The role of museums in bilateral tourist flows: evidence from Italy

The role of museums in bilateral tourist flows: evidence from Italy Abstract This paper estimates the causal relationship of supply of art on domestic tourist flows. To this aim, we use aggregate bilateral data on tourist flows and various data on museums in the twenty Italian regions. To solve the potential endogeneity of the supply of museums, we use three different empirical strategies: we use a fixed effects model controlling for bilateral macro-area dummies, we compute the degree of selection on unobservables relative to observables which would be necessary to drive the result to zero and, finally, we adopt a two-stage least squares approach that uses a measure of historical patronage, the number of noble families, as an instrument for the number of museums. For each empirical strategy, there is strong evidence of a positive effect of the number of ‘net-museums’ on bilateral tourist flows. 1. Introduction An article from the Economist (2013b) shows that the number of museums around the world has risen from about 23,000 two decades ago to at least 55,000 now. In 2012, according to the American Alliance of Museums, American museums received 850 million visits, that is more than all the big-league sport events and the theme parks combined together. In England, more than half of the adult population visited at least a museum or a gallery in 2012, while in Sweden the percentage is close to 67%. Museum-building is also flourishing in developing countries, where governments want to signal that their countries are culturally sophisticated and want their cities to catch up with the great cities of the world. The rise of a large middle class increases the demand for art consumption: China, for example, is investing large sums of money in culture and currently has almost 4,000 museums (thus doubling the number of museums that it had in 2000) (Economist, 2013a).1 In 2011, China opened 386 new museums—more than one per day. To better understand the magnitude of this growth, just think that at the peak of America’s recent museum boom (from the mid-1990s to late 2000s), the number of museums constructed a year was only 20–40 (Johnson and Florence, 2012). Despite such numbers, very little is known about why this is happening and how it is going to influence the economy. From a sociocultural perspective, the role of museums has been deeply changing over time. Besides being places of collection, preservation, and sharing of artworks, nowadays they have an important role in constructing local identity, promoting inter-cultural dialogue, develop educational programmes, and fostering participation. While all these factors have a strong value per se, they also, indirectly, affect the economy. The first thing that comes to mind when thinking about potential channels through which museums might affect the economy is tourism. Indeed, tourism represents the main industry and a sizeable portion of total GDP for many countries. According to the World Travel and Tourism Council (2016), worldwide, the direct contribution of tourism to total GDP is estimated to be around 3%, employing about 108 million workers. Considering its direct, indirect, and induced impacts, tourism accounts for 9.8% of global GDP and 1 in 11 jobs. A significant portion of tourists is believed to travel to visit cultural attractions like museums, churches, etc. (Richards, 2001; Bedate et al., 2006), but apart from simple correlations there is little evidence about the importance of culture in generating tourist flows (Blaug, 2001; Bonet, 2003). Moreover, the relationship between cultural supply and tourism might not be as simple as it might seem at first: localities compete to attract ‘culture-driven tourists’ and to restrain their residents from going to other regions by increasing their supply of cultural goods. However, if domestic consumers learn about their true preferences through consumption (Levy-Garboua and Montmarquette, 2003) or become addicted to the arts (McCain, 1979; Becker and Murphy, 1988; Throsby, 1994; Barros and Brito, 2005), an increase in local supply may also stimulate the local demand for culture and induce residents to visit other places in search of more cultural goods. In this paper, we use bilateral data on tourist flows across Italian regions to uncover the relationship between tourism and museums. There are two reasons why Italian data are well suited for identifying and measuring the relationship between the supply of museums and tourist flows. First, due to its historical heritage, Italy accumulated an impressive quantity of cultural supply, which is why it is called the ‘Bel Paese’ (in English: ‘Beautiful Country’).2 Indeed, Italy has the greatest number of UNESCO (United Nations Educational, Scientific, and Cultural Organization) World Heritage sites in the world (see UNESCO World Heritage Centre web page). Still, as shown in Table 1, there is considerable variation in the supply of museums (in all the measures that we use) across regions in Italy that can be exploited to estimate its impact on tourism. Second, the largest part of the Italian supply of museums has been accumulated when mass tourism did not even exist, thus reducing concerns about reverse causality. We also control for a large set of observables and unobservables (exploring only variations within macro-regions). We show that such historical supply depends on the historical distribution of noble families across the country, and that such distribution can be used to break the potential endogeneity between tourism flows and the supply of art (museums, etc.). The main finding is that regions with a larger supply of museums attract more tourists and retain more local cultural consumers from travelling to other regions in search of art. Table 1 Number of museums and population by region Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Notes: Number of museums using different sources: the Italian Statistic Bureau (ISTAT), the websites http://www.museionline.it (a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on museums in Italy) and ‘http://www.tripadvisor.it’ (as a measure of the perceived quality of the museums). Table 1 Number of museums and population by region Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Notes: Number of museums using different sources: the Italian Statistic Bureau (ISTAT), the websites http://www.museionline.it (a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on museums in Italy) and ‘http://www.tripadvisor.it’ (as a measure of the perceived quality of the museums). The paper is organised as follows. In Section 2, we discuss the literature review. In Section 3, we present the empirical strategy. In particular, in subsection 3.2 we discuss the OLS strategy, while, respectively, in Sections 3.3, 3.4, and 3.5, we present the three different strategies we use to cope with the potential endogeneity: fixed effects, degree of selection on unobservables relative to observables that would explained away our result, and instrumental variable. In Section 4, we discuss our results; in Section 5, we perform some robustness checks; and conclusions are given in Section 6. 2. Literature review Most of the research that has investigated the relationship between art supply and tourist flows finds a positive association. Borowiecki and Castiglione (2014) analyse the inflows of tourists into Italian provinces in two years: 2006 and 2007. Their results show a significant and positive association between the demand for leisure activities (among others, visits to museums, concerts, and theatrical performances) and tourism flows, though there is no use of bilateral data and thus there is no attempt to evaluate the importance in the relative supply of culture in origin and destination. There are three papers that use bilateral tourism flows for different years to study the relationship between tourism and cultural supply in Italy and are therefore related to our study. The first one, Candela et al. (2014), uses a panel data of Italian regions over the period 1998–2009. Based on a spatial interaction model, they highlight that distance can modify the association between tourism flows and cultural supply. Using a number of different measures for cultural supply, including public spending in cultural activities, the average number of visitors per museum, the number of tickets sold per inhabitant for theatrical and musical events and, finally, the number of UNESCO World Heritage Sites, they document a large degree of heterogeneity in the effects of cultural supply on tourism flows with respect to distance. In a similar vein, Cafiso et al. (2016), who again focus on Italian domestic tourism, this time over the period 2000–2012, show that the associations between tourist flows and distance are heterogeneous depending on the business cycle, with tourists preferring to visit close destinations during years of recession. The last paper that uses bilateral tourist flows, Etzo and Massidda (2012), uses a rich number of variables to explain bilateral tourism flows. A dynamic panel model over the period 2004–2007 which uses lagged values of the variables as instruments reveals that tourism responds to art supply. Rather than relying on the validity of lagged variables as instruments in our paper, we use a historical instrument, the number of noble families. There are two papers that analyse the importance of UNESCO World Heritage Sites, certainly another important measure of cultural supply, in shaping tourism inflows, one focussing on China (Han et al., 2010) and one focussing on Italy (Candela et al., 2013). Both find a positive association between UNESCO sites and tourism flows, which is why, in one of our robustness checks, we control for the number of UNESCO World Heritage Sites. Finally, Cellini and Cuccia (2013) use a monthly time series of museum attendance in the whole of Italy and tourist flows to estimate an error-correction model. They find that in the short run, museum attendance increases tourist flows, while in the long run, the direction of the causality is the opposite. While these papers generally find a positive relationship between tourist flows and art supply, most of them do not expressly tackle the issue of endogeneity, thus making it difficult to interpret the results in terms of causality. Solving for the potential endogeneity using macro-area fixed effects, the degree of selection on unobservables relative to observables which would be necessary to drive the result to zero and, finally, a novel empirical strategy that uses art patronage in the past centuries as an instrument for museums is the main contribution of our paper. 3. Empirical analysis 3.1 Road map In this section, we describe the data and the methodology we use to estimate the effect of museums on tourist flows. Our empirical analysis is based on a gravitational model estimated using ordinary least squares (OLS) for the 20 Italian regions. The dependent variable is the tourist flows from one region (the region of origin) to the other (the region of destination), while the variable of interest is the difference in the number of museums between the region of origin and that of destination. Given that Italy has 20 regions, we have a 20-by-20 matrix; that is, 400 observations. Since we are not interested in intra-regional tourism, we end up with 380 observations. As first preliminary evidence, we show raw data and simple correlations. The arrows in Figure A1 (in the online Appendix) represent outgoing per capita regional tourist flows, and their thickness is proportional to the magnitude of such flows (normalised by the population in the region of destination3). The shade of grey of each region is related to the number of per capita museums; darker regions have a larger number of museums. Looking at the figure, shorter arrows tend to be thicker, indicating that distance plays an important role in the choice of the destination. Furthermore, it seems that tourists prefer regions in the north and centre of Italy, which display a higher density of museums (darker shades of grey). Figure 1 shows the raw correlation between the incoming tourist flows in the region of destination (log per capita) and the difference in the availability of museums between the region of destination and that of origin, controlling for the population (log per capita). From this figure, it seems that regions with more museums attract more tourists, as there is clearly a positive correlation, with the slope equal to 0.29 and statistically significant. But in this figure, we do not control for other variables, observable and unobservable, that could affect tourism and bias our results. To rule out the possibility that reverse causality or some omitted variables might bias our results, we use three different empirical strategies: we control for bilateral macro-area dummies, we calculate the degree of selection on unobservables relative to observables which would be necessary to drive our result to zero, and finally we adopt a two-stage least squares (2SLS) approach using the number of noble families in Italy during the Renaissance as an instrument for the presence of museums. Fig. 1 View largeDownload slide Incoming tourist flows in the region of destination (per capita) and the difference in the availability of museums between the region of destination and that of origin. We control for the population. Circles are proportional to population size. Fig. 1 View largeDownload slide Incoming tourist flows in the region of destination (per capita) and the difference in the availability of museums between the region of destination and that of origin. We control for the population. Circles are proportional to population size. 3.2 OLS strategy We use aggregate data on tourism inflows and outflows for the twenty Italian regions, complemented with other geographic data and with data on the supply of museums, in order to estimate a model of tourism demand.4 In particular, we use a gravity model, a spatial model where the degree of interaction between two geographic areas (tourist flows in our case) varies directly with the size of population in the two areas and inversely with the square of the distance between them (Witt and Witt, 1995). To isolate the effect of cultural goods on tourism, we control for factors that might be correlated with both the supply of art and tourism, like income, geographical characteristics, etc. Lim (1997) compares all methods used in around 100 published empirical studies of international tourism demand and