Information and communication technology and poverty alleviation in Nigeria

Abstract

There is a disconnect between the abundant resources at the national level and the severe poverty at individual and household levels in Nigeria. Information and communication technology is an efficient tool that the Asian economies have used to alleviate poverty. Information and communication technology is regarded as a tool which helps create an information and communication technology-based economy. This quantitative study examined how Nigeria can use information and communication technology to alleviate the enormous poverty levels. Data were sourced from the World Bank and International Telecommunication Union, for the period 1992–2020. In terms of the research methodology, the Autoregressive Distributed Lag model was employed in the research. The empirical findings showed that information and communication technology can have a projected 50% positive effect in reducing poverty. Foreign direct investment had a positive effect on reducing income inequality. Primary research findings depicted that information and communication technology, economic growth, foreign direct investment and private domestic credit were key tools for reducing poverty in Nigeria. Concerning policy recommendations, it was suggested that private domestic credit and foreign direct investment should be channelled to the information and communication technology sector to have a greater impact on poverty alleviation.

Keywords:

• economic growth
• foreign direct investment
• information and communication technology
• poverty
• private domestic credit

Introduction

Nigeria has bulk oil reserves and vast stores of minerals, such as bitumen, over 3 billion metric tonnes of iron ore, gold, zinc, and tin, worth over US$657 billion (Ajaja 2023). Despite the abundant resources in this economy, the nation has been battling to address poverty (Anigbogu et al. 2016; Madueke, Anisiobi, and Ozoh 2022; Sanusi and Owoyele 2016, 1). Seventy per cent of the total population in Nigeria is subjected to poverty (Oladimeji and Folayan 2018, 84; Olawale 2018). In 2019, Nigeria had a population of over 200 million people with a total gross domestic product (GDP) of US$448.12 billion in 2019 (World Bank 2020a). Poverty can be defined as the absence of opportunities, coupled with high levels of undernourishment, hunger, illiteracy, lack of access to education, physical and mental illnesses, and socio-economic instability (Beegle et al. 2016).

Poverty is a multifaceted aspect and can be described using diverse proxies, such as access to healthcare and education, to mention just two (Agboola and Balcilar 2012; Kithinji 2022). Nigeria has endured ethnic clashes, as well as insurgent group attacks by Boko-Haram (Shuaibu, Salleh, and Shehu 2015). The result has been increased poverty levels, as some Nigerians lost property and livestock, while children attending the schools attacked by Boko-Haram had reduced opportunities for access to education (Shehu, Saleh, and Shuaibu 2015). In 2017, Boko Haram (Adebisi, Azeez, and Oyedeji 2017) displaced 1.5 million Nigerians including farmers. Such displacements adversely impacted the agricultural sector as the affected farmers stopped producing different crops. In terms of the fishing industry, more than one million fishermen were displaced (Adebisi, Azeez, and Oyedeji 2017). The result was limited supplies of a wide range of goods, which led to an increase in the prices of goods and services by 70% (Adebisi, Azeez, and Oyedeji 2017).

In 2018, the economy received a total of US$1.997 billion as foreign direct investments (FDI) (World Bank 2020a). Despite such investments, a large number of Nigerians continue to suffer in dire poverty. The net value of foreign direct investments has varied over time. Nigeria registered a sharp decrease in FDI levels from US$8.841 billion in 2011 to US$1.997 billion in 2018 (World Bank 2020b). This sharp decline in FDI levels may have had an adverse impact on the welfare of the people.

However, fundamentals of economics assume that FDI can help employment creation and increase industrial outputs, as well as reduce poverty, ceteris paribus (Babatunde 2018; Iruoma 2022; Ucal 2014). However, in Nigeria, this is not the case.

In terms of Internet usage, Nigeria in 2018 had the lowest value of 0.038 fixed broadband subscriptions per 100 people (World Bank 2020c). This implies that an average of four people per 10 000 have access to the Internet. The Internet in this fourth industrial revolution has aided in improving business through web-based transactions, as well as other e-commerce activities (Bilinska-Reformat and Dewalska-Opitek 2021).

Information and communication technology (ICT) can be channelled in different ways for effective poverty reduction. First, the use of the Internet, which can be used to enhance e-commerce activities, helps entrepreneurs to maximize diverse business opportunities (Peng, Ma, and Zhang 2021). This can lead to the expansion of businesses as entrepreneurs penetrate different markets. As businesses expand, jobs are created which then help to reduce poverty. The second mechanism of using ICT for poverty reduction is by using mobile and fixed phones which are used at the macro and micro levels.

