The Ripple Effect: Analyzing the Impact of Rupee Depreciation on Exports and Imports

The INR has been fluctuating from around ₹ 39.31 in 2008 to ₹ 72.90 in 2021. Moreover, the exchange rate has been depreciating since 2018, only witnessing a decline of 6.16% between ₹ 68.09 in 2016 to ₹ 63.76 in 2018, reaching ₹ 81.5 on Jan 2023. This trend is of concern because it can have significant effects on economic development.

The depreciation of the INRX can affect the Indian economy in several ways. It makes imports more expensive. This is because it takes more Rupees to buy the same amount of foreign goods or services. Since India is a net importer of goods and services, a weaker Rupee would lead to higher import costs, which could lead to an increase in inflation. This could lead to a rise in the cost of living for Indian consumers.

Moreover, a weaker INRX could affect the Indian economy by affecting the country’s trade balance. India is a country that exports goods and services to other countries. A weaker Rupee would make Indian exports cheaper for foreign buyers, which would lead to an increase in exports. However, a stronger US Dollar would also mean that India would have to pay more for imports, which would offset the gains from exports. If this imbalance becomes too significant, it could lead to a trade deficit, which would be detrimental to the Indian economy.

Additionally, the depreciation of the INRX could also lead to an outflow of capital from India. If foreign investors feel that the Rupee will continue to weaken, they may be less likely to invest in India, which could lead to a decrease in foreign investment. This could affect the Indian economy by reducing the amount of capital available for investment in the country. This could result in a slower rate of economic growth.

Furthermore, The depreciation of the INRX can have significant effects on economic development in India. It can lead to higher import costs, a trade imbalance, and a reduction in foreign investment. Therefore, it is important for policymakers in India to monitor the exchange rate and take steps to mitigate the effects of a weaker Rupee. This could involve implementing policies to increase exports, reduce import costs, and increase foreign investment in the country.

The Indian government has been grappling with the challenge of boosting exports and bringing down the exchange rate to improve the country’s economic growth. However, a recent study has found that the government’s efforts to increase exports are not having a significant impact on the economy as imports are leading to exports and not government policies.

The world bank data suggests that the Indian government needs to consider alternative measures to increase competitiveness in the market. The current policies are not sufficient to improve the economic conditions, and the increasing imports could be detrimental to the economy in the long run.

The Indian economy heavily relies on exports, which generate revenue and provide jobs for millions of people. However, in recent years, the exchange rate has been a concern for policymakers, as it has been on an upward trend, making Indian exports more expensive and less competitive in the international market. To address this issue, the government has been implementing measures such as export incentives and currency interventions to bring down the exchange rate.

However, we found that the policies implemented by the government have not been successful in bringing down the exchange rate and increasing exports. The researchers analyzed the data from 1996 to 2021 and applied the Augmented Dickey-Fuller (ADF) test to check for the stationarity of the variables. The ADF test is a statistical method used to determine whether a time series is stationary or not.

The results of the ADF test showed that the variables were not stationary. To address this, the researchers applied differencing to make the variables stationary. After differencing, the variables became stationary, and the researchers proceeded to use the Toda-Yamamoto Granger Causality test to determine if there was a causal relationship between imports and exports.

The Toda-Yamamoto Granger Causality test showed that the Indian Rupee (INR) Granger-causes export at lag 1 with a p-value of 0.041. This means that there is a 4.1% chance that the results were due to random chance, which is below the commonly used threshold of 0.05 for statistical significance. Therefore, the Toda-Yamamoto Granger Causality test suggests that there is a statistically significant relationship between the INR and exports. However, it does not necessarily mean that INR is causing exports and that government policies aimed at boosting exports are not effective. Granger causality only identifies a statistical relationship between two variables, but it does not necessarily imply a causal relationship.

Additionally, the study found that increased imports led to an increase in exports, which contradicts the previous statement that INR Granger causes exports. If INR is causing exports, then increasing imports would not be the cause of an increase in exports.

The Indian government needs to consider alternative measures to improve the economy and make exports more competitive. For instance, the government can invest in infrastructure and reduce bureaucratic red tape to attract foreign investments. Additionally, the government can increase productivity and innovation to improve the quality of goods produced, making them more competitive in the global market.

The Indian government’s efforts to increase exports and bring down the exchange rate have not been successful. The study shows that imports are leading to exports and not government policies aimed at increasing exports. The government needs to consider alternative measures to make exports more competitive, such as investing in infrastructure, reducing bureaucratic red tape, and increasing productivity and innovation. Policymakers should consider the results of studies and statistical methods such as the Granger Causality test when deciding on policies to improve the economy.

Data Used

Test Results

ADF Statistic: -1.523
p-value: 0.522
Critical Values:
1%: -4.332
5%: -3.233
10%: -2.749
Series is not stationary
Applying differencing…
ADF Statistic: -4.199
p-value: 0.001
Critical Values:
1%: -3.924
5%: -3.068
10%: -2.674
Series is stationary

ADF Statistic: -1.794
p-value: 0.384
Critical Values:
1%: -4.332
5%: -3.233
10%: -2.749
Series is not stationary
Applying differencing…
ADF Statistic: -2.979
p-value: 0.037
Critical Values:
1%: -3.964
5%: -3.085
10%: -2.682
Series is stationary

ADF Statistic: -1.898
p-value: 0.333
Critical Values:
1%: -4.332
5%: -3.233
10%: -2.749
Series is not stationary
Applying differencing…
ADF Statistic: -6.143
p-value: 0.000
Critical Values:
1%: -3.924
5%: -3.068
10%: -2.674
Series is stationary

Lag 1 is statistically significant. p-value = 0.0248
Lag 2 is statistically significant. p-value = 0.0473
INRX Granger-causes Export at lag 1, p-value=0.041