identifies the most widely used specifications. The dependent variable is generally classified as tourist arrivals and/or departures, tourist expenditures and/or receipts and length of stay, while the explanatory variables are usually income, transportation costs, relative prices, exchange rates, and qualitative factors such as destination attractiveness and tourists’ attributes (like gender, age, education level, and occupation). We test whether the sum of coefficients of the museums in the region of origin, βo, and in that of destination, βd, is equal to zero. In other words, we test whether it is the difference in the availability of museums between regions (Md-Mo) that really matters. An advantage of using differences as opposed to the two variables taken separately (Md and Mo) is that by construction differences will vary at the bilateral level. Since we cannot reject that the coefficients sum up to zero, we are going to use the difference in the number of museums in the region of destination and in the region of origin as our variable of interest (see footnote 16). We use bilateral data on tourism flows and differences in the number of museums between regions in the year 2006 (as Etzo and Massidda, 2012; Borowiecki and Castiglione, 2014; Borowiecki, 2015). The reason is that for that year we manage to collect a large amount of information. Since Italy has a rather static supply of museums, almost the entire variation in the number of museums is across space rather than over time. Moreover, the instrument that we will use later in the 2SLS, based on the historical presence of art patronage (the number of noble families during Renaissance in Italy), is fixed over time as many historical instruments are.5 We use the following specification: log⁡Tod=βdo(log⁡Md−log⁡Mo)+βoXo   +βdXd+βγlog⁡Distod+μod, (1) where o is the region of origin, d the region of destination. Tod is the per capita tourist flow from region o (origin) to region d (destination), Mo and Md are, respectively, indicators of the supply of (per capita) museums in the regions of origin and destination,6Xo and Xd are other characteristics of the two regions (like income, opportunity for mountain or sea tourism, etc.), and Distod is the distance between the capital cities in the two regions. The price of tourism is generally based on travel cost and on relative prices; that is, the difference in the price levels in the regions of origin and destination. We measure travel cost with the distance between the capital cities of the regions of origin and destination (Walsh, 1996). To proxy for relative prices across regions, we use the Consumer Price Index (CPI). In order to capture any residual difference in the attractiveness of regions within macro-areas, we add landscape characteristics (possibility of trekking/hiking/skiing, sea tourism, presence of natural parks). To measure them, we use the following variables: Mountains, that is, the ratio between the mountain area and the total area of a region; Ski, that is, a dummy equal to 1 if the region hosts ski resorts; Mountain x Ski, that is, the interaction between the variables Mountains and Ski; Parks, that is, the ratio between the surface covered by parks and the total surface of a region; and Coasts, that is, the ratio between the coastline length of a region and the total coastal length of Italy. Note that any additional attractiveness is captured by the number of foreign tourists in a region (per capita).7 The data sources are reported in online Appendix A1. Table 2 shows the descriptive statistics of the variables and outlines some characteristics of the Italian regions: most of the variables we consider in our analysis vary considerably; income is distributed unevenly, in particular, the South is relatively poor and the North is relatively rich, despite similar levels of education; and Italy’s dramatic population aging drives the dependency ratio up to almost 57%. Table 2 Summary statistics Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Note: Regional income (in Euro) is divided by 1,000,000,000; population by 1,000 and Foreign Tourists by 1000. Table 2 Summary statistics Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Note: Regional income (in Euro) is divided by 1,000,000,000; population by 1,000 and Foreign Tourists by 1000. In our specification, we cluster the standard errors at both the region of origin and destination level (two-way clustering). Cameron and Golotvina (2005) suggest that in cross-sectional regression models for region-pair data, such as gravity models, that allow for the presence of region-specific errors, it is important to cluster the standard errors; if not, OLS standard errors are greatly underestimated. Our main focus is on the sign of the coefficient of cultural endowments (Md-Mo) (the difference in the availability of museums in the region of destination and origin) in the gravity model shown in eq. (1). Given the log-log specification, the coefficient of the variable representing the cultural endowment can be interpreted as an elasticity. In principle, we should expect a positive coefficient on (Md-Mo). A null coefficient would signal that art is not a motivation for tourism from o to d, while a positive and significant coefficient would mean that the cultural supply is effective in attracting tourists from other regions. 3.3 The fixed effects estimator In addition, we can exploit the bilateral nature of the data, restricting the variation that is used to identify the coefficient on the difference in the supply of museums. In particular, we generate up to five macro-areas and combine them by origin and destination (for a total of up to 24 bilateral dummies8). When adding such fixed effects, we only exploit variation within a pair of origin and destination macro-areas. For example, within the Northeast to South group we use only variation across regions of origin that are located in the Northeast (Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige and Veneto) and regions of destinations that are located in the South (Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia).9 The fixed effects would capture any fixed preference for a set of similar regions of destination that is common across a set of similar regions of origin (e.g. preferences for climatic, geographic, or cultural differences between the set of regions). In order to capture any residual variation that might bias the coefficients on the supply of museums, we control for several other variables that are likely to influence tourism flows as well as museums (for both, origin and destination regions): resident population, per capita income, as well as the Gini coefficient, education, and the demographic dependency ratio.10 3.4 Degree of selection on unobservables relative to observables Even though we control for many observables that are likely to be correlated with both the number of museums and tourist flows, our results might still be biased by unobservable factors that vary within macro-areas. To rule out the possibility that omitted variables might bias our results, we compute the degree of selection on unobservables relative to observables (the so-called ‘implied ratio’) which would be necessary to drive the result to zero. This approach is based on the idea that the bias generated by the observed controls provides information on the bias that is generated by the unobserved ones (Altonji et al., 2005; Oster, 2013). In other words, we investigate how the inclusion of additional regressors change the coefficient on our variable of interest (Md-Mo). If the coefficient on the difference in the number of museums changes substantially, it would be possible that the inclusion of other regressors would significantly reduce the estimated effect. On the contrary, if the coefficient does not vary substantially, we are more confident of the causal interpretation of the relationship.11 3.5 Instrumental variable strategy As an alternative to the degree of selection strategy, we devise an instrument that is plausibly exogenous: the number of Italian noble families from a region as an instrument for museums. There is a historical explanation for the reason why this is likely to be a valid instrument. Between the fifteenth and the eighteenth centuries, the Renaissance characterised Europe and, in particular, Italy, which was well known for its cultural achievements. Art was often financed by wealthy noble families and important representatives of the Church (high-ranking officers such as the Pope, cardinals, and bishops) who used patronage of the arts to signal their status, power and, for religious commissions, piety (Nelson and Zeckhauser, 2008), and not as a means to attract tourism. In a similar vein, Borowiecki (2015) links data on the number of music composers in Italy during Renaissance with contemporary data on cultural activities at the province level, and finds evidence of path-dependence in the supply of arts, driven by historical factors. Provinces with a high number of composers during the Renaissance are also characterised by a lower supply of other forms of entertainment (like, for example, sport events). Wealth inequality was an important driver of the Renaissance. Artistic developments depended on the patronage of an elite of very wealthy people who wanted to distinguish themselves from those of lesser status and needed to demonstrate ‘magnificence’ (Hollingsworth, 1994): to be rich meant to be a patron of the arts (Pullan, 1973; Gerulaitis and Goldthwaite, 1995). Many of the most important and visited Italian museums were built before the start of mass tourism. Only the rise of the bourgeoisie in the nineteenth century caused the move from patronage to a publicly supported system of the arts, a system where investments could depend on tourism flows. In particular, tourism began in the eighteenth and nineteenth centuries, when European aristocrats and rich bourgeois started to travel to Mediterranean countries for the so-called ‘Grand Tour’ (Towner and Wall, 1991). This elite form of tourism was replaced by mass tourism in Western Europe only after World War II (Costa, 1989). Hence cultural goods dating back more than 70 years from now were not created as a response to (high or low) tourist flows; they were just a way to celebrate the power and magnificence of the patrons. Some famous examples are the ‘Vatican Museums’ in Rome, the ‘Galleria degli Uffizi’ (Uffizi Gallery) in Florence, the ‘Palazzo Ducale’ (Doge’s Palace) in Venice, the ‘Reggia di Caserta’ (the Royal palace of Caserta) in the Kingdom of Naples, or the ‘Reggia di Venaria Reale’ (the Royal palace of Venaria Reale) in the Duchy of Savoy. Looking at the general ranking of the most-visited Italian museums in 2011 (Il Giornale dell’Arte, 2012, see Table 3), the mentioned museums are ranked, respectively: first (with 5,078,004 visitors), second (with 1,766,345 visitors), third (with 1,403,524 visitors), tenth (with 571,368 visitors), and eleventh (with 534,777 visitors). Table 3 Italian museums by number of visits Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Source:Il Giornale dell’Arte, 2012. Table 3 Italian museums by number of visits Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Source:Il Giornale dell’Arte, 2012. In order to provide the intuition for our instrumental variable strategy, we briefly review the history of some of them to highlight the fundamental role of nobility during Renaissance in patronizing the art. The Vatican Museums (included in the Lazio region in our dataset) were founded in the sixteenth century by Pope Julius II, as a part of a more general project aimed at making Rome an impressive centre that could demonstrate the prestige of the Pope as the supreme head of the church patronage. The Uffizi Gallery is, nowadays, the most important and visited museum in Florence. The building of the Uffizi palace started in 1560 when Cosimo de’Medici, first Grand Duke of Tuscany, was consolidating his power, with the aim to host the administrative and judicial offices. He clearly filled the palace with art to impress those who visited the palace and to show his economic and political power. The Doge’s Palace in Venice (the Palace of the head of state, the ‘Doge’) was the headquarters of power of the Venetian Republic, hosting the political institutions of the state. It is regarded as a masterpiece of Gothic architecture. It acquired its actual aspect in the Renaissance period, when famous architects and painters worked on it. The Royal Palace of Caserta was started in 1752 for Charles III of Naples as the new centre of the Kingdom of Naples, and it is a masterpiece of baroque architecture. Since 1997, it has been a UNESCO World Heritage Site. The Royal Palace of Venaria Reale was one of the royal residences of Savoy located in Venaria Reale, close to Torino, in northern Italy. The construction of the palace started in 1675 under the patronage of the Duke Carlo Emanuele II, who wanted to celebrate his magnificence by building a hunting residence that could compete with the Palace of Versailles In France. To collect data on patrons in the Renaissance, we went as far back in time as possible through the story and genealogy of the around 1,800 noble families in Italy in the ‘The Golden Book of Italian Nobility’ (Libro d’oro della Nobiltà Italiana), and we use all of them in our analysis. ‘The Golden Book of Italian Nobility’ is the first and most important official source of the Italian monarchy, and it is published by the Collegio Araldico of Rome. Such publication has a comprehensive list of the Italian noble families with the indication of their history and origins which predates mass tourism. Included are those listed in the earlier register of the Libro d’Oro della Consulta Araldica del Regno d’Italia and the later Elenchi Ufficiali Nobiliari of 1921 and of 1933. The process of expropriation of important buildings owned by noble families started with the unification of Italy (1861), continued in the 1920s and 1930s by the Mussolini government, but gained real momentum after World War II. In 1946 the Italian Savoy Kingdom was replaced by a Republic and titles of nobility lost their legal status. With the Republican Constitution, all property owned by the Savoy family was transferred to the State (e.g. the Royal Palace of Venaria Reale, the Royal Palace of Turin, etc.). But the State expropriated many additional buildings owned by other families, as for example the Villa Doria Pamphilj in 1957, and Palazzo Barberini in 1949. Moreover, in 1950 the Italian government expropriated land