At the macro level, research shows that the GDP can increase due to mobile services (Bhavnani et al. 2015). An increase in mobile phone usage creates work for people from sales, use of airtime, and handsets. The mobile industry can expand its business to offer more services to clients, and in so doing, more jobs are created (Bhavnani et al. 2015). Mobile phones can improve productivity, for example, with mobile banking and entrepreneurship. Lastly, at the macro level, mobile telephone service providers also pay tax from the business proceeds, The government uses these for different purposes, such as grants for the less privileged, and the provision of public and merit goods.

Mobile and fixed phones facilitate business transactions; in this fourth industrial revolution, people use phones for e-commerce and e-business activities (Ćuzović and Labović 2020). Evidence from research shows that businesses shifted to the use of phones and the Internet to connect with customers, advertise, sales of services and products, among others, during the COVID era (Higueras-Castillo, Liébana-Cabanillas and Villarejo-Ramos 2023). The ultimate effect was the growth of some businesses, and the creation of job opportunities which indirectly helped to reduce poverty (Peng, Ma, and Zhang 2021). All these diverse benefits can be enjoyed in Nigeria if the economy fully utilizes ICT, and then the poverty levels may fall, ceteris paribus.

At the micro level, research shows that fishermen in India use phones to escape information asymmetry in the fish market and maximize arbitrage opportunities, which in turn, increases their profits and helps to reduce poverty as they make more income (Bhavnani et al. 2015). To add, at the micro level, phones help to reduce market inefficiencies. The Palliathya helpline in Bangladesh has been used to offer diverse services which has been useful in rescuing rural people from poverty (Bhavnani et al. 2015).

Apart from that, research shows that phones allow a saving in transport costs in the business sector. Traders and people can access all information via the phone; thus, they will not need to travel to source the information, hence financial savings which can help meet other necessities of life. In terms of intangible benefits, from research, phones can be used for the dissemination of healthcare and educational information, as well as helping with disaster relief (Bhavnani et al. 2015).

Lastly, in a study by Spulbar et al. (2022), they found that access to the Internet helps people search for employment opportunities, which to some extent helps in alleviating poverty. Thus, the ability to channel Internet usage to search for jobs is useful in reducing poverty, on condition that employment is finally secured.

For this economy in question, with such a low fixed broadband subscription, the opportunities for people engaging in e-commerce activities that improve the standard of living are extremely low, ceteris paribus. This also applies to fixed telephone subscriptions. The economy suffered a sharp drop in fixed telephone subscriptions from 1 687 972 in 2006 to 140 491 in 2018 (World Bank 2020d).

In terms of mobile cellular subscriptions, Nigeria in 2018 had a total of 88.18 per 100 people (World Bank 2020e). This indicates that 88 people of 100 owned mobile phones. With such a high rate, ownership of mobile phones helps people to engage in economic activities that help improve their living standards. This needs to be further explored.

In as much as Nigeria ranks 27th world-wide in terms of nominal GDP value, poverty levels have been soaring (Oladimeji and Folayan 2018, 84). Singapore, Hong Kong, Taiwan and South Korea, also known as the Asian Tigers, invested heavily in human capital development, backed by modern technology (Briceño 2013). The skills acquired in the educational sector were used to transform business in these economies. Research shows that the Asian Tigers’ businesses adopted modern technology to enhance the efficiency in the production of goods and services for local and export use (Briceño 2013). This led to an increase in economic growth and reduced poverty levels (Briceño 2013). Thus, ICT made an indirect contribution to lessening poverty in the context of the Asian Tigers.

In addition, research shows that due to the access to the Internet and fixed telephones, small to medium enterprises in Asia managed to engage in e-commerce activities and improve business communication, access production information, and coordinate economic activities (Briceño 2013). This accounted for 50% of the total export value, which shows how ICT can be used indirectly for poverty reduction (Briceño 2013). Although this can be partially viewed from the lens of growth strategies, it is important to recognize the essential tools that enabled growth to lead to poverty reduction; these are the Internet, as well as mobile phones. It can also be concluded that poverty reduction in Asian economies was partly due to both growth and poverty reduction strategies.

Research carried out in Tanzania, found that mobile phones helped rural traders and farmers to access market information and enhance business communications, which in turn helped to improve productivity and reduce poverty levels (Sife, Kiondo, and Lyimo-Macha 2010). Lastly, a similar study by Miyajima (2022) pointed out that people who own mobile phones in South Africa have access to information on market efficiency, networking opportunities, as well as the ability to start a business which ultimately increases disposable income and reduces poverty levels. This shows the usefulness of mobile phones in poverty reduction. Given the above evidence of how the Internet, mobile phones and fixed telephones can be used for poverty reduction, this can also work in the context of Nigeria. Research shows that grants and foreign aid can be used for poverty reduction, but in Nigeria these were only useful in the short term (Shitile and Sule 2019).

