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Is tourism pro-poor in India? An empirical investigation using ARDL approach
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DOI: 10.1108/JEAS-02-2021-0031
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Is tourism pro-poor in India? An
empirical investigation using
ARDL approach
Is tourism propoor in India?
Manu Sharma, Geetilaxmi Mohapatra and Arun Kumar Giri
Department of Economics and Finance, BITS Pilani, Pilani, India
Abstract
Received 16 February 2021
Revised 15 March 2021
Accepted 15 March 2021
Purpose – The purpose of this paper is to examine the relationship between tourism sector development and
poverty reduction in India using annual data from 1970 to 2018. The paper attempts to answer the critical
question: Is tourism pro-poor in India?
Design/methodology/approach – Stationarity properties of the series are checked by using the ADF unit
root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to cointegration
to examine the existence of long-run relationships; error-correction mechanism for the short-run dynamics, and
Granger non-causality test to test the direction of causality.
Findings – The cointegration test confirms a long-run relationship between tourism development and poverty
reduction for India. The ARDL test results suggest that tourism development and economic growth reduces
poverty in both the long run and the short run. Furthermore, inflation had a negative and significant short-run
impact on the poverty reduction variable. The causality test confirms that there is a positive and unidirectional
causality running from tourism development to poverty reduction confirming that tourism development is propoor in India.
Research limitations/implications – This study implies that poverty in India can be reduced by tourism
sector growth and price stability. For a fast-growing economy with respect to economic growth and tourism
sector growth, this may have far-reaching implications toward inclusive growth in India.
Originality/value – This paper is the first of its kind to empirically examine the causal relationship between
tourism sector development and poverty reduction in India using modern econometric techniques.
Keywords Tourism development, Autoregressive distributed lag (ARDL), Poverty reduction, India
Paper type Research paper
1. Introduction
Tourism has become the world’s fourth-largest export industry after fuels, chemicals, and
food (Tugcu, 2014; Balli et al., 2015). Specifically, tourism accounts for 6 percent of the world’s
total merchandise and service exports that represent 30 percent of international trade in
services in the year 2014. Besides, 9.8 percent of the world’s entire gross domestic product
(GDP) originates in the tourism sector during the same period. The influence of inbound
tourism on national economies is becoming increasingly important because of the growing
size of the tourist market (Ohlan, 2017).
The type of relationship between tourism development and economic growth has received
emphasis since the last decade (Balaguer and Cantavella-Jorda, 2002). There is also a
substantial number of studies that provide support to the tourism-led growth for many
regions and countries (Hye and Khan, 2013; Brida et al., 2016; Ohlan, 2017; Tang et al., 2016).
The tourism sector enhances foreign exchange earnings through the trade of commodities
and import of capital goods, required services, and manufacturing segments of an economy
(Durbarry, 2004). The sector is also viewed as a driver for employment generations and,
therefore, can contribute to augment the aggregate demand leading to increased income in an
economy (Khalil et al., 2007; Nowak et al., 2007).
Recently, few researchers have supported the argument that governments in developing
countries should support and promote tourism sector development for enhancing sustainable
JEL Classification — C32, L83, O11
Journal of Economic and
Administrative Sciences
© Emerald Publishing Limited
1026-4116
DOI 10.1108/JEAS-02-2021-0031
JEAS
economic growth and human development. It is argued that this sector has high multiplier
effects in terms of employment generation, increasing foreign exchange earnings which is
further leading to a positive impact on the balance of payments of the country, stimulating the
supply sectors of tourism, all these activities helping to alleviate poverty (Croes and Vanegas,
2008; Zhao and Ritchie, 2007).
Moreover, the relationships between tourism sector development and poverty alleviation
are not confirmed. Further, it cannot be ascertained that the growth of the economy spurred
by the tourism sector reaches the poor. There are conflicting pieces of evidence on the
relationship between tourism sector development and poverty reduction. Different
researchers analyzed the direct and indirect channels through which tourism sector
development can have an impact on poverty. Blake et al. (2008) and International Trade
Centre (2009) are of the view that there are four channels through which tourism influences
the poverty level; income channel, tax channel, price channel, and risk channels. Firstly, poor
households earn their income through direct or indirect participation in the tourism sector.
Secondly, the promotion of tourism also contributes to the tax base of the government by
generating revenue, and individuals to further to improve the social infrastructure uses this.
Thirdly, as there is an expansion of the tourism sector, the demand for goods and services
that the tourists use increases and, as a result, the prices of those goods may rise. The impact
of the price channel on the poor will depend on the amount of tourism-related goods and
services among the goods and services purchased by the poor. Finally, the fourth channel
relates to risks and other long-term dynamic influences. The dynamic impact of tourism on
local economic development can be positive (e.g. biodiversity conservation measures;
allocation of funds for natural, cultural, and historical resources) and negative (e.g.
destruction of environmental resources, pollution of air, water, noise). Thus, Blake et al. (2008)
state that the relative efficacy of tourism in poverty reduction depends on the participation of
the poor in the tourism sector.