from large-scale land properties, called latifundia, which were mainly in the hands of noble families. The sudden loss of agricultural revenues forced many families to give up their real estate properties. The expropriations and the corresponding loss of power of the nobility add credibility to the exclusion restriction of the instrument, which is less likely now to have a direct effect on tourism. The data we collected include records on high-ranking officers of the Church, which most times were second-born sons of noble families. Amidst the 28 Popes who were heading the Church between the beginning of the fifteenth and the end of the seventeenth century, 24 belonged to noble families (restricting our attention to the 24 Italian Popes, 21 were of noble origins). Despite the fact that many of these buildings became museums before the advent of mass tourism, the origin of noble families might proxy for additional amenities, like wealth, income, landscape, etc. For this reason, it is important to control for these amenities, meaning that the instrumental variable is only conditionally independent. Another objection could be that noblemen are a subset of tourists, thus violating the exclusion restriction. But the number of noble families is extremely small compared to the size of tourist flows, and the region of origin of the noble families is in most cases different from the region where they reside today. Table 4 shows the number of noble families in each Italian region. There is substantial variability across regions, and most of the museums are located in the Central and Northern part of the country. In Figure 2, we plot the difference in the presence of noble families in the region of destination and in the region of origin (over population) and the difference in the presence of museums in the region of destination and in the region of origin (over population) at the regional level. The correlation between noble families (per capita) and museums (per capita) is strongly positive (the β coefficient is around 18% and is significant). Below, we show that the correlation survives even in the 2SLS setup, after controlling for other regressors, including the amenities. Table 4 Noble families Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Table 4 Noble families Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Fig. 2 View largeDownload slide Correlation between the difference in the number of per capita noble families between the region of destination and that of region (per 100,000 inhabitants) and the difference in the number of per capita museums between the region of destination and that of region (per 100,000 inhabitants). Circles are proportional to population size. Fig. 2 View largeDownload slide Correlation between the difference in the number of per capita noble families between the region of destination and that of region (per 100,000 inhabitants) and the difference in the number of per capita museums between the region of destination and that of region (per 100,000 inhabitants). Circles are proportional to population size. 4. Results Table 5 shows the coefficients of the gravity model estimated by OLS (Table A1 in the online Appendix shows the results of the OLS with all the regressors we use in our specification). We use both robust standard errors (in the left parenthesis) and clustered standard errors at the region of origin and destination (in the right parenthesis). In the first column, we do not control for bilateral macro-area dummies, while in the second column, we control for 3 bilateral macro-area dummies,12 in the third for 8 bilateral macro-area dummies,13 and in the fourth for 24 bilateral macro-area dummies.14 When adding a larger number of bilateral macro-area dummies, we are restricting the available variation in the data, controlling for an increasing set of unobserved fixed preferences across macro-regions that might bias our coefficient on the log difference in museums (per capita). Table 5 Estimates of the OLS regressions Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Notes: Regression results for the log of region-to-region tourist flows (divided by the population in the region of origin) Log Tourist flows_od (per capita) on the log difference in the number of museums in the region of destination and origin (per capita) Md-Mo with all the regressors we use in our specification. For the complete list of the controls we use in our specification, see Table A1. Column 1 shows results not controlling for bilateral area dummies. Column 2 controls for 3 bilateral area dummies (north-north, north-south and south-north). Column 3 controls for 8 bilateral area dummies (north-north, north-south, north-center, center-north, center-center, center-south, south-north, south-center). Column 4 controls for 24 bilateral area dummies (northwest-northwest, northwest-northeast, northwest-center, northwest-south, northwest-islands, northeast-northwest, northeast-northeast, northeast-center, northeast-south, northeast-islands, center-northwest, center-northeast, center-center, center-south, center-islands, south-northwest, south-northeast, south-center, south-islands). In the last two rows, we show the implied ratios and the selection on the unobservables that would be needed to drive our results to zero and the value of the coefficient if selection of the observable was identical to the one on the unobservables. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 5 Estimates of the OLS regressions Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Notes: Regression results for the log of region-to-region tourist flows (divided by the population in the region of origin) Log Tourist flows_od (per capita) on the log difference in the number of museums in the region of destination and origin (per capita) Md-Mo with all the regressors we use in our specification. For the complete list of the controls we use in our specification, see Table A1. Column 1 shows results not controlling for bilateral area dummies. Column 2 controls for 3 bilateral area dummies (north-north, north-south and south-north). Column 3 controls for 8 bilateral area dummies (north-north, north-south, north-center, center-north, center-center, center-south, south-north, south-center). Column 4 controls for 24 bilateral area dummies (northwest-northwest, northwest-northeast, northwest-center, northwest-south, northwest-islands, northeast-northwest, northeast-northeast, northeast-center, northeast-south, northeast-islands, center-northwest, center-northeast, center-center, center-south, center-islands, south-northwest, south-northeast, south-center, south-islands). In the last two rows, we show the implied ratios and the selection on the unobservables that would be needed to drive our results to zero and the value of the coefficient if selection of the observable was identical to the one on the unobservables. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Not controlling for area dummies, the elasticity of the difference in the number of museums in the region of destination and in that of origin is statistically significant and is equal to 0.383. When we add bilateral macro-region dummies, we get larger elasticities, and the elasticities get larger as we increase the number of macro-regions (1.469 controlling for 3 bilateral macro-area dummies; it increases to 1.473 controlling for 8 bilateral macro-area dummies and to 1.829 controlling for 24 bilateral macro-area dummies).15 This suggests that restricting the variability tends to reduce a bias that is driving the coefficients towards 0. This is consistent with local governments with disappointingly low numbers of visitors opening up a larger number of museums or, simply, with attractive regions having no interest in managing public museums. Controlling for bilateral macro-area fixed effects, the coefficient on the museums variable increases dramatically, meaning that there are some important unobserved preferences that affect bilateral tourism within bilateral macro-regions (e.g. over the last 50 years Italy has experienced large-scale migration flows from the south, which is poorer and has fewer museums to the north of the country, which is richer and has more museums. Most of these internal migrants have maintained strong links with their region of origins, where they still have relatives. Part of the flows we observe might be driven by these migrants, and more generally by individuals that are attracted to the south despite the smaller number of museums. The bilateral macro-region effects would be able to capture the phenomena, reducing the bias of the estimates. We cannot observe this kind of tourism, but it is likely to be quite large)16 In the last two rows of Table 5, we compute the implied ratios and the selection on the unobservables that would be needed to drive our results to zero. In all the specifications, we find ratios far below 1, meaning that, in fact, the coefficients are even larger. Without bilateral macro-area dummies, the selection on unobservables would have to be almost 8 times as strong as selection on the observables to produce a treatment effect of zero and should go in the opposite direction because its sign is negative. When we use bilateral macro-area dummies, we find that the selection on the unobservables would have to be between 2.58 and 4.05 to explain away the full estimated effect and should go in the opposite direction because its sign is negative. Using the heuristic cut-off equal to 1 suggested by Altonji et al. (2005) and Oster (2013) for the ratio between selection on observables and selection on the unobservables (meaning that the selection of the observable is identical to the one on the unobservables), the coefficient on the variable of interest would actually be even larger (43% without bilateral macro-area dummies and 200–228% with bilateral macro-area dummies).17 These results imply that it is highly unlikely that our estimates can be fully attributed to unobserved heterogeneity. Let us discuss the size of the effects that we estimate. If we take a region with 200 museums, which is close to the average number (238 museums), and we open additional 20 museums, the expected number of incoming tourists would increase by about 3.383% ( 10%×0.383) when using our most conservative OLS estimates. Assuming a close-to-average annual flow of 100,000 visitors from each of the other 19 regions, this amounts to 64,277 more visits inside the region. These results represent a lower bound of the role of museums in attracting tourists because they do not include the number of foreign tourists. According to Borowiecki and Castiglione (2014), domestic tourists mainly attend theatrical performances, while foreign ones are more likely to visit museums and attend concerts. This is an important element to take into account when it comes to policy implications. We now turn to the IV estimates. The results from the first stage, the reduced form and the IV (2SLS) regression, are shown in Table 6. The coefficient on the number of noble families is positive and significant, equal to 0.318. Since none of the regressors in the first stage vary at the bilateral level, the reported coefficients are all symmetric. We use both robust and two-way cluster-robust standard errors by region of origin and region of destination. The first stage F-statistic of the excluded instrument is equal to 943.33 using robust standard errors and to 144.11 using two-way cluster-robust standard errors, that is, well above the rule of thumb of 10 indicated in the literature on weak instruments (Bound et al., 1995; Stock and Yogo, 2002). Table 6 Results of the first stage and IV FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 Notes: First-stage results using the instrumented variable Log Museums_d (per capita) - Log Museums_o (per capita) as dependent variable and the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as an independent variable. Reduced form results using the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as a regressor. 2SLS results using logTourist flows as dependent variable, Log Museums_d (per capita) - Log Museums_o (per capita) as independent variable and Log Noble families_d (per capita) - Log Noble families_o (per capita) as its instrumental variable. For the complete list of the controls we use in our specification see Table A1. We perform a Hausman test, where the null hypothesis is that OLS estimates are identical to the IV ones, and we do not find evidence of endogeneity. Standard errors are in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 6 Results of the first stage and IV FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 Notes: First-stage results using the instrumented variable Log Museums_d (per capita) - Log Museums_o (per capita) as dependent variable and the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as an independent variable. Reduced form results using the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as a regressor. 2SLS results using logTourist flows as dependent variable, Log Museums_d (per capita) - Log Museums_o (per capita) as independent variable and Log Noble families_d (per capita) - Log Noble families_o (per capita) as its instrumental variable. For the complete list of the controls we use in our specification see Table A1. We perform a Hausman test, where the null hypothesis is that OLS estimates are identical to the IV ones, and we do not find evidence of endogeneity. Standard errors are in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Column 2 shows the estimates for the reduced form. The coefficient on the number of noble families is positive and significant when we use robust standard errors (it is almost significant, at 14%, when we cluster the standard errors) and equal to 0.073. The last column in Table 6 reports the results of the IV (2SLS). The coefficient Md-Mo is equal to 0.229 and is close to that of the OLS estimation without bilateral area dummies. These results confirm that museums help attracting tourists from other regions and retaining the local residents to go to other regions to consume art.18 When we introduce bilateral area fixed effects in the 2SLS regression, the first stage F-statistic is far below the rule of thumb of 10 (2.47 with 2 bilateral area dummies, 2.80 with 8 bilateral area dummies, and 4.51 with 24 bilateral area dummies), indicating that the instrument is too weak. The regression of the number of noble families on just the bilateral area fixed effects has an R-squared that is around 0.5, meaning that fixed effects explain most of the variation. For this reason, we cannot use bilateral area fixed effects in the IV specification. 