Research Article |
Corresponding author: Charles Saba ( sabacharlesshaaba@yahoo.com ) Academic editor: Marina Sheresheva
© 2024 Charles Saba, Marinda Pretorius.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Saba C, Pretorius M (2024) The mediating role of governance in creating a nexus between investment in artificial intelligence (AII) and human well-being in the BRICS countries. BRICS Journal of Economics 5(2): 5-44. https://doi.org/10.3897/brics-econ.5.e117358
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The BRICS countries (Brazil, Russia, India, China, and South Africa) aim to achieve Sustainable Development Goals 3 and 16, which involve promoting human well-being for all and building strong institutions and governance. This study examines the AII-HWBG nexus contingent on governance indicators within the BRICS nations in 2012-2022 using the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique. Its findings reveal a long-term relationship among variables with varied causality directions and point to the necessity of integrating governance quality into AII to boost HWBG in both the short- and long-term perspective. Since AII has not so far been used to support HWBG there is a dire need for caution when considering AII’s interaction with institutional governance, economic governance, control of corruption, political stability, regulatory quality and voice and accountability. The paper highlights the crucial role of governance quality in shaping the way AI investment impacts the human well-being. To ensure an overall improvement of well-being, priority should be given to strategies that promote positive synergy between AI investment and governance while mitigating possible harmful effects. Carefully targeted measures in governance areas can create an environment conducive to AI development where it will significantly benefit the citizens of the BRICS countries.
Страны БРИКС (Бразилия, Россия, Индия, Китай и Южная Африка) стремятся достичь Целей устойчивого развития 3 и 16, которые направлены на содействие человеческому благополучию для всех и построение сильных институтов и управления. В этом исследовании рассматривается взаимосвязь AII-HWBG в зависимости от показателей управления в странах БРИКС в 2012-2022 годах с использованием метода перекрестной расширенной авторегрессии с распределенным лагом (CS-ARDL). Его результаты показывают долгосрочную связь между переменными с различными направлениями причинно-следственной связи и указывают на необходимость интеграции качества управления в AII для повышения HWBG как в краткосрочной, так и в долгосрочной перспективе. Поскольку AII до сих пор не использовался для поддержки HWBG, существует острая необходимость проявлять осторожность при рассмотрении взаимодействия AII с институциональным управлением, экономическим управлением, контролем над коррупцией, политической стабильностью, качеством регулирования, правом голоса и подотчетностью. В документе подчеркивается решающая роль качества управления в формировании того, как инвестиции в ИИ влияют на благосостояние человека. Чтобы обеспечить общее улучшение благосостояния, приоритет следует отдавать стратегиям, которые способствуют положительной синергии между инвестициями в ИИ и управлением, одновременно смягчая возможные вредные последствия. Целенаправленные меры в сфере управления могут создать среду, благоприятствующую развитию ИИ, где он принесет значительную пользу гражданам стран БРИКС.
Artificial intelligence investment, governance dimensions, human well-being, CS-ARDL technique, BRICS countries
Инвестиции в искусственный интеллект, аспекты управления, благополучие человека, методика CS-ARDL, страны БРИКС
Until the middle of the 20th century, economic growth and income levels were primarily used to gauge a country’s development. However, in recent times, the focus has shifted towards measuring human well-being or human development (
Both developed and developing countries have been the subjects of research into the drivers of their well-being, with the aim of helping nations achieve desirable levels of sustainable development in the context of globalization. To this day, economists disagree about how to define and measure human well-being.
The primary goal of development is to enhance human well-being, which is closely linked to sustainable development (
Information and communication technologies (ICTs) have a profound impact on various aspects of human societies, including health, education, employment and many others. (
AI is revolutionizing human interactions and business practices, paving the way for the fourth industrial revolution (
To mitigate the adverse impact of AI on human development, governments can use a number of mechanisms: they can (i) enact regulations and establish oversight bodies to ensure that AI development conforms to ethical and safety standards; (ii) enhance transparency and accountability; (iii) design ethical frameworks for AI development and usage; (iv) invest in public awareness campaigns and educational programs to inform citizens about AI technologies, their benefits, and potential risks (
Conversely, poor governance is sure to have a negative impact on human well-being. According to
This paper uses six governance indicators suggested by
Variables | Description | Sources | |
---|---|---|---|
Variables | Description | Sources | |
Dependent variable | |||
HWBG | Log of human development index (HDI) serves as proxy for human well-being | UN database | |
Independent variables | |||
LAII | Log of capital investments in artificial intelligence (AI) serves as proxy for AI investment | OECD database | |
LGDPPC | Log of GDP per capita (constant 2015 US$) proxy for levels of income | WDI database | |
LEMPL | Log of employment to population ratio, 15+, total (%) serves as proxy for employment | WDI database | |
LHMN | Log of School enrollment, secondary (% gross) serves as proxy for human capital | WDI database | |
LAII* OGV | Computed interaction between AI and Overall governance | Authors | |
LAII* POLG | Computed interaction between AI and political governance | Authors | |
LAII*INSTG | Computed interaction between AI and institutional governance | Authors | |
LAII*ECOG | Computed interaction between AI and economic governance | Authors | |
LAII*CRT | Computed interaction between AI and control of corruption | Authors | |
LAII*POLV | Computed interaction between AI and political stability and absence of violence/terrorism | Authors | |
LAII*GF | Computed interaction between AI and government effectiveness | Authors | |
LAII*RG | Computed interaction between AI and regulatory quality | Authors | |
LAII*RLW | Computed interaction between AI and Rule of law | Authors | |
LAII*VACC | Computed interaction between AI and voice and accountability | Authors | |
General/Overall governance (OGV) variable computed via PCA using the six governance indicators below | Authors | Authors | |
CRT | Log of Control of Corruption | WGI database | |
POLV | Political stability and absence of violence/terrorism | WGI database | |
GF | Log of government effectiveness | WGI database | |
RG | Log of regulatory quality | WGI database | |
RLW | Log of rule of law | WGI database | |
VACC | Log of voice and accountability | WGI database | |
POLG | It represents political governance which is computed via PCA using the voice and accountability and political stability and absence of violence/terrorism | Authors | |
INSTG | It represents institutional governance which is computed via PCA using the rule of law and control of corruption | Authors | |
ECOG | It represents economic governance which is computed via PCA using the regulatory quality and government effectiveness | Authors |
According to
Figure
(A): Capital Investment in Artificial intelligence for BRICS countries (OECD database); (B): Governance Quality for BRICS countries (WGI database); (C): HDI for the BRICS Countries (UN database); (D): Economic Governance for BRICS Countries; (E) Political Governance for BRICS Countries; (F) Institutional Governance for BRICS Countries (B, D, E and F – Authors’ estimations from PCA).