While other groups of researchers support the pro-poor tourism (PPT) approach, pro-poor
tourism is an approach where poor people are a priority (Ashley et al., 2001). Taking the poor
at the core, the approach advocates opening up new vistas of opportunities by greater
participation of poor in the tourism activities (Ashley et al., 2001). Increasing tourism
activities may take a toll on the poor in various ways, for instance, displacement. The rising
tourism sector may spoil privacy and force them to leave. Therefore, forcing them to migrate
to other places (Roe et al., 2001). The PPT approach highlights the vital areas where tourism
can play a massive role in helping the poor. However, the pro-poor tourism approach (PPT)
attempts to address the negatives of the tourism industry and argues for greater participation
of the poor in economic activities. To minimize the negative impact on the poor. The approach
does not limit itself to economic perspectives but expands itself to the social and cultural wellbeing of the poor as well (Ashley et al., 2001).
Some researchers also support the argument that tourism promotes job creation and
having the linkage with other sectors, it can play an essential role in employing the poor, thus
having a pro-poor link (Croes and Rivera, 2015). While some others argue that tourism can
make the price rise for non-tradable goods. If the poor’s consumption basket consists of those
goods, it negatively affects them (Blake et al., 2008). Some argue that increased tourism
activities in the nation raise the exchange rate (exchange rate appreciation), thus making the
exports of the economy incompatible in the market and thus reducing the exports of the
economy. If poor people are engaged in other export sectors, the exchange rate appreciation
may hurt them (Hazari and Nowak, 2003).
Wattanakuljarus and Coxhead (2008) argue that an increase in the tourism sector can be
successful in reducing poverty if it is labor-intensive. Moreover, the tax revenue generated
from the tourism sector can be used for development purposes, like improving social
infrastructure such as health and education facilities (Croes and Rivera, 2016).
Hence, now the focus has shifted from the relationship between tourism sector
development and economic growth to more dynamic aspects of tourism sector
development relating it to poverty reduction of the individuals. Now the researchers are
exploring deeper questions like whether tourism sector development reduces poverty,
whether promoting the tourism sector leads to more equitable distribution of income, and so
on (Alam and Paramati, 2016; Kozic, 2019; Mahadevan and Suardi, 2019).
Is tourism propoor in India?
2. Tourism sector development and poverty situation in India
Before 2000, the numbers of tourist arrivals and earnings have remained almost stagnant,
mainly because other countries have moved much faster than it and taken away the market
share. The average annual growth rate of tourism in India has been reported as 4.13 percent
compared to the total average of 4.56 percent between 1980 and 1997. However, India
remained a predominant nation within South Asia, which accounted for about 45 percent
share in the South Asia Region (UNWTO, 2005).
Realizing the importance of the tourism sector in employment generation and in
accelerating economic growth, the Indian government has made significant changes in its
international tourist visa policy. India is making substantial improvements in infrastructure
and making luxury taxes on par with international practices (Ministry of Tourism and
Culture, 2002).
Now, globally India ranks 3rd in terms of the tourism sectors’ contribution to GDP
(UNWTO, 2018). In the total job creation, 4.2 crore jobs were created by the tourism sector in
2018 in the country. From the figure given below (Figure 1), it is identifiable found that during
the last two decades, there has been a tremendous increasing trend in foreign exchange
earnings from tourism in India.
Further, the importance of tourism sector development in the Indian economy can be
inferred from Table 1. It is visible from the table that during the last decade, the tourism
sector’s contribution is increasing in many spheres, and the tourism sector has the potential to
act as an input in the growth process in the Indian economy.
More recently, Travel and Tourism generated revenue of 247 billion US dollars for India in
2018. More importantly, India is among those countries where the growth rate of the tourism
sector outpaces the world average (WTTC, 2018). India improved its competitiveness from
40th rank to 34th from 2017 to 2018 in the Travel and Tourism Competitive Index (TTCI).
India comes in the top 35 nations in the TTCI list. However, improving the necessary
infrastructure required for boosting this sector remains a big challenge (The Travel and
Tourism Competitiveness Report, 2019).
Together with high development in the tourism sector, during the last two decades, India
has also witnessed a phenomenal growth in the reduction of poverty. Based on the estimates
of the National Sample Survey Office (NSSO), the percentage of the population lying below
TOURISM RECEIPTS
(IN RS CRORES)
Tourism receipts in India
200000
150000
100000
50000
0
2002
2004
2006
2008
2010
YEAR
Source(s): Based on authors own calculation
2012
2014
2016
2017
Figure 1.