5. Robustness checks We perform different robustness checks to make sure that our results do not depend on the particular specification we used. Like we did in the main regressions, we use both robust standard errors and two-way cluster-robust standard errors by region of origin and region of destination. We use four different specifications (see Tables A2 and A3 in the online Appendix): the first one (column 1) without bilateral macro-area dummies and the other three with, respectively, 3, 8, and 24 bilateral macro-area dummies (columns 2–4). Table 7 shows the main results of Tables A2 and A3 based on the specification with 24 bilateral macro-area dummies. Since the OLS estimates appear to be a conservative estimate of the effect of museums on tourist flows, the robustness checks are based on the OLS specifications. Table 7 Robustness checks: OLS estimates Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Notes: The variable of interest is LogMuseumsd(pc)−LogMuseumso(pc). In panel A, we show the robustness checks weighting for the population in the region of origin, controlling for the regional land area (both in the region of origin and in that of destination), for the number of UNESCO World Heritage Sites (both in the region of origin and in that of destination), not using per capita values (both in the dependent variable and in the regressors), controlling for the number of international flight passengers (both in the region of origin and in that of destination) and, finally, using the number of museums in the region of origin and destination taken separately. In panel B, we show the robustness checks using different measures of museums. We also generate a composite index (the cultural index), that is, an aggregated measure of three different cultural goods (museums, theatrical performances, concerts). Finally, we show the estimates using all the different goods that enter the cultural index. We show all the results controlling for 24 bilateral area dummies (Northeast, Northwest, Center, South, Islands). For the complete list of the controls we use in our specification, see Table A1. The number of observations is 380. The only exception is when we control for international flight passengers (240 observations) because four regions do not have airports (Basilicata, Molise, Trentino Alto Adige, and Valle d’ Aosta) and are excluded given the log specification. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 7 Robustness checks: OLS estimates Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Notes: The variable of interest is LogMuseumsd(pc)−LogMuseumso(pc). In panel A, we show the robustness checks weighting for the population in the region of origin, controlling for the regional land area (both in the region of origin and in that of destination), for the number of UNESCO World Heritage Sites (both in the region of origin and in that of destination), not using per capita values (both in the dependent variable and in the regressors), controlling for the number of international flight passengers (both in the region of origin and in that of destination) and, finally, using the number of museums in the region of origin and destination taken separately. In panel B, we show the robustness checks using different measures of museums. We also generate a composite index (the cultural index), that is, an aggregated measure of three different cultural goods (museums, theatrical performances, concerts). Finally, we show the estimates using all the different goods that enter the cultural index. We show all the results controlling for 24 bilateral area dummies (Northeast, Northwest, Center, South, Islands). For the complete list of the controls we use in our specification, see Table A1. The number of observations is 380. The only exception is when we control for international flight passengers (240 observations) because four regions do not have airports (Basilicata, Molise, Trentino Alto Adige, and Valle d’ Aosta) and are excluded given the log specification. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Let’s start by discussing the results of Table A2 (its short version is panel A in Table 7). To be sure that our results are not biased by the different dimension of the regions, we estimate a weighted regression, weighting for population in the region of origin. Again, the coefficient on (Md-Mo) is significant and positive (its elasticities is between 0.461 without bilateral macro-area dummies and 1.732 with 24 bilateral macro-area dummies). Since regional land area is another important characteristic that might explain tourist flows, we control for it (both that in the region of destination and in that of origin). Results are very close to those of our main regression. We use a specification where we control for the number of UNESCO World Heritage Sites in the Italian regions (both in that of origin and in that of destination) in 2006 because they are a potential substitute to museums. Estimates are, again, very close to the main ones. We estimate a regression without per capita values controlling for the population in the region of origin and in the region of destination. The coefficient on (Md-Mo) is still positive and significant in all the specifications, but the first one without bilateral fixed effects (its elasticities is between 0.145 without bilateral macro-area dummies and 0.715 with 3 bilateral macro-area dummies). We also adopt a specification that includes the fraction of international flight passengers in the region of origin and destination as a proxy for efficient transports: the coefficient on (Md-Mo) is still positive and significant (its elasticities are between 0.733 with 8 bilateral macro-area dummies and 2.623 with 24 bilateral macro-area dummies). We consider the number of international passengers because the number of Italian passengers would clearly be endogenous. Finally, we use a specification with the number of museums (per capita) in the region of origin and in that of destination taken separately. Our results show that tourists tend to travel from regions with a significantly lower number of museums to those with a significantly larger number of museums. This is in line with our main results. In Table A3 (its shorter version is panel B in Table 7), we cope with the potential measurement error using two different measures of museums, and we also take into account the fact that museums are not the only typology of cultural goods considering other two additional important cultural goods: theatre performances and concerts. First, we take into account as an alternative measure of the number of museums provided by the web site ‘museionline.it’, a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on over 3,000 museums in Italy. The coefficient on (Md-Mo) is statistically significant. Its elasticity is between 0.282 without bilateral macro-area dummies and 0.539 with 24 bilateral macro-area dummies. Then we use a measure of the (perceived) quality of the museums: the list of the top cultural attractions on the web site ‘tripadvisor.com’ at a regional level. The coefficient on (Md-Mo) is between 0.237 (without bilateral macro-area dummies) and 0.473 (with 24 bilateral macro-area dummies). Finally, we perform a robustness check using a composite index (the cultural index), that is, an aggregated measure of three different cultural goods: museums, theatre performances, and concerts. The index is constructed with a factor analysis and represents a weighted average of the three cultural measures, where the weights are based on the correlation structure of these variables. The difference in the supply of art between the region of destination and that of origin measured by the cultural index has a positive and significant effect on tourist flows, and its elasticity is between 0.260 (without bilateral macro-area dummies) and 0.371 (with 24 bilateral macro-area dummies). We also show the estimates with the three cultural goods taken separately. The difference in the supply of theatrical performances between the region of destination and that of origin increases tourist flows by an elasticity that is between 0.235 (without bilateral macro-area dummies) and 1.152 (with 24 bilateral macro-area dummies). The difference in the supply of concerts in the region of destination and that of origin has a positive and significant effect on tourist flows (the elasticities is between 0.392 and 0.564). 6. Conclusions This paper identifies a causal relationship between the number of museums and tourist flows. Based on bilateral tourist flows between Italian regions, cultural attractions are shown to have a positive and significant effect on domestic tourist flows. To address the potential endogeneity problem, we use a series of different identification strategies, and results are similar across all methods. These findings are consistent with the recent investments undertaken by several countries, like China, Saudi Arabia, Australia, Albania, Brazil, and Ukraine (see the Economist, 2013c), to increase the number of museums, in an effort to attract more and more tourists. In our analysis, we focus on a country which is characterised by a large supply of museums but with important differences across regions. Another advantage of Italy is that nobility has been abolished after World War II, adding credibility to the exclusion restriction of our instrument, the number of noble families residing in a region. As is often the case, improvements in the internal validity of an estimation come at the expense of the external validity of them. To judge the external validity of our findings, we have to consider the peculiarities of the country we have analysed and call for extending our methodology to other countries. Italy has an internationally renowned cultural heritage and represents a clear outlier in terms of wealth of cultural supply. As a consequence, Italians may have developed a preference for cultural tourism, generating estimates that are larger than for a random citizen in a random country. For this reason, it is important to replicate our study in other countries that experience art patronage. Since art patronage tended to arise wherever a royal or imperial system dominated a society, our instrument could be appropriate for those countries that were ruled by an aristocracy before the nineteenth century: among others, France, Germany, the UK, Spain, the Netherlands, Denmark, Sweden, Belgium, and Austria. Another limitation of the study is that the cultural supply coming from museums has been approximated by their sheer number. Better data on the characteristics of museums, including their detailed exhibitions, special events, the price of the admission ticket, the marketing (including the online one), as well as their capacity and visibility, would allow for a more detailed analysis of how museums shape tourism. An avenue of future research is to understand how digital technologies are changing the demand for and the consumption of museums.19 The ‘MuseiD-Italia’ project, a digital library of Italy’s most important museum collections, started in 2012, allows users to browse the art collections online, and this could either crowd out real visits or, instead, promote additional ones. Supplementary material Supplementary material (the Appendix) is available online at the OUP website. Footnotes 1 Jeffrey Johnson, the founding director of China Megacities Lab at Columbia University (New York City), called this unprecedented museum building boom the ‘museumification’ of China (Johnson and Florence, 2012). 2 Dante Alighieri and Francesco Petrarca were probably the first ones to use this expression in their poetic works: ‘del bel paese là dove ’l sì sona’ (Alighieri Dante, 1993, verse 80) and ‘il bel paese Ch’Appennin parte e ’l mar circonda e l’Alpe’ (Petrarca Francesco, 2015, verses 13–14). 3 Without normalising, the arrows would tend to be thicker whenever the size of the region of destination is larger. Since larger regions tend to have more museums, this could generate a spurious correlation between the number of tourists and the number of museums. One obtains similar figures when dividing by the area of the regions of destination. There is no need to divide by the population of origin because each map focuses on only one region of origin. 4 Despite the universally recognised importance of culture as a source of attraction for tourism, data on cultural tourism are still very limited. Information on the relevance of cultural tourism is scattered and indirect, and often based on ad hoc surveys. 5 See for example settler mortality in Acemoglu et al. (2012), the literacy rate at the end of the nineteenth century and past political institutions in Tabellini (2010), and the presence of a bishop before the year 1000 and foundation by Etruscans in Guiso et al. (2016). 6 Note that here each museum is treated symmetrically no matter the importance, but that later we will use different sources to check robustness. 7 In our study, we focus on domestic tourism and control for the number of foreign ones. The reason is that for foreign tourism in our bilateral strategy, we would not know the number of museums in the country of origin and also would not have a measure of the number of the corresponding noble families. Furthermore, using just domestic tourist flows does not rise concerns in terms of selection. Italy is an extraordinary country in terms of wealth of cultural heritage and, for this reason, could attract a special typology of international tourist with strong preferences for cultural attractions, thus generating a problem of selection. 8 There are 52 = 25 combination available, and we drop one dummy variable from the regressions. 9 To divide regions into broad geographic areas (North, South, Centre, etc.), we follow the Italian National Institute of Statistics—ISTAT classification). 10 The population of the region of origin represents the potential demand for tourism. The population of the region of destination is likely to influence its attractiveness as well, at least through visits to friends and relatives. The budget constraint of tourists depends on the level of income in the region of origin (thus we control for the per capita regional income) and possibly also on its distribution as measured by the regional Gini index. We also include two other socio-demographic variables of the region of origin in the model: the level of education, measured by the percentage of people with at least a middle school diploma, and the demographic dependency ratio, equal to the ratio between the population aged 65 or over and the population aged 20–64. The level of education is expected to be positively correlated with tourism, while the demographic dependency ratio has an a priori ambiguous effect on tourist flows (traveling for business being more likely for prime-age individuals, with pilgrimages being more frequently associated with the elderly). 