Introduction

Remittances sent by migrant workers are an important source of household income and foreign exchange in South Asia. This paper seeks to determine whether dependence on remittances is associated with unemployment, inflation, and GDP growth in Nepal, through comparisons to other countries such as Bangladesh and Sri Lanka. The study combines comparisons of World Bank data with regressions from RStudio.

Background:

Remittances are funds sent by people working abroad to families in their home countries. They can help families pay for education, health care, housing, and food. At the same time, heavy reliance on money earned abroad raises an important question: does this income provide a foundation for financial stability, or does it harm domestic employment and growth? The goal is not to assume that the remittances are either completely beneficial or harmful, but to examine how their potential benefits react when placed next to the economic risks. 

Data

Data from the World Bank approximates remittances at about 26% of GDP in Nepal in 2024, in comparison to 6.1% in Bangladesh and 6.7% in Sri Lanka. In the same comparison, unemployment is reported at 10.5% in Nepal, 3.6% in Bangladesh, and 4.2% in Sri Lanka.¹ These figures illustrate how much more heavily Nepal relies on remittances relative to the size of its economy. However, this alone is not enough to claim that remittances caused the differences in unemployment. 

However, in Nepal, the unemployment rate from 2015–2024 stays relatively high and fairly stable, mostly between about 10.4% and 13%, with a noticeable spike to 13% in 2020 and 12.2% in 2021, followed by a decline to 10.5% by 2024. In contrast, data for Bangladesh over the same period generally shows a lower and more stable unemployment rate with only small changes, with the highest at 5.5 while the lowest was at 3.4. This is significantly more stable than Nepal. While Nepal’s unemployment appears high despite changing GDP growth rates, Bangladesh’s unemployment tends to remain steady. 

Bangladesh has a large garment industry, where they manufacture and exports clothing. This huge industry creates a steady demand for labor and therefore could be a reason why the employment rate stays so consistent. Nepal relies more heavily on industries such as telecommunications and banking. These jobs are more focused on merit and what skills you can bring to improve the business. Additionally, the recent growth of artificial intelligence could make telecommunications a significantly harder career path as AI takes over jobs. This could lead to further unemployment issues. 

It’s important to recognize that correlation and causation are not the same. Correlation means that two events are connected, whereas causation implies that one event leads to another. For example, the data shows that Nepal’s unemployment rises to 13% in 2020 while GDP growth falls to –2.4%. This is a correlation because both variables move in opposite directions at the same time. The negative relationship means that when unemployment increases, GDP decreases, which does make sense. However, it is important to note that the data alone does not prove that the drop in GDP directly caused the increase in unemployment. There could have been other factors, such as anything involving issues/instability in the government. The dataset also shows that in 2022 GDP growth increases to 5.6%, but unemployment stays around 10.7%, which weakens the causal interpretation. To make a stronger claim, we would need more detailed information such as policy changes. 

Remittances:

At the household level, remittances can reduce financial insecurity by supporting basic consumption when wages are insufficient or jobs are scarce. Money transfers from abroad may also help families fund education or businesses. For Nepal, a high volume of remittances relative to GDP means this income can provide significant support for recipients during political and economic disruptions. Although the benefits are reasonable to infer, data do not directly measure how the transfers are spent. 

Over-reliance:

A large remittance share can also indicate that domestic employment does not meet the needs of the workers. If workers must seek opportunities abroad, households may become dependent on these overseas markets and migration policies. Additionally, money used primarily for consumption does not automatically generate long-term productivity growth. However, it is not accurate to assume that receiving remittances makes people unwilling to work: migration, unemployment, and remittance flows can influence one another in several directions. 

Research:

The research uses World Bank economic data compiled for Nepal, Bangladesh, and Sri Lanka across multiple years. The independent variable in each regression is remittances received as a percentage of GDP, while the dependent variables are unemployment, inflation, and GDP growth, all estimated separately in RStudio. 

Regression and what it is:

A regression is a statistical method that allows us to understand the relationship between two or more variables. For this research, regressions are extremely important because it allows me to answer the question whether or not unemployment, inflation, and GDP growth are correlated. 

