Technology and AI—Impact on Country’s Growth and Unemployment

 

Brij Dave ¹

 

¹ Retd. IAS Officer, India

 

1. INTRODUCTION

The emergence of artificial intelligence (AI) has ignited both optimism and trepidation regarding its impact on the labour market, economic inequality and GDP growth rate. As AI technologies evolve, they present opportunities for enhancing productivity and shifting tasks, particularly affecting low-virtuoso labour in developing nations. Concerns about job displacement resonate due to existing historical precedents, as the trend of automation tends to elevate certain types of tasks at the expense of others, resulting in social and economic implications that could exacerbate inequality unless properly managed. A call for revised policy frameworks is essential to harness the benefits of AI, which includes addressing regulatory needs, protecting data privacy, and implementing fair profit-sharing mechanisms. The transformational potential of AI hinges on the nuanced interplay between technology and the economic landscape. While previous waves of automation have historically led to job displacement, they have also paved the way for innovative job creation. Looking back reveals the cyclical nature of technological impacts, as shifts towards skill acquisition and new occupational opportunities routinely emerge alongside fears of job losses. Policymakers are urged to encourage inclusive growth, ensure equitable access to technological advancements, and establish robust safety nets that can safeguard against the risks posed by further economic stratification. The successful integration of AI into the workforce requires a balanced approach that recognizes the dual nature of innovation: as a creator of possibilities while simultaneously acknowledging and addressing the challenges it introduces. Adoption of AI is happening very fast in all the sectors and the employment opportunities are soaring as shown in the Graph 1 below.

The empirical studies of endogenous growth models generally involve testing the effect of R&D variables on total factor productivity (TFP) growth. For example, Jones (1995b) uses the time series plots of the TFP growth and the growth rate of the numbers of scientists and engineers in France, Germany, Japan and United States to test the validity of R&D based growth models. However, he finds no evidence that these variables are positively related. Aghion and Howitt (1998) provide explanations for the contradicting results of Jones(1995b). First, the increasing complexity of technology makes it necessary to raise R&D over time just to keep the innovation rate constant for each product.

Graph 1

Graph 1

 

Second, as the number of products increases, an innovation in any one product affects a smaller proportion of the economy, and therefore, has a smaller proportional spillover effect on the aggregate stock of knowledge. They then argue that instead of the number of the scientists and engineers, GDP share of R&D investment should be used to take into account the size of the economy.

In order to study the impact of technological and AI on growth of a country and unemployment, this paper analyses the data relating to G7 countries, which are believed to be the richest countries in the world where the adoption of technology is quite fast as compared to others. But in case of spread of AI, China and India and many smaller developing countries are found to have invested greatly in AI and have been able to utilize this technology in various sectors of the economy in a much accelerated and better way than the developed countries of G7 group. Hence, along with the G7 countries, India has been included in the study, as the digital revolution in India coupled with the development of digital infrastructure and Fin Tech, as well as being the country with the maximum number of digital transactions in a year, appears to have enhanced productivity. Another reason for including India is one of the fastest developing countries in the world. The following figure (Graph 2) shows this:

Graph 2

Graph 2

 

Also India has been on the front to adopt AI in various sectors comparable to these advanced countries.

 

2. Literature Review

In the contemporary landscape of technological evolution, Artificial Intelligence (AI) stands as a beacon of transformative power, reshaping industries, economies, and societies globally. From enhancing productivity across industries to driving innovation and fostering inclusive development, AI holds the promise of reshaping economic trajectory. The intersection of technology, artificial intelligence (AI), and economic performance has garnered significant scholarly attention in recent years. A growing body of literature suggests that technological advancements, particularly in AI, can have profound implications for labour markets and economic growth. For instance, Brynjolfsson & McAfee (2014) argue that while technological progress can displace certain jobs, it simultaneously creates new opportunities, leading to a net positive effect on employment in the long run. This perspective aligns with findings that AI adoption is associated with lower unemployment rates, suggesting a transformative rather than a purely destructive impact on the labour market.

Moreover, the role of investment in technology as a driver of economic growth has been extensively documented. According to Acemoglu & Restrepo (2018), increased investment in AI and automation technologies can enhance productivity, which is a critical determinant of GDP growth. Their research indicates that regions that invest more heavily in technology experience faster economic growth, corroborating observations that productivity gains are essential for GDP growth.