Furthermore, foreign direct investment and remittances proved to have an insignificant impact on poverty reduction in Nigeria due to non-modernised institutions among other reasons (Arabyat 2017). Remittances were not effective because these depend on the financial status and needs of the remitter (Ewubare and Okpoi 2018). If a person is in a bad financial position, he or she may not remit money; thus, it is not a sustainable solution to poverty. Nigeria, therefore, requires a sustainable solution to poverty; one way of modernizing financial institutions may be with the use of ICT (Kyeremeh, Prempeh, and Forson 2019). This study, therefore, examined how ICT can be used for poverty reduction in Nigeria. The article is structured as follows: the next section is based on the literature review of a theoretical and empirical nature. The research methodology, results and conclusion follow.

Theoretical literature review

This section presents the literature review, the Schumpeter’s theory and the classical theory of poverty.

Schumpeter’s theory

The theory is based on four key features which are the circular flow, the role of the entrepreneur, the end of capitalism and the cyclical process (Engelman 1994). The theory is based on the premise that a change in investments, accompanied by monetary expansion, are the key variables behind business fluctuations (Bodrožić and Adler 2018). Schumpeter’s theory posits that innovation in the corporate world is the main reason for the increased investments and business fluctuations in an economy (Sledzik 2013). From this perspective, technological innovations by entrepreneurs can be useful in addressing poverty. Thus, new technology directed to the production of goods and services can be beneficial in increasing economic growth and economic development of any economy. This is in line with the theory of production and income distribution. When a business expands due to innovation, there will be a demand for labour to fill the new jobs created because of the expansion of business operations. The employed people in turn earn wages or an income. Thus, this helps to reduce poverty as the newly employed people will have access to food, healthcare, as well as other basic necessities of life, ceteris paribus.

Classical theory of poverty

The classical theory of poverty is built on the assumption that markets operate efficiently, and individual productivity is directly correlated to the wages earned (Davis and Sanchez-Martinez 2014). Poverty is, therefore, regarded as a sign of poor individual choices that emanate from poor productivity. However, according to the classical hypothesis, poverty can also result from people’s genetic heterogeneity (Davis and Sanchez-Martinez 2014, 15). The classical view also holds that when poverty levels rise, government involvement is required. According to studies, Nigeria’s government was unable to combat poverty since ICT was not utilized in a variety of economic sectors (Ochanja 2017).

Review of recent literature

Ochanja (2017) conducted research on ICT’s contribution to eradicating poverty in Nigeria. According to the study, the Nigerian government’s failure to use ICT as a strategic instrument prevented it from reducing poverty as from 1960 (Ochanja 2017, 20). However, when used on a smaller scale in Nigeria, ICT did show promise in reducing poverty by 50% (Ochanja 2017). The study, however, fell short of adequately articulating how ICT could be applied more broadly to fight poverty. There is no clear justification for how the anticipated 50% ICT usage will help to reduce poverty. This demonstrates that there is a research gap to be closed.

Igbasi (2020) researched ICT and poverty reduction in the Abambra State in Nigeria. The study’s findings demonstrated how ICT helps to reduce poverty by facilitating e-learning, capacity building and employment opportunities, among others (Igbasi 2020). However, there are several obstacles to using ICT to reduce poverty, such as a high prevalence of illiteracy, inadequate human resources and bad infrastructure. The study concluded that the government should create sound ICT policies and provide financial aid for ICT (Igbasi 2020). Poverty in Nigeria is found at the national level and household level; however, the research is silent on the appropriate solutions that can be used at the household level to reduce poverty using ICT tools. This means that the research does not offer or present any possible ways of addressing the high poverty levels in Nigeria.

Ewubare and Okpoi (2018) investigated the impact of remittances on poverty in Nigeria. Evidence from their study proved that remittances were useful for poverty reduction in the short run. The recommendation is that the state creates a conducive environment for the in-flows of remittances (Ewubare and Okpoi 2018). However, the research does not explain how a conducive environment for the in-flows of remittances will help poverty reduction in the long run.

Shitile and Sule (2019) investigated the effect of foreign aid and grants towards poverty reduction in Nigeria. Research findings proved that foreign aid and grants were not effective in poverty reduction (Shitile and Sule 2019). This demonstrates that Nigeria requires long lasting solutions for effective poverty reduction. This study examined how ICT can be used to fight poverty in the economy in question.

