Digitalization for Reducation of Poverty: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Adrian Tudor Mitroi.

The Sustainable Development Goals can be divided into five pillars: people, planet, prosperity, partnership and peace. One of the first stipulated goals of the UN agenda is the eradication of poverty and famine.  An increase in digital development will lead to a reduction in the poverty headcount rate. by encouraging digital development and through adopting new technologies, the government can lead to the eradication of poverty. This seems counterintuitive due to the fact that investment in shelter and primary goods can be seen as one of the primary ways of developing the economy. Better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of a country.

  • poverty
  • panel data
  • digitalization index
  • economic development

1. Introduction

One of the most discussed problems of the present is sustainability. The problem of sustainable development was defined for the first time in its present form in the Brundtland Report [1] published in October 1987, where the concept gained additional focus regarding the building of a socially inclusive and environmentally sustainable form of economic development (at first, the concept had a bigger and greater focus on the environment, such as in the definition offered by the International Union for the Conservation of Nature [2] in 1980).
Taking into account the importance of the concept, the present paper proposes a model for analyzing the influence of digital development on the poverty headcount ratio as calculated by the World Bank. We considered the poverty headcount ratio to be a determinant factor of sustainable development due to its priority on the agenda of the United Nations in the 2030 Sustainable Development Goals [3]. In the following sections, we will review the articles that we considered to be of the highest importance in the development of the present paper, and we will develop a digitalization index and quantify its influence by using the methodology of the vector autoregressive model for the panel data.

