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Middle East and Central Asia > Jordan

You are looking at 1 - 4 of 4 items for :

  • Type: Journal Issue x
  • Technological Change: Choices and Consequences; Diffusion Processes x
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Cover IMF Working Papers
Digitalization and Resilience: Firm-level Evidence During the COVID-19 Pandemic
Nordine Abidi
,
Mehdi El Herradi
, and
Sahra Sakha
The COVID-19 pandemic has resulted in an unprecedented shock to firms with adverse consequences for existing productive capacities. At the same time, digitalization has increasingly been touted as a key pathway for mitigating economic losses from the pandemic, and we expect firms facing digital constraints to be less resilient to supply shocks. This paper uses firm-level data to investigate whether digitally-enabled firms have been able to mitigate economic losses arising from the pandemic better than digitally-constrained firms in the Middle East and Central Asia region using a difference-in-differences approach. Controlling for demand conditions, we find that digitally-enabled firms faced a lower decline in sales by about 4 percentage points during the pandemic compared to digitally-constrained firms, suggesting that digitalization acted as a hedge during the pandemic. Against this backdrop, our results suggest that policymakers need to close the digital gap and accelerate firms’ digital transformation. This will be essential for economies to bounce back from the pandemic, and build the foundations for future resilience.
Keywords:
COVID-19; digitalization; technology adoption; economic resilience; Middle East and Central Asia; digitally-enabled firm; digitally-constrained firm; firms in the Middle East; firm-level data; digital transformation; Digitalization; Financial inclusion; Foreign corporations; Asia and Pacific; Africa; Middle East; North Africa
Cover IMF Working Papers
Gender Equality and Inclusive Growth
Asel Isakova
and
Francesco Luna
This paper considers various dimensions and sources of gender inequality and presents policies and best practices to address these. With women accounting for fifty percent of the global population, inclusive growth can only be achieved if it promotes gender equality. Despite recent progress, gender gaps remain across all stages of life, including before birth, and negatively impact health, education, and economic outcomes for women. The roadmap to gender equality has to rely on legal framework reforms, policies to promote equal access, and efforts to tackle entrenched social norms. These need to be set in the context of arising new trends such as digitalization, climate change, as well as shocks such as pandemics.
Keywords:
gender gap; inequality; wage gap; wage segregation; gender discrimination; active labor market policies; gender budgeting; GGGI: Global Gender Gap Index; inclusive growth; WP; fiscal policy; parental leave; justice system; gender earnings gap; age-employment profile; commonwealth country; earnings gap; job portal; Women; Gender inequality; Global; South Asia; Sub-Saharan Africa; West Africa; Central Asia
Cover IMF Working Papers
Harnessing Digital Technologies to Promote SMEs in the MENAP Region
Ms. Inutu Lukonga
Policy makers in the MENAP region have been formulating policies and designing programs to develop small and medium sized enterprises (SMEs) with a view to create jobs and achieve inclusive growth. But while the programs have helped increase the number of enterprises, growth of SMEs continues to face barriers to growth. As a result, microenterprises predominate and SMEs contribution to employment remains below potential. Partial implementation of reforms explain some of the underperformance, but frictions in strategy design also played an important role. Sustaining current reforms is, therefore, not sufficient to achieve inclusive growth. Digital technologies have potential to boost SMEs productivity and growth and economies are rapidly digitalizing, thus SMEs need to embrace digital solutions to compete and survive. Therefore, for SMEs to be effective engines of inclusive growth, a rethinking of the SME development strategy is needed that makes SMEs’ digital transformation a priority.
Keywords:
WP; SME growth; MENAP country; financing SMEs; SME sector; SME support policy; SMEs in MENAP; SME lending; government service; digital economy; Small and medium enterprises; Business environment; Employment; Digitalization; Global; Middle East; North Africa; Sub-Saharan Africa; Digital Technologies; Inclusive Growth; MENAP Region; SME strategy; private equity; SME policy; SME data
Cover IMF Working Papers
FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk
Majid Bazarbash
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Keywords:
WP; ML model; bears risk; machine learning technique; ML analysis; ML evaluation; FinTech Credit; Financial Inclusion; Machine Learning; Credit Risk Assessment; ML analyst; credit risk driver; FinTech credit company; credit scoring; supervised machine learning model; capital structure; borrower default; neural network; Credit risk; Credit; Credit ratings; Loans; Global