11 These bounds are now often computed in empirical work. For example, this approach has been used by Bellows and Miguel (2009) in their study on the impact of the Sierra Leone civil war on individuals who have been victimised in terms of their postwar socio-economic status, their political mobilization, and engagement, by Nunn and Wantchekon (2011) in their paper on the impact of slave trade on mistrust in Africa and by Adhvaryu et al. (2014) in their paper on the effect of cocoa price shocks at birth on adult mental health outcomes. 12 We generated two area dummies: North, which includes the region of Liguria, Lombardia, Piemonte, Valle d’Aosta, Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, Veneto, Lazio, Marche, Toscana, and Umbria, and South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, and Sicilia. 13 We generated three area dummies: North, which includes the region of Liguria, Lombardia, Piemonte, Valle d’Aosta, Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, and Veneto; Center, which includes the region of Lazio, Marche, Toscana, and Umbria; and South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, and Sicilia. 14 We generated five area dummies: Northwestern, which includes the region of Liguria, Lombardia, Piemonte, and Valle d’Aosta; Northeastern, which includes the region of Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, and Veneto; Central, which includes the region of Lazio, Marche, Toscana, and Umbria; South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia; and Islands, which include the region of Sardegna and Sicilia. There are 52=25 combination available, and we drop one dummy variable from the regressions. 15 We also run the regressions using a Poisson estimator, as suggested by Silva and Tenreyro (2006): under heteroscedasticity, the parameters of log-linearised models estimated by OLS might lead to biased estimates of the true elasticities. The estimated effect of the difference in the number of museums is positive and significant at the 1% level (the coefficient on Md-Mo is equal to around 0.29 without bilateral fixed effects and increases up to 0.89 with bilateral fixed effects). 16 Our preferred specification is the one that uses the largest number of bilateral area dummies. The specification in first differences between destination and origin that we use relies on the assumption that adding a museum in the region of destination has the same effect as reducing the number of the museum in the region of origin. For this reason, we also regressed tourist flows on the number of museums in destination and in origin separately and then test the assumption that the coefficients sum up to zero or, in another words, are symmetric. We find that the two coefficients taken separately are not significantly different from zero (the p-value is equal to 0.21 with robust standard errors and to 0.13 with clustered standard errors). 17 One reason to favour this cut-off is that researchers typically focus their data collection efforts (or their choice of regression controls) on the controls they believe ex ante are the most important (Angrist and Pischke, 2010). 18 While without bilateral macro-areas a Hausman test rejects the hypothesis that there is endogeneity, the instrument varies too little within macro-areas to run the IV using such dummies. 19 For a discussion on how new technologies are shaping cultural consumption, see Borowiecki and Navarrete (2017, 2016) and Borowiecki et al. (2016). Acknowledgements Special thanks go to Giovanni Mastrobuoni for his valuable suggestions and constant encouragement. We are grateful to Bruce Weinberg, Francesc Ortega, Orley Ashenfelter, Eugene Smolensky, Mika Kortelainen, and Andrea Vindigni for their useful comments. We also thank all the participants at the seminars at the Industrial Relations Section at Princeton University, at the Department of Economics at Queens College CUNY (New York), at the Department of Economics at the University of Essex, and at the Collegio Carlo Alberto (Moncalieri, Italy). Finally, we gratefully acknowledge the comments by the participants at the 69th annual conference of the International Institute of Public Finance (Palermo, Italy), the 5th Applied Economics Workshop (Petralia Sottana, Italy), and the 4th European Workshop on Applied Cultural Economics (Aydin, Turkey). References Acemoglu D. , Johnson S. , Robinson J. A. ( 2012 ) The colonial origins of comparative development: An empirical investigation: Reply , American Economic Review , 102 , 3077 – 110 . Google Scholar CrossRef Search ADS Adhvaryu A. , Fenske J. , Nyshadham A. ( 2014 ) Early life circumstance and mental health in Ghana , Technical Report, Centre for the Study of African Economies , Oxford . Alighieri Dante ( 1993 ) La Divina Commedia. Inferno , Vol. Canto XXXIII , Zanichelli, Bologna . Altonji J. G. , Elder T. E. , Taber C. R. ( 2005 ) Selection on observed and unobserved variables: assessing the effectiveness of Catholic schools , Journal of Political Economy , 113 , 151 – 84 . Google Scholar CrossRef Search ADS Angrist J. D. , Pischke J. ( 2010 ) The credibility revolution in empirical economics: how better research design is taking the con out of econometrics , Journal of Economic Perspectives , 24 , 3 – 30 . Google Scholar CrossRef Search ADS Barros C. , Brito P. ( 2005 ) Learning-by-consuming and the dynamics of the demand and prices of cultural goods , Journal of Cultural Economics , 29 , 83 – 106 . Google Scholar CrossRef Search ADS Becker G. S. , Murphy K. M. ( 1988 ) A theory of rational addiction , Journal of Political Economy , 96 , 675 – 700 . Google Scholar CrossRef Search ADS Bedate A. , del Barrio M. , Devesa M. , Herrero L. , Sanz J. ( 2006 ) The economic impact of cultural events. A case-study of Salamanca 2002, European capital of culture , European Urban and Regional Studies , 13 , 41 – 57 . Google Scholar CrossRef Search ADS Bellows J. , Miguel E. ( 2009 ) War and local collective action in Sierra Leone , Journal of Public Economics , 93 , 1144 – 57 . Google Scholar CrossRef Search ADS Blaug M. ( 2001 ) Where are we now on cultural economics , Journal of Economic Surveys , 15 , 123 – 43 . Google Scholar CrossRef Search ADS Bonet L. ( 2003 ) Cultural tourism , in Towse R. (ed.) A Handbook of Cultural Economics , Edward Elgar Publishing , Cheltenham . Borowiecki K. J. ( 2015 ) Historical origins of cultural supply in Italy , Oxford Economic Papers , 67 , 781 – 805 . Google Scholar CrossRef Search ADS Borowiecki K. J. , Castiglione C. ( 2014 ) Cultural participation and tourism flows: an empirical investigation of Italian provinces , Tourism Economics , 20 , 241 – 62 . Google Scholar CrossRef Search ADS Borowiecki K. J. , Forbes N. , Fresa A. ( 2016 ) Cultural Heritage in a Changing World , Springer , New York . Google Scholar CrossRef Search ADS Borowiecki K. J. , Navarrete T. ( 2016 ) Changes in cultural consumption: ethnographic collections in Wikipedia , Cultural Trends , 25 , 233 – 48 . Google Scholar CrossRef Search ADS Borowiecki K. J. , Navarrete T. ( 2017 ) Digitization of heritage collections as indicator of innovation , Economics of Innovation and New Technology , 26 , 227 – 46 . Google Scholar CrossRef Search ADS Bound J. , Baker R. , Jaeger D. ( 1995 ) Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak , Journal of the American Statistical Association , 90 , 443 – 50 . Cafiso G. , Cellini R. , Cuccia T. ( 2016 ) Do economic crises lead tourists to closer destinations? Italy at the time of the Great Recession , Papers in Regional Science . Google Scholar CrossRef Search ADS Cameron A. , Golotvina N. ( 2005 ) Estimation of models for country-pair data controlling for clustered errors: with international trade applications , manuscript, University of California, Davis . Candela G. , Mussoni M. , Patuelli R. ( 2013 ) The effects of World Heritage Sites on domestic tourism: a spatial interaction model for Italy , Journal of Geographical Systems , 15 , 369 – 402 . Google Scholar CrossRef Search ADS Candela G. , Mussoni M. , Patuelli R. ( 2014 ) Cultural offer and distance in a spatial interaction model for tourism , Economics and Business Letters , 3 , 96 – 108 . Google Scholar CrossRef Search ADS Cellini R. , Cuccia T. ( 2013 ) Museum and monument attendance and tourism flow: a time series analysis approach , Applied Economics , 45 , 3473 – 82 . Google Scholar CrossRef Search ADS Costa N. ( 1989 ) Sociologia del Turismo , Cooperativa libraria IULM , Milan . The Economist ( 2013a ) Mad about museums . The Economist ( 2013b ) Temples of delight . The Economist ( 2013c ) The Bilbao effect: If you build it, will they come? Etzo I. , Massidda C. ( 2012 ) The determinants of Italian domestic tourism: a panel data analysis , Tourism Management , 33 , 603 – 10 . Google Scholar CrossRef Search ADS Gerulaitis L. , Goldthwaite R. ( 1995 ) Wealth and the Demand for Art in Italy, 1300–1600 , Vol. 23 , Johns Hopkins University Press , Baltimore, MD . Guiso L. , Sapienza P. , Zingales L. ( 2016 ) Long-term persistence , Journal of the European Economic Association , 14 , 1401 – 36 . Google Scholar CrossRef Search ADS Han C.-C. , Lin H.-L. , Yang C.-H. ( 2010 ) Analysis of international tourist arrivals in China: the role of World Heritage Sites , Tourism Management , 31 , 827 – 37 . Google Scholar CrossRef Search ADS Hollingsworth M. ( 1994 ) Patronage in Renaissance Italy: From 1400 to the Early Sixteenth Century , J. Murray , Baltimore, MD . Il Giornale dell’Arte ( 2012 ) ‘I visitatori dei musei italiani nel 2011’ . Johnson J. , Florence Z. A. ( 2012 ) The museumification of China , Technical Report, M+ Matter , Hong Kong, China . Levy-Garboua L. , Montmarquette C. ( 2003 ) Cultural tourism , in Towse R. (ed.) A Handbook of Cultural Economics’ Edward Elgar Publishing , Cheltenham . Lim C. ( 1997 ) Review of international tourism demand models , Annals of Tourism Research , 24 , 835 – 49 . Google Scholar CrossRef Search ADS McCain R. ( 1979 ) Reflections on the cultivation of taste , Journal of Cultural Economics , 3 , 30 – 52 . Google Scholar CrossRef Search ADS Nelson J. , Zeckhauser R. ( 2008 ) The Patron’s Payoff: Conspicuous Commissions in Italian Renaissance Art , Princeton University Press , Princeton, NJ . Nunn N. , Wantchekon L. ( 2011 ) The slave trade and the origins of mistrust in Africa , American Economic Review , 101 , 3221 – 52 . Google Scholar CrossRef Search ADS Oster E. ( 2013 ) Unobservable selection and coefficient stability: theory and validation , Technical Report, National Bureau of Economic Research , Cambridge, MA . Petrarca Francesco ( 2015 ) Il Canzoniere , Giulio Einaudi , Torino . Pullan B. ( 1973 ) A History of Early Renaissance Italy: From Mid-Thirteenth to the Mid-Fifteenth Century , Allen Lane , London . Richards G. ( 2001 ) The development of cultural tourism in Europe , in Richards G. (ed.) Cultural Attractions and European Tourism’ CABI Publishing , Wallingford , 1 – 269 . Google Scholar CrossRef Search ADS Silva J. S. , Tenreyro S. ( 2006 ) The log of gravity , Review of Economics and Statistics , 88 , 641 – 58 . Google Scholar CrossRef Search ADS Stock J. , Yogo M. ( 2002 ) Testing for weak instruments in linear IV regression , Technical Report, National Bureau of Economic Research , Cambridge, MA . Tabellini G. ( 2010 ) Culture and institutions: economic development in the regions of Europe , Journal of the European Economic Association , 8 , 677 – 716 . Google Scholar CrossRef Search ADS Throsby D. ( 1994 ) The production and consumption of the arts: a view of cultural economics , Journal of Economic Literature , 32 , 1 – 29 . Towner J. , Wall G. ( 1991 ) History and tourism , Annals of Tourism Research , 18 , 71 – 84 . Google Scholar CrossRef Search ADS Walsh M. ( 1996 ) Demand analysis in Irish tourism , Statistical and Social Inquiry of Ireland . Witt C. , Witt S. ( 1995 ) Forecasting tourism demand: a review of empirical research , International Journal of Forecasting , 11 , 447 – 75 . Google Scholar CrossRef Search ADS World Travel and Tourism Council ( 2016 ) The economic impact of travel & tourism: 2016 annual update—summary , Technical Report , London . © Oxford University Press 2017 All rights reserved This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Oxford Economic Papers Oxford University Press

The role of museums in bilateral tourist flows: evidence from Italy

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0030-7653
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Abstract