2. Sustainable Development Goals and Their Implementation at a Worldwide Scale

One of the most cited preseapersrch that analyzed the concept of the Sustainable Development Goals is the one written by Griggs et al. [4][3] with the title “Policy: Sustainable development goals for people and planet”. The conclusions of this paper indicate that global stability depends on integration of the goals, such as combating poverty and securing human well-being in the plans of the United Nations. Another interesting articleresearch is the one written by French and Koze [5][4]. This article analyzes the ways in which statistics regarding poverty are calculated and their accuracy for the indicators that measure the level of poverty. This paper eestimates that in 2013, approximately 385 million children were living on less than USD 1.90 per day. These data are, however, stated as being an approximation due to the fact that 63% of countries do not publish data regarding child poverty, with this being in the context of the UN Sustainable Development Goals agenda for 2030, in which the eradication of poverty is the first priority. An interesting overview of the subject is described in “A Systematic Study of Sustainable Development Goal (SDG) Interactions” by Pradhan [6][5]. WResearchers decided to analyze the Sustainable Development Goals set by the UN Agenda for 2030 for potential synergies between them. It is stated that the first goal of the agenda (the eradication of poverty) has a synergistic relation with most of the other goals, and the twelfth goal (responsible consumption and production) is described by the authors as being the most likely to suffer trade-offs. This is due to the implications of reducing the use of coal and oil use in industry which, if carried out in an unprepared economy, will lead to unemployment and poverty. The articleresearch concludes that in order for the goals to become obtainable by the 227 analyzed economies, they must be adopted in a non-obstructive way, and the current strategies of implementation should take into account the level of development of the analyzed countries. Another interesting paperresearch is the one written by Filho et al. [7][6], which presents a series of three case studies to show how the Sustainable Development Goals are an opportunity to advance equal opportunities and foster the economic development of countries by promoting sustainable development. The topic of sustainable development has been linked in the literature with the resilience of the economy. One such preseaperrch is the one written by Folke et al. [8][7]. In tThis article, the authors describe two fundamental errors in the design of environmental policies: the implicit assumption that the ecosystem’s responses to the influence generated by humans are defined by linearity and predictability and that the environment and human society can be treated separately when designing a policy. The authors used the concept of resilience, defined as the capacity to change, learn and develop, to analyze the best strategies to increase the economy’s capacity and adapt in the present climate. Another interesting view on the subject is presented in the researticlech written by Hickel [9][8]. According to tThe author, therere in is an inherent contradiction in the two sides of the sustainable development concept, as stated in the Sustainable Development Goals in of the United Nations between the goal of yearly global economic growth of 3% and the protection of the environment (as stated in goals 6, 12, 13, 14, and 15). The paperresearch states that the by accepting the global economic growth rate at 3%, it is almost impossible to achieve any reductions in the aggregate global resource use. In oura opinion, this offers an interesting view, due to the alternative of downscaling resource use in order to reach the target of climate change rate reduction in high-income nations by introducing quantified objectives for resource use. In the scientific literature, there are views [10,11,12][9][10][11] that state that the evolution of sustainable development is difficult to quantify, and its influence on macroeconomic indicators is challenging to analyze. In this paper, we aim to present the means of measuring the impact of digital development on an essential part of sustainable development: the reduction of the poverty headcount (this being part of the first two Sustainable Development Goals (reducing poverty and eradicating famine) stated by the United Nations). In other artiaddition to the mentioned scles [13,14], we can see that the relevant scientific literature considers using indicators for assessing the evolution of the Sustainable Development Goals agenda. These attempts deal with studying the progress for a short period of time and for various regions. Due to the fact that, in the present paper, we seek to analyze the progress toward the reduction of poverty which has been manifesting in the last two decades, we decided to use the poverty headcount rate as a proxy for sustainable development, and we aimed to determine its correlations and relations with a technology development index. In addition to the mentioned scientific papers, a masearch, a major contribution in the advancement of the measurement of poverty is the 2030 Agenda itself [3][12]. This represents a holistic approach to the problems that the United Nations consider to be fundamental to solve until 2030. In the case of the first goal, which is the eradication of poverty in all its forms, the agenda offers several targets: eradicate extreme poverty, reduce poverty by at least 50%, implement nationally appropriate social protection systems, equal rights to ownership, basic services, technology and economic resources, build resilience to environmental economic and social disasters, the mobilization of resources to end poverty and the establishment of poverty eradication frameworks at all levels. As stated, wresearchers are interested in the eradication of extreme poverty. The indicator that the UN considers to be the most important is the proportion of the population living below the international poverty line, aggregated by sex, age, employment status and geographical location. The UN considers the poverty line to be USD 1.90 per day, and in this paper, we used the USD 5.50 poverty line indicator due to its availability for more countries and because weresearchers considered that for developed countries, the USD 5.50 per day threshold was closer to the national poverty line (which is linked to an indicator of the second target—reduce poverty by at least 50%—with the indicator of the proportion of the population living below the national poverty line). For example, the USA poverty line was USD 35 per day in 2020 [15][13], and India’s was USD 12 per day in urban areas and USD 7.50 in rural areas in 2005 [16][14].
By analyzing the literature regarding the Sustainable Development Goals and their relation to the economic development of a country, weit can statebe stated that they represent more than a list of goals. They represent a development program for bettering the future of the world and a blueprint for sustainable development. With that being said, in the present paper, we researchers attempt to analyze the relation between digital development and the poverty rate. This is due to the attempt to obtain a focused view on the relation between these two variables in order to observe if encouraging digital development (e.g., by subsidizing the acquisition of computers) could lead to advancement in the Sustainable Development Goals. In addition, the adoption of technology could also lead to an increase in equity due to more access to information and opportunities.

3. Measuring the Impact of Digital Development on Poverty

In the scientific literature, there has been a number of researticlesch that focus on analyzing the effect of digital development on the poverty level. One such paperresearch is the one written by Kwilinski et al. [17][15], where the digital economy and society index were used to evaluate the digitalization of the countries of the European Union and were analyzed along with the AROPE indicator (people at risk of poverty and social exclusion). As the main research methods, the preseaperrch implements a correlation analysis and uses the Monte Carlo method to take into consideration the probability that a change in the value of the AROPE indicator will happen in 2021. The conclusions state clearly that the countries with a higher digitalization level have a lower percentage of people in poverty and lower social exclusion risk. Other researticlesch [18,19][16][17] argue that in the case of the African continent, mobile phone development has led to a significant increase in informal financial development, even though its effects are less noticeable at the macroeconomic level, and that the use of mobile phones with internet access in 44 African countries in the period between 2000 and 2016 has led to an increase in financial inclusion. Other literature review-based studies [20][18] claim that there are few preseapersrch that can present a causal inference between ICT development and poverty. The interaction between the internet and mobile phone access or other technologies and poverty is a topic of focus for many papersresearch [21[19][20][21][22][23],22,23,24,25], which have applied a multitude of methodologies in order to analyze this relation for different countries. Additionally, in the scientific literature, there has been a trend toward analyzing the impacts of technology as a means of inclusion and access to information on poverty in either South Asia or Sub-Saharan Africa [26][24] or in Latin America [27][25]. The studies conclude that in the case of South Asia and Sub-Saharan Africa, the adoption of new technologies is an important factor in sustaining the reduction of poverty in developing countries. In the case of Latin America, the study proposes a heterodox type of growth strategy in order to counter the perceived inequality generated by the acceleration of wealth creation. In st Fudying individual countries, from several studies that we considered to be of interest [28,29,30,31], due to their ithermplications for the present article, we found that the majority of the results indicate that the impact of internet adoption was mainly a positive one, as it reduced the rates of poverty. However, a problem still remains regarding the affordability of computers and internet access. Furtre, in several researchermore, in several articles [32[26][27],33], there has been a focus on the relation between the internet and technology and the knowledge economy. This relation is significant because the growth in the percentage of internet users can increase the transition to the knowledge economy, and this favors the reduction of the poverty rate.