This paper aims to investigate the impact of AI investment on human well-being taking into account the mediating role of various dimensions of governance in the BRICS countries over the period of 2012-2022, which is important for several reasons. First, the BRICS countries represent some of the world’s fastest-growing economies. They have been actively investing in AI technologies to improve performance of various sectors, from healthcare to manufacturing. Research into the governance dynamics in these economies is essential to understand how AI investments impact their socioeconomic development. Second, the BRICS nations exhibit diverse governance models, ranging from democratic to authoritarian systems. Studying the governance mediating role in AI investment allows us to find out how different governance indicators and structures influence the relationship between AI adoption and human well-being. This may also provide valuable insights into the global AI landscape with particular regard to developing countries. Third, AI investment is expected to substantially affect the human well-being through changes in employment, healthcare, education, and overall quality of life. Understanding how governance mechanisms mediate this impact is critical for policymakers who need to make informed decisions about AI adoption and regulation. Fourth, research focusing on the BRICS countries can generate ideas on how to address governance gaps and promote responsible AI deployment, which may subsequently be used to create policies that will maximize the benefits of AI, minimize potential harms and ultimately enhance the well-being of their populations. Fifth, as the BRICS countries are some of the major players in the global economy, their AI policies and practices have far-reaching implications for the global AI industry. Insights gained from studying these nations can therefore inform international discussions on AI governance, standards, and cooperation. Focusing on BRICS can contribute to shaping the global AI landscape by promoting ethical and sustainable AI practices.
The present study addresses a significant gap in the empirical literature in several ways. Firstly, it is the first endeavor to examine the impact of AI investment on the human well-being while considering various dimensions of governance. Secondly, although researchers have explored the influence of governance on human development, such studies are relatively scarce, and none could be found that specifically focused on the G-7 economies. Lastly, the study makes use of the novel Cross-Sectional Autoregressive-Distributed Lag (CS-ARDL) technique proposed by
This paper is structured as follows: Section 2 offers a literature review. The methodology and data are outlined in Section 3. Section 4 presents and discusses the findings of the empirical analysis. Section 5 outlines the policy implications. The study is brought to a conclusion in Section 6.
In empirical literature, researchers have examined the factors that influence the human well-being or human development, with some studies specifically concentrating on the role of governance. Human well-being has often been proxied using the Human Development Index (HDI). For instance, in a study focusing on the role of technological progress and ICT development,
Research has also delved into the impact of emerging technologies, including artificial intelligence (AI), on economic growth, human development, governance, employment and total factor productivity (TFP). As economic growth is closely linked to human well-being (
Using the SGMM approach for the case of Sub-Saharan Africa (SSA),
It appears that so far there has been no study on the relationship between AI investment and the human well-being determined by the quality of governance, its dimensions or indicators. Nor has it been shown how general governance quality, dimensions or indicators affect human well-being in the BRICS countries. The present paper is looking into these issues with a view to identifying policy measures that could help the BRICS countries achieve the SDG 3 & 16, thus promoting human well-being for all and building strong institutions and governance.
The initial empirical methodology employed in this research comprises various techniques, such as the principal components analysis (PCA), descriptive analysis, and scatter plot visualization. It also includes tests for panel unit root (both first- and second-generation), tests for slope homogeneity and cross-sectional dependence (CD), as well as CIPS panel unit root tests. The study also uses first- and second-generation panel cointegration tests, fully modified ordinary least squares (FMOLS), dynamic OLS (DOLS), and Dumitrescu-Hurlin (
Two normative perspectives underlie
HDI = f (AII,OGV) (1a)
Where HDI = Human Development Index proxy for human wellbegin (HWBG).
AII = Artificial intellegence investment.
OGV = Over governance quality.
The functional form equation above can be linearized and augmented by incorporating other factors influencing human well-being, as indicated in the previously mentioned studies.
Model 1:
LHWBGi,t = β1 + ℶ1LAIIi,t + ℶ2LGDPPCi,t + ℶ3LEMPLi,t + ℶ4LHMNi,t +
