The paper is a requirement for Econometrics senior seminar class in a Ivy school. It should address a well-defined economics questions that include some of the econometrics methods (using STATA or R) discussed in the classes. The methods are 2. Linear Time Series Analysis and Its Applications.. 3. Autocorrelation and Partial Autocorrelation. 4. Unit Root and Nonstationarity. 5. Stochastic process. 6. Modeling Univariate Time Series: Autoregressive Models (AR). 7. Autoregressive Integrated Moving Average Models (ARMA and ARIMA). 8. Conditional Heteroskedastic Models. 9. Modeling Volatility: ARCH, GARCH, GARCH-M and EGARCH. 10. Multivariate Time Series Analysis and Its Applications. 11. Vector Autoregression (VAR) Models. 12. Impulse Response Functions. 13. Vector ARMA Models, (VARMA, VMA) 14. Structural VAR 15. Variance Decomposition. 16. Cointegration. 17. Error Correction and VAR representation. 18. Cointegration Test. 19. Forecasting under Cointegrated VAR. 20. VECM (Vector Error Correction Models). 21. Panel Data Applications I prefer a simple and straightforward topic that does not delve into complicated economic issues. The past example topics I attached below are 1. The impact of “March Madness” on college yield rates and 2. the impact of unemployment on suicidal rate in the US. As one of options, I have thought of a relation between the number of UNESCO World Heritage Sites (WHS) and the number of inbound tourists in different countries. However, I am not sure if the number of UNESCO WHS could be regarded as a time-series variable. Also, this topic would have to deal with panel data (multiple cases throughout time), which seems more complicate than time-series (one case through times) such as the past example papers. I will attach the outline at the bottom anyway, so please tell me what you think about it. You can proceed with this topic if you think the topic would be feasible. If you have a simpler or better idea in mind, I would be happy to check that. 6.Required resource : data for econometrics

The Impacts of World Heritage Sites on Tourism

Abstract
This study examines the effect of World Heritage Sites on the number of tourists in a country. The statistics consist of a panel data from 66 countries that cover the years 2000 to 2008. The results obtained show that many tourists visit a country with many world heritage sites. Essentially, the number of world heritage sites positively affects the number of tourists. It is also found that the relationship is stronger for natural as opposed to sites of cultural heritage. The results also indicate that increase in a country’s WHSs increases the number of tourists who visit it. The two relationships are strong despite the different patterns that have been found to exist in the various countries.


Table of Contents
Abstract 2
Introduction 4
Literature Review 6
Other Factors that Affect Tourism 8
Empirical Methodology 10
Findings and Discussion 12
Conclusion and Further Research 14
Conclusion 14
Appendix 16

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Introduction
According to WTTC, tourism is one of the sectors that contribute a large portion of the global economy (Su & Lin, 2014). It is a major source of tax, exports, employment and income in many of the countries in the world. The World Trade and Tourism Council (WTTC) explain that the tourism sector provides close to 300 million job opportunities which represent 10% of the global employment. Besides, the industry produced about 6 trillion of the global economy in 2011. Furthermore, the tourism industry is more environment-friendly than the manufacturing industry. The World Bank Carbon Finance Unit further argues that the development contributed by the tourism sector is sustainable (Bento, 2014). As a result, several governments are promoting tourism in their countries both locally and international so that they can boost their economic growths. The number of tourists has consistently been rising with the increase in the awareness of the significance of leisure and increase in disposable incomes.
According to the World Tourism Organization, the number of international tourists has grown from 540 million in 1995 to 1 billion in 2010 (Su & Lin, 2014). This represents about 5% increase per year. The World Heritage Centre of the UNESCO provides that there has been a steady growth in the number of WHSs across the world. Principally, the number of the sites rose from 470 in 1995 to 940 in 2011 (Su & Lin, 2014). This represents an average increase of 6% per year. Therefore, the trend can be used to determine whether there is a positive effect of World Heritage Sites on the number of tourists in a particular country. If proved it will indicate that increase the number of international tourists and as a result their expenditure (Chasapopoulos, Butter & Mihaylov, 2014). This has the effect of improving the economies of the countries visited by the tourists. Few researchers have studied this subject.
To enhance the understanding of the above subject, this paper focuses on the investigation of the relationship between the number of WHSs in a particular country and the number of tourists. This will be done by analyzing the data from sixty-six countries for nine years from 2000 to 2008 to determine the impacts of the number of WHSs on the number of tourists’ arrival in a country. Secondly, the sample will be divided into several groups according to the number of World Heritage Sites in each of the countries and study the effects of these sites on the number of tourists arrival within the groups. Finally, the variables that are time-invariant variables in the data have been eliminated by using more countries in the panel data.
Literature Review
This section describes literature review and the factors that affect tourism. The analytical and conceptual framework of the number of tourists and the number of UNESCO WHSs are also considered.
Factors that affect demand for tourism
According to Eilat & Einaiv (2004), the increase in the number tourists has made private companies and businesses and governments increase the campaign to create awareness on their tourism. Several researchers have investigated the main factors that affect the demand for tourism. These studies have established that countries with either natural or cultural UNESCO heritage sites are visited more frequent. Since natural and cultural features attract tourists, it can be considered that the features already inscribed by UNESCO as a WHSs, would attract more tourists, especially inbound tourists (Mckercher, (2016). The studies have also established that inscription by the UNESCO promotes both local and international tourism. This has been true in countries such as England and Spain and Hungary.
Because data is available, most of the studies conducted have employed the model of panel data. The disadvantage of panel data is the factor of the time-invariant variables (Mckercher, 2016). This paper also makes use of panel data. However, to reduce the problem caused by variables that are time-invariant, the number of countries researched has been increased.
According to Su & Lin (2014), the geographical imbalance in the distribution of the sites has increased despite the increase in the number of WHSs around the world. The figure below shows the top 20 countries with the highest number of WHS. About half of the WHSs and the half of the WHSs are found in Europe. The next figure shows the top 20 countries that experience the highest number of tourists. As can be seen from the graphs, countries with many numbers of world heritage sites are visited by more tourists.