4. Using an Index to Measure Digital Development

The use of an index to measure digital development has been widely described in the scientific literature, and several researticlesch [34,35,36,37][28][29][30][31] have proposed and used indices for measuring digital development, such as the one written by Archibugi and Coco [34][28], which had a focus on the developing countries and calculated a proprietary index—ArCo—based on three main components: the creation of technology, the available technological infrastructure, and the level of development of human skills. Another important field of study in the scientific literature is the analysis of the differences in digital development between different regions of a country or between countries [42,43,44,45,46,47,48,49,50,51,52][32][33][34][35][36][37][38][39][40][41][42]. These studies analyze the concept of the digital divide. The digital divide can be defined in a simple way as the gap present between the part of the population that has access to technology and the one that does not. In this context, some preseapersrch [45,47,49][35][37][39] analyze the digital divide for countries in a region in order to observe the level of development of each country and compare their indicators. A method that was used in the aworticlek written by Beynon-Davies and Hill [45][35] was the use of the digital divide index, which was used in analyzing the Wales region of the United Kingdom at two points in time: 1997 and 2000. In addition, in the scientific literature, there have been studies [43,46,50,51,52][33][36][40][41][42] that maintain the idea that the adoption of technology increases the participation of the population in the economy, promotes the sustainable development of the economy and leads to the eradication of poverty. The relation between the reduction of poverty and digital development appears in the preseaperrch written by Dawood [52][42]. This paper observes a relation between the digital development and the social and economic progress in the case of the rural communities of northern Malaysia, stating that there is a correlation that could be made stronger by correlated action at a central level and pragmatic action at the grass roots level.

5. Measuring Influence Using Panel Data Vector Autoregressive Models

In order to quantify the influence of digital development on the poverty headcount rate, we decided to implement a panel data vector autoregressive model. The method of modeling using the vector autoregressive model was developed for the first time in the paperresearch written by Sims [53][43]. The methodology has been improved since its introduction in 1980, and important landmarks are represented by several researticlesch [54,55,56][44][45][46]. One of the researticlesch of interest in developing the present articleresearch is the one written by Andrews and Lu [57][47], in which the methodology for GMM estimation on dynamic panel data models was developed. In order to measure the effect of digital development on the poverty headcount, the authors used the methodology presented in the paper written by Dahlberg and Johansson [58] to develop the present article.