+ ℶ5OGVi,t + ℶ6LAIIi,t * OGVi,t + ε1i,t (1b)
Where β, ℶ1, ..., ℶ4, and εit represents the constants, coefficient and the error term, respectively. Model 1 excludes the interaction terms between: LAII and OGV; LAII and POLG; LAII and INSTG; LAII and ECOG; LAII*CRT; LAII*POLV; LAII*GEF; LAII*RQE; LAII*RLW; and LAII*VCAC while the rest of the models (that is, model 2-5) does in a systemic manner one after the other.
Model 2: Capturing the interaction between LAI and OGV
LHWBGi,t = β1 + ℶ1LAIIi,t + ℶ2LGDPPCi,t + ℶ3LEMPLi,t + ℶ4LHMNi,t +
+ ℶ5OGVi,t + ℶ6LAIIi,t * OGVi,t + ε1i,t (2)
Model 3: Capturing the interaction between LAI and POLG
LHWBGi,t = β1 + ℶ1LAIIi,t + ℶ2LGDPPCi,t + ℶ3LEMPLi,t + ℶ4LHMNi,t +
+ ℶ5POLGi,t + ℶ6LAIIi,t * POLGi,t + ε1i,t (3)
Model 4: Capturing the interaction between LAI and ECOG
LHWBGi,t = β1 + ℶ1LAIIi,t + ℶ2LGDPPCi,t + ℶ3LEMPLi,t + ℶ4LHMNi,t +
+ ℶ5ECOGi,t + ℶ6LAIIi,t * ECOGi,t + ε1i,t (4)
Model 5: Capturing the interaction between LAI and INSTG
LHWBGi,t = β1 + ℶ1LAIIi,t + ℶ2LGDPPCi,t + ℶ3LEMPLi,t + ℶ4LHMNi,t +
+ ℶ5INSTGi,t + ℶ6LAIIi,t * INSTGi,t + ε1i,t (5)
We specify the CS-ARDL model below which took its bearing from the above equations:
ΔLHWBGi,t = 𝔉i + ξi (LHWBGi,t – 1 – ℶiXi,t – 1 – β1iLHWBGt – 1 – β2iXt – 1)+
ΔLHWBGi,t – j + ΔXi,t – j + ø1iΔLHWBGt + ø2iXt + uit (6)
Where ΔLHWBG, Xi,t, LHWBGt – 1 & Xt – 1, ΔLHWBGi,t – j & ΔXi,t – 1, ΔLHWBGt & ΔXt and uit are dependent variable, all independent variables during the long-run, mean of the dependent and explanatory variables in the long-run, dependent and independent variables in the short-run, mean dependent and independent variables during the short-run and the error term, respectively. Furthermore, where j, t, ℶ1i, γ1i, Гi,j, ø1i and ø2i denotes cross-sectional dimension, time, coefficients of the independent variables, short-run coefficient of the dependent variable, short-run coefficients of the independent variables, mean of dependent variables and mean of independent variables in the short-run, respectively. The details of the dependent and independents variables regressors can be found in Table
This research study employed annual panel data encompassing the Group of BRICS countries, namely Brazil, Russia, India, China, and South Africa, for the period spanning from 2012 to 2022. The data sources included three primary databases which can be found in Table
Table
Panel A: Overall governance | |||||||
Principal component results | |||||||
Compnnt | Eigenvalue | Difference | Proportion | Cumulative | |||
Compnnt 1 | 3.2890 | 1.9194 | 0.5482 | 0.5482 | |||
Compnnt 2 | 1.3697 | 0.4856 | 0.2283 | 0.7764 | |||
Compnnt 3 | 0.8840 | 0.6146 | 0.1473 | 0.9238 | |||
Compnnt 4 | 0.2694 | 0.1372 | 0.0449 | 0.9687 | |||
Compnnt 5 | 0.1322 | 0.0765 | 0.0220 | 0.9907 | |||
Compnnt 6 | 0.0557 | 0.0093 | 1.0000 | ||||
Principal components eigenvectors results | |||||||
Variables | Compnnt 1 | Compnnt 2 | Compnnt 3 | Compnnt 4 | Compnnt 5 | Compnnt 6 | Unexplained |
CRT | 0.5095 | -0.1061 | -0.0384 | 0.5855 | -0.4555 | 0.4213 | 0.131 |
GF | 0.3811 | -0.5119 | 0.3439 | -0.0998 | 0.6296 | 0.2610 | 0.1634 |
POLV | 0.0210 | 0.6257 | 0.7017 | 0.2931 | 0.1704 | -0.0269 | 0.4624 |
RG | 0.4774 | 0.1699 | 0.2358 | -0.7190 | -0.4110 | 0.0418 | 0.2109 |
RLW | 0.5327 | -0.0088 | -0.1466 | 0.1917 | 0.1138 | -0.8031 | 0.0665 |
VACC | 0.2883 | 0.5534 | -0.5574 | -0.0876 | 0.4305 | 0.3270 | 0.3072 |
Panel B: Political governance | |||||||
Compnnt | Eigenvalue | Difference | Proportion | Cumulative | |||
Compnnt 1 | 1.1506 | 0.3012 | 0.5753 | 0.5753 | |||
Compnnt 2 | 0.8494 | 0.4247 | 1.0000 | ||||
Principal components eigenvectors results | |||||||
Variables | Compnnt 1 | Compnnt 2 | Unexplained | ||||
POLV | 0.7071 | 0.7071 | 0.4247 | ||||
VACC | 0.7071 | -0.7071 | | 0.4247 | ||||
Panel B: Institutional governance | |||||||
Compnnt | Eigenvalue | Difference | Proportion | Cumulative | |||
Compnnt 1 | 1.9034 | 1.8068 | 0.9517 | 0.9517 | |||
Compnnt 2 | 0.0966 | 0.0483 | 1.0000 | ||||
Principal components eigenvectors results | |||||||
Variables | Compnnt 1 | Compnnt 2 | Unexplained | ||||
CRT | 0.7071 | 0.7071 | 0.0483 | ||||
RLW | 0.7071 | -0.7071 | 0.0483 | ||||
Panel C: Economic governance | |||||||
Compnnt | Eigenvalue | Difference | Proportion | Cumulative | |||
Compnnt 1 | 1.5366 | 1.0733 | 0.7683 | 0.7683 | |||
Compnnt 2 | 0.4634 | 0.2317 | 1.0000 | ||||
Principal components eigenvectors results | |||||||
Variables | Compnnt 1 | Compnnt 2 | Unexplained | ||||
RG | 0.7071 | 0.7071 | 0.2317 | ||||
GEF | 0.7071 | -0.7071 | 0.2317 | ||||
Panel A: Correlation matrix results for the governance variable | |||||||
i | ii | iii | iv | v | vi | ||
(i) CRT | 1.000 | ||||||
(ii) GF | 0.6537*** (0.0000) |
1.000 |
|||||
(iii) POLV | -0.0443*** (0.5136) |
-0.1932*** (0.0040) |
1.000 |
||||
(iv) RG | 0.6795*** (0.0000) |
0.5366*** (0.0000) |
0.2588*** (0.0001) |
1.000 |
|||
(v) RLW | 0.9034*** (0.000) |
0.6219*** (0.0000) |
-0.0428*** (0.5274) |
0.7586*** (0.0000) |
1.000 |
||
(vi) VACC | 0.3896*** (0.0000) |
-0.1532*** (0.0230) |
0.1506*** (0.0255) |
0.4596*** (0.0000) |
0.5580*** (0.0000) |
1.000 |
Mean | Median | Max | Mini | Std. Dev. | Skewness | Kurtosis | Jarque-Bera | Prob. | |