Figure 2.1: Countries with the most number of tourists

Figure 2.2: Top 20 preferred countries by tourists
Two principle reasons exist for the countries that have many sites enlisted by the UNESCO sites as WHSs to get an increase in inbound tourist numbers. Firstly, both governments and the travel agencies widely use the world heritage sites to advertise tourism (Lorde, Li & Airey, 2015). The strict procedure of the inscription as a WHSs increases the global visibility of countries with such sites. The sites mainly attract the attention the international tourists.
Secondly, to conserve the sites in different countries, the UNESCO WHS offer aid to provide support for the establishment of the repair of the sites (Lorde, Li & Airey, 2015). The money if well spent can improve the tourism standards so that more inbound tourists could be attracted. However, sites are inscribed as WHSs not for tourism development of tourism but for the mobilization of sustainable resources and to increase awareness for long term WHS
Other Factors that Affect Tourism
The difference in tourism is affected by several factors. Several studies have been done over the last two decades which have focused on economic factors. Firstly, the income of the country where the tourists originate is a major factor. International tourism requires money for flight and hotel charges (Ledesma-Rodríguez, Navarro-Ibáñez & Pérez-Rodríguez, 2001). These are then determined by the amount of money left after spending. Hence the GDP and the HDI of a country are important factors that affect international tourism.
Price is also a factor that largely affects tourism. The prices consist of the cost of staying at the destination and the flight charges. However, it is difficult to determine the prices paid by the tourists while in international countries. Hence, many researchers have not studied it as a factor that affects tourism. The prices also include the cost of traveling to an alternative destination. The alternative destination is a destination which has similar cultural and geographical features such as the main destination. For instance, the alternative destination for Hong Kong is Taiwan, Thailand, Singapore and South Korea (Li, Wu, & Cai, 2008).
Furthermore, Eilat & Einaiv (2004), argue that exchange rate is a major factor that affects tourism. The exchange rate varies with time consequently changing the number of tourists who visit a particular country. A friendly exchange rate makes the tourists buy more of the commodities that they would have bought (Eilat & Einaiv (2004). Additionally, they will buy other commodities that they would not have bought if the exchange rate was higher. When the rate is higher, the tourists may postpone their trip or cancel it altogether.