6. The Economic Effect of the COVID-19 Pandemic

The connection between digitalization and the economy has been best observed in the last unique period, more precisely during the pandemic. In this sense, works such as the one written by Fernández-Portillo et al. [59][48] tracked the impact of innovation on the relationship between the digitalization of companies and their economic and financial performance. The conclusion that the authors reached was that to reach a certain level of performance, not only is digitization needed, but a new strategy that will lead to the improvement of the company’s performance is needed as well. Khera et al. [60][49] showed that digital financial services have been a key factor in economic growth. Thus, for the developing countries studied, the notes from the results indicated that digital financial inclusion is positively associated with GDP growth per capita and accelerating economic growth, with their recommendations being related to the digitization of financial services. Dirk Kohnert [61][50] showed that in Africa, digitalization and mobile telecommunications have made a positive contribution to economic growth during the pandemic, even for less-developed regions. However, the population here is facing, with new forms of the digital divide, the gap between the poor and rich, between advanced and less advanced African countries as well as between Africa and the rest of the world. According to Amankwah-Amoaha et al. [62][51], this shows how the pandemic has driven or constrained the digitalization of business around the globe, moving to global acceleration in the use of modern, digitized technologies that have changed working patterns and business strategies in a word lifestyle. Guo et al. [63][52] showed that the pandemic has put small- and medium-sized enterprises under enormous pressure to survive, which has forced them to adopt various digital technologies to cope with the crisis. The empirical results of the analysis show that digitalization has allowed small- and medium-sized enterprises to respond effectively to the public crisis. In their study, Almeida et al. [64][53] analyzed the impact of digital transformation processes during the pandemic in three business areas: labor and social relations, marketing and sales and technology. The result was that digitalization would increase in each of these areas and would encourage the emergence of new digital products and services. Härting et al. [65][54] showed that the key driver of business development is digital transformation, and with the pandemic, the need for digital solutions became more acute considering the opportunities for digitalization, especially for small and medium enterprises. Singh et al. [66][55] conducted a survey to distribute and meet food demand during the pandemic, and the results confirmed the positive impact of information on cost-saving performance and supply chain relationships, where the online distribution and application process was used. Abidi et al. [67][56] showed that the pandemic has led to an unprecedented shock for businesses and the economy in general, while digitalization has acted as a fence or as a popular key used to mitigate economic losses. The results obtained by the authors illustrate that digitally activated companies were able to mitigate the economic losses resulting from the unique situation better than companies with digital restrictions in the Middle East and Central Asia regions. Döhring et al. [68][57] showed in their work that in a pandemic, even if a persistent increase in the demand for digital services was expected, the estimated economic impact was unknown. This paper states that competition policy and the labor market have come to support the digital transition, making digitalization grow at the same pace as economic growth. Ragoussis and Timmis [69][58] showed the crucial role played by digital technologies in helping companies cope with the shock caused by the pandemic and found that digitalization has transformed the trajectory of the online market, leading to significant growth. Xiang et al. [70][59] illustrated in their study that the sectors severely affected by the pandemic did not use the necessary technological and digital strategies to sustain their economies, showing as a conclusion the vital role of information technology and digitalization in supporting economies and helping them sustain themselves during crises. Chauhan et al. [71][60] showed that global blockages due to the pandemic from different economic branches have accelerated the digitalization of various sectors of the economy from retail to finance, education and healthcare, but at the same time, they have intensified inequalities at the national level and between countries. The COVID-19 (or Coronavirus) pandemic has exacerbated inequalities in nationality, occupation, income, sex and race as well as, in fact, a decrease in global productivity. Claeys et al. [72][61] showed that the pandemic has led to a global recession, and although both developing and advanced countries have lost about the same proportion of production, the real annual decline in GDP was higher in advanced countries, except for China, which saw an increase in GDP but below the pre-pandemic forecasts. Dannenberg et al. [73][62] in their study showed the impact of the pandemic in online food retail in Germany and the fact that there has been a strong increase in food and a disproportionate increase in online food trade because of digitalization. Katz et al. [74][63] showed through empirical evidence the important role of digitalization and technology in mitigating the disruption of economic and social effects created by the pandemic while assessing the vulnerable population groups, unemployment rates and level of readiness of developing countries to meet the challenge. Chakravorti et al. [75][64] illustrated in their study the growth and development of pandemic digitalization. This has helped people to work, learn, shop and socialize safely during a pandemic, a unique situation, and to maintain a semblance of normalcy. With the expansion of digitalization, e-commerce has grown, video conferencing has become more widely used, and the Zoom platform has reached high levels, competing with IBM.

