Technology penetration and human development nexus in middle-income countries: the synergy effect of inclusive resources distribution

ABSTRACT

This paper intends to examine how interactions between equal distribution of resources and the information and communication technology (ICT) influence inclusive human development (inequality-adjusted human development) for 81 countries from middle-income countries within the period 2005–2017. We use a double-censored Tobit regression as it accounts for the dependent variable with a limited range. It exhibits the behavior that is consistent with the method of estimation. We employ the instrumental variable (IV) for the independent variables of interest to deal with simultaneity or reverse causality due to endogeneity. In light of established findings for this study, we conclude that equal distribution of public goods such as technologies could play a critical role in promoting inclusive human development. Supplementary policy repercussions are highlighted.

KEYWORDS:

• Inclusive human development
• technological penetration
• inclusive distribution of resources
• lower and upper-middle-income countries

JEL CLASSIFICATION:

• O55
• O40
• G20
• I38
• D30

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Note that Information Communication Technology is used interchangeably with Technology Penetration. The technology penetration refers mobile penetration, internet penetration and telephone penetration (see Appendix 1 for measurement of variables).

2 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

3 List of sampled countries – Albania; Algeria; Angola; Armenia; Azerbaijan; Bangladesh; Belarus; Belize; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Bulgaria; Cabo Verde; Cambodia; Cameroon; China; Colombia; Congo, Rep.; Costa Rica; Cote d'Ivoire; Cuba; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt, Arab Rep.; El Salvador; Equatorial Guinea; Eswatini; Fiji; Gabon; Georgia; Ghana; Grenada; Guatemala; Guyana; Honduras; India; Indonesia; Iran, Islamic Rep.; Iraq; Jamaica; Jordan; Kazakhstan; Kenya; Kiribati; Kyrgyz Republic; Lao PDR; Lebanon; Lesotho; Libya; Malaysia; Maldives; Mauritania; Mauritius; Mexico; Micronesia, Fed. Sts.; Moldova; Mongolia; Montenegro; Morocco; Myanmar; Namibia; Nicaragua; Nigeria; Pakistan; Papua New Guinea; Paraguay; Peru; Philippines; Romania; Russian Federation; Samoa; Sao Tome and Principe; Serbia; Solomon Islands; South Africa; Sri Lanka; Sudan; Suriname; Thailand; Timor-Leste; Tonga; Tunisia; Turkey; Ukraine; Uzbekistan; Vanuatu; Venezuela, RB; Vietnam; Zambia

4 In accordance to the World Bank classification, lower-middle-income countries are those with a GNI per capita between $996 and $3,895; upper middle-income countries are those with a GNI per capita between $3,896 and $12,055.

5 We employ the instrumental variable (IV) for the independent variables of interest to deal with the issue of simultaneity or reverse causality as a result of endogeneity. It is expedient to identify that endogeneity could be caused by four factors (i.e., omitted variables, simultaneity or reverse causality, autoregression of the auto-correlated errors and errors of measurement (see Greene, 2002). However, these issues are hardly addressed by a single estimation strategy simultaneously. Thus, this study chooses the employment of internal instrument to address reverse causality using IV approach. Whereas control for the unobserved heterogeneity or country-fixed effect answers the issue of omitted variable, this unobserved heterogeneity or country-fixed effect is addressed when we discompose the analysis into different income intervals (i.e., Lower Middle-Income Countries and Upper Middle-Income Countries).

6 One lag is enough to capture past information. In other words, the correlation between the level series and first lag series is high while the correlation between the level series and second lag series is not quite high. Hence, only one lag is necessary. This is in accordance with prior studies (Asongu & Nwachukwu, 2016b).

7 Conditional or marginal impact is the interaction between the ICT variables and the equal distribution of resource whereas the unconditional impact is the effect from the corresponding ICT proxy that remains not interacted.

Additional information

Notes on contributors

Alex Adegboye

Alex Adegboye is a faculty member and PhD candidate at Covenant University, Nigeria. His research areas include: Sustainable Development, Taxation, Public Finance, Accountability and Governance Quality, and Knowledge economy.

Stephen A. Ojeka

Stephen A. Ojeka is a senior lecture and PhD holder from Covenant University, Nigeria.

Olawunmi Tolase

Olawunmi Tolase has MSc in Finance from Covenant University, Nigeria.

Oluwatayo Omoremi

Oluwatayo Omoremi has MSc in Accounting from Covenant University.

Yvonne Jude-Okeke

Yvonne Jude-Okeke has MSc in Accounting from Covenant University.