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The Ministry of Finance has begun the preparations for the transition strategy to be adopted when Nepal is upgraded from the Least Developed Country Group to a Developing Country status.

How does the World Bank determine interest rates?

If we think that the World Bank is a global central bank that determines interest rates for the world, that would be wrong. There is no global currency, so there is no global central bank to manage it and determine interest rates for it. The Washington, DC-based World Bank is a global financial assistance institution with membership of about 200 countries, whose objective is to carry out various activities, including poverty alleviation, by providing funds for capital investment.

Loan from the World Bank

The World Bank is an international financial institution that provides loans to countries around the world for capital projects. It consists of two institutions: the International Bank for Reconstruction and Development (IBRD), and the International Development Association (IDA). The World Bank is a component of the World Bank Group. The IBRD provides assistance to middle-income and poor but creditworthy countries, and it also acts as an umbrella for more specialized agencies under the World Bank.

The IBRD was the original arm of the World Bank responsible for the reconstruction of post-war Europe. Before becoming a member of its affiliated institutions (the International Finance Corporation, the Multilateral Investment Guarantee Agency, and the International Center for Settlement of Investment Disputes), a country must be a member of the IBRD. As a least developed country, Nepal has long been receiving large amounts of official development assistance (ODA) and grants. This aid used to be free or at very low interest rates (concessional loans). Now, with the upgrade, the form of this assistance is likely to change dramatically.

This simply means that as a poor country, the money that is available at free or very low interest rates will now decrease. The interest rate on loans is likely to increase, meaning that the money needed for development may have to be paid at higher interest rates than before. Nepal’s public debt increased by Rs 231.8bn in the last fiscal year. Nepal’s public debt is now Rs 264.9bn. Of this, Rs 138.2bn is foreign debt. According to public data, the World Bank and the Asian Development Bank contribute 80 percent to Nepal’s development assistance.

As of June 2024, the government’s outstanding public debt was Rs 243.8bn. During this period, the appreciation of the US dollar against the Nepalese currency has been adding additional burden to public debt. There is also concern that this may increase Nepal's external debt burden. Similarly, Nepal will lose its access to special funds such as the Least Developed Countries Fund (LDCF). These funds have been playing an important role in climate change adaptation and various development projects. Now, the reduced availability of these funds is likely to make it more difficult for Nepal to raise financial resources for climate risk reduction and other development programs.

The reduction in the availability of grants and concessional loans will also put Nepal under increasing pressure to finance projects in key social sectors such as infrastructure development, education and health. As development partners are changing their development assistance policies, Nepal needs to find new sources of finance for its development. The International Development Association provides loans to the world’s poorest countries.

In Nepal, the World Bank has been providing loans to Nepal at an interest rate of 0.75 percent, but the Ministry of Finance recently has announced that it has now increased it to 1.5 percent. As the day of Nepal’s graduation from LDC is approaching, the possibility that the World Bank may have increased the interest rate on loans it provides cannot be ruled out. It is worth noting that Nepal is scheduled to formally graduate from LDC status in 2026. This is an initial indication that its impact is starting to be seen in the interest rate that Nepal has to pay on its foreign debt.

In this context, it seems that the World Bank has increased the interest rate on loans it has been providing to Nepal with effect from this July. The Asian Development Bank has also been charging 1.5 percent interest. However, this is considered a concessional loan. The World Bank has been charging the lowest interest rate on loans among development partners.

After the upgrade, loans will be available from donors only at a relatively high interest rate and this may put Nepal in the grip of additional debt, and it has come to light that the World Bank has increased the interest rate.

In addition, it has been made public that the World Bank has not only increased the interest rate but also reduced the loan repayment period. Nepal used to take loans to repay within 40 years. Now, it is said that the maturity period of the loan has also been reduced from 24 to 30 years by adding six more years.

Earlier, such periods were of two types, 40 and 38 years. Now, the Ministry of Finance has stated that it has been made for 30 years. It is also necessary to move forward in coordination with all parties, making the transitional strategy relevant to the time. Although it is estimated that the upgrade will have positive effects on Nepal's development and commercial investment, contribute to the development of new trade and economic partnerships, build a sustainable development, build a national image, and increase credibility, it is not that easy.