An overview of
tourism earnings
in India
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the poverty line has reduced from 54.9 percent in 1973 to 27.5 percent in 2004 and slightly
increased in 2009–10. The number of poor people has decreased by 52.4 million during this
period from the year 2006–2016; India has lifted 271 million people out of the shackles of
poverty (UNDP 2019). India ranks 129th out of 185 countries in the Human Development
Index issued by the United Nations development program. With the rapid economic
expansion and proactive policies in the last three decades, India has seen a drastic
improvement in life expectancy, education, and health care sectors. However, around a
quarter of the world’s poor live in India, and it has to go a far way with poverty alleviation
programs to reduce poverty in India. Exploring pro-poor sectors is vital for the country
(United Nations Development programme, 2019). In this backdrop, the study takes up the
empirical question of whether tourism sector development helps in poverty eradication or not.
In the above situation, the main objective of the present study is to examine the
relationship between tourism sector development and poverty reduction in India using
annual data from 1970 to 2018. The paper attempts to answer the critical question; what the
impact of tourism sector development on poverty in India? The current study uses the
autoregressive distributed lag (ARDL) bound testing approach to cointegration to examine
the long-run relationship, the error-correction mechanism (ECM) for testing the short-run
dynamics and the Toda-Yamamoto procedure is followed for causality analysis. The
remainder of the paper is organized as follows: Section 2 gives an overview of the tourism
sector development and poverty situation in India. Section 3 represents the literature review
on the concerned issue. Section 4 presents the variables, data, sources of data, and
methodology, while Section 5 provides the empirical results. Section 6 concludes the study.
3. Review of literature
Recently the relationship between the performance of the tourism sector and poverty is
gaining importance among scholars, international organizations, and governments alike all
over the world. The potential of the tourism sector in enhancing economic growth has been
widely recognized. There are a good number of researches taking place linking the tourism
sector to economic growth both for individual countries and panel of countries (Dogru and
Bulut, 2018; Dritsakis, 2004; Lee and Chang, 2008; Sokhanvar et al., 2018). The tourism-led
growth hypothesis postulates that tourism can be a driving force of an economy (Balaguer
and Cantavella-Jorda, 2002), and it has a positive linkage with other sectors also which makes
the impact even stronger (Brida et al., 2016).
However, its multidimensional impact via various channels in an economy on human
development, poverty, income inequality remains a very interesting aspect of the current
research, which is not confined to economic growth. Some researchers used the general
equilibrium framework, while others used Vector Autoregressive (VAR) and autoregressive
distributed lag model (ARDL) to analyze the relationship between tourism and poverty.
Concerning poverty, Blake et al. (2008), by using a general equilibrium model in Brazil,
analyzed the impact of the tourism sector on different income groups of households. Their
findings reveal that although benefits accrue to all income groups, the lowest income group
benefits the least, indicating that tourism can reduce poverty. Similarly, Saayman et al. (2012),
Contribution
Table 1.
Role of tourism sector
in Indian economy
2011
2012
In exports (US$ in bn)
16.79
17.78
In investment (US$ in bn)
30.73
33.61
In employment (in thousands)
37,136
37,555.9
In GDP (US$ in bn)
153.41
162.49
Source(s): World Travel and Tourism Council (2018)
2013
2014
2015
2016
2017
18.87
35.33
38,064
172.88
20.46
36.98
39,003
185.55
21.76
39.53
39,621
201.32
23.98
40.22
40,520
219.67
27.18
41.26
41,624
231.91
while analyzing the impact of tourism on poverty in South Africa in general equilibrium
analysis, find that tourism receipts (a proxy for the tourism sector) can be a potent tool for
poverty alleviation in the region.
Jiang et al. (2011) examine the linkage between tourism intensity and GDP per capita in
small island developing states (SIDS) in the Asia Pacific, Caribbean, and Africa. The proxy
used for the tourism sector is tourism intensity, which is the ratio of tourist arrivals to
residents, while poverty is proxied by HDI. They found a positive correlation between
tourism intensity and GDP per capita, as well as a positive relationship between tourism
intensity and HDI. However, the authors acknowledge that correlation did not imply causal
inference and advocated the need for future studies to be based on causality analysis.
Croes and Vanegas (2008) applying VAR by using annual data from 1980 to 2004 in
Nicaragua, found that a long-run relationship exists between poverty, economic expansion,
and tourism. They used GDP as the proxy for economic expansion is GDP, the headcount
ratio the proxy for poverty, and tourism receipt is used for tourism. Their study unearths
causal linkage existing from tourism to poverty reduction and bidirectional causality existing
between economic expansion and poverty. The study is important in bringing into light the
direct linkage of tourism and poverty. However, in their study, the short-run impacts of the
variables were not considered.