 Unemployment:

The unemployment regression estimates a remittance coefficient of 0.2188, significant at the 1% level. If both measures are expressed in percentages, an increase of one percentage point in remittances as a share of GDP is associated with about 0.219 percentage points higher unemployment in the sample. Its R2 of 0.2085 indicates that the model accounts for approximately 20.85% of the observed variation in unemployment. This finding is consistent with the possibility that high-unemployment economies rely more on migration and remittances. 

Inflation + GDP:

The remittance coefficient for inflation is -0.05613, while the coefficient for GDP growth is –0.02345. Neither is statistically significant. Their R2  values of 0.004678 (Inflation)  and 0.003268 (GDP Growth) show that remittances alone explain very little of the variation in these outcomes. These results do not prove that remittances have no economic effects. Rather, the regression does not provide enough evidence of a linear association with inflation or GDP growth in this sample. 

Literary Review:

Existing research has examined both the positive and negative effects of remittances on developing economies. In Chandan Sapkota’s study,² he investigated the economic consequences of Nepal’s growing dependence on remittances. Although remittances provide financial support to households, Sapkota identifies potential long-term economic challenges associated with this dependence. One concern is the possibility of Dutch disease, in which large inflows of foreign currency can reduce the competitiveness of domestic industries. This can weaken Nepal’s ability to develop its own industries and cause an inability to create sustainable employment opportunities. His research suggests that while remittances provide short-term economic benefits, over-reliance on remittances can create weaknesses that undermine economic development. 

Additionally, Dilip Ratha’s study examines the broader effects of remittances on developing economies.³ Unlike Sapkota, who emphasizes the potential challenges associated with dependence, Ratha focuses more on the economic benefits that remittances could potentially provide. His research highlights how remittances can reduce poverty, increase household consumption, and improve access to healthcare and education. Remittances can be especially important in developing countries where employment opportunities and access to resources are limited. He also discusses how remittances can contribute to economic development when they are used correctly. 

Both these studies demonstrate that the effects are extremely complex and multifaceted. While remittances can improve household welfare and provide short-term stability, they do not provide the necessary support that guarantees sustainability. This is extremely relevant when considering Nepal, Sri Lanka, and Bangladesh, as they differ in structure and dependence on foreign income. 

Comparison:

The comparison makes Nepal a useful case for examining the difference between economic support and economic dependence. Its remittance share is substantially higher than those of Bangladesh and Sri Lanka, while the recorded unemployment is also higher. 

The results suggest that remittances can be a foundation for household stability without necessarily solving underlying labour-market problems. Nepal’s 2020 experience also shows why they need to be careful in trying to understand the economic patterns. In the project’s earlier data, GDP growth fell to –2.4% as unemployment reached 13%. The pandemic and other factors could have played into both these variables at the same time. 

For Nepal, dependence on remittances appears best understood as both a source of support and a potential sign of financial instability, rather than strictly beneficial or strictly harmful. Transfers from abroad can help households manage financial needs, but Nepal’s comparatively high dependence also raises questions regarding domestic employment and sustainability of growth driven by money sent from overseas. The RStudio regression identifies a statistically significant positive association between remittances and unemployment, but not with inflation or GDP growth. The most reasonable conclusion is that remittances may stabilize household finances while leaving broader economic challenges unresolved. 

Notes

1. World Bank, “Nepal,” World Bank Open Data, accessed October 25, 2026, https://data.worldbank.org/country/nepal ↩
2. Chandan Sapkota, “Remittances in Nepal: Boon or Bane?,” The Journal of Development Studies 49, no. 10 (2013): 1316–1331, https://doi.org/10.1080/00220388.2013.812196 ↩
3. Dilip Ratha, The Impact of Remittances on Economic Growth and Poverty Reduction, Policy Brief No. 8 (Washington, DC: Migration Policy Institute, 2013), https://www.migrationpolicy.org/publication/impact-remittances-economic-growth-and-poverty-reduction ↩