Mebawondu et al. (2012) researched the impact of ICT on poverty alleviation in Nigeria. Research findings showed that the government and the private sector of Nigeria need to have proactive measures that will help poverty reduction in different sectors of the economy (Mebawondu et al. 2012). However, there is no clarity on what the state and the private sector should implement as specific measures. This current research sought to find a way to close this gap by providing a method in which ICT can be channelled towards poverty reduction.

It can be fruitful if the government creates a safe investment platform to attract foreign and local investors to invest in the ICT sector. This implies that apart from designing sound economic policies, the state should address key areas of concern that impact investments in ICT (Hussain and Ahmad 2020). With regards to the private sector introducing measures to address poverty, this can be in the form of availing loan facilities to small and medium enterprises, as well as other key players who have business interests in the ICT sector (Hussain and Ahmad 2020). This will help to increase ICT diffusion for positive poverty reduction in the long run. From this discussion, it can be concluded that foreign direct investment and private domestic credit can be useful in supporting ICT initiatives in reducing poverty levels.

Kuliya (2014) investigated the influence of ICT on poverty reduction in Nigeria. From the study, it was established that ICT is useful to impact the socio-economic and political lives of people (Kuliya 2014). It was also realised that some people remain poverty stricken due to technological illiteracy and the lack of financial resources to acquire ICT devices (Kuliya 2014). The study gives little attention to the effective transmission mechanism that can be used to ensure that the less fortunate Nigerians have access to ICT services and how to use these as tools to escape the vicious jaws of poverty.

ICT and its impact on Nigerian development were the subject of research by Oladimeji and Folayan (2018). Oladimeji and Folayan’s (2018, 84) research findings indicated that Nigeria had a relatively low degree of Internet business and that more ICT deployment was necessary, particularly in the rural areas. Major advances in eradicating poverty may lead to the development of rural areas, particularly in the fields of agriculture, healthcare, and education. ICT’s potential use in various Nigerian economic areas to combat poverty is the crucial missing piece. ICT can clearly be a potent remedy for reducing poverty, according to the literature. These findings support Latif’s et al. (2018) assertion that ICT has a favourable impact on economic growth. According to this viewpoint, high GDP levels frequently correlate with high levels of economic growth and employment opportunities. As a result, fewer people are living in poverty as more individuals find employment and new business opportunities arise.

Methodology

This section presents the research methodology used in the study. A brief description of the variables is presented under Table 1.

Table 1: Summary of data set.

Variable	Indicator	Variable description	Unit of measurement	Source of data
HDI	Human development Index	Proxy for poverty measurement	Index	United Nations 2021
ICT	Composite Index of Telecommunication	Principal component value of connected mobile phones, percentage of people with access to Internet as well as penetration of fixed lines.	Index	Author Computation based on International Telecommunication Union (2023)
SS	Social Security	Social protection and labour assess government policies in social protection and labour market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people.	Country Policy Institutional Assessment Score	World Bank (2023)
REM	Remittances	comprise current transfers in cash or in kind made or received by resident households to or from non-resident households and compensation of employees	% of GDP	World Bank (2023)
EG	Economic Growth	Refers to an increase in an economy’s gross domestic product value on yearly basis.	Annual Percentage (%)	World Bank (2023)
FDI	Foreign direct investment	An aggregated value comprising of equity capital, reinvestment of earnings, long-term and short term- capital.	Current US$	World Bank (2023)
PDC	Private domestic credit	This incorporates all financial resources provided to the private sector such as loans, purchases of non-equity securities, trade credits as well as accounts receivable.	(% of GDP)	World Bank (2023)

Source: Researcher’s construct (2023)