LHDI | -0.3125 | -0.3011 | -0.1684 | -0.5142 | 0.0903 | -0.4597 | 2.4931 | 10.1044 | 0.0064 |
LGDPPC | 8.6993 | 9.0392 | 9.3227 | 7.1985 | 0.6596 | -1.2762 | 3.0224 | 59.7256 | 0.0000 |
LAII | 18.6243 | 18.2463 | 24.5859 | 11.0021 | 3.0930 | 0.0820 | 2.6718 | 1.2335 | 0.5397 |
LEMPL | 3.9756 | 4.0355 | 4.2075 | 3.6818 | 0.1608 | -0.2460 | 1.5889 | 20.4736 | 0.0000 |
LHMN | 4.6281 | 4.6255 | 4.7034 | 4.5622 | 0.0382 | 0.2947 | 2.0422 | 11.5930 | 0.0030 |
OGV | 1.82E-09 | -0.1151 | 5.5839 | -2.8866 | 1.8136 | 0.8177 | 4.5900 | 47.6917 | 0.0000 |
ECOG | -5.45E-09 | -0.0746 | 4.5463 | -1.5332 | 1.2396 | 1.6153 | 6.4278 | 203.378 | 0.0000 |
INSTG | 1.27E-08 | 0.0745 | 4.2903 | -2.4014 | 1.3796 | 0.6032 | 4.3330 | 29.6303 | 0.0000 |
POLG | 3.27E-08 | 0.0815 | 2.1391 | -4.3384 | 1.0727 | -0.9342 | 5.9449 | 111.5028 | 0.0000 |
VACC | -0.2072 | 0.2954 | 1.1127 | -1.6608 | 0.8994 | -0.4972 | 1.5660 | 27.9135 | 0.0000 |
RLW | -0.1843 | -0.1360 | 1.0352 | -0.8698 | 0.4072 | 0.4442 | 4.0956 | 18.2357 | 0.0001 |
RG | -0.1303 | -0.1852 | 0.9212 | -0.5600 | 0.3432 | 1.2540 | 4.4014 | 75.6580 | 0.0000 |
POLV | -0.5565 | -0.5226 | 1.0747 | -4.2696 | 0.6563 | -2.9610 | 19.7817 | 2903.041 | 0.0000 |
GF | 0.0995 | 0.0689 | 1.8407 | -0.5336 | 0.4300 | 1.5651 | 6.8065 | 222.6350 | 0.0000 |
CRT | -0.2897 | -0.2890 | 1.0537 | -1.0516 | 0.4372 | 0.8568 | 4.7053 | 53.5753 | 0.0000 |
We began by conducting a slope homogeneity test following
Test statistics (Delta) | Value | p-value |
Δdelt | 11.971*** | 0.000 |
Δadj delt | 13.054*** | 0.000 |
Pesaran test | Breusch-Pagan LM test | |||
Variables | Statistic | P-value | Statistic | P-value |
HWBG | 17.39*** | 0.000 | 150.725*** | 0.000 |
LGDPPC | -1.02 | 0.309 | 192.510*** | 0.000 |
LAII | 12.42*** | 0.000 | 85.780*** | 0.000 |
LEMPL | 13.67*** | 0.000 | 65.855*** | 0.000 |
LHMN | -2.74*** | 0.006 | 101.260*** | 0.000 |
OGV | 13.85*** | 0.000 | 240.249*** | 0.000 |
POLG | -3.97*** | 0.000 | 283.476*** | 0.000 |
ECOG | 3.82*** | 0.000 | 309.340*** | 0.000 |
INSTG | 18.82*** | 0.000 | 244.938*** | 0.000 |
CRT | 18.35*** | 0.000 | 291.576*** | 0.000 |
GF | 2.67*** | 0.008 | 178.136*** | 0.000 |
POLV | -4.26*** | 0.000 | 267.838*** | 0.000 |
RG | 4.84*** | 0.000 | 240.343*** | 0.000 |
RLW | 16.27*** | 0.000 | 271.125*** | 0.000 |
VACC | 5.23*** | 0.000 | 142.674*** | 0.000 |
Series | Model | Levels | First Difference |
---|---|---|---|
Series | Model | Levels | First Difference |
HWBG | LLC | -3.4510*** (0.0003) | -8.1866*** (0.0000) |
IPS | -1.9719** (0.0243) | -8.8373*** (0.0000) | |
LGDPPC | LLC | -1.5217* (0.0640) | -7.7317*** (0.0000) |
IPS | 0.4820 (0.6851) | -9.0149*** (0.0000) | |
LAII | LLC | -0.9696 (0.1661) | -6.5198*** (0.0000) |
IPS | 0.5063 (0.6937) | -7.7185*** (0.0000) | |
LEMPL | LLC | 0.1874 (0.5743) | -3.2116*** (0.0007) |
IPS | 0.0059 (0.5024) | -7.3500*** (0.0000) | |
LHMN | LLC | 0.8661 (0.8068) | -7.8598*** (0.0000) |
IPS | 1.4207 (0.9223) | -7.3869*** (0.0000) | |
OGV | LLC | 3.9196 (1.0000) | -9.2173*** (0.0000) |
IPS | 2.7555 (0.9971) | -7.5137*** (0.0000) | |
POLG | LLC | 5.0740 (1.0000) | -9.3104*** (0.0000) |
IPS | 3.3999 (0.9997) | -7.6536*** (0.0000) | |
ECOG | LLC | 4.6239 (1.0000) | -8.9634*** (0.0000) |
IPS | 3.6116 (0.9998) | -7.8113*** (0.0000) | |
INSTG | LLC | 5.2553 (1.0000) | -8.7998*** (0.0000) |
IPS | 3.4401 (0.9997) | -7.6270*** (0.0000) | |
CRT | LLC | 5.0740 (1.0000) | -9.3104*** (0.0000) |
IPS | 3.3999 (0.9997) | -7.6536*** (0.0000) | |
GF | LLC | 3.8799 (0.9999) | -7.5759*** (0.0000) |
IPS | 2.9365 (0.9983) | -7.7854*** (0.0000) | |
POLV | LLC | 4.8784 (1.0000) | -8.5996*** (0.0000) |
IPS | 3.7380 (0.9999) | -7.7717*** (0.0000) | |
RG | LLC | 4.4832 (1.0000) | -8.5459*** (0.0000) |
IPS | 3.2718 (0.9995) | -7.7101*** (0.0000) | |
RLW | LLC | 4.4047 (1.0000) | -8.3746*** (0.0000) |
IPS | 2.8854 (0.9980) | -7.5409*** (0.0000) | |
VACC | LLC | 2.1604 (0.9846) | -8.9917*** (0.0000) |
IPS | 2.3327 (0.9902) | -7.7901*** (0.0000) |
Variables | Levels | 1st Difference |
---|---|---|
Variables | Levels | 1st Difference |
HWBG | -1.553 | -6.187*** |
LGDPPC | -1.223 | -6.190*** |
LAII | -1.915 | -6.188*** |
LEMPL | -1.709 | -6.185*** |
LHMN | -0.762 | -6.193*** |
OGV | -1.380 | -6.194*** |
POLG | -0.459 | -6.186*** |
ECOG | -1.454 | -6.188*** |
INSTG | -1.292 | -6.179*** |
CRT | -1.688 | -6.182*** |
GF | -1.709 | -6.183*** |
POLV | -1.303 | -6.189*** |
RG | -1.669 | -6.184*** |
RLW | -1.127 | -6.188*** |
VACC | -0.517 | -6.191*** |
Tables
Trace test | Maximum Eigenvalue test | ||||||
H0 | H1 | λ-trace statistic | p-value | Ho | H1 | λ-max statistic | p-value |
r = 0 | r ≥ 1 | 0.000 | 1.0000 | r = 0 | r ≥ 1 | 0.000 | 1.0000 |
r ≤ 1 | r ≥ 2 | 214.1 | 0.0000 | r ≤ 1 | r ≥ 2 | 166.9 | 0.0000 |
r ≤ 2 | r ≥ 3 | 245.7 | 0.0000 | r ≤ 2 | r ≥ 3 | 217.6 | 0.0000 |
r = 3 | r ≥ 4 | 156.1 | 0.0000 | r = 3 | r ≥ 4 | 136.3 | 0.0000 |
r ≤ 4 | r ≥ 5 | 78.85 | 0.0000 | r ≤ 4 | r ≥ 5 | 73.24 | 0.0000 |
r ≤ 5 | r ≥ 6 | 32.01 | 0.0004 | r ≤ 5 | r ≥ 6 | 32.01 | 0.0004 |
Statistic | Value | Z-value | P-value | Robust P-value |
Gt | -1.367*** | 3.002 | 0.099 | 0.000 |
Ga | -3.787*** | 3.035 | 0.099 | 0.000 |
Pt | -2.871*** | 2.253 | 0.088 | 0.000 |
Pa | -3.118*** | 2.200 | 0.086 | 0.000 |
Lag | AIC | SIC | HQIC |