It is also expected to have an impact on sectors such as commodity exports, prices, and employment. Nepal is scheduled to graduate from LDC to developing country status in November 2026.

Key challenges and measures

After the upgrade, Nepal will lose some of the special benefits of LDC status, such as preferential market access for goods and services, flexibility in implementing World Trade Organization (WTO) rules, international development measures, and special financing. Nepal has embarked on a new journey after meeting the per capita Gross National Income (GNI) criteria and qualifying for development from a LDC. The United Nations Committee for Development Policy (UN CDP) had previously recommended the Himalayan nation for upgrade in its final assessment.

Graduation for Nepal is an important step towards realizing the national aspiration of a prosperous and developed Nepal. Nepal will continue to access all LDCs-specific assistance measures by 2026. Apart from Nepal, Bangladesh and the Lao People’s Democratic Republic have also been recommended for graduation by the CDP, which has been good for the country. ‘Developing’ is a relative term, a stage like a work in progress—a stage before something reaches its final state. Nepal in 2025  is certainly more developed than Nepal in 2010, which was more developed than Nepal in 2000. But looking at the global average trend, Nepal has not been able to keep pace with the development happening around the world.

So can Nepal be considered developing? No, not when compared to other countries, but when compared to Nepal in previous years. The majority of Nepalis are only concerned with survival, development is a long way off. It is necessary to dispel the illusion that the Nepali people as a society are still very uneducated, very deeply ‘superstitious’, rarely know the meaning of ‘human rights’ and have not yet reached the complexity and sophistication in thinking required to compete with other developing countries of the world.

Nepal must make proper use of the available resources. Peace and security must be maintained in the country. Since Nepal can commercially produce up to 43 thousand megawatts of hydropower, it should try to produce as much hydropower as possible from fast-flowing rivers. There is a risk of reversal at every graduation, mainly due to outflow shocks such as the impact of the Covid-19 pandemic, climate-induced disasters, and trade shocks, which are also major threats to the Nepalese economy.

Similarly, Nepal presents a unique case as it is the first country to be upgraded from the LDC category without meeting the Gross National Income (GNI) criteria. Due to the persistence of many problems, including low standards, it faces the difficult task of achieving sustainable economic growth to progress from the current low-middle income country to a high-income country. There are several economic implications of the upgrade. After upgrade, Nepal will lose some of the special benefits that come with LDC status, such as preferential market access for goods and services, flexibility in implementing WTO rules, international development measures, and special financing.

Although Nepal will be eligible for the Generalized System of Preferences (GSP) available to developing countries, it is much less generous than the duty-free, quota-free market access that many advanced economies offer to LDCs. Raising the necessary financial resources for the investments needed to put LDCs on a rapid growth path has been a major challenge in implementing the Doha Development Agenda, adopted by the United Nations to provide differential treatment that is closely aligned with the Sustainable Development Goals.

In addition, Nepal will lose access to Aid for Trade (AfT) under the Enhanced Integrated Framework (EIF) and the United Nations Capital Development Fund (UNCDF) five years after upgrade.

Additionally, once a country graduates from the Least Developed Country (LDC) category, the minimum grant element of Official Development Assistance (ODA) loans decreases until it is classified as a Low Income Country (LIC); however, Nepal has also become a ‘Low-Middle Income Country’ (LMIC), due to which the lending conditions have become relatively tighter. That being so, the coming years will be a period of both opportunities and challenges for Nepal to navigate its way from a LDC to a developing middle-income country. Consequently, a smooth, irreversible, inclusive, resilient, and sustainable transition is critical for an upgrade.

The country needs to make serious efforts towards poverty alleviation to develop its productive capacity, expand its export base, diversify its economy, and sustain its tertiary education levels in the long term.   Engaging the private sector, civil society, and the international community is equally important as it pursues the Sustainable Graduates agenda. To foster innovation, job creation and economic diversification, it is important to provide incentives and technical support to micro, small and medium-sized enterprises (MSMEs).