Model

The research adopted the Auto Regressive Distributed Lag model (ARDL) to test the effects of ICT on poverty. The ARDL model, developed by Pesaran, Shin, and Smith (1999), was used in the research because it helps depict the relationship between ICT and poverty. It is applicable where variables are stationary at level or after the first difference (Boukhatem 2022). It was used because it helps to reduce the chances of spurious regression (Gholami, Sang-Vong Tom, and Heshmati 2005). E-views econometric software was used to run the regressions. The model is specified below: $Y_{t-1}=m+{\alpha }_1{Y}_{t-1}+{\alpha }_{2\mathrm{xt}-1}+{\alpha }_{3\mathrm{xt}-1}+{\alpha }_{4\mathrm{xt}-1}+{\epsilon }_t$where $Y_{t-1}$ represents poverty and $\mathrm{xt}$ represents the variables: ICT, PDC FDI, EG, REM and SS. $\alpha 1$ denotes the coefficient for the short run.${\alpha }_1,{\alpha }_2,{\alpha }_3,{\alpha }_4,$ represents row vectors of the dimension as vector xt, $\epsilon t$ is white noise. The model, in its functional form, is presented below: $\begin{array}{rl}\mathrm{HD}{I}_{t-1}=& m+{\alpha }_1\mathrm{HD}{I}_{t-1}+{\alpha }_2\mathrm{IC}{T}_{t-1}+{\alpha }_3\mathrm{PD}{C}_{t-1}\\ & +{\alpha }_4\mathrm{FD}{I}_{t-1}+{\alpha }_{5}E{G}_{t-1}+{\alpha }_{6}S{S}_{t-1}\\ & +{\alpha }_7\mathrm{RE}{M}_{t-1}+{\epsilon }_t\end{array}$Private domestic credit (PDC) was included because it has an impact on poverty reduction. Research shows that PDC can be used for investments which ultimately creates employment, thereby reducing the levels of poverty (Nsiah et al. 2021). Foreign direct investment can be in the ICT sector, and this helps to create technologically related jobs (Tobondo, Nurdin, and Jokolelono 2021). As people are employed, their poverty levels lessen to some extent. HDI was used as a proxy for poverty. The index is useful for poverty measurement as it incorporates income, education and healthcare. Research shows that it produces reliable estimates as far as poverty measurement is concerned (Bejar 2021; Korankye et al. 2020). In addition, the HDI uses healthcare and education, and these factors form part of the multidimensional index which is one of the most robust proxies for poverty measurement (United Nations Development Programme 2022; Vollmer and Alkire 2022).

Economic growth was added as a variable because research shows high levels of economic growth can be useful for reducing poverty levels (Balasubramanian, Burchi, and Malerba, 2023; Erlando, Riyanto, and Masakazu 2020). Social security was included as a control variable because it impacts poverty reduction (Yu and Li 2021). Social security entails diverse government initiatives and policies aimed at reducing poverty levels (Mediaty, Syahrir, and Aini Indrijawati 2015). Finally, REM was added as a control variable because when people receive this it helps them to buy some of the basic goods and services needed for human survival, thus reducing poverty to some extent (Barkat, Alsamara, and Mimouni 2023).

Results

This section presents a discussion of the results that relate to the key variables used in the study. The first quarter in this research refers to the period 1992–1999, the second quarter is the period 1999–2006, the third quarter is the period 2006–2013 and the last quarter refers to 2013–2020.

Figure 1 shows the ICT composite index. To compute this index, the author used a bundled value of fixed Internet usage per 100 people, fixed telephone usage per 100 people and mobile phone usage per 100. A principal component analysis was used to add the three indicators. The rationale behind using this approach is that it gives room for suggesting robust policies on the subject matter (Asongu and Le Roux 2017; David 2019). This technique has been used by other researchers and is regarded as useful for inferential validity (Asongu and Nwachukwu 2016; Tchamyou 2016).

Figure 1: ICT composite index: 1992–2020.

Source: Researcher’s calculation (2023)

For the first quarter, ICT was extremely low in Nigeria. However, in the second quarter, a slight increase in the ICT index was recorded. In the third quarter the Nigerian economy experienced a sharp increase in the ICT index, although there was a drop toward the end of the same quarter. The low usage of ICT could be attributed to low adoption rates of using ICT in the diverse sectors of the Nigerian economy.

Foreign direct investment in Nigeria

Figure 2 shows the foreign direct investment in Nigeria for the period 1992–2020. It shows that Nigeria experienced extremely low levels of foreign direct investment for the first quarter, from 1992–1999. In the second quarter, the FDI started at a low level, below US$94 million but rose to above US$300 million by the end of 2006. In the third quarter, FDI levels increased sharply though there were some fluctuations. The highest value recorded in this quarter was an estimated US$1.53 billion. In the last quarter, FDI levels for Nigeria increased only for one year to US$1.61 billion. A drastic drop in FDI levels was experienced from US$1.61 billion to an estimated US$285 million. This sharp decrease in FDI levels was caused by the poor performance of the Nigerian financial market, political violence from Boko Haram, as well as the poor infrastructure in the country (Osabohien, Adeleye, and Alwis 2020). All these factors drive investors away and this led to a sharp drop in FDI levels. Overall, the FDI performance in Nigeria has been characterized by a slow pace and uneven fluctuations.

Figure 2: Foreign direct investment in Nigeria: 1992–2020.