0 | 0.8342 | 0.9349 | 0.8750 |
1 | -22.1541 | -21.4492* | -21.8687 |
2 | -21.8515 | -20.5423 | -21.3214 |
3 | -21.5855 | -19.6720 | -20.8108 |
4 | -21.4374 | -18.9197 | -20.4180 |
5 | -23.4418* | -20.3199 | -22.1778* |
For the estimation of the long-run coefficients of the explanatory variables, this study employed the FMOLS and DOLS methods as advocated by
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
---|---|---|---|---|---|---|---|---|---|---|---|
PANEL A: FMOLS | |||||||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
Variables | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff |
LGDPPC | 0.1509*** | 0.1409*** | 0.1294*** | 0.1239*** | 0.1187*** | 0.1271*** | 0.1205*** | 0.1135*** | 0.1243*** | 0.1197*** | 0.1306*** |
(0.0166) | (0.0076) | (0.0014) | (0.0013) | (0.0013) | (0.0011) | (0.0013) | (0.0011) | (0.0014) | (0.0015) | (0.0012) | |
LAII | 0.0017* | 0.0014*** | -0.0041*** | -0.0022*** | -0.0062*** | 0.0002*** | -0.0050*** | -0.0071*** | -0.0040*** | -0.0006* | -0.0002 |
(0.0010) | (0.0004) | (0.0003) | (0.0003) | (0.0002) | (0.0003) | (0.0003) | (0.0002) | (0.0003) | (0.0003) | (0.0003) | |
LEMPL | -0.1090** | -0.1068 | -0.0224*** | -0.0519*** | 0.0360*** | -0.0457*** | 0.0584*** | 0.0718*** | 0.0169*** | -0.0352*** | -0.0477*** |
(0.0462) | (0.0000) | (0.0074) | (0.0071) | (0.0058) | (0.0056) | (0.0053) | (0.0047) | (0.0063) | (0.0078) | (0.0077) | |
LHMN | -0.0394 | -0.0406 | -0.2739*** | -0.2481*** | -0.2972*** | -0.2717*** | -0.3255*** | -0.3143*** | -0.3004*** | -0.2627*** | -0.2708*** |
(0.0551) | (0.1150) | (0.0046) | (0.0043) | (0.0036) | (0.0031) | (0.0030) | (0.0031) | (0.0037) | (0.0047) | (0.0060) | |
OGV | -0.0026** | -0.0076*** | |||||||||
(0.0010) | (0.0024) | ||||||||||
LAII* OGV | 0.0003** | ||||||||||
(0.0001) | |||||||||||
POLG | -0.0976*** | ||||||||||
(0.0039) | |||||||||||
LAII* POLG | 0.0044*** | ||||||||||
(0.0002) | |||||||||||
INSTG | -0.0782*** | ||||||||||
(0.0028) | |||||||||||
LAII* INSTG | 0.0034*** | ||||||||||
(0.0001) | |||||||||||
ECOG | -0.0576*** | ||||||||||
(0.0030) | |||||||||||
LAII* ECOG | 0.0027*** | ||||||||||
(0.0001) | |||||||||||
CRT | -0.2201*** | ||||||||||
(0.0072) | |||||||||||
LAII*CRT | 0.0094*** | ||||||||||
(0.0004) | |||||||||||
POLV | -0.0728*** | ||||||||||
(0.0064) | |||||||||||
LAII* POLV | 0.0031*** | ||||||||||
(0.0003) | |||||||||||
GF | -0.0841*** | ||||||||||
(0.0092) | |||||||||||
LAII* GF | 0.0038*** | ||||||||||
(0.0004) | |||||||||||
RG | -0.2640*** | ||||||||||
(0.0109) | |||||||||||
LAII*RG | 0.0126*** | ||||||||||
(0.0006) | |||||||||||
RLW | -0.2673*** | ||||||||||
(0.0106) | |||||||||||
LAII* RLW | 0.0118*** | ||||||||||
(0.0005) | |||||||||||
VACC | -0.1688*** | ||||||||||
(0.0043) | |||||||||||
LAII* VACC | 0.0080*** | ||||||||||
(0.0002) | |||||||||||
R-squared | 0.9864 | 0.9869 | 0.9043 | 0.9187 | 0.8921 | 0.9122 | 0.8812 | 0.8785 | 0.9043 | 0.9180 | 0.9237 |
Adj. R-squared | 0.9858 | 0.9863 | 0.9020 | 0.9168 | 0.8895 | 0.9101 | 0.8783 | 0.8756 | 0.9021 | 0.9161 | 0.9219 |
Obs | 215 | 215 | 215 | 215 | 215 | 215 | 215 | 215 | 215 | 215 | 215 |
PANEL B: DOLS | |||||||||||
Variables | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff | Coeff |
LGDPPC | 0.1809*** | 0.1266*** | 0.1312*** | 0.1263*** | 0.1243*** | 0.1335*** | 0.1306*** | 0.1165*** | 0.1275*** | 0.1199*** | 0.1264*** |
(0.0441) | (0.0051) | (0.0061) | (0.0050) | (0.0066) | (0.0054) | (0.0079) | (0.0073) | (0.0062) | (0.0046) | (0.0040) | |
LAII | 0.0029*** | -0.0007 | -0.0014 | -0.0000 | -0.0021** | 0.0022 | 0.0013 | -0.0028** | -0.0009 | 0.0019 | -0.0005 |
(0.0011) | (0.0010) | (0.0011) | (0.0011) | (0.0011) | (0.0014) | (0.0016) | (0.0015) | (0.0011) | (0.0012) | (0.0011) | |
LEMPL | 0.1180** | -0.0640** | -0.0248 | -0.1035*** | -0.0143 | -0.1185*** | 0.0139 | 0.0256 | -0.0095 | -0.0547* | -0.0120 |
(0.0572) | (0.0276) | (0.0351) | (0.0283) | (0.0298) | (0.0283) | (0.0335) | (0.0395) | (0.0271) | (0.0295) | (0.0328) | |
LHMN | -0.1149 | -0.2492*** | -0.2859*** | -0.2177*** | -0.2820*** | -0.2297*** | -0.3322*** | -0.2984*** | -0.2965*** | -0.2570*** | -0.2914*** |
(0.0728) | (0.0181) | (0.0225) | (0.0171) | (0.0207) | (0.0166) | (0.0207) | (0.0290) | (0.0178) | (0.0189) | (0.0272) | |
OGV | -0.0046*** | -0.0709*** | |||||||||
(0.0013) | (0.0096) | ||||||||||
LAII* OGV | 0.0032*** | ||||||||||
(0.0005) | |||||||||||
POLG | -0.1115*** | ||||||||||
(0.0195) | |||||||||||
LAII* POLG | 0.0053*** | ||||||||||
(0.0009) | |||||||||||
INSTG | -0.0985*** | ||||||||||
(0.0121) | |||||||||||
LAII* INSTG | 0.0044*** | ||||||||||
(0.0006) | |||||||||||
ECOG | -0.0941*** | ||||||||||
(0.0179) | |||||||||||
LAII* ECOG | 0.0043*** | ||||||||||
(0.0009) | |||||||||||
CRT | -0.2652*** | ||||||||||
(0.0409) | |||||||||||
LAII*CRT | 0.0118*** | ||||||||||
(0.0020) | |||||||||||
POLV | -0.1628*** | ||||||||||
(0.0429) | |||||||||||
LAII* POLV | 0.0074*** | ||||||||||
(0.0020) | |||||||||||
GF | -0.2285** | ||||||||||
(0.0871) | |||||||||||
LAII* GF | 0.0100*** | ||||||||||
(0.0041) | |||||||||||
RG | -0.3472*** | ||||||||||
(0.0543) | |||||||||||
LAII*LRG | 0.0169*** | ||||||||||
(0.0029) | |||||||||||
LRLW | -0.3135*** | ||||||||||
(0.0435) | |||||||||||