This support could include e-commerce platforms, digitally enabled green innovations, and tools for digital and financial literacy. Public-Private Partnerships (PPP): Encouraging public-private partnerships, especially in infrastructure development, will leverage the capabilities of the private sector to deliver large projects.

Many of us have a problem saying no to people. It makes us uncomfortable. Sometimes we have to explain ourselves or make elaborate excuses. So, we end up saying yes to things we would rather not be a part of. I guess it’s one of the most common human conditions—one that we would like to correct but find ourselves unable to most of the time. 

I’m horrible at saying no to people. My default response is always a ‘sure’ or an ‘okay’ even as my mind is screaming otherwise. I don’t want to disappoint people or come across as someone who is difficult. But saying yes doesn’t always guarantee I will follow through on my promises. I will often skip lunches and invites despite having said yes to them. I’ll find ways to back out last minute and feel relieved when someone cancels engagements I’ve agreed to be a part of. I realize if I could only say no to things I don’t feel like doing, I don’t have to be unnecessarily stressed out or eventually do things half heartedly. 

I always vow to do better—to speak my mind and turn down offers I’m not interested in. I’m envious of people who can say no. Every year, it’s one of my top five resolutions. I’m trying to learn how to say no without offending people. But it’s not an easy thing. Whenever I say no (or try to say no) I can clearly see the hurt on the other person’s face and I start to explain myself, sometimes even making up stories as I go. I hate myself for it. But I fall into the trap every single time. 

I have a few friends, colleagues, and mentors who can say no politely and with ease. One thing they all seem to have in common is clearly sorted priorities. They know they won’t be able to give time to certain things and have no qualms about rejecting those offers. I have spoken to a few of them and they have all maintained that how the other person feels isn’t in their control. The best they can do is be direct and clearly state where they stand. It prevents future misunderstandings and complications, they say. 

I have said yes to things that I’ve had to cancel at the last minute and this is even worse than not being able to say no in the first place. I realize it makes me lose face and people aren’t likely to take my words seriously in the future. I can’t remember the number of times I’ve said yes to invitations and engagements knowing full well that I might not be able to make it and then regretted it later. 

I’ve also had people say yes to me only to disappear at the last minute. Once a senior female journalist agreed to participate in a roundtable event I was organizing for research purposes. She even confirmed a week before the discussion. Then she wouldn’t pick up her phone or respond to texts a day prior to the event. I know she wasn’t ill or had had no emergencies and could have responded to the calls and texts as she was out and about town. Some people I knew had even caught up with her for coffee and chitchat. 

Needless to say, I hated being on the receiving end of this kind of unprofessional behavior and I wondered how many times I might have disappointed people in a similar fashion. I would like to say that I’ve always made it a point to cancel if I wouldn’t be able to do something I had said yes to but I must have pulled the disappearing act too a few times when it has been too awkward to cancel. 

This one incident has made me think deeply and seriously about the importance and perhaps kindness of saying no rather than saying yes to seem amicable and nice and then later backing out. Though initially alarming, it gives the other person clarity on where things stand. It’s a nicer thing to do for the sake of the other person and also a kind thing to do for yourself. You will feel better about yourself and won’t be stressed. 

Saying no takes practice and it’s not something I hope to achieve overnight or through resolutions but to start with I’m definitely going to force myself to speak my mind instead of saying yes to everything that comes my way. 

One of my colleagues told me a great way to start saying no is to tell people you will think it through when they ask you something and not give an immediate answer. This allows you space to gather your thoughts and give a dignified answer without offending anyone or without having to compromise. 

Although the use of generative AI has significantly improved efficiency and productivity in the creative industry, it has also raised concerns about reinforcing biased worldviews related to gender, caste, ethnicity, geography and other social dimensions. Against this backdrop, this article begins by presenting findings from this writer’s experiments that reveal how generative AI responds to key gender-related prompts. It then reviews past research to explore whether generative AI perpetuates traditional notions of gender inequality and stereotypes, or whether it represents a more progressive shift. The article then analyzes the root causes of biased outputs, and proposes pathways for more equitable, inclusive and socially responsible AI development.