In another study, Croes (2014), while examining the impact of tourism in Nicaragua and
Costa Rica, discovers that the tourism sector has varying effects depending on the different
degrees of poverty. In Nicaragua, the findings indicate that tourism is pro-poor. However, for
Costa Rica, it does not have any significant impact. It is noticeable that Nicaragua has a
higher degree of poverty in contrast to Costa Rica.
Vanegas (2014), doing an unbalanced panel study for a period of 1980–2012 in five
countries of Central America, discovers that tourism is helpful in poverty reduction. On the
other hand, Vanegas et al. (2015) examine the impacts of three different sectors, namely
tourism, agriculture, and manufacturing sector on poverty reduction in two countries;
Nicaragua and Costa Rica using the autoregressive distributed lag model (ARDL) for an
annual period of 1980–2012. The finding indicates that the tourism sector is the most
influential in reducing poverty among all the sectors for both countries. The authors use the
poverty headcount index as a proxy variable for poverty measurement and exports of all the
three sectors as regressors.
Sharpley and Naidoo (2010) find that tourism can be a short-run solution for poverty
alleviation, but in the long run, it has a limited impact on Mauritius. For the long run
strategies, policies should be around the core sectors of an economy. While Job and Paesler
(2013) in Wasini island unearth that the economic impact of the tourism sector is pro-poor, and
it helps to improve the living standard of the people engaged in tourism-oriented business.
Mahadevan et al. (2017), using a dynamic computable general equilibrium (DCGE) model
for Indonesia, find that both inbound tourism and domestic tourism are significant in
reducing poverty. However, the authors find that tourism is worsening income inequality in
both rural and urban regions. The higher the impact of tourism on poverty reduction, the
worse gets income inequality.
Similarly, Njoya and Seetaram (2018) also used a computable general equilibrium (CGE)
model to study the impact of tourism on the Kenyan economy. The findings indicate that
tourism is efficient in reducing poverty both in urban and rural areas. However, it has a
greater impact in urban areas. The authors conclude that tourism can be used as an efficient
tool for poverty reduction.
While examining a panel of top 43 tourist countries (based on tourist arrivals) covering a
period of 1995–2012, Raza and Shah (2017) discover that the tourism sector can help to reduce
income inequality. Thus, promoting tourism can bring development that is more inclusive for
an economy.
Is tourism propoor in India?
JEAS
Llorca-Rodrıguez et al. (2020), analyzing the relative efficacy of inbound and domestic
tourism in tackling poverty using the generalized method of moments (GMM) for a panel of 60
countries from 1995 to 2014, discover that both types of tourism are effective in reducing
poverty. However, domestic tourism is more efficient due to its stronger pro-poor backward
linkages.
After exploring the available literature, we conclude that the results are not general. Also,
the findings vary across the panel and country-specific studies. With this backdrop, the
present study proposes to analyze the tourism-poverty nexus in India. By doing so, the
present study will add a new country-specific study to the existing studies on the empirical
relationship between the tourism sector development and poverty reduction.
4. Data and methodology
4.1 Data Source and variable identification
The study uses annual time series data starting from 1970 to 2018. The data collected from
various national and international sources, including world development indicators
published by World Bank, World Travel and Tourism Council (WTTC), Ministry of
Tourism (India), Handbook of Indian economy published by Reserve bank of India (RBI).
4.1.1 Tourism sector development. For tourism sector development, there are two ways to
identify the growth of the inbound tourism sector. First is a monetary way, the earning
accruing from the tourism sector to the government, i.e. tourism receipt (TR) for a country
(Balaguer and Cantavella-Jorda, 2002; Ohlan, 2017; Shahbaz et al., 2017) and the second way is
a physical measure that is, number of tourist arrivals (TA) in a country (Kumar et al., 2019;
Turan Katircio
glu, 2010). The study uses both the indicators for two different model
specifications.
4.1.2 Poverty. Measuring the poverty level is a complex issue. According to the World
Bank, the definition of poverty is “the inability to attain a minimal standard of living”
measured in terms of basic consumption needs (World Bank, 1990). Simply stated that a
person is poor if it is unable to meet the fundamental requirements of life. In line with it, the
study uses per capita consumption expenditure as a proxy for poverty reduction (PR) because
consumption expenditure among the poor is usually more reliably reported and more stable
than income (Datt and Ravallion, 1992; Sehrawat and Giri, 2016, 2018).
4.1.3 Control variables. Inflation measured by consumer price index (CPI), with base year
2010 5 100). Inflation reduces the purchasing power of money. The poor who has limited
means of living are adversely affected by it (Romer and Romer, 1998). And, the economic
growth is measured by real GDP per capita (Kumar et al., 2019; Shahbaz et al., 2017).
4.2 Bound test approach
For checking the cointegration among the variables, the autoregressive distributed lag (ARDL)
model is employed. Unlike the traditional cointegration approaches, the ARDL approach does
not require the series to be stationary of the same order. It works well in the mixed order of
integration, i.e. I(0) and I(1). However, one limitation of the ARDL approach is that it cannot be
applied when the series is of I(2) order. If the series is of I(2) order, F-statistic gives misleading
results that nullify the entire estimation (Pesaran et al., 2001). To take care of the problem,
Augmented Dickey and Fuller (1979) unit root test is employed. Hence, it must be ascertained
that no series is of order I(2). Once the order of integration is identified, the bound test is done. If
the value of the calculated F- statistic comes outside the bound, the result can be drawn.
However, if the calculated value falls between the upper and lower bound, no conclusion
regarding cointegration can be reached. For the acceptance of the cointegration, the calculated
F-value must be higher than the upper bound value given by Pesaran et al. (2001).
Now the equation be transformed into ARDL formatΔlnPRt ¼β þ β0 þ β1 lnPRt−1 þ β2 lnTRt−1 þ β3 lnPGDPt−1 þ β4 CPIt−1
p
m
n
X
X
X
þ
δ1i ΔlnPRt−i þ
δ2i ΔlnTRt−i þ
δ3i ΔlnPGDPt−i
i¼1
þ
i¼0
i¼0
q
X
Is tourism propoor in India?
(1)
δ4i ΔCPIt−i þ mt
i¼0
ΔlnTRt ¼ β þ β0 þ β1 lnPRt−1 þ β2 lnTRt−1 þ β3 lnPGDPt−1 þ β4 CPIt−1
p
m
n
X
X
X
þ
δ1i ΔlnPRt−i þ
δ2i ΔlnTRt−i þ
δ3i ΔlnGDPIt−i
i¼0
þ
i¼0
i¼1
q
X
(2)
δ4i ΔCPIt−i þ mt
i¼0
ΔlnPRt ¼ β þ β0 þ β1 lnPRt−1 þ β2 lnTAt−1 þ β3 lnPGDPt−1 þ β4 CPIt−1
p
m
n
X
X
X
þ
δ1i ΔlnPRt−i þ
δ2i ΔlnTAt−i þ
δ3i ΔlnPGDPt−i
i¼1
þ
q
X
i¼0
i¼0
(3)
δ4i ΔCPIt−i þ mt
i¼0
ΔlnTAt ¼ β þ β0 þ β1 lnPRt−1 þ β2 lnTAt−1 þ β3 lnPGDPt−1 þ β4 CPIt−1
þ
m
X
δ1i ΔlnPRt−i þ
i¼0
þ
q
X
n
X
δ2i ΔlnTAt−i þ
p
X
δ3i ΔlnPGDPt−i
i¼0
i¼1
δ4i ΔlnCPIt−i þ mt
(4)
i¼0
where Δ indicates the different form of the variable. β; β0 stand for trend; ln implies that the
variables have been transformed in natural logs and intercept β1 ; β2, β3, β4 are the long-run
coefficients and δ1i, δ2i , δ3i, δ4i are the short-run coefficients, and μt is a random error. The lags
of the series are denoted by m, n, p, q.
After confirming the bound test with the ARDL approach, the unrestricted error correction
model will be run for finding the short-run dynamics. ECM form can be written as here:
p
m
n
X
X
X
ΔlnPRt ¼ α þ α0 þ
A1i ΔlnPRt−i þ
A2i ΔlnTRt−i þ
A3i ΔlnPGDPt−i
i¼1
þ
p
X
i¼0
i¼0
A4i ΔCPIt−i þ ΦECTt−1 þ νt
(5)
i¼0
ΔlnPRt ¼ α þ α0 þ
m
X
i¼1
þ
q
X
i¼0
A1i ΔlnPRt−i þ
n
X
A2i ΔlnTAt−i þ
i¼0
A4i ΔCPIt−i þ ΦECTt−1 þ νt
p
X
A3i ΔlnPGDPt−i
i¼0
(6)
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where the ECT stands for error correction term, and Φ shows the speed of adjustment with
which equilibrium is achieved. α, α0 are the intercept, trend respectively and A1i, A2i; A3i, A4i are
the short-run coefficients and m, n, p, q are the lags of the respective variables.
After conducting ECM, various tests are carried out to find the robustness of the model,
and cumulative sum (CUSUM) and cumulative sum of square (CUSUMSQ) are employed to
check the stability of the model (Brown et al., 1975).