LAII *RLW | 0.0141*** | ||||||||||
(0.0022) | |||||||||||
VACC | -0.1344*** | ||||||||||
(0.0218) | |||||||||||
LAII* VACC | 0.0062*** | ||||||||||
(0.0011) | |||||||||||
R-squared | 0.9956 | 0.9682 | 0.9623 | 0.9701 | 0.9578 | 0.9638 | 0.9492 | 0.9415 | 0.9658 | 0.9693 | 0.9669 |
Adj. R-squared | 0.9922 | 0.9405 | 0.9295 | 0.9440 | 0.9209 | 0.9322 | 0.9051 | 0.8905 | 0.9360 | 0.9425 | 0.9381 |
Obs | 205 | 205 | 205 | 205 | 205 | 205 | 205 | 205 | 205 | 205 | 205 |
In Column 2 of Table
This section examines the causative links between the series under consideration. Based on whether their p-values were below or above the 10% significance threshold, we either dismissed or upheld the null hypothesis asserting the absence of causality for each Chi-square statistic. To begin with, the outcomes of the panel causality tests are depicted in Table
Model | Null hypothesis | W-statistic | Zbar-statistic | p-value | Direction of relationship observed | Conclusion |
---|---|---|---|---|---|---|
Model | Null hypothesis | W-statistic | Zbar-statistic | p-value | Direction of relationship observed | Conclusion |
1 | HWBG LAII | 1.9724* | -0.1418 | 0.0873 | HWBG ↔ LAII | Bidirectional causality |
LAII HWBG | 0.8779** | -1.2349 | 0.0169 | |||
2 | CRT HWBG | 0.2689* | -1.8431 | 0.0653 | CRT → HWBG | Unidirectional causality |
HWBG CRT | 0.4888 | -1.6236 | 0.1045 | |||
3 | GF HWBG | 0.4254* | -1.6868 | 0.0916 | GF → HWBG | Unidirectional causality |
HWBG GF | 0.6336 | -1.4789 | 0.1392 | |||
4 | RG HWBG | 0.3443* | -1.7678 | 0.0771 | RG → HWBG | Unidirectional causality |
HWBG RG | 0.6879 | -1.4246 | 0.1543 | |||
5 | LRLW HWBG | 0.3162* | -1.7959 | 0.0725 | RLW → HWBG | Unidirectional causality |
HWBG RLW | 0.4704 | -1.6419 | 0.1006 | |||
6 | VACC HWBG | 0.5825 | -1.5299 | 0.1260 | HWBG VACC | No causality |
HWBG VACC | 0.6856 | -1.4270 | 0.1536 | |||
7 | POLV HWBG | 0.2675* | -1.8445 | 0.0651 | POLV → HWBG | Unidirectional causality |
HWBG POLV | 0.4896 | -1.6227 | 0.1046 | |||
8 | POLG HWBG | 0.2010* | -1.9110 | 0.0560 | POLG→ HWBG | Unidirectional causality |
HWBG POLG | 0.4863 | -1.6261 | 0.1039 | |||
9 | ECOG HWBG | 0.3884* | -1.7239 | 0.0847 | ECOG→ HWBG | Unidirectional causality |
HWBG ECOG | 0.5288 | -1.5836 | 0.1133 | |||
10 | INSTG HWBG | 0.2922* | -1.8199 | 0.0688 | HWBG ↔ INSTG | Bidirectional causality |
HWBG INSTG | 0.4311* | -1.6812 | 0.0927 | |||
11 | OGV HWBG | 0.3935* | -1.7187 | 0.0857 | OGV→ HWBG | Unidirectional causality |
HWBG OGV | 1.1775 | -0.9357 | 0.3494 | |||
12 | CRT LAII | 0.2473* | -1.8648 | 0.0622 | CRT→ LAII | Unidirectional causality |
LAII CRT | 2.9517 | 0.8364 | 0.4030 | |||
13 | GF LAII | 0.3284* | -1.7838 | 0.0745 | GF→ LAII | Unidirectional causality |
LAII GF | 1.6409 | -0.4729 | 0.6363 | |||
14 | LRG LAII | 0.4269* | -1.6853 | 0.0919 | RG→LAII | Unidirectional causality |
LAII RG | 3.1557 | 1.0402 | 0.2983 | |||
15 | RLW LAII | 0.2157* | -1.8963 | 0.0579 | RLW→LAII | Unidirectional causality |
LAII RLW | 3.0466 | 0.9312 | 0.3518 | |||
16 | VACC LAII | 1.6823 | -0.4315 | 0.6661 | VACC LAII | No causality |
LAII VACC | 1.1274 | -0.9857 | 0.3243 | |||
17 | POLV LAII | 0.3240* | -1.7881 | 0.0738 | POLV→LAII | Unidirectional causality |
LAII POLV | 2.7830 | 0.6679 | 0.5042 | |||
18 | POLG LAII | 0.2077** | -1.9043 | 0.0569 | POLG→LAII | Unidirectional causality |
AII POLG | 2.6794 | 0.5644 | 0.5725 | |||
19 | ECOG LAII | 0.3309* | -1.7813 | 0.0749 | ECOG→LAII | Unidirectional causality |
LAII ECOG | 2.4297 | 0.3150 | 0.7527 | |||
20 | INSTG LAII | 0.1005** | -2.0114 | 0.0443 | INSTG→LAII | Unidirectional causality |
LAII INSTG | 3.2690 | 1.1533 | 0.2488 | |||
21 | OGV LAII | 0.1973** | -1.9147 | 0.0555 | OGV→LAII | Unidirectional causality |
LAII OGV | 2.8811 | 0.7659 | 0.4438 |
Table
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
Variables | |||||||||||
Short Run Est. | |||||||||||
ΔLGDPPC | -0.0193** | -0.0392** | 0.0868** | -0.0167* | 0.0101* | 0.1255*** | 0.1357*** | 0.1277*** | 0.1265*** | 0.0551** | 0.0980* |
(0.0093) | (0.0176) | (0.0336) | (0.0093) | (0.0056) | (0.0351) | (0.0415) | (0.0328) | (0.0360) | (0.0243) | (0.0524) | |
ΔLAII | -0.0000 | 0.0005 | 0.0028 | -0.0003 | -0.0011 | -0.0008*** | -0.0002*** | -0.0003 | -0.0004*** | 0.0004 | 0.0000 |
(0.0002) | (0.0012) | (0.0028) | (0.0002) | (0.0008) | (0.0002) | (0.0001) | (0.0002) | (0.0001) | (0.0006) | (0.0001) | |
ΔLEMPL | 0.0417*** | -0.1821 | 0.0726** | -0.0254 | 0.0536** | 0.0190*** | 0.0062** | 0.0255 | 0.0204** | 0.0268 | -0.0158 |
(0.0139) | (0.1708) | (0.0373) | (0.0336) | (0.0233) | (0.0090) | (0.0027) | (0.0218) | (0.0092) | (0.0328) | (0.0216) | |
ΔLHMN | -0.1257 | 0.1478*** | -0.0850 | 0.0917* | 0.0958 | 0.0871*** | 0.1180** | 0.0807*** | 0.0861*** | 0.1333*** | 0.1236** |
(0.2452) | (0.0251) | (0.0851) | (0.0533) | (0.0716) | (0.0267) | (0.0460) | (0.0269) | (0.0274) | (0.0478) | (0.0555) | |
ΔOGV | 0.0014* | 0.0125** | |||||||||
(0.0008) | (0.0057) | ||||||||||
Δ LAII*OGV | 0.0003* | ||||||||||
(0.0002) | |||||||||||
Δ POLG | -0.1040*** | ||||||||||
(0.00357) | |||||||||||
Δ LAII*POLG | 0.0058*** | ||||||||||
(0.0016) | |||||||||||
ΔINSTG | 0.0082* | ||||||||||
(0.0047) | |||||||||||
ΔLAII*INSTG | -0.0004*** | ||||||||||