In addition, the relationship between technological infrastructure and economic performance has been explored by Chen et al. (2020), who found that robust technological infrastructure significantly contributes to economic resilience and growth. Their findings support the analysis that technological infrastructure plays a crucial role in shaping economic outcomes, although results indicate it may not have a direct effect on unemployment rates.

Furthermore, the implications of R&D investment on economic development have been highlighted by Hall & Rosenberg (2010), who emphasize that sustained investment in research and development is vital for fostering innovation and maintaining competitive advantage. This aligns with findings regarding the positive correlation between investment in R&D/AI and economic performance, albeit with a nuanced understanding of its immediate effects on unemployment.

However, realizing this potential requires strategic investments in research, infrastructure, and human capital development. Recently, Yong et al. (2023) show that the proliferation of AI in the economy has been unprecedented. In particular, the advent of the post-pandemic era has intensified the reliance on and desire for AI for economic development. Most relevant studies emphasize that AI has the potential to significantly impact economic growth in various ways. However, it is worth noting that the impact of AI on economic growth is not uniform across all sectors and regions. Some industries may experience more significant changes and growth, while others may face challenges or disruptions. Additionally, the successful adoption and integration of AI technologies need adequate infrastructure, data availability and supportive policies, which can vary across different economies Mohamed Ali Trabelsi (2024). Accenture in their article “Enabling strong and inclusive AI-driven Economic Growth” have forecasted that by 2035, AI has the potential to double annual growth rates in terms of gross value added. As Zhang et al. (2012) state, there is a significant relationship between scientific innovation and economic growth. In China and in several other Asian countries like Korea, Taiwan and Singapore, aggressive technology acquisition and efficient use of these technologies in production processes played a significant role in the economic development of these countries. In order to increase their international competitiveness, the mentioned states further developed these acquired technologies by improving their research and development (R&D) capabilities. Consequently, as the scientific innovation contributes more to economic growth, governments give more importance to technological investments. Stefan Calimanu (2023) observes that traditional models of development are also changing due to advancements in technology, with the digital divide and inequality becoming pressing concerns. The need for digital infrastructures is greater than ever, and the lack of it in many places is threatening development. As unequal access to digital technologies worsens existing social and economic disparities and restricts opportunities for education, employment, healthcare, and civic participation, it also challenges economic development efforts. The positive relationship between countries’ own R&D and productivity growth has been also confirmed by studies using international panel data, such as Frantzen (2000) and Griffith, Redding and Reenen (2002). There is also strong evidence that R&D spillovers from industrialized countries to developing countries have positive effects on the TFP (Total Factor Productivity) growth of the latter (Coe, Helpman and Hoffmaister (1995); Griffith, Redding and Reenen (2002)). In another study Savvides and Zachariadis (2003) show that both domestic R&D and foreign direct investment increase the domestic productivity and value-added growth. Zachariadis (2003) compares the effect of R&D on aggregate and manufacturing output and finds that the effect of R&D is much higher for aggregate economy than the manufacturing sector.

In summary, the literature suggests a complex interplay between technology, AI adoption, and economic indicators. While concerns about job displacement due to AI are prevalent, evidence indicates that technological advancements can lead to job creation and economic growth when accompanied by strategic investments and robust infrastructure.

 

3. Research Objectives

The primary objectives of this research paper are as follows:

1)    To Analyse the Impact of AI Adoption on Unemployment Rates: This objective aims to investigate the relationship between the adoption of artificial intelligence technologies and the fluctuations in unemployment rates across various sectors. The hypothesis posits that increased AI adoption may correlate with lower unemployment rates, challenging the prevailing narrative of job displacement.

2)    To Examine the Role of Investment in Technological Infrastructure: This objective focuses on assessing how investments in technological infrastructure influence economic growth and employment dynamics. It seeks to determine whether enhanced technological capabilities can mitigate potential job losses associated with automation.

3)    To Evaluate the Contribution of Productivity Gains to GDP Growth: This objective aims to quantify the impact of productivity gains resulting from technological advancements on the overall GDP growth rate. It will explore the mechanisms through which productivity improvements drive economic expansion.

4)    To Investigate the Relationship Between R&D Investment and Economic Development: This objective seeks to analyse the significance of research and development investments in fostering innovation and economic resilience, particularly in the context of AI and automation technologies.