To examine gender bias in generative AI, I conducted a series of prompt-based experiments using a widely-used generative AI tool. When I asked the tool to write a hypothetical story about a nurse, it immediately assigned a female name and used the pronoun “she.” This pattern continued across other professions. Scientist, engineer, and security guard, Army, Police, were consistently given male names and pronouns, while kitchen helpers, dancers and Early Childhood Development (ECD) teachers were presented as female. Even in the health sector, roles like gynecologist were portrayed as female, whereas doctors were more often assigned male or mixed-gender identities.

Next, I tested how the AI assigned roles in hierarchical professional settings. When prompted to generate hypothetical names of CEOs and their secretaries, the AI consistently provided male names for CEOs and female names for secretaries, reinforcing traditional occupational gender roles. And when asked to list 20 fictional nurses, it provided all female names. A prompt for 20 ECD teachers also resulted in exclusively female names. In contrast, prompts for teachers and head teachers produced a mix of male and female names, though still reflecting gendered assumptions depending on the level of authority or setting.

Across multiple attempts, the results were consistent: generative AI tools tend to reflect and reproduce entrenched gender stereotypes. While they may occasionally offer mixed or neutral outputs, the overall trend favors traditional associations between gender and profession. 

The outcome of the experiment aligns closely with findings from a 2024 UNESCO study titled “Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models.” The report reveals that generative AI systems consistently exhibit pervasive biases related to gender, sexuality and race. These systems often associate female names with traditional domestic roles, generate negative or harmful content about LGBTIQA+ individuals, and assign stereotypical professions based on gender and ethnicity.

According to the research report entitled  Gender and Ethnicity Representation of University Academics by Generative Artificial Intelligence Using DALL-E 3 by Currie, Hewis and Wheat (2025), published in the Journal of Further and Higher Education, generative AI tools continue to reproduce systemic biases in visual representation. The analysis revealed that 82.2 percent of AI-generated academic characters were male and 94.2 percent were light-skinned. Women, people with darker skin tones and individuals with disabilities were significantly underrepresented.

This apart,  a recent study in Australia titled Gender Bias in Generative Artificial Intelligence Text-to-Image Depiction of Medical Students by Currie, G, Currie, J, Anderson, S, and Hewis, J (2024), published in the Health Education Journal, examined how DALL-E 3 generates images of medical students. Although more than half of Australia’s actual medical students are women, as claimed by the research report, the AI overwhelmingly portrayed men being 92 percent. 

Another study, which asked large language models like ChatGPT and Alpaca to generate recommendation letters for hypothetical employees, found clear gender bias in the language used. Men were often described as “experts” and “thinkers,” while women were labeled with terms like “beauty” and “emotional, the study revealed. These patterns highlight deep-rooted gender stereotypes embedded in AI systems.

A 2025 study published in Computers in Human Behavior: Artificial Humans offers how AI wrongly represents females in healthcare.  The research, conducted by Ho, Hartanto, Koh, and Majeed, revealed that women’s heart disease symptoms are often misdiagnosed or wrongly linked to other conditions, despite being identical to men’s. Diagnostic AI tools also consistently performed better for male patients, resulting in more frequent underdiagnosis and misdiagnosis for women.

Why biased outputs?

The AI and tech industries remain overwhelmingly male-dominated, with women occupying only a small fraction of development roles. This gender imbalance directly influences how AI systems are conceived and built. As a consequence of this, male-centered perspectives and assumptions into the architecture of artificial intelligence are dominant. This apart, there is the lack of robust fairness testing in many AI tools, especially across gender, race and cultural dimensions. 

Another reason is the quality of the data these systems are trained on. Many AI tools, particularly text-to-image models, rely on massive datasets like LAION-5B—scraped from the internet, where misinformation, sexism and xenophobia are widespread. Without meaningful filtering and oversight, these flawed inputs lead to the replication and amplification of harmful stereotypes and discriminatory narratives.