(0.0001) | |||||||||||
ΔECOG | 0.0057* | ||||||||||
(0.0033) | |||||||||||
ΔLAII*ECOG | -0.0002** | ||||||||||
(0.0001) | |||||||||||
ΔLCRT | 0.0005 | ||||||||||
(0.0004) | |||||||||||
ΔLAII*LCRT | -0.0003*** | ||||||||||
(0.0001) | |||||||||||
ΔPOL | 0.0027* | ||||||||||
(0.0014) | |||||||||||
ΔLAII*POLV | -0.0000 | ||||||||||
(0.0000) | |||||||||||
ΔLGF | -0.0333*** | ||||||||||
(0.0127) | |||||||||||
ΔLAII*LGF | 0.0020** | ||||||||||
(0.0009) | |||||||||||
ΔLRG | 0.0002 | ||||||||||
(0.0006) | |||||||||||
ΔLAII*LRG | -0.0001*** | ||||||||||
(0.0000) | |||||||||||
ΔLRLW | -0.0308*** | ||||||||||
(0.0131) | |||||||||||
ΔLAII*LRLW | 0.0016** | ||||||||||
(0.0008) | |||||||||||
ΔLVACC | 0.0378*** | ||||||||||
(0.0133) | |||||||||||
ΔLAII*LVACC | -0.0022*** | ||||||||||
(0.0008) | |||||||||||
Adjust. Term | |||||||||||
ECT | -1.0434*** | -0.6978*** | -1.1475*** | -1.0440*** | -1.0552*** | -1.0218*** | -1.0169*** | -1.0300*** | -1.0232*** | -1.0294*** | -1.0202*** |
(0.0489) | (0.1051) | (0.0623) | (0.0172) | 0.0121 | (0.0133) | (0.0125) | (0.0144) | (0.0145) | (0.0120) | (0.0158) | |
Long Run Est. | |||||||||||
LR_LGDPPC | -0.0195** | -0.0724** | 0.0798*** | -0.0156** | 0.0096* | -0.0002*** | 0.1332*** | 0.1243*** | 0.1235*** | 0.0538** | 0.0950* |
(0.0094) | (0.0323) | (0.0319) | (0.0088) | (0.0054) | (0.0001) | (0.0406) | (0.0321) | (0.0353) | (0.0238) | (0.0511) | |
LR_LAII | -0.0001 | 0.0009 | 0.0030 | -0.0003 | -0.0011 | -0.0008*** | -0.0002*** | -0.0003 | -0.0004*** | 0.0004 | 0.0001 |
(0.0002) | (0.0017) | (0.0027) | (0.0002) | (0.0008) | (0.0002) | (0.0001) | (0.0002) | (0.0001) | (0.0007) | (0.0007) | |
LR_LEMPL | 0.0393*** | -0.2430 | 0.0604* | -0.0235 | .00508** | 0.0184** | 0.0060** | 0.0242 | 0.0198** | 0.0256 | -0.0145 |
(0.0128) | (0.2975) | (0.0328) | (0.0325) | (0.0223) | (0.0086) | (0.0027) | (0.0207) | (0.0090) | (0.0316) | (0.0203) | |
LR_LHMN | -0.1098 | 0.2299*** | -0.0671 | 0.0901* | 0.0926 | 0.0853*** | 0.1161** | 0.0785*** | 0.0845*** | 0.1295*** | 0.1227** |
(0.2437) | (0.0594) | (0.0700) | (0.0534) | (0.0689) | (0.0268) | (0.0455) | (0.0267) | (0.0274) | (0.0466) | (0.0566) | |
LR_OGV | 0.0014** | 0.0194** | |||||||||
(0.0007) | (0.0096) | ||||||||||
LR_LAII*OGV | 0.0005* | ||||||||||
(0.0003) | |||||||||||
LR_POLG | -0.0977*** | ||||||||||
(0.0387) | |||||||||||
LR_ LAII*POLG | 0.0048**** | ||||||||||
(0.0017) | |||||||||||
LR_INSTG | 0.0080* | ||||||||||
(0.0045) | |||||||||||
LR_ LAII*INSTG | -0.0004*** | ||||||||||
(0.0001) | |||||||||||
LR_ECOG | 0.0055* | ||||||||||
(0.0031) | |||||||||||
LR_LAII*ECOG | -0.0002** | ||||||||||
(0.0001) | |||||||||||
LR_LCRT | 0.0005 | ||||||||||
(0.0004) | |||||||||||
LR_LAII*LCRT | -0.0003*** | ||||||||||
(0.0001) | |||||||||||
LR_POL | 0.0027** | ||||||||||
(0.0015) | |||||||||||
LR_ LAII*POL | -0.0000 | ||||||||||
(0.0000) | |||||||||||
LR_LGF | -0.0320*** | ||||||||||
(0.0122) | |||||||||||
LR_ LAII*LGF | 0.0020*** | ||||||||||
(0.0008) | |||||||||||
LR_LRG | 0.0001 | ||||||||||
(0.0006) | |||||||||||
LR_LAII*LRG | -0.0001*** | ||||||||||
(0.0000) | |||||||||||
LR_LRLW | -0.0296** | ||||||||||
(0.0124) | |||||||||||
LR_LAII*LRLW | 0.0016** | ||||||||||
(0.0008) | |||||||||||
LR_LVACC | 0.0375*** | ||||||||||
(0.0135) | |||||||||||
LR_ LAII*LVACC | -0.0022*** | ||||||||||
(0.0008) | |||||||||||
Observation | 210 | 210 | 210 | 210 | 210 | 210 | 210 | 210 | 210 | 210 | 210 |
R-squared | 0.60 | 0.94 | 0.61 | 0.84 | 0.81 | 0.89 | 0.91 | 0.90 | 0.89 | 0.87 | 0.91 |
For the first model, without the interaction term variables, in Column 1 of Table
In Column 2, 3, 8, and 10 of Table
The interaction between AI investment and explanatory variables such as institutional governance, economic governance, control of corruption, political stability and absence of violence, regulatory quality and voice and accountability has a negative and significant impact on HWBG at a 10% level of significance in both the short and long run (see Column 4, 5, 6, 9 and 11 of Table
These findings do not align with the studies conducted by
Although good governance is often viewed as a crucial factor in promoting human well-being, it is important to recognize that there are situations in developing countries where some dimensions or indicators of good governance may not consistently lead to the expected improvements in human well-being. The reasons why it is so may include the following: (i) the BRICS countries differ in size, economic structure, and social dynamics so governance indicators and policies may have different effects
It is obvious that, for example, effective control of corruption typically leads to more efficient and transparent government processes
Firstly, the study shows that, on the one hand, unconditional AI investment did not enhance human well-being (HWBG) in both the short- and long-run; on the other hand, unconditional overall governance quality impacts HWBG. Therefore, while prioritizing AI-related initiatives to boost HWBG, the policymakers should not neglect governance quality as it also produces certain effect. This suggests that a balanced policy approach, focusing on both AI investment and governance quality, can contribute to comprehensive improvements in HWBG.