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Empirical Methodology
In this case, we will apply the Augmented Dickey-Fuller (ADF) test. This test can be depicted in three different ways which are:
No intercept and trend: ∆ = −1+ ∑t ∆ −1+
Intercept available but no trend: ∆ = + −1+ ∑t ∆ −1+
Both intercept and trend available: ∆ = + + −1+ ∑t ∆ −1+
The null hypothesis that is to be tested is H0: = 0 . H1: ≠ 0. The Mackinnon test is used in the calculation of the p-values. Johansen’s multivariate likelihood technique is applied in the evaluation of the co-integration diagnosis. The resulting model is given by: = 1 −1+ 2 −2+ ⋯ + −+ . In this case, − is the lag length. When the above signs are differenced the following solution is obtained: ∆ = ∑ ∆ −+ − + . Short-term relationships are adjusted using the short-term parameter while the long term vector contributes the variable long-term relatio nships. There exist three properties that concern the properties of . These are the () = , () = 0 and 0 < () = < . The approach by Johansen applies () to test factors which co-integrate. Since rank testing is equivalent to non-testing eigenvalue testing, two approaches can be used. Firstly, trace test takes into consideration the null hypothesis n0 () = while the second one tries to adopt maximum eigenvalue test and uses a similar test hypothesis. However, different test statistics are used in the two approaches. Schwarz Bayesian Information Criterion is used to enable appropriate selection of lag. Additionally, the effectiveness offered by different parallel time series to estimate the number of tourists by using the Granger causality. Granger causality is a study on the cause and effect among variables, in this case, the WHSs and the number of tourists. The study considers two time series factors. The two are: Four null hypotheses are verified to show the type of relationship that exists between the time series variables. The four are: A. H0: 1= 0 and α2≠ 0 which means that X lags Y. Secondly, 1≠ 0 and 2= 0 which means that Y lags X. Thirdly, H0: 2= 0 and β1= 0 means that both X and Y are independent. The last one involves feedback relationship between the variables: H0: 2≠ 0 and β1≠ 0. The statistical tool Ljung-Box Q is used to test the linear dependence between the two variables.   Findings and Discussion The results in Table 2 are the predicted results. The first to third models use the number of world heritage sites to explain their variables. On the other hand, fourth to sixth models have separated the sites into natural and cultural so that the contribution of each could be determined. In the third model, the number of world heritage sites affects tourism positively. In other words, the addition of one world heritage site would increase the number of inbound tourists by about 380, 000. Therefore, the addition of a site promotes the tourism of a particular country. Furthermore, the possession of more heritage sites brings in more visitors to the country. When the sites are separated into natural and cultural, it is established that both have positive effects on tourism. Inscription of one cultural site increases the number of tourists by about 397,000 while a natural site enhances the number by 419,000. In summary, both natural and cultural sites increase the number of tourists. However, the effect is more for natural sites than for cultural sites. The third and the sixth models make the assumption that the WHSs have a constant influence on tourism in different countries. However, the number of world heritage sites can cause a variance in the marginal effect. Essentially, the addition of a site may cause a different increase in the population of inbound tourists for countries with scarce sites and those with abundant sites. For a country that already many sites, the inscription of a single world heritage sites results in increase in the number of tourists that visit the country. However, when the country concerned is a country with less number of sites, the inscription of a single site will increase the number of tourists visiting the particular country to be much more as compared to that with many sites. The other variable coefficients are almost similar despite the number of sites. For instance, the marginal effect of the GDP of a country on its tourists is positive. Therefore, development in the GDP has the effect of increasing the inbound tourists in a particular country. There is an increase of about 1200 tourists when the GDP of a country increases by $1B. On the contrary, countries with large populations experience a reduced number of tourists. In essence, when two countries, have the same number of WHSs, similar infrastructure, and equal economic development but different population, the tourists will choose to go to the country with less population. A less crowded or a small country is preferable because it is easier traveling through it. It is also established that countries with more freedom attract more tourists. Basically, tourists will want to go where there is civil and political freedom. Furthermore, the amount spent on health by different countries affects the number of tourists. Spending money on health improves the sanitation. However, the effect of the value of foreign exchange and amount spent on education is insignificant in determining the number of inbound tourists. The is as shown by the results in Table 1.   