Source: World Bank (2023)

Human development index in Nigeria

This was used as a proxy for poverty measurement in the research. This is because the human development index is a composite index that encompasses life expectancy, education and per capita income (Al-Hilani 2012). Figure 3 indicates, that for the first quarter, the HDI levels were extremely low. In the second quarter, from 1999–2006 there was an increase in the HDI levels to an estimated index of 0.45. This implies that there were positive changes in terms of the welfare of Nigerians. In the third quarter, from 2006–2013, there was a fair increase in the HDI levels that reached close to 0.5. In the last quarter, Nigeria experienced higher levels of HDI relative to the third quarter. Overall, the HDI levels for Nigeria for the period in question are low and this depicts a certain degree of poverty.

Figure 3: Human development index in Nigeria: 1992–2020.

Source: World Bank (2023)

Descriptive statistics

Table 2 shows all the variables used in the study, which were: FDI = Foreign direct investment, ICT = Information and communication technology, HDI = Human development Index, PDC = private domestic credit, EG = economic growth, REM = remittances and SS = social security. The results show that the mean FDI for Nigeria was US$94,864,411 and the maximum was US$3.51 billion. The standard deviation was US$1.18 billion and the Jarque-Bera was 4.21. The mean HDI was 0.30 and the maximum was 0.53. The Jarque-Bera was 4.59. In terms of the ICT index, the mean was 11.95 and the maximum was 45.39 while the Jarque-Bera was 5.06. Lastly, in terms of PDC, the mean was 10.4, the maximum was 19.62 and the Jarque-Bera was 6.26. In terms of EG, the mean was 4.4%, and the maximum was 4.8%. The standard deviation was 3.8%. The Jarque-Bera was 1.51. With regards to REM, the mean was 3.5% and the maximum was 8.3%. The standard deviation was 2.34 and the Jarque-Bera was 1.80. Finally, SS had a mean of 3, a maximum of 4 and a standard deviation of 0.76. The Jarque-Bera was 2.52.

Table 2: Descriptive statistics.

	HDI	ICT	FDI	PDC	EG	REM	SS
Mean	0.41	11.95	94864411	10.43	4.43	3.55	3.14
Median	0.47	1.164	133000000	9.39	4.82	3.82	3.25
Maximum	0.53	45.39	3.15E + 08	19.62	15.39	8.33	4
Minimum	0.00	0.00	−50000.00	6.17	−2.03	0.11	2
Std. Deviation	0.246	16.38	1.18	3.43	3.80	2.34	0.76
Skewness	−0.41	0.98	0.75	1.10	0.41	0.17	−0.42
Kurtosis	1.20	2.30	1.85	3.68	3.78	1.80	1.80
Jarque-Bera	4.59	5.06	4.21	6.26	1.51	1.80	2.52

Source: Researcher’s calculation (2023)

Correlation analysis

Table 3 shows that the correlation between ICT and HDI was positive at 23% while the correlation between FDI and HDI was 57%. PDC was positively associated with HDI at 66%, while EG and HDI had a positive association at 87%. REM and HDI were positively correlated at 77%. The correlation between SS and HDI was 89%. In addition, FDI and ICT were negatively associated at 17%, and PDC had a positive relationship with ICT at 8%. EG and ICT had a positive correlation at 22% while REM was positively associated with ICT at 16%. SS was positively associated with ICT at 24%. With regards to PDC and FDI there was a positive association at 56%. EG and FDI were positively associated at 67%, while REM and SS were positively associated with FDI at 48% and 53%, respectively. EG, REM and SS were positively associated with PDC at 65%, 59% and 62%, respectively. REM and SS had a positive relationship with EG at 64% and 88%, respectively. Finally, SS was positively associated with REM at 69%.

Table 3: Correlation analysis.

	HDI	ICT	FDI	PDC	EG	REM	SS
HDI	1
ICT	0.23	1
FDI	0.57	−0.17	1
PDC	0.66	0.08	0.56	1
EG	0.87	0.22	0.67	0.65	1
REM	0.77	0.16	0.48	0.59	0.64	1
SS	0.89	0.24	0.53	0.62	0.88	0.69	1

Source: Researcher’s calculation (2023)

Unit root tests

In the research, to test for unit root, the Augmented Dickey-Fuller, Phillips-Perron and the Zivot-Andrew’s tests were used. The results are presented below.

Table 4 shows that the p-values of all the variables were less than 0.05; hence, it was concluded that the series was stationary. PDC, REM, SS, EG, ICT and HDI were stationary after the first difference and FDI was stationary at level. The results also depicted that PDC, ICT, FDI and HDI had structural breaks in 2007, 2008, 2009 and 2011, respectively. Finally, EG, SS and REM had structural breaks in 2010, 2006 and 2007, respectively. All the unit root tests used in this study confirmed that the series was stationary.

Table 4: Unit root tests.