Secondly, when investment in AI and overall governance quality jointly have a positive and significant impact on HWBG in the BRICS countries, it suggests that quality of governance plays a crucial role in harnessing the benefits of AI for the well-being of citizens. It means that governments should prioritize the development and enforcement of robust governance and regulatory frameworks specifically tailored to investment in AI technologies. This should include clear guidelines for ethical AI development, data privacy, accountability mechanisms and some others. Governments should devise and enact policies that foster continued support and encouragement for AI research, development, and adoption. Such support can manifest itself through mechanisms like grants, tax incentives, and partnerships with private sectors, aiming to promote innovation within the AI industry while simultaneously reinforcing political governance structures, government effectiveness and rule of law in the BRICS countries both in the short- and long-run. Policymakers should prioritize enhancing economic governance by reducing bureaucracy, streamlining regulations, and ensuring fair competition within the AI industry. Effective economic governance fosters an environment conducive to AI innovation, thereby promoting both short-term and long-term improvements in human well-being.
Thirdly, when the interaction between investment in AI and institutional governance has a negative and significant impact on human well-being in the BRICS countries, it suggests that certain aspects of institutional governance may weaken the positive effects of AI investments both in the short- and long-run. To address this issue, it will be necessary to streamline the institutional governance regulations related to AI investments and applications. Policies that establish institutional regulatory environment that fosters innovation while incorporating essential safeguards to maximize the benefits of AI for human well-being should be promoted. Given that institutional governance structures often entail lengthy decision-making processes that could hinder the timely implementation of AI investment programs with societal benefits, governments and policymakers should proactively identify and address issues causing delays at the institutional level. Policymakers should prioritize policies that streamline complex bureaucratic procedures, which can otherwise impede AI initiatives at the institutional level. This is because simplifying processes for obtaining approvals, licenses, or funding for AI investment projects could accelerate the delivery of AI-driven services, promoting human well-being. Institutional governance decisions about resource allocation should prioritize AI investment initiatives that directly contribute to well-being.
Fourthly, the interaction between investment in AI and control of corruption making a negative and significant impact on human well-being in BRICS countries suggests that control of corruption has not played a crucial role in harnessing the benefits of AI for citizens’ well-being. Therefore, policies should strengthen anti-corruption measures and enforcement to maintain a clean and transparent investment environment for AI and other technologies in BRICS countries. Governments and policymakers should develop and enforce ethical AI guidelines to prevent corrupt practices in AI procurement, development and deployment processes. This will ensure fairness and transparency in AI project selection and implementation. The results indicate that a peaceful and stable political environment has not played a crucial role in realizing the benefits of AI for citizens’ well-being, so the governments of the BRICS countries should implement policies aimed at ensuring the continuity of political stability through effective governance, conflict resolution mechanisms, and investments in conflict prevention strategies. These measures could help minimize the risk of violence and social unrest, which could otherwise disrupt AI projects and overall economic stability. As the absence of violence reduces the need for resources and efforts to manage conflicts, governments will be able to allocate more resources to AI projects that could positively impact human well-being both in the short- and long-term. The result of the interaction between investment in AI and regulatory quality suggests that regulatory quality policies should foster innovation by providing clear rules and incentives for businesses to invest in AI research and development. This competition among AI providers can lead to improved AI technologies that have a positive impact on various aspects of human well-being, such as healthcare, education, and public services.There is a need for regulations that support innovation while safeguarding against potential negative consequences and balance safety and ethical considerations while encouraging AI research and development.
Finally, given that the BRICS countries are still classified as developing countries, the interaction between investment in AI and government effectiveness has a positive and significant impact on human well-being, which indicates the need for policies that further enhance government effectiveness by implementing reforms aimed at improving efficiency, transparency, and accountability in the use of AI investments. Since the interaction between investment in AI and the rule of law has a positive and significant impact on human well-being in BRICS countries, they are in need of policies addressing legal and regulatory bottlenecks that could hinder the development, deployment, and utilization of AI technologies effectively. Such policies should target complex or outdated legal frameworks, legal uncertainties, or inconsistent enforcement of AI-related laws and regulations, which could impede innovation and the full realization of AI’s potential benefits for human well-being. The interaction between investment in AI and voice and accountability has a negative and significant impact on human well-being in BRICS countries, which may indicate that policies aimed at enhancing public participation, transparency, and accountability in AI decision-making processes are needed. These policies should encourage citizen engagement, ensure that AI applications align with societal values, and provide mechanisms for oversight and accountability in AI development and deployment in BRICS countries both in the short- and long-run. The negative impact might suggest that if these elements are lacking, AI technologies could be deployed in ways that do not fully consider the well-being and interests of the population, leading to adverse outcomes. Therefore, strengthening voice and accountability mechanisms becomes crucial in guiding AI investments and applications to benefit human well-being. Policies should promote robust collaboration among the BRICS countries to establish shared AI regulatory standards and norms aimed at enhancing human well-being. Sharing best practices and knowledge can help address BRICS AI investment challenges. BRICS governments should involve the public in AI-related decision-making processes to address concerns, build trust, and enhance government legitimacy. Policies should encourage sharing best practices and knowledge to address BRICS AI challenges, especially considering some of their prominent positions in AI development.
The BRICS countries (Brazil, Russia, India, China, and South Africa) have shown commitment to achieving and maintaining Sustainable Development Goal (SDG) 3 and 16 of the United Nations, which includes promoting human well-being for all and building strong institutions and governance. However, the empirical research question of how to leverage artificial intelligence (AI) investment to promote human well-being in the context of governance dynamics remained unexplored, especially concerning the BRICS economies; hence the need to examine the AI investment (AII) and human well-being (HWBG) nexus contingent on various dimensions or indicators of governance in the BRICS countries between 2012 and 2022. We applied the novel Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) estimation and other novel econometric techniques. The research findings reveal a long-term relationship among variables, with various causality directions. Based on CS-ARDL results, policymakers should prioritize integrating governance quality into AII to boost HWBG in the short- and long-term perspective. However, caution is needed when considering AII’s interaction with institutional governance, economic governance, control of corruption, political stability, regulatory quality and voice and accountability as it did not support HWBG either in the short or the long run.
Overall, these findings underscore the significance of governance quality in shaping the impact of AI investment on human well-being. Policymakers should pursue strategies that foster positive interactions between AI investment and governance dimensions while addressing potential negative impacts to ensure the overall enhancement of human well-being. In addition, targeted improvements in governance can help create an environment where AI contributes significantly to the overall well-being of citizens in the BRICS countries. Based on the CS-ARDL results, the study recommends that BRICS governments and policymakers prioritize and enhance the integration of AII into their governance systems to stimulate HWBG in both the short- and long-term perspective. However, the study cautions against overlooking the interaction between AII and variables such as institutional governance, economic governance, control of corruption, political stability, regulatory quality and voice and accountability, as it did not support HWBG either in the short or the long run. Therefore, the study recommends to develop AII-friendly governance policies within the BRICS countries, considering the nascent nature of AI as one of the technologies of the Fourth Industrial Revolution.
Future research should examine whether the conclusions of this study hold up to empirical inspection within country-specific or regional settings to further enhance our understanding of the research topic. The study acknowledges its scope and limitations, and future research is encouraged to include a broader range of variables, such as physical capital investments and other macroeconomic variables, to provide a more comprehensive analysis of the factors affecting human well-being.
Conflict of interest: The author(s) declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgement: We appreciate the editor(s) and anonymous reviewer(s) for their valuable comments that helped improve the quality of this study. The usual disclaimer applies.