References

  1. United Nations World Commission on Environment and Development (Ed.) Report of the World Commission on Environment and Development: Our Common Future; Oxford University Press: Oxford, UK, 1987.
  2. International Union for Conservation of Nature and Natural Resources. 1980. World Conservation Strategy: Living Resource Conservation for Sustainable Development. Available online: https://www.a21italy.it/medias/31C2D26FD81B0D40.pdf (accessed on 17 March 2022).
  3. Griggs, D.J.; Stafford-Smith, M.; Gaffney, O.; Rockström, J.; Öhman, M.C.; Shyamsundar, P.; Steffen, W.; Glaser, G.; Kanie, N.; Noble, I.R. Policy: Sustainable development goals for people and planet. Nature 2013, 495, 305–307.
  4. French, D.; Kotzé, L.J. Sustainable Development Goals; Edward Elgar Publishing: Cheltenham, UK, 2018.
  5. Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J.P. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earth’s Future 2017, 5, 1169–1179.
  6. Leal Filho, W.; Tripathi, S.K.; Andrade Guerra, J.B.; Giné-Garriga, R.; OrlovićLovren, V.; Willats, J. Using the sustainable development goals towards a better understanding of sustainability challenges. Int. J. Sustain. Dev. World Ecol. 2018, 26, 179–190.
  7. Folke, C.; Carpenter, S.; Elmqvist, T.; Gunderson, L.; Holling, C.S.; Walker, B. Resilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations. Ambio 2002, 31, 437–440.
  8. Hickel, J. The contradiction of the sustainable development goals: Growth versus ecology on a finite planet. Sustain. Dev. 2019, 27, 873–884.
  9. Coscieme, L.; Mortensen, L.F.; Anderson, S.J.; Ward, J.D.; Donohue, I.; Sutton, P.C. Going beyond Gross Domestic Product as an indicator to bring coherence to the Sustainable Development Goals. J. Clean. Prod. 2020, 248, 119232.
  10. Kubiszewski, I.; Mulder, K.; Jarvis, D.; Costanza, R. Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction. Sustain. Dev. 2022, 30, 139–148.
  11. MacFeely, S. Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge. J. Off. Stat. 2020, 36, 361–378.
  12. United Nations Statistics Division (UNSD). SDG Indicators—Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development. Available online: https://unstats.un.org/sdgs/indicators/indicators-list (accessed on 17 March 2022).
  13. Federal Register: Annual Update of the HHS Poverty Guidelines. Available online: https://www.govinfo.gov/content/pkg/FR-2020-01-17/pdf/2020-00858.pdf (accessed on 24 April 2022).
  14. Government of India, Press Information Bureau, Poverty Estimates for 2004-05. Available online: https://web.archive.org/web/20120917001050/http://planningcommision.gov.in/news/prmar07.pdf (accessed on 24 April 2022).
  15. Kwilinski, A.; Vyshnevskyi, O.; Dzwigol, H. Digitalization of the EU Economies and People at Risk of Poverty or Social Exclusion. J. Risk Financ. Manag. 2020, 13, 142.
  16. Asongu, S.A. How has Mobile Phone Penetration Stimulated Financial Development in Africa? J. Afr. Bus. 2013, 14, 7–18.
  17. Evans, O. Connecting the poor: The internet, mobile phones and financial inclusion in Africa. Digit. Policy Regul. Gov. 2018, 20, 568–581.
  18. May, J.; Diga, K. Progress Towards Resolving the Measurement Link Between ICT and Poverty Reduction. In Impact of Information Society Research in the Global South; May, J., Diga, K., Eds.; Springer: Singapore, 2015; pp. 83–104.
  19. James, J. The Internet and poverty in developing countries: Welfare economics versus a functionings-based approach. Futures 2006, 38, 337–349.
  20. Adomi, E.E. Frameworks for ICT Policy: Government, Social and Legal Issues; IGI Global: Hershey, PA, USA, 2011.
  21. Ramírez, R. Appreciating the Contribution of Broadband ICT with Rural and Remote Communities: Stepping Stones toward an Alternative Paradigm. Inf. Soc. 2007, 23, 85–94.