 

4. Research Methodology

The research methodology employed in this study is a quantitative approach utilizing multiple regression analysis to evaluate the relationships between the identified variables. The methodology includes the following steps:

1)    Data Collection: Data were collected from various sources, including national labour statistics, investment reports, and technological adoption metrics. Key variables such as unemployment rates, levels of AI adoption, investment in R&D, and productivity gains were compiled for analysis. Data of AI adoption rate were obtained from OECD, NASSCOM, or Statista. GDP Growth Rates (2001 to 2023) data were taken from The World Bank (World Bank GDP Data). Data of other economic indicators were taken from the International Monetary Fund (IMF). Some of these data were taken from the OECD Economic Outlook. Other sources are Our World in Data from (Our World in Data GDP) and Statista (Statista GDP Data). Comprehensive year-wise and country-wise data on productivity gains attributed specifically to technology or AI for G7 countries and India from 2001 to 2023 is not consistently available in open sources. However, general trends and estimates were synthesized from multiple credible sources: World Economic Forum, McKinsey Company Data on Historical Technology Contributions have been taken from OECD i-Library (https://www.oecd-ilibrary.org/science-and-technology/the-impact-of-artificial-intelligence-on-productivity-distribution-and-growth_8d900037-en).

2)    Model Specification: A multiple regression model was constructed to assess the impact of independent variables (e.g., Log (Investment), AI adoption percentage, productivity gains, technological infrastructure, and R&D investment) on the dependent variable (unemployment rate/GDP growth rate). The model was specified to include control variables that may influence the outcomes.

3)    Statistical Analysis: The collected data were analysed using statistical software to perform regression analysis. The significance of each variable was evaluated based on p-values, and coefficients were interpreted to understand the direction and strength of the relationships.

4)    Interpretation of Results: The results of the regression analysis will be interpreted to draw conclusions regarding the hypotheses. Insights will be derived on how AI adoption and technological investments affect unemployment rates and GDP growth, contributing to the broader discourse on economic development.

5)    Validation and Robustness Checks: To ensure the reliability of the findings, robustness checks will be conducted, including sensitivity analyses and potential multicollinearity assessments among the independent variables.

 

5. Empirical Analysis

In this study, GDP growth rate per cent, log of Investment in R&D and AI, AI adoption in per cent, productivity gain in per cent, Technological Infrastructure Index, Tech and AI labour force (number per 10,000 labours) and finally, to examine the impact on unemployment, Gross Unemployment in per cent have been taken as the variables to inquire and analyse.

1)    Descriptive Statistics

·        GDP Growth Rate: The mean is 1.85%, with a standard deviation of 3.05%, indicating moderate variability. The range is from -9.3% to 8.95%.

·        Log (Investment): The mean is 4.90, with a relatively small standard deviation of 0.40, suggesting less variability.

·        AI Adoption in Percent: The mean is 9.35%, with a wide range from 0.5% to 40%, showing significant variability.

·        Productivity Gain in %: The mean is 0.02%, with a small standard deviation of 0.008, indicating low variability.

·        Technological Infrastructure: The mean is 74.33, with a range from 42.5 to 96.2, showing moderate variability.

·        Tech and AI Labour Force: The mean is 89.59 per 10,000, with a range from 45.1 to 124.2, indicating moderate variability.

 

2)    Correlations

The correlation analysis reveals several interesting relationships: The descriptive statistics and correlation analysis show relationships between variables. Regression analysis was also conducted, revealing significant coefficients for AI adoption but with potential multicollinearity issues.

·        Productivity Gain shows a moderate positive correlation (0.54) with GDP Growth Rate

·        Technological Infrastructure and Tech/AI Labour Force show very strong positive correlation with each other (0.98)

·        Log (Investment) has strong positive correlations with both Technological Infrastructure (0.68) and Tech/AI Labour Force (0.74)

·        AI adoption shows strong positive correlations with Technological Infrastructure (0.79) and Tech/AI Labour Force (0.77)

·        GDP Growth Rate shows slight negative correlations with Technological Infrastructure (-0.19) and Tech/AI Labour Force (-0.19)

The scatter plots help visualize these relationships and confirm the patterns seen in the correlation matrix.

Graph 3

Graph 3

 

Variance Inflation Factor (VIF): This table shows the VIF values for each independent variable. A VIF above 10 suggests significant multicollinearity, which could affect the robustness of the analysis.