Conclusion and Further Research Conclusion This has studied the relationship between the numbers of tourists who visit a country and the world heritage sites available by analyzing the panel data available from sixty-six countries for ten years. The study has also investigated the effect of the addition of new sites in a country and their impacts over time. According to the estimated results, a country that has at least one WHS will experience an increase in the number of tourists by about 400,000 per year. Although both cultural and natural WHSs enhance the number of tourists in a country, natural sites causes a more effect than the cultural WHSs. The marginal effect for natural WHS is about 419000 while that of cultural WHS is 397000. For countries that have less UNESCO WHSs and unknown for sites, there is an increase in the number of tourists immediately a site has been added to the WHSs lists. On the other hand, for countries that already have many sites that have been inscribed as UNESCO WHSs and are well-known for beautiful sites, the addition of a single site to the WHSs will have little effect on the number of tourists in the country. There will only be a slight increase. However, countries with abundant WHSs (more than 22 sites) experience increase in the WHSs marginal effect. In other words, gearing effect emerges in countries with sufficient UNESCO WHSs. In conclusion, increasing the number of world heritage sites in a country will increase the number of both local and international tourists. Hence, a country that has a UNESCO heritage site is not only preserving the cultural and natural achievements and resources but is also developing its tourism economy. Further research could focus on the number of tourists in a country and the UNESCO WHSs maintaining costs. This should be conducted by the application of the cost-benefit analysis. This will be determined from the number of tourists who arrive in a country and the income generated. Furthermore, the data to be used in the study should be extended to make the analysis of data to be reliable and meaningful. The future research could also focus on the coefficient of the relationship studied above.   Appendix Appendix 1: Statistical definitions Variable Description Mean S.D. Min Max ARRIVAL Number of inbound tourists (1000) 5355.10 1100.00 3.00 809.00 WHS Number of WHSs 5.50 7.29 0.00 44.00 CULTURAL No. of cultural WHSs 4.23 6.33 0.00 42.00 NATURAL No. of natural WHSs 1.09 1.89 0.00 12.00 GDP Gross Domestic Product(billions) 246.21 1029.56 0.25 11670.80 POP Population (million) 42.68 143.00 0.03 1331.38 EX Exchange rate of local currency per US dollar 659.77 2078.67 0.00 17065.08 FREEDOM Level of civil rights and political freedom 5.19 2.18 1.00 7.00 HEALTH Percentage of Gross Domestic Product on health 6.35 2.22 0.01 16.21 EDU Expenses by government on education 15.48 5.50 6.20 71.09 Table 2 First model Pooled OLS Second model Fixed effects Third model Random effects Fourth model Pooled OLS Fifth model Fixed effects Sixth model Random effects WHS 533384.15*** [11.77] 89416.14 [0.64] 382637.04*** [4.69] CULTURAL 563357.95*** [10.79] 15548.61 [0.09] 396658.60*** [4.16] NATURAL 637057.13*** [4.57] 292458.01 [1.07] 418605.71** [2.10] Control variable Available Available Yes Yes Yes Yes BeP test Hausman test (p-value 1/4) R-square 116.26*** (p-value ¼ 0.000) 0.815 22.00 (p-value ¼ 0.143) 0.539 0.737 111.78*** (p-value ¼ 0.000) 0.818 25.27* (p-value ¼ 0.089) 0.547 0.739 Chi-square 461.239*** 460.099*** Observations 359 359 359 359 359 359 *denote indicates significance level at 10% **denote indicates significance level at 5% ***denote indicates significance level at 1%   References Bento, J. P. (2014). The determinants of international academic tourism demand in Europe. Tourism Economics, 20(3), 611-628. doi:10.5367/te.2013.0293 Chasapopoulos, P., Butter, F. A., & Mihaylov, E. (2014). Demand for tourism in Greece: a panel data analysis using the gravity model. International Journal of Tourism Policy, 5(3), 173. doi:10.1504/ijtp.2014.063105 Deng, J., King, B., & Bauer, T. (2002). Evaluating natural attractions for tourism. Annals of Tourism Research, 29(2), 422-438. doi:10.1016/s0160-7383(01)00068-8 Eilat, Y., & *, L. Einaiv. (2004). Determinants of international tourism: a three-dimensional panel data analysis. Applied Economics, 36(12), 1315-1327. doi:10.1080/000368404000180897 Ledesma-Rodríguez, F. J., Navarro-Ibáñez, M., & Pérez-Rodríguez, J. V. (2001). Panel data and tourism: a case study of Tenerife. Tourism Economics, 7(1), 75-88. doi:10.5367/000000001101297748 Li, M., Wu, B., & Cai, L. (2008). Tourism development of World Heritage Sites in China: A geographic perspective. Tourism Management, 29(2), 308-319. doi:10.1016/j.tourman.2007.03.013 Lorde, T., Li, G., & Airey, D. (2015). Modeling Caribbean Tourism Demand: An Augmented Gravity Approach. Journal of Travel Research, 55(7), 946-956. doi:10.1177/0047287515592852 Mckercher, B. (2016). Do Attractions Attract Tourists? A Framework to Assess the Importance of Attractions in Driving Demand. International Journal of Tourism Research, 19(1), 120-125. doi:10.1002/jtr.2091 Su, Y., & Lin, H. (2014). Analysis of international tourist arrivals worldwide: The role of world heritage sites. Tourism Management, 40, 46-58. doi:10.1016/j.tourman.2013.04.005 Yang, C., Lin, H., & Han, C. (2010). Analysis of international tourist arrivals in China: The role of World Heritage Sites. Tourism Management, 31(6), 827-837. doi:10.1016/j.tourman.2009.08.008

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