  22. Rao, S.S. Role of ICTs in India’s rural community information systems. Info 2004, 6, 261–269.
  23. Cecchini, S.; Scott, C. Can information and communications technology applications contribute to poverty reduction? Lessons from rural India. Inf. Technol. Dev. 2003, 10, 73–84.
  24. Lyons, A.; Kass-Hanna, J.; Greenlee, A.J. Impacts of Financial and Digital Inclusion on Poverty in South Asia and Sub-Saharan Africa. Food Sustain. Secur. Ejournal 2020.
  25. Arocena, R.; Senker, P. Technology, Inequality, and Underdevelopment: The Case of Latin America. Sci. Technol. Hum. Values 2003, 28, 15–33.
  26. Pînzaru, F.; Zbuchea, A.; Anghel, L.C. Knowledge Development through the Internet. In Proceedings of the International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, Sydney, Australia, 6–7 November 2014.
  27. Massimo, G.C.; Luca, G. Technology policy for the knowledge economy: Public support to young ICT service firms. Telecommun. Policy 2007, 31, 573–591.
  28. Archibugi, D.; Coco, A. A New Indicator of Technological Capabilities for Developed and Developing Countries (Arco). SSRN Electron. J. 2004, 32, 629–654.
  29. Álvarez, I.G.; Magaña, G. ICT and development: A new indicator for international comparisons. Int. J. Technol. Glob. 2008, 4, 170–185.
  30. Nevado-Peña, D.; López-Ruiz, V.; Alfaro-Navarro, J. An analysis of the key role of human and technological development in the smart specialization of smart European regions. Inf. Technol. Dev. 2020, 26, 728–741.
  31. Hongxi, C.; Juan, P. A Study on the Dynamic Relationship between Digital Financial Development, Social Consumption and Economic Growth. J. Econ. Public Financ. 2021, 7, 56–69.
  32. Cavalcanti, D.R.; Oliveira, T.; Santini, F.D. Drivers of Digital Transformation Adoption: A Weight and Meta-Analysis. Heliyon 2022, 8, e08911.
  33. Jovanović, M.; Dlačić, J.; Okanovic, M. Digitalization and society’s sustainable development—Measures and implications. Proc. Rij. Fac. Econ. J. Econ. Bus. 2018, 36, 905–928.
  34. Bagchi, K.K. Factors Contributing to Global Digital Divide: Some Empirical Results. J. Glob. Inf. Technol. Manag. 2005, 8, 47–65.
  35. Beynon-Davies, P.; Hill, R.A. Evaluating a digital divide index in a regional context. J. Syst. Inf. Technol. 2007, 9, 46–59.
  36. Taylor, D.; Packham, G.A. Social Inclusion through ICT: Identifying and Overcoming Barriers to ICT Use. Strateg. Change 2016, 25, 45–60.
  37. Ayanso, A.; Cho, D.I.; Lertwachara, K. The digital divide: Global and regional ICT leaders and followers. Inf. Technol. Dev. 2010, 16, 304–319.
  38. Ayanso, A.; Cho, D.I.; Lertwachara, K. Information and Communications Technology Development and the Digital Divide: A Global and Regional Assessment. Inf. Technol. Dev. 2014, 20, 60–77.
  39. Chinn, M.D.; Fairlie, R.W. ICT Use in the Developing World: An Analysis of Differences in Computer and Internet Penetration. Inf. Syst. Behav. Soc. Methods 2006, 18, 153–167.
  40. Dijk, J.V. The Deepening Divide: Inequality in the Information Society; SAGE Publications Inc.: Thousand Oaks, CA, USA, 2005.
  41. Warren, M. The digital vicious cycle: Links between social disadvantage and digital exclusion in rural areas. Telecommun. Policy 2007, 31, 374–388.
  42. Dawood, S.R. Digital Divide and Poverty Eradication in the Rural Region of Northern Peninsular Malaysia. Indones. J. Geogr. 2019, 51, 172–182.
  43. Sims, C.A. Macroeconomics and Reality. Econometrica 1980, 48, 1–48.
  44. Kiviet, J. On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models. J. Econom. 1995, 68, 53–78.
  45. Bond, S.R. Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice. Port. Econ. J. 2002, 1, 141–162.
  46. Anderson, T.; Hsiao, C. Formulation and Estimation of Dynamic Models Using Panel Data. J. Econom. 1982, 18, 47–82.