Abstract This paper estimates the causal relationship of supply of art on domestic tourist flows. To this aim, we use aggregate bilateral data on tourist flows and various data on museums in the twenty Italian regions. To solve the potential endogeneity of the supply of museums, we use three different empirical strategies: we use a fixed effects model controlling for bilateral macro-area dummies, we compute the degree of selection on unobservables relative to observables which would be necessary to drive the result to zero and, finally, we adopt a two-stage least squares approach that uses a measure of historical patronage, the number of noble families, as an instrument for the number of museums. For each empirical strategy, there is strong evidence of a positive effect of the number of ‘net-museums’ on bilateral tourist flows. 1. Introduction An article from the Economist (2013b) shows that the number of museums around the world has risen from about 23,000 two decades ago to at least 55,000 now. In 2012, according to the American Alliance of Museums, American museums received 850 million visits, that is more than all the big-league sport events and the theme parks combined together. In England, more than half of the adult population visited at least a museum or a gallery in 2012, while in Sweden the percentage is close to 67%. Museum-building is also flourishing in developing countries, where governments want to signal that their countries are culturally sophisticated and want their cities to catch up with the great cities of the world. The rise of a large middle class increases the demand for art consumption: China, for example, is investing large sums of money in culture and currently has almost 4,000 museums (thus doubling the number of museums that it had in 2000) (Economist, 2013a).1 In 2011, China opened 386 new museums—more than one per day. To better understand the magnitude of this growth, just think that at the peak of America’s recent museum boom (from the mid-1990s to late 2000s), the number of museums constructed a year was only 20–40 (Johnson and Florence, 2012). Despite such numbers, very little is known about why this is happening and how it is going to influence the economy. From a sociocultural perspective, the role of museums has been deeply changing over time. Besides being places of collection, preservation, and sharing of artworks, nowadays they have an important role in constructing local identity, promoting inter-cultural dialogue, develop educational programmes, and fostering participation. While all these factors have a strong value per se, they also, indirectly, affect the economy. The first thing that comes to mind when thinking about potential channels through which museums might affect the economy is tourism. Indeed, tourism represents the main industry and a sizeable portion of total GDP for many countries. According to the World Travel and Tourism Council (2016), worldwide, the direct contribution of tourism to total GDP is estimated to be around 3%, employing about 108 million workers. Considering its direct, indirect, and induced impacts, tourism accounts for 9.8% of global GDP and 1 in 11 jobs. A significant portion of tourists is believed to travel to visit cultural attractions like museums, churches, etc. (Richards, 2001; Bedate et al., 2006), but apart from simple correlations there is little evidence about the importance of culture in generating tourist flows (Blaug, 2001; Bonet, 2003). Moreover, the relationship between cultural supply and tourism might not be as simple as it might seem at first: localities compete to attract ‘culture-driven tourists’ and to restrain their residents from going to other regions by increasing their supply of cultural goods. However, if domestic consumers learn about their true preferences through consumption (Levy-Garboua and Montmarquette, 2003) or become addicted to the arts (McCain, 1979; Becker and Murphy, 1988; Throsby, 1994; Barros and Brito, 2005), an increase in local supply may also stimulate the local demand for culture and induce residents to visit other places in search of more cultural goods. In this paper, we use bilateral data on tourist flows across Italian regions to uncover the relationship between tourism and museums. There are two reasons why Italian data are well suited for identifying and measuring the relationship between the supply of museums and tourist flows. First, due to its historical heritage, Italy accumulated an impressive quantity of cultural supply, which is why it is called the ‘Bel Paese’ (in English: ‘Beautiful Country’).2 Indeed, Italy has the greatest number of UNESCO (United Nations Educational, Scientific, and Cultural Organization) World Heritage sites in the world (see UNESCO World Heritage Centre web page). Still, as shown in Table 1, there is considerable variation in the supply of museums (in all the measures that we use) across regions in Italy that can be exploited to estimate its impact on tourism. Second, the largest part of the Italian supply of museums has been accumulated when mass tourism did not even exist, thus reducing concerns about reverse causality. We also control for a large set of observables and unobservables (exploring only variations within macro-regions). We show that such historical supply depends on the historical distribution of noble families across the country, and that such distribution can be used to break the potential endogeneity between tourism flows and the supply of art (museums, etc.). The main finding is that regions with a larger supply of museums attract more tourists and retain more local cultural consumers from travelling to other regions in search of art. Table 1 Number of museums and population by region Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Notes: Number of museums using different sources: the Italian Statistic Bureau (ISTAT), the websites http://www.museionline.it (a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on museums in Italy) and ‘http://www.tripadvisor.it’ (as a measure of the perceived quality of the museums). Table 1 Number of museums and population by region Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Region Population Museums ISTAT Musei online Tripadvisor Abruzzo 1,305,307 135 94 19 Basilicata 594,086 74 19 9 Calabria 2,004,415 131 108 15 Campania 5,790,929 236 141 86 Emilia-Romagna 4,187,557 411 348 130 Friuli-V.Giulia 985,128 178 86 27 Lazio 5,304,778 369 269 129 Liguria 1,610,134 166 137 52 Lombardia 9,475,202 377 315 111 Marche 1,528,809 326 269 44 Molise 320,907 42 18 3 Piemonte 4,341,733 447 201 83 Puglia 4,071,518 156 134 38 Sardegna 1,655,677 220 96 42 Sicilia 5,017,212 261 181 65 Toscana 3,619,872 526 343 123 Trentino-A. Adige 985,128 164 79 45 Umbria 867,878 146 95 50 Valle d’ Aosta 123,978 53 26 6 Veneto 4,738,313 324 231 106 Total 58,528,561 4,742 3,190 1,183 Notes: Number of museums using different sources: the Italian Statistic Bureau (ISTAT), the websites http://www.museionline.it (a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on museums in Italy) and ‘http://www.tripadvisor.it’ (as a measure of the perceived quality of the museums). The paper is organised as follows. In Section 2, we discuss the literature review. In Section 3, we present the empirical strategy. In particular, in subsection 3.2 we discuss the OLS strategy, while, respectively, in Sections 3.3, 3.4, and 3.5, we present the three different strategies we use to cope with the potential endogeneity: fixed effects, degree of selection on unobservables relative to observables that would explained away our result, and instrumental variable. In Section 4, we discuss our results; in Section 5, we perform some robustness checks; and conclusions are given in Section 6. 2. Literature review Most of the research that has investigated the relationship between art supply and tourist flows finds a positive association. Borowiecki and Castiglione (2014) analyse the inflows of tourists into Italian provinces in two years: 2006 and 2007. Their results show a significant and positive association between the demand for leisure activities (among others, visits to museums, concerts, and theatrical performances) and tourism flows, though there is no use of bilateral data and thus there is no attempt to evaluate the importance in the relative supply of culture in origin and destination. There are three papers that use bilateral tourism flows for different years to study the relationship between tourism and cultural supply in Italy and are therefore related to our study. The first one, Candela et al. (2014), uses a panel data of Italian regions over the period 1998–2009. Based on a spatial interaction model, they highlight that distance can modify the association between tourism flows and cultural supply. Using a number of different measures for cultural supply, including public spending in cultural activities, the average number of visitors per museum, the number of tickets sold per inhabitant for theatrical and musical events and, finally, the number of UNESCO World Heritage Sites, they document a large degree of heterogeneity in the effects of cultural supply on tourism flows with respect to distance. In a similar vein, Cafiso et al. (2016), who again focus on Italian domestic tourism, this time over the period 2000–2012, show that the associations between tourist flows and distance are heterogeneous depending on the business cycle, with tourists preferring to visit close destinations during years of recession. The last paper that uses bilateral tourist flows, Etzo and Massidda (2012), uses a rich number of variables to explain bilateral tourism flows. A dynamic panel model over the period 2004–2007 which uses lagged values of the variables as instruments reveals that tourism responds to art supply. Rather than relying on the validity of lagged variables as instruments in our paper, we use a historical instrument, the number of noble families. There are two papers that analyse the importance of UNESCO World Heritage Sites, certainly another important measure of cultural supply, in shaping tourism inflows, one focussing on China (Han et al., 2010) and one focussing on Italy (Candela et al., 2013). Both find a positive association between UNESCO sites and tourism flows, which is why, in one of our robustness checks, we control for the number of UNESCO World Heritage Sites. Finally, Cellini and Cuccia (2013) use a monthly time series of museum attendance in the whole of Italy and tourist flows to estimate an error-correction model. They find that in the short run, museum attendance increases tourist flows, while in the long run, the direction of the causality is the opposite. While these papers generally find a positive relationship between tourist flows and art supply, most of them do not expressly tackle the issue of endogeneity, thus making it difficult to interpret the results in terms of causality. Solving for the potential endogeneity using macro-area fixed effects, the degree of selection on unobservables relative to observables which would be necessary to drive the result to zero and, finally, a novel empirical strategy that uses art patronage in the past centuries as an instrument for museums is the main contribution of our paper. 3. Empirical analysis 3.1 Road map In this section, we describe the data and the methodology we use to estimate the effect of museums on tourist flows. Our empirical analysis is based on a gravitational model estimated using ordinary least squares (OLS) for the 20 Italian regions. The dependent variable is the tourist flows from one region (the region of origin) to the other (the region of destination), while the variable of interest is the difference in the number of museums between the region of origin and that of destination. Given that Italy has 20 regions, we have a 20-by-20 matrix; that is, 400 observations. Since we are not interested in intra-regional tourism, we end up with 380 observations. As first preliminary evidence, we show raw data and simple correlations. The arrows in Figure A1 (in the online Appendix) represent outgoing per capita regional tourist flows, and their thickness is proportional to the magnitude of such flows (normalised by the population in the region of destination3). The shade of grey of each region is related to the number of per capita museums; darker regions have a larger number of museums. Looking at the figure, shorter arrows tend to be thicker, indicating that distance plays an important role in the choice of the destination. Furthermore, it seems that tourists prefer regions in the north and centre of Italy, which display a higher density of museums (darker shades of grey). Figure 1 shows the raw correlation between the incoming tourist flows in the region of destination (log per capita) and the difference in the availability of museums between the region of destination and that of origin, controlling for the population (log per capita). From this figure, it seems that regions with more museums attract more tourists, as there is clearly a positive correlation, with the slope equal to 0.29 and statistically significant. But in this figure, we do not control for other variables, observable and unobservable, that could affect tourism and bias our results. To rule out the possibility that reverse causality or some omitted variables might bias our results, we use three different empirical strategies: we control for bilateral macro-area dummies, we calculate the degree of selection on unobservables relative to observables which would be necessary to drive our result to zero, and finally we adopt a two-stage least squares (2SLS) approach using the number of noble families in Italy during the Renaissance as an instrument for the presence of museums. Fig. 1 View largeDownload slide Incoming tourist flows in the region of destination (per capita) and the difference in the availability of museums between the region of destination and that of origin. We control for the population. Circles are proportional to population size. Fig. 1 View largeDownload slide Incoming tourist flows in the region of destination (per capita) and the difference in the availability of museums between the region of destination and that of origin. We control for the population. Circles are proportional to population size. 3.2 OLS strategy We use aggregate data on tourism inflows and outflows for the twenty Italian regions, complemented with other geographic data and with data on the supply of museums, in order to estimate a model of tourism demand.4 In particular, we use a gravity model, a spatial model where the degree of interaction between two geographic areas (tourist flows in our case) varies directly with the size of population in the two areas and inversely with the square of the distance between them (Witt and Witt, 1995). To isolate the effect of cultural goods on tourism, we control for factors that might be correlated with both the supply of art and tourism, like income, geographical characteristics, etc. Lim (1997) compares all methods used in around 100 published empirical studies of international tourism demand and identifies the most widely used specifications. The dependent variable is generally classified as tourist arrivals and/or departures, tourist expenditures and/or receipts and length of stay, while the explanatory variables are usually income, transportation costs, relative prices, exchange rates, and qualitative factors such as destination attractiveness and tourists’ attributes (like gender, age, education level, and occupation). We test whether the sum of coefficients of the museums in the region of origin, βo, and in that of destination, βd, is equal to zero. In other words, we test whether it is the difference in the availability of museums between regions (Md-Mo) that really matters. An advantage of using differences as opposed to the two variables taken separately (Md and Mo) is that by construction differences will vary at the bilateral level. Since we cannot reject that the coefficients sum up to zero, we are going to use the difference in the number of museums in the region of destination and in the region of origin as our variable of interest (see footnote 16). We use bilateral