	ADF test		PP test		ZA test with structural break point
Variable	Level	1st Diff	Level	1st Diff	Level	1st Diff	Break Year
HDI		0.00		0.00		0.00	2011
FDI	0.00		0.00		0.00		2009
ICT		0.00		0.00		0.00	2008
PDC		0.00		0.00		0.00	2007
EG		0.00		0.00		0.00	2010
SS		0.00		0.00		0.00	2006
REM		0.00		0.00		0.00	2007

Source: Researcher’s calculation (2023)

Heteroskedasticity tests

To test for heteroskedasticity, the Breusch–Pagan Godfrey test was used. The p-value of 0.06 was greater than 0.05, hence, it was concluded that there was no heteroskedasticity.

Autocorrelation test

The Durbin-Watson t-statistic was used to test for autocorrelation in the study. The results showed a Durbin-Watson statistic of 1.94. This is between the acceptable ranges of 1.5–2.0 and therefore, it was concluded that there was no autocorrelation. This implies that the validity of inferential statements associated with the hypothesis test and confidence interval is not affected (Huitema and Laraway 2006).

Lag length selection

The VAR model was used to determine the optimal lag length in this study. The results are presented in Table 5. The Akaike criterion was used for decision purposes on the appropriate lag length. Table 5 shows that Lag 2 was selected because it had the lowest AIK value of 39.74.

Table 5: VAR model for optimal lag selection.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−598.6899	NA	1.60e + 15	46.36076	46.55432	46.41650
1	−504.6650	151.8863	4.04e + 12	40.35885	41.32662*	40.63753
2	−480.7229	31.30901*	2.42e + 12*	39.74791*	41.48989	40.24954*

Source: Researcher’s construct (2023)

Bounds test

To test for co-integration, the bounds test (Pesaran, Shin, and Smith 2001) was used and the F-statistic of 2.50 was less than the I(0) value of 2.88. It was, therefore, concluded there was no long-run relationship between variables used in the research, which were foreign direct investment, human development index (HDI), private domestic credit (PDC), economic growth, (EG) remittances, (REM) social security (SS), as well as information and communication technology (ICT). In other words, the research shows that there was a short-run relationship between the variables used in the study. Consequently, the short-run ARDL was estimated and is presented next.

Auto regressive distributed lag short-run results

The following results show the short-run ARDL results.

Table 6 shows that ICT, PDC, FDI and EG were statistically significant as their p-values were below 0.05. If ICT is increased by 1%, HDI will increase by 3.7% in the short run. An increase in HDI implies a decrease in poverty levels. This implies that ICT is useful in poverty reduction using HDI as a proxy. With regards to PDC, a 1% increase in PDC will lead to a 1% increase in HDI in the short-run. Thus, PDC helps to reduce poverty levels in Nigeria. For FDI, a 1% increase in FDI will lead to a 4.3% decrease in HDI levels in the short-run. This implies an inverse relationship between FDI and HDI.

Table 6: Auto regressive distributed lag short-run results.

Variable	Coefficient	T-statistic	Probability
ICT	3.7	2.4	0.00
PDC	0.01	3.17	0.00
FDI	−4.25	−2.85	0.01
EG	1.06	2.70	0.02

Source: Researcher’s calculation (2023)

Discussion of the research findings

The research findings proved that ICT, PDC and EG had a positive effect on HDI in the short-run. This demonstrates that these factors can be used for effective poverty reduction. These research findings are in line with the views of Balasubramanian, Burchi, and Malerba (2023), Nsiah et al. (2021) and Ochanja (2017) who concluded that ICT, PDC and EG help to reduce poverty levels. The existence of a relationship between ICT and poverty alleviation means that ICT is a useful tool that should be used in diverse sectors of the Nigerian economy to reduce poverty levels. ICT can be used for business expansion and when new businesses open, people are employed. Such employment enables people to access certain goods and services needed for survival, hence, the level of poverty will be lessened to some extent (Peng, Ma, and Zhang 2021). PDC can be channelled towards supporting small to medium enterprises, as well as infant industries. This enables expansion and business expansion creates employment which then helps poverty reduction. Apart from that when people are employed, they can help their families with some remittances; thus, this helps in poverty reduction in a non-deterministic way. In terms of economic growth, high productivity levels in an economy generate a demand for labour which helps poverty reduction (Lee and Sissons 2016). Also, as more goods and services are produced, the government earns more revenue from diverse taxes, such as Value Added Tax (VAT) and such funds can be used for the provision of social security programmes which helps in reducing poverty. Overall, it can be concluded that Nigeria needs to maximize the use of PDC, EG and ICT for effective poverty reduction.