  47. Andrews, D.; Lu, B. Consistent Model and Moment Selection Procedures for GMM Estimation with application to dynamic panel data models. J. Econom. 2001, 101, 123–164.
  48. Fernández-Portillo, A.; Almodóvar-González, M.; Sánchez-Escobedo, M.C.; Coca-Pérez, J.L. The role of innovation in the relationship between digitalisation and economic and financial performance. Eur. Res. Manag. Bus. Econ. 2022, 25, 100190.
  49. Khera, P.; Ng, S.; Ogawa, S.; Sahay, R. Is Digital Financial Inclusion Unlocking Growth? IMF Working Paper. 2021, p. 167. Available online: https://ssrn.com/abstract=4026364 (accessed on 23 July 2022).
  50. Kohnert, D. The Impact of Digitalization on Poverty Alleviation in Africa. Munich Personal RePEc Archive (MAPRA). 2021, p. 110296. Available online: https://mpra.ub.uni-muenchen.de/110296/ (accessed on 25 July 2022).
  51. Amankwah-Amoaha, J.; Khan, Z.; Wood, G.; Knight, G. COVID-19 and digitalization: The great acceleration. J. Bus. Res. 2021, 136, 602–611.
  52. Guo, H.; Yang, Z.; Guo, A. The digitalization and public crisis responses of small and medium enterprises: Implications from a COVID-19 survey. Front. Bus. Res. China 2020, 19, 1–25.
  53. Almeida, F.; Santos, J.D.; Monteiro, J.A. The Challenges and Opportunities in the Digitalization of Companies in a Post-COVID-19 World. IEEE Eng. Manag. Rev. 2020, 48, 20022139.
  54. Härting, R.-C.; Rösch, A.-L.; Serafino, G.; Häfner, F.; Bueechl, J. Opportunities and Barriers of Digitization in the COVID-19 Crisis for SMEs. In Smart and Sustainable Technology for Resilient Cities and Communities. Advances in Sustainability Science and Technology; Springer: Singapore, 2022; pp. 47–60.
  55. Kashav, S.; Singh, R.; Centobelli, P.; Cerchione, R. Digitization of Information Sharing to Minimize the Impact of COVID-19 in the Food Supply Chain. In Handbook of Research on the Platform Economy and the Evolution of E-Commerce; IGI Global: Hershey, PA, USA, 2021; pp. 251–272.
  56. Abidi, N.; Herradi, M.E.; Sakha, S. Digitalization and Resilience: Firm-Level Evidence During the COVID-19 Pandemic; IMF Working Papers; International Monetary Fund: Washington, DC, USA, 2022; p. 034.
  57. Döhring, B.; Hristov, A.; Maier, C.; Roeger, W.; Thum-Thysen, A. COVID-19 acceleration in digitalisation, aggregate productivity growth and the functional income distribution. Int. Econ. Econ. Policy 2021, 18, 571–604.
  58. Ragoussis, A.; Timmis, J. Global Transition Online. World Bank Group. Working Paper 2022. Available online: https://openknowledge.worldbank.org/handle/10986/37103 (accessed on 10 June 2022).
  59. Xiang, S.; Rasool, S.; Hang, Y.; Javid, K.; Javed, T.; Artene, E.A. The Effect of COVID-19 Pandemic on Service Sector Sustainability and Growth. Front. Psychol 2021, 1178.
  60. Chauhan, N.; Corredera, M.; Lukasik, K.; Reis, F. Post-pandemic future of work—How does digitization impact labour? Explor. Econ. 2021. Available online: https://www.exploring-economics.org/en/discover/post-pandemic-future-of-work-digitization-labour/ (accessed on 10 June 2022).
  61. Claeys, G.; Darvas, Z.; Demertzis, M.; Wolff, G.B. The Great COVID-19 Divergence: Managing a Sustainable and Equitable Recovery in the EU. Intereconomics 2021, 56, 211–219.
  62. Dannenberg, P.; Fuchs, M.; Riedler, T.; Wiedemann, C. Digital Transition by COVID-19 Pandemic? The German Food Online Retail. J. Econ. Human Geogr. 2020, 111, 543–560.
  63. Katz, R.; Jung, J.; Callorda, F. Can digitization mitigate the economic damage of a pandemic? Evidence from SARS. Telecommun. Policy 2020, 44, 102044.
  64. Chakravorti, B.; Chaturvedi, R.S.; Filipovic, C.; Brewer, G. Digital in the Time of COVID; The Fletcher School at Tufts University: Medford, MA, USA, 2020.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback