📈 economics

Dynamic inter-relationships among tourism, exchange rate, financial development and economic growth in India

This paper analyzes the dynamic interrelationships between tourism, exchange rates, financial development, and economic growth in India from 1994–95 to 2021–2022, revealing long-run cointegration and a short-term bidirectional causality between tourism and growth that necessitates independent policy interventions for sustainable development.

Original authors: Suraj Sharma, Pooja Singh

Published 2026-07-13

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Suraj Sharma, Pooja Singh

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine India's economy as a giant, bustling rollercoaster. For decades, economists have been trying to figure out what makes the cart go faster: the thrill of the ride (tourism), the strength of the track (financial development), or the wind in the sails (exchange rates). A new study by Dr. Suraj Sharma and Pooja Singh takes a deep dive into this ride, looking at data from 1994-95 to 2021-22 to see how these forces interact.

Here is the story of what they found, told without the heavy math.

The Big Discovery: A Long-Term Dance

First, the researchers checked if these four variables—tourism, money supply, exchange rates, and GDP growth—were even dancing to the same beat. Using a statistical tool called the ARDL model (think of it as a super-precise dance instructor), they found that yes, they are all linked in the long run. They move together over time.

However, when they looked at who is actually leading the dance, the results were a bit surprising.

The "Short-Term Spark" vs. The "Long-Term Engine"

Many people believe that tourism is the magic wand that makes a country rich forever. This study suggests that for India, tourism is more like a sparkler than a bonfire.

The Short-Term Magic: In the short run, tourism is a fantastic booster. The data shows that when tourism receipts go up, the economy gets a massive, immediate jolt. Specifically, a 1 percentage point increase in tourism (as a share of real GDP) leads to a 12.87 percentage point increase in the economic growth rate. That is a huge, instant pop!
The Long-Term Reality Check: But here is the twist: that sparkler fizzles out. When the researchers looked at the long run, tourism's effect on economic growth turned out to be statistically insignificant. It's not that tourism is bad; it's just that it can't carry the whole economy on its own for the long haul. The paper explicitly rules out the idea that tourism alone is a sustainable, long-term growth engine for India without help from other factors.

The Wind in the Sails: Exchange Rates

While tourism was the short-term spark, the Real Effective Exchange Rate (REER) was the steady wind in the sails. The study found a positive, significant link between the exchange rate and economic growth in the long run.

In fact, the data suggests that a 1 percentage point increase in the annual change of the REER leads to a 0.15 percentage point increase in real GDP growth. This challenges the old-school idea that a stronger currency (appreciation) always hurts a country by making exports expensive. Instead, in India's case, a stronger currency seems to signal a healthy, productive economy that can handle it.

The Money Machine: Financial Development

What about the financial system (represented by "broad money" or M3)? The study found that while more money in the system seems to help growth, the link wasn't strong enough to be statistically significant in the long run. It's like having a full gas tank, but the car still needs a good driver (stable policies) and a clear road (tourism and exchange rates) to really zoom.

The "Who Starts It?" Question

The researchers also asked: "Who is chasing whom?"

Tourism and Growth: They found a two-way street (bidirectional causality) in the short run. Tourism boosts the economy, and a growing economy boosts tourism. They feed off each other like two dogs chasing their own tails, but only for a little while.
The Fade: When they used "Impulse Response" functions (which act like a shockwave test), they saw that when a shock hits the tourism sector, the economy jumps up happily. But this happiness doesn't last forever. The effect starts high, then slowly declines and stabilizes after about fifteen periods. It's a wave that crashes and then settles down.

What This Means for the Future

The authors are careful not to call this a "solved problem." They suggest that while tourism is a great tool for a quick economic boost, it isn't a magic bullet for the future.

To keep India's economy growing, the study argues you can't just rely on tourists. You need a three-legged stool:

Tourism to give the short-term kick.
Stable Exchange Rates to keep the long-term engine running smoothly.
A Strong Financial System to support the whole structure.

If you try to build the economy on just one leg (like tourism alone), the stool will wobble. The paper suggests that for India to keep growing, policymakers need to balance all three, ensuring that the money flows, the currency stays stable, and the tourists keep coming, but with the understanding that tourism alone won't carry the load forever.

In short: Tourism is the fun, fast start, but the exchange rate and financial system are the brakes and the engine that keep the ride going for the long distance.

Technical Summary: Dynamic Inter-relationships Among Tourism, Exchange Rate, Financial Development, and Economic Growth in India

Problem Statement and Research Gap
This study investigates the dynamic interrelationships between international tourism receipts, real effective exchange rates (REER), financial development (proxied by broad money supply), and economic growth in India. While existing literature extensively documents the tourism-led growth hypothesis, there is a notable scarcity of empirical research focusing on the simultaneous interplay of these four specific macroeconomic variables within the Indian context, particularly using recent data. Most prior studies rely on linear models or focus on developed economies, often overlooking the bidirectional complexities and the specific structural dynamics of emerging economies like India. Furthermore, previous Indian studies often utilize data ending prior to 2014 or rely solely on Granger causality and Johansen cointegration tests, lacking advanced dynamic analysis techniques such as impulse response functions and variance decomposition. This research aims to fill these gaps by analyzing data from 1994-95 to 2021-22, a period encompassing significant economic shifts and the recent pandemic-induced disruptions.