4.3 Causality analysis
After the bound test estimation, to give further support to the above cointegration analysis,
Toda and Yamamoto (1995) procedure of causality is followed. The advantage of the
procedure over the conventional Granger causality test is that it can be applied irrespective of
the order of the integration of the series, i.e. I(0), I(1), or I(2). The VAR formats for the variables
are listed in equations (7)–(10).
lnPRt ¼ δ0 þ
k
X
dmax
X
δ1i lnPRt−i þ
i¼1
þ
dmax
X
δ2j lnPRt−j þ
j¼kþ1
k
X
π 1i lnTRt−i
i¼1
(7)
π 2j lnTRt−j þ εi
j¼kþ1
lnTRt ¼ δ0 þ
k
X
dX
max
δ1i lnTRt−i þ
i¼1
þ
dX
max
δ2j lnTRt−j þ
j¼kþ1
k
X
π 1i lnPRt−i
i¼1
(8)
π 2j lnPRt−j þ εi
j¼kþ1
lnPRt ¼ δ0 þ
k
X
i¼1
þ
dX
max
dmax
X
δ1i lnPRt−i þ
δ2j lnPRt−j þ
j¼kþ1
k
X
π 1i lnTAt−i
i¼1
(9)
π 2j lnTAt−j þ εi
j¼kþ1
lnTAt ¼ δ0 þ
k
X
δ1i lnTAt−i þ
i¼1
þ
dmax
X
dmax
X
j¼kþ1
δ2j lnTAt−j þ
k
X
i¼1
π 1i lnPRt−i
(10)
π 2j lnPRt−j þ εi
j¼kþ1
Although the existence of a cointegrating relationship between the major determinants of
tourism, economic growth, inflation, and poverty suggests that there might be causality in at
least one direction, it does not indicate the direction of causality among the variables. Hence,
the causality test needs to be performed. The null hypothesis of no-causality is not accepted
when the p values fall within the conventional 1–10% of the level of significance. Hence, for
the rejection of the null hypothesis, for example in equation (7), Granger causality running
from lnTRt to lnPRt can be concluded if π 1i ≠ 0∀i. Similarly, in (9), from lnTAt to lnPRt if
π 1i ≠ 0∀i. Then we can conclude that tourism sector development has a unidirectional
causality running to poverty reduction.
5. Analysis of empirical results
The basic requirement of the ARDL model is none of the series should be integrated of order
higher than I(1). Therefore, it is important to ascertain the level of integration among the
variables. Augmented Dickey and Fuller (1979) test is applied in order to check the
stationarity. The test is applied for both cases. One is only with intercept, and the other is
intercept with the trend. We find that for case 1, all the variables are not stationary at levels.
However, it is noticed that after the first difference, the variables are stationary, i.e. I(1). There
is a slight change in the results when the trend is added. Two series, namely, lnTR and lnTA
become stationary at levels. Rests of the variables are stationary at I(1) (see Table 2).
After making sure that none of the series is of I(2) order, i.e. not integrated of order higher
than I(1), the bound test is performed. However, it is important that the lag length of the
variables is optimal in the estimation process. It is also important to ascertain that optimal lag
length selected so that error terms in equations (1)–(4) chosen, so that error terms not serially
correlated. Thus, Akaike information criteria (AIC) considered for estimating equations (1)–(4).
F-statistic for the bound test is reported in the table. It is evident that in all models, there is
cointegration among the variables. As the calculated F-statistic is greater than the upper bound
critical value at 1%, the null hypothesis for no cointegration among the variables can be rejected.
The study follows the ARDL estimation with the unrestricted constant and unrestricted
trend (Pesaran et al., 2001). The F-statistic confirms the long-run relationship among the
variables (see Table 3).
As for the focus of the study, it is crucial to confirm the cointegration in equations (1) and
(3). The result shows that for both models, the null hypothesis of no cointegration among the
variables is rejected at a 1% level of significance.
It is clear from Table 3 that the values of the calculated F-statistic for equations (1) and (3)
exceed the critical value of the upper bound level at 1% significance level. Hence, the null
hypothesis (Ho) of no cointegration is rejected for the model specification of equations (1) and
(3). Therefore, there exists a long-run relationship between poverty reduction, tourism sector
development, price level, and economic growth. Besides, the estimation results of equation (2)
and (4) reject the null hypothesis.
In what follows, next is the long-run estimates of the ARDL model. Since the focus of the
study is the role of tourism sector development in poverty reduction, therefore, model A and
model B with equations (1) and (3) respectively, analyzed for the long-run and short-run
estimates. The long-run estimates are presented in Table 4. It is evident from the results that
both the proxies of tourism sector development used are have a positive and significant
relationship with poverty reduction. The long-run elasticity represents that a 1 percent
increase in tourism receipt or tourist arrivals brings an 8 percent reduction in poverty. Our
results support the evidence provided by existing literature supporting the dynamic role of
tourism sector development in poverty alleviation in India. The result of the present study is
in line with Croes and Vanegas (2008), Jiang et al. (2011), Llorca-Rodrıguez et al. (2020),
Mahadevan et al. (2017), Saayman et al. (2012).
Series
Level
Constant without trend
First difference
lnPR
5.60
2.98**
lnPGDP
4.28
5.71*
lnTR
2.25
4.88*
lnTA
2.24
4.99*
CPI
1.34
4.98*
Note(s): *, ** indicate the level of significance at 1 and 5%
Level
0.04
1.68
3.73**
4.02**
2.67
Is tourism propoor in India?
