consistency in Innovation: Switzerland, although not among the highest in ease of doing business, leads in innovation indicators, indicating that a lower business ranking does not necessarily limit a country’s capacity for innovation.

  • Correspondence with Innovation Indicators: The relationship between ease of doing business scores and innovation indicators such as patent applications and R&D spending is not directly visible in these graphs. However, one could hypothesize that a more favorable business environment could facilitate investment in R&D and increase the number of patents. Countries with agile business environments can provide the necessary conditions for innovations to be developed and commercialized successfully.

Conclusion:

The observed patterns suggest that countries with better ease of doing business scores provide fertile ground for innovation and business development. However, high scores on the ease of doing business are not exclusive to countries with strong innovation indicators, suggesting that other factors also play a crucial role in fostering innovation.

4.1.2. How does easy to Doing Business correspond with Innovation indicators like patent application and R&D expenditures?

Main points: The graph suggests a high positive correlation (0.92), which is somewhat counterintuitive given that a lower Doing Business score is better. Typically, we would expect a negative correlation if higher patent applications were associated with better (lower) Doing Business scores. The positive correlation here could imply that in the dataset, countries with higher scores (indicating a less favorable business environment) are registering more patent applications. This could be due to a variety of factors, such as larger countries having both more complex business regulations and more patents, or it could be an artifact of the data representation or a data error.

Main points:

The correlation is negative (-0.53), which aligns with expectations. As R&D expenditure increases, the Doing Business score decreases (improves), indicating that countries that spend more on R&D tend to have a more favorable business environment. This suggests that investment in research and development may contribute to or coincide with conditions that facilitate business operations, such as streamlined regulations, better infrastructure, and more efficient administrative services.

Conclusions: The negative correlation between R&D expenditures and Doing Business scores supports the hypothesis that innovation activity is higher in countries where it’s easier to do business. However, the unexpected positive correlation with patent applications warrants a closer look to ensure that the data is correct and to explore the reasons behind this pattern.

5. Conclusions

After having carried out all the data analysis, we can conclude that:

  • Relationship between Innovation and Economic Growth:

There is a discernible, statistically significant relationship between investment in Research and Development (R&D) with a five-year lag and annual GDP growth. Specifically, an incremental increase in R&D investment correlates with a marginal increase in GDP growth, although the magnitude of this effect is relatively small.

  • Impact of Technology on Productivity:

The analysis suggests a strong positive correlation between technological advancements, as measured by indicators like high-technology exports, internet usage, and mobile subscriptions, and economic development. This correlation is particularly pronounced in high-income countries, which also show high levels of innovation. These countries have consistently led in global innovation rankings, indicating a potential link between technological advancement and increased productivity.

  • Educational and Business Environment’s Influence on Innovation:

The analysis underscores the importance of an educational and business environment conducive to innovation. Countries leading in innovation indexes tend to have robust intellectual property regulations and fruitful collaborations between industry and universities. This implies that investment in education, especially higher education and research, can be crucial for fostering an innovative economy.

Recommendations Based on Economic Models of Innovation:

We must consider that while R&D investment is critical, it is not a sole predictor of GDP growth. Hence, policy recommendations should consider a broader set of economic factors. Policies encouraging innovation should also address the additional determinants of economic growth not captured in the current model, such as macroeconomic stability, infrastructure, health, and social factors.

  • Overall Economic Impact:

The economies studied account for a significant portion of the world’s GDP, with a particular note on the substantial growth rates of emerging economies like China and India. The classification of countries into high-income, balanced economic powerhouses, and emerging market giants provides a framework for understanding the differential impact of innovation and technology on economic development across various economic contexts.

In summary, the analysis confirms that innovation and technology play critical roles in economic development. However, they operate within a complex system where multiple factors intersect. Thus, while innovation and technology are pivotal, they are parts of a broader economic ecosystem that collectively drives growth. This nuanced understanding should shape both policy formulation and strategic economic planning.