Methodology
The study employs a quantitative time-series approach utilizing the Autoregressive Distributed Lag (ARDL) bounds testing framework developed by Pesaran et al. (2001). This method was selected to accommodate variables with mixed orders of integration (I(0) and I(1)).

Data Sources: Data were sourced from the Reserve Bank of India (RBI) database and the Ministry of Tourism, Government of India. The dataset covers the period 1994-95 to 2021-22 (27 years).
Variables:
- Dependent Variable ( $Y_t$ ): Real GDP growth rate.
- Independent Variables: Tourism receipts as a percentage of real GDP ( $T_t$ ), annual percentage change in Real Effective Exchange Rate ( $R_t$ ), and annual percentage change in Broad Money ( $F_t$ ) representing financial development.
Analytical Steps:
1. Stationarity Checks: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests confirmed that $T_t$ and $F_t$ are integrated of order 1 (I(1)), while $Y_t$ and $R_t$ are stationary at levels (I(0)).
2. Cointegration: The ARDL bounds test was used to determine the existence of a long-run relationship.
3. Estimation: Long-run and short-run coefficients were estimated using the ARDL model. An Error Correction Model (ECM) was employed to analyze short-run dynamics and the speed of adjustment to long-run equilibrium.
4. Causality: The Toda-Yamamoto (1995) augmented VAR procedure and Block Exogeneity Wald tests were utilized to determine the direction of causality.
5. Dynamic Analysis: Impulse Response Functions (IRF) and Variance Decomposition Analysis (VDA) were conducted to assess the persistence of shocks and the relative contribution of each variable to economic growth fluctuations.
6. Diagnostic Testing: The model validity was confirmed through serial correlation (Breusch-Godfrey LM), heteroskedasticity (White test), normality (Jarque-Bera), and stability tests (CUSUM and CUSUMsq).

Key Results

Cointegration: The ARDL bounds test yielded an F-statistic of 26.590, which exceeds the upper critical bound at the 1% significance level, confirming the existence of a long-run cointegrating relationship among tourism, exchange rates, financial development, and economic growth.
Long-Run Dynamics:
- Tourism: The long-run coefficient for tourism is negative and statistically insignificant, suggesting that tourism does not drive long-term economic growth in India within this sample period.
- Exchange Rate: The REER shows a positive and statistically significant long-run relationship with economic growth (coefficient 0.15), indicating that currency appreciation is associated with growth, possibly reflecting strong macroeconomic fundamentals.
- Financial Development: The coefficient for financial development is positive but statistically insignificant in the long run.
Short-Run Dynamics:
- Tourism: Tourism has a strong, statistically significant positive impact on economic growth in the short run. A 1-unit increase in tourism receipts leads to a 12.87-unit increase in GDP growth.
- Adjustment Speed: The Error Correction Term (ECT) is negative (-1.215) and significant, indicating a rapid adjustment to long-run equilibrium, though the magnitude suggests potential overshooting.
Causality:
- Bidirectional: There is bidirectional causality between tourism and economic growth in the short run, as well as between tourism and exchange rates.
- Unidirectional: Causality runs unidirectionally from tourism to financial development.
Shock Persistence (IRF & VDA):
- Impulse Response: A positive shock to tourism initially boosts economic growth, but this effect diminishes over time, stabilizing near zero after approximately fifteen periods. This contrasts with studies suggesting persistent long-term effects.
- Variance Decomposition: While GDP growth is primarily explained by its own shocks initially, the contributions of tourism, financial development, and exchange rates show a steady upward trend, stabilizing after 3–7 periods.

Key Contributions

Temporal Scope: The study provides the most recent empirical analysis of these variables in India, extending the data range to 2021-22, thereby capturing the post-pandemic economic landscape.
Methodological Rigor: Unlike many prior studies that rely solely on causality tests, this paper integrates ARDL bounds testing with Toda-Yamamoto causality, Impulse Response Functions, and Variance Decomposition to offer a comprehensive view of both short-term dynamics and long-term structural relationships.
Multivariate Framework: The research uniquely combines tourism, exchange rates, and financial development in a single multivariate model for India, addressing the interdependent nature of these macroeconomic factors rather than isolating them.

Significance and Policy Implications
The paper concludes that while tourism acts as a potent engine for short-term economic stimulation in India, it is not a standalone driver of long-term growth. The findings suggest that the sustainability of tourism-led growth is contingent upon the stability of the exchange rate and the robustness of the financial system.

The authors posit that the independence of these factors is crucial for effective policymaking. Specifically:

Short-Term vs. Long-Term: Policymakers should recognize tourism as a short-term growth booster but must rely on sound exchange rate policies and a well-functioning financial system to sustain long-term economic development.
Strategic Focus: To maximize the sector's potential, India must focus on improving tourism infrastructure, enhancing financial intermediation, and maintaining macroeconomic stability.
Interdependence: The bidirectional relationship between tourism and the broader economy implies that growth-oriented policies in tourism can create macroeconomic feedback loops, provided they are supported by stable currency and financial conditions.

The study acknowledges limitations, including the potential structural breaks caused by the COVID-19 pandemic and the use of aggregate national data which may obscure regional variations. Nevertheless, the results offer a nuanced understanding of the Indian economic landscape, challenging the notion of a purely tourism-led growth model in favor of a more integrated, macroeconomically supported approach.