Constant with trend
First difference
9.63*
8.33*
5.36*
4.46*
3.99**
Table 2.
Unit root test
(ADF test)
JEAS
Estimated models
Lag length
F-statistics
(Model A)
lnPR/lnTR, lnPGDP, CPI (equation 1)
lnTR/lnPR, lnPGDP, CPI (equation 2)
3,4,4,2
3,3,0,0
10.27*
7.06*
(Model B)
lnPR/lnTA, lnPGDP, CPI (equation 3)
lnTA/InPR, lnPGDP, CPI (equation 4)
4,4,1,2
3,0,0,0
8.74*
8.20*
Bounds
Table 3.
ARDL bound test
results
Level of significance
I (0)
I (1)
1%
5%
10%
Note(s): * indicate the level of significance at 1%
5.17
4.01
3.47
4.45
5.07
6.36
Independent variables
lnTR
lnTA
lnPGDP
CPI
CONSTANT
TREND
Model A (t-value)
Model B (t-value)
0.08(3.36)*
0.93(6.96)*
0.002(1.80)
0.05(0.08)
0.01(4.33)*
0.08(2.26)**
0.67(4.79)*
0.001(1.03)
0.84(1.21)
0.003(2.11)**
Diagnostics
0.9997
0.9997
R2
0.9995
0.9996
adj R2
F-statistic
6305.3(0.00)
7393.2(0.00)
Normality
1.01(0.60)
0.76(0.68)
D.W. stat
2.20
2.40
Autocorrelation
F 5 0.59(0.55)
F 5 3.35(0.07)
Table 4.
F 5 0.96(0.52)
F 5 1.58(0.13)
Long run estimates for Heteroscedasticity
Note(s): t-values are depicted in the parenthesis, and * and ** represent the level of significance at 1 and 5%
ARDL model
Further, it is noticeable from the result that GDP per capita is having positive and significant
in explaining poverty reduction at the 1% level for both models. For model A, a 1% increase
in GDP per capita reduces poverty by 9%. Similarly, for model B, a 1% increase in GDP per
capita decreases poverty by around 6%. Therefore, it can imply that economic expansion
brings about a rapid reduction in the poverty level in the country. Results align with Datt and
Ravallion (1992), Dollar and Kraay (2004), Sehrawat and Giri (2016). However, inflation, in the
long run, does not have any significant impact on poverty. It is noticeable that the trend is
negative and significant in both models. It implies that certain structural and institutional
factors are hurting poverty. However, those factors are beyond the scope of the study and can
be a good avenue for future research.
The short-run estimates are presented in Table 5. The results obtained from the short-run
estimates implied that the tourism sector fails to impact poverty reduction in the short run.
However, economic growth has a positive and significant impact on poverty in the short-run.
It again reaffirms the dynamic effect of economic expansion in poverty reduction. One
percent increase in GDP per capita brings a 6% reduction in poverty.
Regressors
Model A(t-values)
ΔlnTR
ΔlnTA
ΔlnPGDP
ΔCPI
ECTt1
0.006(0.69)
0.67(10.93) *
0.001(1.62)
6.75*
Model B(t-values)
Is tourism propoor in India?
0.004(0.84)
0.69(11.65) *
0.002(2.12) **
6.21*
Robustness indicators
0.93
0.93
R2
0.90
0.90
Adjusted- R2
D.W. stat
2.20
2.40
SE regression
0.007
0.007
RSS
0.001
0.001
F-stat
29.99(0.00)
35.85(0.00)
Note(s): values are given in parenthesis, Δ denotes the differenced series and *and **represents the
significance level at 1 and 5% respectively
Interestingly, for model B, inflation is seen to have a negative and significant impact on
poverty. Moreover, the coefficient sign is also the same for both models. The results
reflect that the rise in general prices has a detrimental effect on the poor in the short run,
and the price instability increases poverty in India. The error correction term symbolizes
the speed of adjustment once out of the equilibrium path. It takes around two years to
get back to equilibrium once the shock occurs. It implies, annually, 48% of the deviations
are corrected. The negative sign reaffirms cointegration and causal link among the
variables.
Thus, it is deciphered from the results that tourism sector development and expansion of
economic growth can be a very effective mechanism for eliminating poverty in India. It is also
found that there exists a long-run relationship for both models. However, it does not state the
direction of the causal link between poverty reduction and the tourism sector. Therefore,
what follows next is identifying the causal nexus between them.