data on tourism flows and differences in the number of museums between regions in the year 2006 (as Etzo and Massidda, 2012; Borowiecki and Castiglione, 2014; Borowiecki, 2015). The reason is that for that year we manage to collect a large amount of information. Since Italy has a rather static supply of museums, almost the entire variation in the number of museums is across space rather than over time. Moreover, the instrument that we will use later in the 2SLS, based on the historical presence of art patronage (the number of noble families during Renaissance in Italy), is fixed over time as many historical instruments are.5 We use the following specification: log⁡Tod=βdo(log⁡Md−log⁡Mo)+βoXo   +βdXd+βγlog⁡Distod+μod, (1) where o is the region of origin, d the region of destination. Tod is the per capita tourist flow from region o (origin) to region d (destination), Mo and Md are, respectively, indicators of the supply of (per capita) museums in the regions of origin and destination,6Xo and Xd are other characteristics of the two regions (like income, opportunity for mountain or sea tourism, etc.), and Distod is the distance between the capital cities in the two regions. The price of tourism is generally based on travel cost and on relative prices; that is, the difference in the price levels in the regions of origin and destination. We measure travel cost with the distance between the capital cities of the regions of origin and destination (Walsh, 1996). To proxy for relative prices across regions, we use the Consumer Price Index (CPI). In order to capture any residual difference in the attractiveness of regions within macro-areas, we add landscape characteristics (possibility of trekking/hiking/skiing, sea tourism, presence of natural parks). To measure them, we use the following variables: Mountains, that is, the ratio between the mountain area and the total area of a region; Ski, that is, a dummy equal to 1 if the region hosts ski resorts; Mountain x Ski, that is, the interaction between the variables Mountains and Ski; Parks, that is, the ratio between the surface covered by parks and the total surface of a region; and Coasts, that is, the ratio between the coastline length of a region and the total coastal length of Italy. Note that any additional attractiveness is captured by the number of foreign tourists in a region (per capita).7 The data sources are reported in online Appendix A1. Table 2 shows the descriptive statistics of the variables and outlines some characteristics of the Italian regions: most of the variables we consider in our analysis vary considerably; income is distributed unevenly, in particular, the South is relatively poor and the North is relatively rich, despite similar levels of education; and Italy’s dramatic population aging drives the dependency ratio up to almost 57%. Table 2 Summary statistics Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Note: Regional income (in Euro) is divided by 1,000,000,000; population by 1,000 and Foreign Tourists by 1000. Table 2 Summary statistics Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Variable Obs Mean Std. Dev. Min Max Between regions tourist flows 380 107,520 171,134 91 1,464,579 Museums (ISTAT) 380 237 134 42 526 Museums (museionline.it) 380 160 103 18 348 Museums (tripadvisor.com) 380 59 42 3 130 Theatrical performances 380 8,424 748,228 201 27,342 Concerts 380 1,731 1,651 75 6,616 Noble families 380 88 71 2 240 Population 380 2,926 2,353 124 9,475 Regional income (billions Euros) 380 74.2 71.0 4.1 307.7 Distance (km) 380 599 340 105 1,642 Mountain 380 0.42 0.25 0.01 1 Ski 380 0.15 0.36 0 1 Park 380 0.11 0.07 0.02 0.28 Coast 380 0.05 0.07 0 0.26 Secondary education or above 380 0.73 0.03 0.69 0.80 Foreign Tourists 380 17,137.7 15,632.79 779 50,309 CPI 380 100.4 7.3 88.0 113.3 Gini Index 380 0.29 0.02 0.26 0.33 Dependency Ratio 380 50.2 3.3 42.8 56.7 Regional land area 380 15,783.91 7,721.13 3,260.9 27,21 UNESCO World Heritage Sites 380 2.55 2.34 0 7 International flight passengers 380 0.05 0.10 0 0.37 Note: Regional income (in Euro) is divided by 1,000,000,000; population by 1,000 and Foreign Tourists by 1000. In our specification, we cluster the standard errors at both the region of origin and destination level (two-way clustering). Cameron and Golotvina (2005) suggest that in cross-sectional regression models for region-pair data, such as gravity models, that allow for the presence of region-specific errors, it is important to cluster the standard errors; if not, OLS standard errors are greatly underestimated. Our main focus is on the sign of the coefficient of cultural endowments (Md-Mo) (the difference in the availability of museums in the region of destination and origin) in the gravity model shown in eq. (1). Given the log-log specification, the coefficient of the variable representing the cultural endowment can be interpreted as an elasticity. In principle, we should expect a positive coefficient on (Md-Mo). A null coefficient would signal that art is not a motivation for tourism from o to d, while a positive and significant coefficient would mean that the cultural supply is effective in attracting tourists from other regions. 3.3 The fixed effects estimator In addition, we can exploit the bilateral nature of the data, restricting the variation that is used to identify the coefficient on the difference in the supply of museums. In particular, we generate up to five macro-areas and combine them by origin and destination (for a total of up to 24 bilateral dummies8). When adding such fixed effects, we only exploit variation within a pair of origin and destination macro-areas. For example, within the Northeast to South group we use only variation across regions of origin that are located in the Northeast (Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige and Veneto) and regions of destinations that are located in the South (Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia).9 The fixed effects would capture any fixed preference for a set of similar regions of destination that is common across a set of similar regions of origin (e.g. preferences for climatic, geographic, or cultural differences between the set of regions). In order to capture any residual variation that might bias the coefficients on the supply of museums, we control for several other variables that are likely to influence tourism flows as well as museums (for both, origin and destination regions): resident population, per capita income, as well as the Gini coefficient, education, and the demographic dependency ratio.10 3.4 Degree of selection on unobservables relative to observables Even though we control for many observables that are likely to be correlated with both the number of museums and tourist flows, our results might still be biased by unobservable factors that vary within macro-areas. To rule out the possibility that omitted variables might bias our results, we compute the degree of selection on unobservables relative to observables (the so-called ‘implied ratio’) which would be necessary to drive the result to zero. This approach is based on the idea that the bias generated by the observed controls provides information on the bias that is generated by the unobserved ones (Altonji et al., 2005; Oster, 2013). In other words, we investigate how the inclusion of additional regressors change the coefficient on our variable of interest (Md-Mo). If the coefficient on the difference in the number of museums changes substantially, it would be possible that the inclusion of other regressors would significantly reduce the estimated effect. On the contrary, if the coefficient does not vary substantially, we are more confident of the causal interpretation of the relationship.11 3.5 Instrumental variable strategy As an alternative to the degree of selection strategy, we devise an instrument that is plausibly exogenous: the number of Italian noble families from a region as an instrument for museums. There is a historical explanation for the reason why this is likely to be a valid instrument. Between the fifteenth and the eighteenth centuries, the Renaissance characterised Europe and, in particular, Italy, which was well known for its cultural achievements. Art was often financed by wealthy noble families and important representatives of the Church (high-ranking officers such as the Pope, cardinals, and bishops) who used patronage of the arts to signal their status, power and, for religious commissions, piety (Nelson and Zeckhauser, 2008), and not as a means to attract tourism. In a similar vein, Borowiecki (2015) links data on the number of music composers in Italy during Renaissance with contemporary data on cultural activities at the province level, and finds evidence of path-dependence in the supply of arts, driven by historical factors. Provinces with a high number of composers during the Renaissance are also characterised by a lower supply of other forms of entertainment (like, for example, sport events). Wealth inequality was an important driver of the Renaissance. Artistic developments depended on the patronage of an elite of very wealthy people who wanted to distinguish themselves from those of lesser status and needed to demonstrate ‘magnificence’ (Hollingsworth, 1994): to be rich meant to be a patron of the arts (Pullan, 1973; Gerulaitis and Goldthwaite, 1995). Many of the most important and visited Italian museums were built before the start of mass tourism. Only the rise of the bourgeoisie in the nineteenth century caused the move from patronage to a publicly supported system of the arts, a system where investments could depend on tourism flows. In particular, tourism began in the eighteenth and nineteenth centuries, when European aristocrats and rich bourgeois started to travel to Mediterranean countries for the so-called ‘Grand Tour’ (Towner and Wall, 1991). This elite form of tourism was replaced by mass tourism in Western Europe only after World War II (Costa, 1989). Hence cultural goods dating back more than 70 years from now were not created as a response to (high or low) tourist flows; they were just a way to celebrate the power and magnificence of the patrons. Some famous examples are the ‘Vatican Museums’ in Rome, the ‘Galleria degli Uffizi’ (Uffizi Gallery) in Florence, the ‘Palazzo Ducale’ (Doge’s Palace) in Venice, the ‘Reggia di Caserta’ (the Royal palace of Caserta) in the Kingdom of Naples, or the ‘Reggia di Venaria Reale’ (the Royal palace of Venaria Reale) in the Duchy of Savoy. Looking at the general ranking of the most-visited Italian museums in 2011 (Il Giornale dell’Arte, 2012, see Table 3), the mentioned museums are ranked, respectively: first (with 5,078,004 visitors), second (with 1,766,345 visitors), third (with 1,403,524 visitors), tenth (with 571,368 visitors), and eleventh (with 534,777 visitors). Table 3 Italian museums by number of visits Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Source:Il Giornale dell’Arte, 2012. Table 3 Italian museums by number of visits Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Ranking Museum Region Visitors Century 1 Musei Vaticani Lazio 5,078,004 XVI 2 Galleria degli Uffizi Toscana 1,766,345 XVI 3 Palazzo Ducale Veneto 1,403,524 XIV 4 Galleria dell’Accademia Toscana 1,252,822 XVIII 5 Museo Nazionale di Castel Sant’Angelo Lazio 981,821 XIII 6 Museo Centrale del Risorgimento Lazio 821,000 XIX–XX 7 Museo Argenti, Museo Porcellane, Boboli Toscana 714,224 XV 8 Museo Nazionale del Cinema Piemonte 608,448 XIX 9 Museo delle Antichità Egizie Piemonte 577,042 XVII 10 Reggia di Caserta Campania 571,368 XVIII 11 Reggia di Venaria Reale Piemonte 534,777 XVIII 12 Museo di Palazzo Vecchio Toscana 533,218 XII–XIV 13 Museo del Novecento Lombardia 522,100 XX 14 Museo e Galleria Borghese Lazio 506,368 XVII 15 Musei Capitolini Lazio 469,351 XVIII Source:Il Giornale dell’Arte, 2012. In order to provide the intuition for our instrumental variable strategy, we briefly review the history of some of them to highlight the fundamental role of nobility during Renaissance in patronizing the art. The Vatican Museums (included in the Lazio region in our dataset) were founded in the sixteenth century by Pope Julius II, as a part of a more general project aimed at making Rome an impressive centre that could demonstrate the prestige of the Pope as the supreme head of the church patronage. The Uffizi Gallery is, nowadays, the most important and visited museum in Florence. The building of the Uffizi palace started in 1560 when Cosimo de’Medici, first Grand Duke of Tuscany, was consolidating his power, with the aim to host the administrative and judicial offices. He clearly filled the palace with art to impress those who visited the palace and to show his economic and political power. The Doge’s Palace in Venice (the Palace of the head of state, the ‘Doge’) was the headquarters of power of the Venetian Republic, hosting the political institutions of the state. It is regarded as a masterpiece of Gothic architecture. It acquired its actual aspect in the Renaissance period, when famous architects and painters worked on it. The Royal Palace of Caserta was started in 1752 for Charles III of Naples as the new centre of the Kingdom of Naples, and it is a masterpiece of baroque architecture. Since 1997, it has been a UNESCO World Heritage Site. The Royal Palace of Venaria Reale was one of the royal residences of Savoy located in Venaria Reale, close to Torino, in northern Italy. The construction of the palace started in 1675 under the patronage of the Duke Carlo Emanuele II, who wanted to celebrate his magnificence by building a hunting residence that could compete with the Palace of Versailles In France. To collect data on patrons in the Renaissance, we went as far back in time as possible through the story and genealogy of the around 1,800 noble families in Italy in the ‘The Golden Book of Italian Nobility’ (Libro d’oro della Nobiltà Italiana), and we use all of them in our analysis. ‘The Golden Book of Italian Nobility’ is the first and most important official source of the Italian monarchy, and it is published by the Collegio Araldico of Rome. Such publication has a comprehensive list of the Italian noble families with the indication of their history and origins which predates mass tourism. Included are those listed in the earlier register of the Libro d’Oro della Consulta Araldica del Regno d’Italia and the later Elenchi Ufficiali Nobiliari of 1921 and of 1933. The process of expropriation of important buildings owned by noble families started with the unification of Italy (1861), continued