Table 1

Table 1
Variable	IF
GDP Growth Rate	.1489
Log (Investment)	30.8812
AI adoption in per cent	.9676
Productivity Gain in per cent	1.7656
Technological Infrastructure	226.1632
Tech and AI labour force per 10,000 of population	786.2127
Unemployment rates in per cent	25.4049

 

The VIF results show that "Technological Infrastructure" and "Log (Investment)" have very high VIF values, suggesting they may significantly inflate the variance of the regression coefficients. Here it is important to notice that the pre-existing economic conditions and extent of technological advancement does influence the early adoption of AI and increase in productivity, consequently positively impacting economic growth. Hence, AI adoption Level is an important variable affecting GDP growth rate by increasing productivity. The following Chart shows this relation.

Graph 4

Graph 4

 

© Fixed Effect Regression

A panel regression between these variables yields the following results in Table 1. As shown in the table, the coefficient of the following variables are positive, AI adoption in per cent, productivity gain in per cent, and technological infrastructure.

Table 2

Table 2 Panel OLS Estimation Summary
SUMMARY OUTPUT
Regression Statistics
Multiple R	0.609706772
R Square	0.371742348
Adjusted R Square	0.350445478
Standard Error	2.4585319
Observations	184
ANOVA
	df	SS	MS	F	Significance F
Regression	6	633.0371721	105.5061953	17.45525778	7.93441E-16
Residual	177	1069.855101	6.044379103
Total	183	1702.892273
	Coefficients	Standard Error	t Stat	P-value	Lower 95%
Intercept	7.0636383	3.2139117	2.1978321	0.0292589208	0.721121
Log(Investment)	-0.5604657	0.7813048	-0.7173458	0.4741063805	-2.102337
AI adoption in Per cent	0.0122045	0.0344376	0.3543959	0.7234640972	-0.055757
Productivity Gain in %	203.6236620	24.3308197	8.3689602	0.0000000000	155.607831
Technological Infrastructure	0.1275803	0.1078798	1.1826149	0.2385479768	-0.085316
Tech and AI Labour Force per 10000 of population	-0.1527927	0.0807881	-1.8912762	0.06022090905	-0.312225
Unemployment Rates in per cent	-0.3390041	0.1241401	-2.7308187	0.00695707889	-0.583989

 

However, it is significant in case of productivity gain in per cent, seeing the p-values. According to these data, 1 per cent increase in productivity gain, may cause the GDP growth rate multiply 203.62. With unemployment coming down by 1 percent, rate of GDP growth will increase by 0.339.

Cluster Analysis

Standardized the independent variables to ensure comparability, applied a clustering algorithm (e.g., K-Means), and determined the optimal number of clusters using the Elbow Method. Below are the outputs:

 

 

Graph 5

Graph 5

 

The Elbow Method suggests the optimal number of clusters, while the pair plot shows how the data points are grouped. The cluster centres provide insights into the characteristics of each cluster. The analysis reveals three distinct clusters with the following characteristics:

The clusters can be characterized as follows:

1)    Cluster 0 (Advanced Economies): Canada, Germany, Japan, and UK

·        Characterized by high technological infrastructure and AI labour force, moderate AI adoption, and low unemployment rates

·        Conservative GDP growth but stable economic indicators

2)    Cluster 1 (Technology Leader): United States

·        Highest investment in R&D/AI ($732B)

·        Leading in technological infrastructure and AI workforce

·        Strong AI adoption rate and moderate unemployment

3)    Cluster 2 (Developing Tech Economies): France, India, and Italy

·        Higher GDP growth rate but lower technological infrastructure

·        Lower AI adoption rates

·        Higher unemployment rates

·        Lower R&D/AI investment

The clustering shows clear differentiation between technology leaders, advanced economies, and developing tech economies, which is primarily driven by their technological infrastructure, AI investment, and economic indicators.

ANOVA RESULTS:

                                                                       F-Statistic                                  p-value

GDP Growth Rate:         36.92900994579501         3.547435140156471e-14

Log(Investment):                          56.84213929110366         6.967958037726596e-20

AI adoption in Per cent:                111.2906978545915         3.041886766718913e-32

Productivity Gain in %:                 92.00263495450793        2.7029581785333875e-28

Technological Infrastructure:         170.4709910375212         2.371915257204045e-42

Tech and AI Labour Force

per 10000 of population:               179.39011723537007        1.133116464452673e-43

 

Interpretation of Results: All ANOVA tests show extremely low p-values (p<0.001). This indicates that the differences between clusters are statistically significant for all metrics. The F-statistics are particularly high for technological infrastructure and tech workforce, suggesting that these are the strongest differentiating factors. The following graph, Graph 6 shows these results visually.