Post estimation tests

Normality test

To test for normality, the Jarque-Bera test was used with a probability value of 0.75 obtained. The p-value of 0.75 is greater than the significance level of 5%. Therefore, the null hypothesis of having a normal distribution is accepted. This implies that the data used were normally distributed.

Serial correlation test

The model was tested for serial correlation using the Breusch Godfrey Serial Correlation LM test; a p-value of 0.52 was obtained. It was concluded that the model was free from serial correlation as the p-value of 0.52 was above the 5% level.

Stability tests

To test for stability, the CUSUM test and CUSUM of squares test were used. The results are presented in Figures 4 and 5.

CUSUM test

Figure 4 shows that the model is stable within the 5% boundary.

Figure 4: CUSUM Test.

Source: Researcher’s calculation (2023)

CUSUM of squares test

Figure 5 shows an insignificant structural deviation from 5%, with a structural break but the model was stable afterwards.

Figure 5: CUSUM of squares test.

Source: Researcher’s calculation (2023)

Policy recommendations

This section presents the policy implication of the findings. The above discussion and results show that Nigeria can benefit from poverty alleviation through the use of ICT, EG and PDC. The following policy recommendations are suggested:

Promoting high usage of mobile phone usage

There is a need for the Nigerian economy to improve and promote mobile phone usage in Nigeria. This can be done by ensuring that all the marginalized people who lack access to mobile phones are given access. To promote this, the government can design effective policies that allow companies that produce and supply mobile phones to meet the untapped market of people who do not have access to mobile phones. An alternative may be to incentivise or provide subsidies to companies that sell mobile phones so that the final price is relatively affordable. This will help the less privileged to afford mobile phones and ultimately use these for the purpose of business communication, e-commerce, as well as other economic activities which in turn improve living standards.

Social security programmes

The Nigerian government can roll out social security programmes to alleviate poverty. These programmes can be implemented and target different people in the economy. For example, grants for the disabled and the elderly. This will help to ensure that people are rescued from the vicious jaws of poverty as they will have some income to meet some of the basic needs in life.

Increase in Internet usage

There is a need for the Nigerian government to increase Internet usage. This can be achieved by opening the telecommunication sector to allow for more players. This will improve efficiency in the sector and reduce the prices of Internet use, ceteris paribus. Policies that promote Internet usage must be developed to ensure that Internet affordability is prioritized especially to the poverty-stricken communities in Nigeria. Thus, access to the Internet will expose more Nigerians to both economic and other commercial activities that can help to promote entrepreneurial activities. In the long run, this will improve living standards as entrepreneurial activities can create job market expansion.

Improvement in ICT policies

Nigerian ICT policies should be tailor made to ensure that fixed telephone usage and Internet usage are increased across the Nigerian populace at large. Thus, ICT should be prioritized at all levels and in different sectors of the economy. This can be in the small to medium enterprises sector, educational sector, government structures, as well as the manufacturing sector. Policies to be developed should ensure that digital illiteracy is eliminated so that all Nigerians can fully benefit from ICT usage.

Channelling foreign direct investment towards the ICT sector

Foreign direct investment can be channelled towards the ICT sector of Nigeria. The long-run effect may lead to the growth of the ICT sector. Growth in the ICT sector is normally accompanied by employment creation and the opening of other micro-ICT businesses which, in turn, add value to economic growth, just to mention a few. In the long run, these benefits will help to improve the livelihoods of Nigerians at large, ceteris paribus. In short, FDI should be used for ICT investments to have a greater effect on poverty reduction.

Channelling of private domestic credit to the ICT sector

It is suggested that the Nigerian economy should attract more private domestic credit and channel it towards the ICT sector. This will help in reducing poverty levels. This is economically justified as increases in private domestic credit help to provide capital to emerging businesses, entrepreneurs, as well as small scale enterprises. These key players will be able to acquire modern ICT equipment for use in the production of goods and services. In so doing, this sector can lead to the expansion of businesses, promotion of ICT-related entrepreneurial projects, as well as job creation. Employment opportunities imply people earn an income, thus living standards are bound to change for the better.

Conclusions

This study sought to examine the role of ICT in poverty reduction in the Nigerian economy. The research findings showed that information and communication technology, economic growth and private domestic credit had a positive impact on the reduction of poverty. ICT had a positive impact on the human development index and this affirms the assertion that ICT is useful to reduce poverty. This implies that the Nigerian economy should channel ICT to different productive sectors for effective poverty reduction. In terms of policy recommendations, it was suggested that there is a need to promote the use of mobile phones, offer social security programmes, increase Internet usage, improve ICT policies, as well as channelling foreign direct investment and private domestic credit towards the ICT sector.

Disclosure statement

No potential conflict of interest was reported by the author.