Why is the afghani currency appreciating?

A friend of mine recently shared a screenshot on our friends’ whatsapp group the other day – 1 Afghani = 1.10 INR, it declared. Another friend explained that only a few months back, 1 Afghani (the official currency of Afghanistan) was worth less than 1 Indian Rupee. Another friend also shared a link of a news article saying that Afghani was one of the best performing currencies in the world in recent times. This triggered a patriotic debate of how the Indian Rupee is depreciating against the dollar, while the currency of a troubled nation is performing better than it. Some of my friends argued that this was a failure of India’s ruling party, while others argued passionately to oppose them. But the central question remained unanswered: does this mean the Taliban is ruling Afghanistan better than the governments of other countries?

As a student of Economics, I wrote a long message explaining the phenomenon in the group, which clarified the doubts of many of my friends. In this article, I write the long form of that message, backed by some data points. 

To start with, I must say that this is a classic case to show how appreciation in the value of a currency is not a direct indicator of an improving economic situation in that country, or better political management. But then why is the Afghani appreciating?

Let’s start with the basics of demand and supply: value or price of anything is determined by the market forces of demand and supply. Anything that sees high demand and low supply is sold at high price, just like mangoes before their season begins.

In line with this logic, appreciation of afghani points to the fact that it is in high demand! Before you conclude that this obviously means economic growth in Afghanistan, let us look at the reasons for this high demand.

You might remember the dramatic videos from the airport of Kabul when the US withdrew its troops from Afghanistan. Living conditions in Afghanistan, which were already bad, worsened rapidly from this point. The Taliban took over the government of the country in the days that followed, which led to an economic collapse as the GDP of the Economy fell by an estimated 20%. According to the UN, 9 out of 10 locals live in poverty even today and 1 out of every 2 locals are dependent on foreign aid for their livelihoods. More than 70% live without access to electricity, leading to numerous deaths during the winter.

In response to this crisis, billions of dollars were donated from around the world and sent to Afghanistan as aid for the local population. In 2023, the United Nations Organisation (UN) alone plans to provide aid worth $3.2 billion to Afghanistan, which is more than 20% of Afghanistan’s entire economy (considering last reliable GDP estimate of 2021 – which has since declined)! Additionally, other organisations and countries have pledged similar amounts of aid to the troubled nation. Part of this aid comes in non-monetary form – as clothes, food grains, supplies, etc., but a considerable amount of it is cash or fund transfer.

The Taliban government has banned using foreign currencies for domestic transactions since November 2021, just like any other national government. This means that all dollars and euros received in international aid must be converted into Afghani first. All the foreign currencies received as aid are sold, and Afghanis are bought in exchange – and thus arises a large part of the high demand for Afghanis. Keeping in line with the basics of demand and supply, as far as the Afghanistan Government restricts supply of the Afghani by printing them in limited quantities, appreciation in its value has been only natural.

Secondly, the present hulla-balloo around appreciation of Afghani is ignorant of its steep fall in 2021 – to the lows of 0.71 per Indian rupee in January 2022. And if one extends their horizon and looks back to the exchange rates of 2013-2014, what does one find? That 1 afghani back then was more or less equivalent to 1.10 INR. This indicates that the Afghani is simply returning to its normal value of the past, after a massive shock of military, political, and economic upheaval.

Now that we have understood the big picture of appreciation of Afghani, I can’t resist sharing a simplistic projection of what the future might be.

If Afghanistan starts to do better in all aspects, we can expect the international aid to reduce considerably. In such a scenario, the cycle that we have just discussed, reverses. Then, the demand for Afghani might fall, and the currency might lose its value from its current highs. Does that mean political stability and Economic development is bad for the value of Afghani? Well, not really. A depreciating currency has its own benefits and it can be a topic of another article. Besides, it is estimated that Afghanistan has $1 trillion worth of deposits of valuable minerals and commodities like gold, copper, precious and semi-precious stones, and lithium. The nation might arise as a successful exporter of these minerals, which can support the value of the Afghani in future.

In conclusion, all of these are just possibilities. We have hardly scraped the surface of currency exchange rates, and I have deliberately not included many other relevant aspects to keep this article simple for the layperson.

To make judgement of the entire picture based on this analysis would be like predicting the depths of the ocean by dipping your feet at the beach. However, I hope you found it just as fascinating as the latter!

Data Science and Analytics Projects Portfolio

Classifying states based on Indian Youth Tobacco Survey

In this notebook, my analysis shows that north-eastern states have high proportion of tobacco users among students. Also, these states witness little effect of any promotional efforts to encourage or discourage the consumption of tobacco. This is done using K-means classifier and EDA. What are the reasons for this phenomenon? Check my python notebook to know more!

Access the kaggle notebook here: https://shorturl.at/gpwG3

Customer Churn EDA & Prediction using RandomForestClassifier and Recall

Accuracy is not always the best metric to evaluate a model. On a case to case basis, we also need to focus on Recall, precision, and specificity. Also, sometimes prediction model might be unusable but simple EDA can generate crucial insights. In this project, I look at a dataset of telecom users and their churn and derive important actionable insight. Further, I try to set up and refine a model for predicting whether a customer of a telecom service provider will switch to their competition or not, using RandomForestClassifier and Recall as the optimization metric.

Access the ipynb file here: https://shorturl.at/FUVZ1

Credit Card Fraud detection – with SMOTE

Here, I use a credit cards transaction dataset and try various supervised ML algorithms on it to predict fraudulence of transactions. I have used SMOTE technique to cure the imbalance in the target variable.

https://shorturl.at/qrM89

Silicon Valley Bank Crisis, Government intervention, and Public Sentiments

This notebook uses NLP and AI to assess impact of government intervention on public perceptions during the Silicon Valley Bank Crisis in USA using 279,000+ tweets. My analysis shows that there is statistically significant improvement in sentiments after the government policy interventions.

https://shorturl.at/dfL78

Online Retail Store analysis – Customer Segmentation – Kmeans

Here, I analyze the orders data of online retail store with the objective of customer segmentation using Kmeans Unsupervised Algorithm.

https://shorturl.at/ACKXZ

HR Analytics and attrition using EDA, Random Forests and GridSearchCV

This project analyses an HR dataset to detect factors that contribute to churn and attrition of employees.

Later in the same project file, I also try to predict whether an employee will leave the employer or not, using a random forest model and perform hyperparameter tuning on the same.

https://shorturl.at/wzINQ

Courier Charges Reconciliation

Here, I analyze the data of an online retail business (titled X) and its courier partner, to reconcile the invoice data from the courier partner and check for mistakes in it. By the end, I create order wise report of difference in charges billed by courier partner and the correct charges that should be charged. I also generate a summary of how much the company has been overcharged or undercharged by the courier partner.

This helps the company X to make sure that its courier partner is not overcharging it for its services.

Link to the python notebook: https://shorturl.at/mrzAT