Graph 6

Graph 6

 

Key Insights from Cluster Analysis:

1)    Technology Adoption Gap: There's a clear divide between high-tech leaders and developing economies in terms of AI adoption and technological infrastructure.

2)    Growth Paradox: Interestingly, Cluster 2 (Developing Tech Economies) shows the highest GDP growth despite having the lowest technological indicators. This might suggest a "catch-up effect" where less developed economies have more room for rapid growth.

3)    Employment Patterns: Higher technological advancement correlates with lower unemployment rates, suggesting that technology adoption might not necessarily lead to job losses.

4)    Investment Levels: There's a positive correlation between investment levels and technological advancement, indicating that higher investment supports technological development.

5)    Size Distribution: The moderate performers (Cluster 2) form the largest group, suggesting that most economies are in a transitional phase of technological adoption. The country-wise comparison of performance on the relevant indicators is reproduced in the following graph.

Graph 7

Graph 7

 

1)    Leaders in AI Adoption:

·        Top 3 Countries by AI Adoption vs Productivity: US, Canada and Japan

·        The United States stands out as the clear leader with 40% AI adoption, highest productivity gains (0.032%), and significantly higher R&D investment ($732,000M)

·        Canada and Japan follow with 38% AI adoption each, but with notably different productivity gains and investment levels

2)    Lower Adoption Countries:

·        Bottom 3 countries by AI adoption: Italy, France, Germany.

·        Italy, France, and Germany show lower AI adoption rates (30-35%) but maintain relatively strong technological infrastructure scores

3)    Correlation Patterns:

·        Strong positive correlation (0.771) between AI adoption and technological infrastructure

·        Moderate positive correlation (0.624) between AI adoption and R&D investment

·        Weaker correlation (0.215) between AI adoption and productivity gains, suggesting that productivity improvements may lag behind AI adoption

4)    Investment-Infrastructure Relationship:

·        The size of the R&D investment generally increases with technological infrastructure.

·        This suggests that countries with better technological infrastructure tend to invest more in R&D and AI

5)    Productivity Variation:

·        Despite similar AI adoption levels, countries show varying productivity gains

·        This suggests that the effectiveness of AI implementation varies significantly across countries, possibly due to differences in implementation strategies and supporting infrastructure

 

This effectively shows that while AI adoption and investment are closely related, the translation into productivity gains is more complex and varies significantly across countries.

(E)  Chi-Square Test: -- The Chi-Square Test was conducted, showing a statistically significant relationship (p-value = 0.036) between AI Adoption Level and Cluster. This suggests that AI adoption levels are not independent of the identified clusters.

(F) Multiple Regression Analysis on Unemployment Rate as dependent variable

Variable                                       Coefficient                                            P-Value

const                                  19.345897722715094                     2.7685872549751992e-11

Log(Investment)        -3.861494434930842                     3.140192649081043e-08

AI adoption in Per cent -0.10412791820295801                   2.7044612164366714e-08

Productivity Gain in %  -0.9867568432516216                   0.9417721374963468

Technological Infrastructure    0.510015355848964        9.861992767122386e-20

Tech and AI Labour Force

per 10000 of population  -0.3487188407849844          9.010556390941843e-17

Investment in R&D/AI

(In million $)                                    8.938382953979042e-06                   9.914032822415434e-09

 

·        Significant Variables: The variables Log (Investment) and AI adoption in Per cent have significant negative coefficients, indicating that increases in these variables are associated with decreases in unemployment rates. This suggests that technological progress and AI adoption may not necessarily lead to higher unemployment.

·        Other variables: Productivity gain in % and technological infrastructure have non-significant p-values, indicating that they do not significantly affect unemployment rates in this model.

 

6. Conclusion and Insights

·        GDP Growth Rate: The analysis indicates that Productivity Gain in % is a key driver of GDP growth, while other factors such as AI adoption and investment levels do not show significant direct effects in this model.

·        Unemployment Rate: Contrary to common concerns, AI adoption and technological progress appear to be associated with lower unemployment rates, suggesting that these factors might contribute to job creation or transformation rather than job loss.