The result of Granger non-causality for both models are given in Table 6. Finding depicts
the chi-square statistic for model A and model B. Bidirectional causality between poverty
reduction and tourist receipt is found. However, for model B, we find there is a unidirectional
causality running from tourist arrivals to poverty reduction. Nevertheless, it can be inferred
that tourism sector development is important for poverty alleviation as the causal link
depicts. The stability of the model is checked by CUSUM and CUSUMSQ tests, as suggested
by (Brown et al., 1975). Figure 2 clearly shows that our model A and model B are robust and
stable. To check the stability of the augmented VAR model used for causality analysis
inverse roots of autoregressive characteristics polynomial diagram is drawn. It is clear from
the graph that our VAR model is stable (see Figure 3).
6. Summary and conclusion
In this study, an attempt is made to test the long-run and causal relationship between tourism
development and poverty reduction in India for the period 1970 to 2018. For this purpose, the
study employs the ARDL bound testing approach to cointegration to test the long-run and
short-run relationship. Toda and Yamamoto Granger causality test is used to investigate the
causal relationship among variables. The study uses two proxy variables of tourism
development, i.e. tourism receipt, and the number of tourist arrivals. Study used per capita
consumption expenditure as a proxy for poverty reduction variable and consumer price
index and per capita gross domestic product as the control variables.
Table 5.
Short-run estimates
of ARDL
–8
–4
0
4
92
94
96
98
00
02
04
06
08
10
12
12
14
14
16
94
96
98
00
02
04
06
08
10
16
18
CUSUM
5% Significance
–0.4
90
92
92
94
Note(s): The straight line denotes the bound value at 5% significance
–16
–0.2
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Model B
–12
92
5% Significance
–0.4
0.0
90
CUSUM
18
–8
–4
0
4
8
12
16
–16
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
8
1.4
Model A
12
–12
Figure 2.
CUSUM and CUSUM
square stability test
16
94
96
96
98
00
02
04
00
02
04
06
CUSUM of Squares
98
CUSUM of Squares
10
12
06
10
12
14
5% Significance
08
5% Significance
08
14
16
16
18
18
JEAS
The main objective of the present study is to examine the linkages between tourism
development and poverty reduction in the context of developing and resource-rich country,
i.e. India. More particularly, the study proposes to generate empirical support to explore the
policy questions of whether tourism sector development can contribute to reducing poverty
in India. In other words, do the benefits that resulted from tourism activities in India trickle
down to the poor directly and indirectly?
The empirical results of the present study confirmed a long-run positive and significant
relationship between tourism development and poverty reduction. The casualty test also
observed positive and significant causality running from tourism development to poverty
reduction. It implies that any measures that promote tourism development in India can help to
reduce the poverty level of the poor. The results also infer that rising inflation may lead to an
increase in the poverty level in the short run, which is not desirable. Hence, high inflation
becomes a hurdle in the process of poverty reduction. This result infers that a stable price is
also a significant prerequisite for poverty reduction in India.
Therefore, the present study proposes that encouraging tourism development,
developing the tourism sector and, increasing efficiency in tourism, and maintaining
price stability will have a positive impact on the population as a whole and the poor
population in particular. For a fast-growing economy like India, the policymakers should be
Dependent variable
Causal direction
Model A-poverty reduction and tourism receipt
Poverty reduction (lnPR)
Tourism receipt (lnTR)
lnTR→lnPR
lnPR→lnTR
Is tourism propoor in India?
Chi Sq ( )
7.56***
8.79**
Model B-poverty reduction and tourist arrivals
Poverty reduction (lnPR)
lnTA→lnPR
Tourist arrivals (lnTA)
lnPR→lnTA
Note(s): ***, ** represent the level of significance at 10 and 5% respectively
3.12***
0.21
Table 6.
Granger noncausality Test
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
0.0
–0.5
–1.0
–1.5
–1.5
–1.0
–0.5
0.0
Note(s): All the roots lie in the circle
0.5
1.0
1.5
Figure 3.
Stability check for
VAR model
JEAS
concerned for GDP growth and a broader network of the tourism sector growth. This will
certainly trickle down the benefits and may have far-reaching implications towards
inclusive growth in the country.
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About the authors
Manu Sharma is a Ph.D. Scholar in the Department of Economics and Finance. He has qualified UGC-JRF
and his specializations are Macroeconomics, Development Economics and Social Economics. He is doing
his Ph.D. in the topic of Tourism Demand in India.
Geetilaxmi Mohapatra holds a Ph.D. in Environmental and Development Economics. She has over
ten years of teaching and research experience in Economics at post graduate level. She is active in
research and consultancy and has authored number of research papers in national journal and
conference proceedings.
Arun Kumar Giri holds a Ph.D. in Macroeconomics. He is currently working as Associate Professor
in the Department of Economics and Finance, at BITS, Pilani, India. He has more than 20 years of
experience in teaching and research in Economics at post graduate level. He has authored a number of
research papers in national and international journal and conference proceedings. Arun Kumar Giri is
the corresponding author and can be contacted at: akgiri.bits@gmail.com
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Is tourism propoor in India?