The digital gender divide further deepens these inequities. Women globally—and in countries like Nepal—have less access to digital tools. They are underrepresented in online spaces, and face disproportionate levels of online hate, algorithmic discrimination, and exclusion from the tech workforce. Cultural and social barriers continue to restrict women’s access to AI education and mentorship, limiting their participation in shaping the technology. As of 2018, only 10–15 percent of AI developers in major tech firms were women; by 2022, over 90 percent of developers remained male. Generative AI tools not only inherit these biases from their training data but also reinforce them through constant user interactions. For example, when prompted about leadership, these systems often emphasize male figures and valorize stereotypically masculine traits like dominance and risk-taking. This happens because the AI reflects dominant cultural narratives found in the training data. Furthermore,  user prompts and feedback—often unconsciously reinforcing existing norms—create a feedback loop that hardens these gendered patterns over time.

The way forward 

In conclusion, as generative AI becomes more powerful and widespread, it is essential that we shape its development in ways that promote fairness, inclusion and accountability. This means going beyond technical solutions and embracing a people-centered approach using diverse and representative data, ensuring transparency in how AI systems work, and involving voices from historically marginalized communities in every stage of design and decision-making. Strong ethical and human rights standards must guide AI governance, with clear oversight and accountability mechanisms in place. If developed responsibly, AI has the potential not only to avoid reinforcing existing inequalities, but also to help build a more just and equitable digital future for all.

In Karnali Province, fatal accidents continue to claim lives as residents are forced to cross rivers using tuins—makeshift cable crossings—due to the absence of proper bridges. Despite repeated tragedies, progress on building safe infrastructure remains slow, putting lives at daily risk.

On 7 Oct 2023, Makar Singh Nepali (38) of Soru Rural Municipality-5, Mugu, died while crossing the Karnali River via a tuin in Sarkegad Rural Municipality, Humla. The cable snapped, sending him plunging into the river. His body was recovered a month later.

Just a day later, on Oct 8, Tula Bohara (55) of Mudkechula Rural Municipality, Dolpa, fell into the Jagadulla River while trying to cross in a canoe—her only option after a bridge was destroyed by floods in 2019. She did not survive.

Another tragedy occurred when Bhuwame Khadka of Junichande Rural Municipality-4, Jajarkot, died after falling from a canoe while crossing the Chhedagarh River. In a similar incident a few years ago, Harisingh Khadka and Bir Bahadur Khadka were seriously injured.

These incidents represent only a small fraction of the accidents occurring across the province. Locals report that injuries, disappearances, and deaths due to unsafe crossings have become tragically routine. Yet, there is no comprehensive record of tuin-related fatalities. The Karnali Provincial Police Office in Surkhet has documented just three deaths and two injuries from tuin incidents since 2018, although locals insist the actual numbers are much higher. “What was meant to be a lifeline has become a symbol of fear and death,” said a resident of Humla.

In Sarkegad and similar areas, both residents and elected officials risk their lives daily crossing rivers on tuins. “There is no bridge here,” said local resident Aiti Phadera. “We cross the Karnali with our eyes closed, praying to our ancestral gods.” From transporting food and firewood to taking the sick for treatment, tuins are still widely used. Children also risk their lives every day on their way to school.

Preliminary data from the Ministry of Physical Infrastructure and Urban Development shows that 55 tuin systems remain active in eight of Karnali’s ten districts. Humla has the highest number (15), followed by Kalikot (12), Jajarkot (12), and Surkhet (eight). Only Rukum Paschim and Jumla have no active tuins. Officials said the data was gathered through public notices and will be verified through on-site surveys.

To address the crisis, the Karnali Province Planning Commission has pledged to construct 496 suspension bridges within five years, increasing the total number from 1,304 to 1,800 by the end of the fiscal year 2025/26. This goal is part of the province’s second five-year development plan.

However, locals and social activists say progress has been slow and uneven. “Some tuins are tied to trees with frayed ropes—disaster can strike at any moment,” said local activist Bindulal Regmi. In some places, people are even charged to cross: Rs 1,000 for motorcycles and Rs 100 per person—an added burden for those already struggling.

Devaki Timalsina, Vice-chairperson of Sarkegad Rural Municipality, emphasized the need for coordinated action. “Our people are dying. This cannot go on,” she said. “The local, provincial, and federal governments must come together to build the bridges Karnali urgently needs.”

Though the region is now connected to the national road network, many remote communities still lack basic infrastructure like bridges. Until that changes, people will continue to cross rivers with prayer on their lips—and fear in their hearts.