Analysis of Results of Tests: -

AI adoption affects unemployment rates globally through a complex interplay of job creation and transformation rather than outright displacement. The findings from the multiple regression analysis indicate that increased AI adoption is associated with a significant decrease in unemployment rates, as evidenced by the negative coefficient for the variable "AI adoption in percent" (coefficient = -0.104). This suggests that as countries adopt AI technologies, they may experience a reduction in unemployment, potentially due to the emergence of new job categories and the enhancement of existing roles.

Moreover, the analysis highlights that investment in AI and technological infrastructure plays a crucial role in this dynamic. The significant negative coefficient for "Log (Investment)" further supports the notion that higher investment levels correlate with lower unemployment rates. This implies that strategic investments in AI not only facilitate technological advancement but also contribute to job creation and economic resilience.

The quantitative analyses conducted in this research highlights the critical role of investment in technological infrastructure as a determinant of AI adoption and its subsequent effects on economic performance. The findings reveal a strong positive correlation (0.771) between AI adoption and technological infrastructure, indicating that countries with robust technological frameworks are more likely to invest in and adopt AI technologies. This relationship suggests that investment in infrastructure is foundational for facilitating the integration of AI into various sectors, thereby enhancing productivity and economic growth. Furthermore, the analysis of R&D investment shows a moderate positive correlation (0.624) with AI adoption, reinforcing the idea that countries that prioritize technological infrastructure and investment in R&D are better positioned to leverage AI for economic advancement. The visualization of investment levels relative to technological infrastructure indicates that countries with superior infrastructure tend to allocate more resources to R&D and AI initiatives, which in turn supports job creation and reduces unemployment rates. The findings also suggest that the effectiveness of AI implementation varies significantly across countries, despite similar levels of AI adoption. This variation can be attributed to differences in technological infrastructure and investment strategies, underscoring the importance of tailored approaches to infrastructure development in maximizing the benefits of AI.

The quantitative analyses conducted in this research indicate that productivity gains are a significant driver of GDP growth. The regression results reveal that the variable "Productivity Gain in %" has a notable impact on GDP, highlighting its role as a key determinant in the economic growth equation. Although the analysis did not find a direct significant effect of AI adoption and investment levels on GDP growth, the strong correlation between productivity gains and GDP suggests that improvements in productivity are essential for enhancing economic performance. The findings emphasize that while AI and technological investments contribute to productivity enhancements, the translation of these gains into GDP growth may not be immediate. This indicates that the effective implementation of AI technologies and the optimization of productivity processes are critical for realizing their full economic potential. Therefore, fostering an environment that encourages productivity improvements through strategic investments in technology and infrastructure is vital for sustained GDP growth.

The analysis indicates a strong positive correlation between R&D investment and economic development, underscoring the critical role that research and development plays in fostering innovation and enhancing productivity. Countries that allocate higher levels of investment towards R&D tend to exhibit more significant advancements in technological infrastructure, which in turn supports AI adoption and overall economic growth. The quantitative findings suggest that R&D investment not only drives technological progress but also contributes to improved employment outcomes, as innovative practices lead to the creation of new job opportunities.

Here are the key takeaways from the research: 1. AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2. The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3. Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4. Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other nations. 5. Three distinct clusters of countries were identified: Advanced Economies, Technology Leaders, and Developing Tech Economies. 6. Productivity gains are a significant driver of GDP growth, with a 1% increase in productivity potentially causing GDP growth to multiply by 203.62. 7. AI adoption and technological progress appear to be associated with lower unemployment rates, suggesting job creation or transformation rather than job loss. 8. There's a clear divide between high-tech leaders and developing economies in terms of AI adoption and technological infrastructure. 9. Higher technological advancement correlates with lower unemployment rates. 10. The United States stands out as the clear leader in AI adoption (40%) and R&D investment. 11. The effectiveness of AI implementation varies significantly across countries, possibly due to differences in implementation strategies and supporting infrastructure. 12. Future research directions include longitudinal studies on AI impact, sector-specific analysis, and investigation of policy implications for AI adoption and economic development.

Sensitivity and Robustness Checks. The correlation matrix indicates several variables may be highly correlated, particularly "Technological Infrastructure" and "Log(Investment)," which could lead to multicollinearity issues. The VIF results show that "Technological Infrastructure" and "Log(Investment)" have very high VIF values, suggesting they may significantly inflate the variance of the regression coefficients, potentially compromising the robustness of the analysis. To address multicollinearity, re-ran the regression analyses without "LogInvestment" and "Technological Infrastructure" variables. The multicollinearity issue has been addressed by removing the variables "LogInvestment" and "Technological Infrastructure" from the regression analyses. The updated models for both "GDP Growth Rate" and "Unemployment Rates" have been successfully run, and the results show improved VIF values, indicating reduced multicollinearity.

 

Updated Results:

1)    Summary of the GDP Growth Rate Regression:

·        The model explains approximately 34.6% of the variance in GDP Growth Rate (R-squared = 0.346).

·        Significant predictors include "Productivity Gain in %" with a coefficient of 203.75, indicating a strong positive relationship.

Summary

                            OLS Regression Results                           

==================================================================

Dep. Variable:        GDP_Growth_Rate   R-squared:          0.346

Model:                            OLS   Adj. R-squared:                  0.331

Method:                 Least Squares   F-statistic:                     23.63

Date:                Sat, 14 Dec 2024   Prob (F-statistic):           1.06e-15

Time:                        12:30:31   Log-Likelihood:                -426.80

No. Observations:                 184   AIC:                             863.6

Df Residuals:                     179      BIC:                             879.7

Df Model:                           4                                        

Covariance Type:            nonrobust                                        

==================================================================

                                                       coef         std err                 t       P>|t|              [0.025         0.975]

---------------------------------------------------------------------------------------------------------------

Intercept                                            2.9082      1.960      1.484      0.140      -0.959       6.775

AI_adoption_in_Per_cent                 0.0402      0.031      1.310      0.192      -0.020       0.101

Productivity_Gain_in_percent          203.7463     24.597      8.283      0.000     155.208     252.285

Tech_and_AI_Labour_Force

per_10000_of_population                 -0.0601      0.023     -2.572      0.011      -0.106      -0.014

Investment_in_RD_AI_

In_million_dollar                         9.541e-07    1.7e-06     -0.562      0.575   -4.31e-06     2.4e-06

==================================================================

Omnibus:                     78.876   Durbin-Watson:                   2.183

Prob(Omnibus):            0.000   Jarque-Bera (JB):                 305.907

Skew:                          -1.671       Prob(JB):                          3.74e-67

Kurtosis:                       8.360   Cond. No.                            2.57e+07

==================================================================

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

[2] The condition number is large, 2.57e+07. This might indicate that there are strong multicollinearity or other numerical problems.

 

2)    VIF Results for GDP Growth Rate:

Variable	IF
AI adoption in per cent	.6376
Productivity Gain in per cent	.4682
Tech and AI labour force per 10,000 of population	.6824
Investment in R&D and AI in million $	2.2165

All VIF values are now below 10, indicating no significant multicollinearity issues.

Scope for further Research

The findings of this research open several avenues for further investigation:

1)    Longitudinal Studies on AI Impact: Future research could focus on longitudinal studies to assess the long-term effects of AI adoption on employment dynamics and productivity across different sectors and regions. This would provide deeper insights into the sustainability of job creation versus displacement.

2)    Sector-Specific Analysis: Investigating the impact of AI adoption and R&D investment on specific industries could yield valuable insights. Different sectors may experience varying degrees of productivity gains and employment changes, necessitating tailored strategies for AI implementation.

3)    Comparative Studies Across Clusters: Given the identified clusters of countries with varying AI adoption levels, comparative studies could explore the factors contributing to successful AI integration and economic outcomes in high-adoption versus low-adoption countries.

4)    Policy Implications: Research could delve into the policy frameworks that facilitate or hinder R&D investment and AI adoption. Understanding the role of government policies in shaping technological infrastructure could inform better policy-making for economic development.

5)    Impact of Technological Infrastructure: Further exploration of how specific components of technological infrastructure influence the effectiveness of AI adoption and productivity gains would be beneficial. This could include examining broadband access, digital literacy, and investment in education.

6)    Socioeconomic Factors: Investigating the socioeconomic factors that influence the relationship between R&D investment, AI adoption, and economic development could provide a more comprehensive understanding of the dynamics at play.

By addressing these areas, future research can contribute to a more nuanced understanding of the interplay between technology, economic development, and labour markets.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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