in the 1920s and 1930s by the Mussolini government, but gained real momentum after World War II. In 1946 the Italian Savoy Kingdom was replaced by a Republic and titles of nobility lost their legal status. With the Republican Constitution, all property owned by the Savoy family was transferred to the State (e.g. the Royal Palace of Venaria Reale, the Royal Palace of Turin, etc.). But the State expropriated many additional buildings owned by other families, as for example the Villa Doria Pamphilj in 1957, and Palazzo Barberini in 1949. Moreover, in 1950 the Italian government expropriated land from large-scale land properties, called latifundia, which were mainly in the hands of noble families. The sudden loss of agricultural revenues forced many families to give up their real estate properties. The expropriations and the corresponding loss of power of the nobility add credibility to the exclusion restriction of the instrument, which is less likely now to have a direct effect on tourism. The data we collected include records on high-ranking officers of the Church, which most times were second-born sons of noble families. Amidst the 28 Popes who were heading the Church between the beginning of the fifteenth and the end of the seventeenth century, 24 belonged to noble families (restricting our attention to the 24 Italian Popes, 21 were of noble origins). Despite the fact that many of these buildings became museums before the advent of mass tourism, the origin of noble families might proxy for additional amenities, like wealth, income, landscape, etc. For this reason, it is important to control for these amenities, meaning that the instrumental variable is only conditionally independent. Another objection could be that noblemen are a subset of tourists, thus violating the exclusion restriction. But the number of noble families is extremely small compared to the size of tourist flows, and the region of origin of the noble families is in most cases different from the region where they reside today. Table 4 shows the number of noble families in each Italian region. There is substantial variability across regions, and most of the museums are located in the Central and Northern part of the country. In Figure 2, we plot the difference in the presence of noble families in the region of destination and in the region of origin (over population) and the difference in the presence of museums in the region of destination and in the region of origin (over population) at the regional level. The correlation between noble families (per capita) and museums (per capita) is strongly positive (the β coefficient is around 18% and is significant). Below, we show that the correlation survives even in the 2SLS setup, after controlling for other regressors, including the amenities. Table 4 Noble families Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Table 4 Noble families Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Region Noble families Abruzzo 17 Basilicata 7 Calabria 52 Campania 147 Emilia-Romagna 145 Friuli-V.Giulia 39 Lazio 120 Liguria 99 Lombardia 240 Marche 90 Molise 2 Piemonte 216 Puglia 33 Sardegna 27 Sicilia 122 Toscana 183 Trentino-A. Adige 27 Umbria 55 Valle d’ Aosta 2 Veneto 137 Fig. 2 View largeDownload slide Correlation between the difference in the number of per capita noble families between the region of destination and that of region (per 100,000 inhabitants) and the difference in the number of per capita museums between the region of destination and that of region (per 100,000 inhabitants). Circles are proportional to population size. Fig. 2 View largeDownload slide Correlation between the difference in the number of per capita noble families between the region of destination and that of region (per 100,000 inhabitants) and the difference in the number of per capita museums between the region of destination and that of region (per 100,000 inhabitants). Circles are proportional to population size. 4. Results Table 5 shows the coefficients of the gravity model estimated by OLS (Table A1 in the online Appendix shows the results of the OLS with all the regressors we use in our specification). We use both robust standard errors (in the left parenthesis) and clustered standard errors at the region of origin and destination (in the right parenthesis). In the first column, we do not control for bilateral macro-area dummies, while in the second column, we control for 3 bilateral macro-area dummies,12 in the third for 8 bilateral macro-area dummies,13 and in the fourth for 24 bilateral macro-area dummies.14 When adding a larger number of bilateral macro-area dummies, we are restricting the available variation in the data, controlling for an increasing set of unobserved fixed preferences across macro-regions that might bias our coefficient on the log difference in museums (per capita). Table 5 Estimates of the OLS regressions Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Notes: Regression results for the log of region-to-region tourist flows (divided by the population in the region of origin) Log Tourist flows_od (per capita) on the log difference in the number of museums in the region of destination and origin (per capita) Md-Mo with all the regressors we use in our specification. For the complete list of the controls we use in our specification, see Table A1. Column 1 shows results not controlling for bilateral area dummies. Column 2 controls for 3 bilateral area dummies (north-north, north-south and south-north). Column 3 controls for 8 bilateral area dummies (north-north, north-south, north-center, center-north, center-center, center-south, south-north, south-center). Column 4 controls for 24 bilateral area dummies (northwest-northwest, northwest-northeast, northwest-center, northwest-south, northwest-islands, northeast-northwest, northeast-northeast, northeast-center, northeast-south, northeast-islands, center-northwest, center-northeast, center-center, center-south, center-islands, south-northwest, south-northeast, south-center, south-islands). In the last two rows, we show the implied ratios and the selection on the unobservables that would be needed to drive our results to zero and the value of the coefficient if selection of the observable was identical to the one on the unobservables. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 5 Estimates of the OLS regressions Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Log Tourist flows_od (per capita) (1) (2) (3) (4) Log Museums_d (per capita) - Log Museums_o (per capita) 0.383 1.469 1.473 1.829 (0.086) (0.108) (0.219) (0.088) (0.219) (0.094) (0.259) (0.195) Log Population_o –0.099 –0.802 –0.790 –0.772 (0.057) (0.080) (0.138) (0.083) (0.138) (0.105) (0.139) (0.117) Log Population_d 0.863 1.539 1.568 1.834 (0.060) (0.124) (0.141) (0.068) (0.140) (0.069) (0.142) (0.130) Log Distance –0.654 –0.723 –0.701 –0.734 (0.054) (0.092) (0.064) (0.096) (0.079) (0.115) (0.057) (0.097) Log Regional Income_o (per capita) 0.352 2.491 2.494 2.674 (0.399) (0.454) (0.572) (0.243) (0.566) (0.237) (0.576) (0.224) Log Regional Income_d (per capita) –2.659 –5.131 –5.166 –6.123 (0.410) (0.716) (0.571) (0.434) (0.549) (0.232) (0.548) (0.350) Log Education_o 2.272 –1.422 –1.751 3.522 (1.004) (0.580) (1.242) (0.548) (1.335) (0.674) (1.582) (0.550) Log Education_d –5.535 –2.639 –3.311 –1.225 (1.094) (2.107) (1.260) (0.851) (1.297) (1.009) (1.565) (1.476) Log Foreign Tourists_o (per capita) 0.094 –0.545 –0.520 –1.141 (0.073) (0.091) (0.124) (0.078) (0.124) (0.096) (0.230) (0.116) Log Foreign Tourists_d (per capita) 0.950 1.525 1.580 1.672 (0.069) (0.110) (0.126) (0.077) (0.125) (0.065) (0.221) (0.167) Controls yes yes yes yes Number of bilateral area dummies 0 3 8 24 Observations 380 380 380 380 R-squared 0.874 0.886 0.889 0.921 Selection on the unobservables that would drive our results to zero –7.652 –2.588 –2.685 –4.055 Coefficient on the variable of interest with a cut-off equal to 1 0.433 2.037 2.021 2.28 Notes: Regression results for the log of region-to-region tourist flows (divided by the population in the region of origin) Log Tourist flows_od (per capita) on the log difference in the number of museums in the region of destination and origin (per capita) Md-Mo with all the regressors we use in our specification. For the complete list of the controls we use in our specification, see Table A1. Column 1 shows results not controlling for bilateral area dummies. Column 2 controls for 3 bilateral area dummies (north-north, north-south and south-north). Column 3 controls for 8 bilateral area dummies (north-north, north-south, north-center, center-north, center-center, center-south, south-north, south-center). Column 4 controls for 24 bilateral area dummies (northwest-northwest, northwest-northeast, northwest-center, northwest-south, northwest-islands, northeast-northwest, northeast-northeast, northeast-center, northeast-south, northeast-islands, center-northwest, center-northeast, center-center, center-south, center-islands, south-northwest, south-northeast, south-center, south-islands). In the last two rows, we show the implied ratios and the selection on the unobservables that would be needed to drive our results to zero and the value of the coefficient if selection of the observable was identical to the one on the unobservables. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Not controlling for area dummies, the elasticity of the difference in the number of museums in the region of destination and in that of origin is statistically significant and is equal to 0.383. When we add bilateral macro-region dummies, we get larger elasticities, and the elasticities get larger as we increase the number of macro-regions (1.469 controlling for 3 bilateral macro-area dummies; it increases to 1.473 controlling for 8 bilateral macro-area dummies and to 1.829 controlling for 24 bilateral macro-area dummies).15 This suggests that restricting the variability tends to reduce a bias that is driving the coefficients towards 0. This is consistent with local governments with disappointingly low numbers of visitors opening up a larger number of museums or, simply, with attractive regions having no interest in managing public museums. Controlling for bilateral macro-area fixed effects, the coefficient on the museums variable increases dramatically, meaning that there are some important unobserved preferences that affect bilateral tourism within bilateral macro-regions (e.g. over the last 50 years Italy has experienced large-scale migration flows from the south, which is poorer and has fewer museums to the north of the country, which is richer and has more museums. Most of these internal migrants have maintained strong links with their region of origins, where they still have relatives. Part of the flows we observe might be driven by these migrants, and more generally by individuals that are attracted to the south despite the smaller number of museums. The bilateral macro-region effects would be able to capture the phenomena, reducing the bias of the estimates. We cannot observe this kind of tourism, but it is likely to be quite large)16 In the last two rows of Table 5, we compute the implied ratios and the selection on the unobservables that would be needed to drive our results to zero. In all the specifications, we find ratios far below 1, meaning that, in fact, the coefficients are even larger. Without bilateral macro-area dummies, the selection on unobservables would have to be almost 8 times as strong as selection on the observables to produce a treatment effect of zero and should go in the opposite direction because its sign is negative. When we use bilateral macro-area dummies, we find that the selection on the unobservables would have to be between 2.58 and 4.05 to explain away the full estimated effect and should go in the opposite direction because its sign is negative. Using the heuristic cut-off equal to 1 suggested by Altonji et al. (2005) and Oster (2013) for the ratio between selection on observables and selection on the unobservables (meaning that the selection of the observable is identical to the one on the unobservables), the coefficient on the variable of interest would actually be even larger (43% without bilateral macro-area dummies and 200–228% with bilateral macro-area dummies).17 These results imply that it is highly unlikely that our estimates can be fully attributed to unobserved heterogeneity. Let us discuss the size of the effects that we estimate. If we take a region with 200 museums, which is close to the average number (238 museums), and we open additional 20 museums, the expected number of incoming tourists would increase by about 3.383% ( 10%×0.383) when using our most conservative OLS estimates. Assuming a close-to-average annual flow of 100,000 visitors from each of the other 19 regions, this amounts to 64,277 more visits inside the region. These results represent a lower bound of the role of museums in attracting tourists because they do not include the number of foreign tourists. According to Borowiecki and Castiglione (2014), domestic tourists mainly attend theatrical performances, while foreign ones are more likely to visit museums and attend concerts. This is an important element to take into account when it comes to policy implications. We now turn to the IV estimates. The results from the first stage, the reduced form and the IV (2SLS) regression, are shown in Table 6. The coefficient on the number of noble families is positive and significant, equal to 0.318. Since none of the regressors in the first stage vary at the bilateral level, the reported coefficients are all symmetric. We use both robust and two-way cluster-robust standard errors by region of origin and region of destination. The first stage F-statistic of the excluded instrument is equal to 943.33 using robust standard errors and to 144.11 using two-way cluster-robust standard errors, that is, well above the rule of thumb of 10 indicated in the literature on weak instruments (Bound et al., 1995; Stock and Yogo, 2002). Table 6 Results of the first stage and IV FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 Notes: First-stage results using the instrumented variable Log Museums_d (per capita) - Log Museums_o (per capita) as dependent variable and the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as an independent variable. Reduced form results using the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as a regressor. 2SLS results using logTourist flows as dependent variable, Log Museums_d (per capita) - Log Museums_o (per capita) as independent variable and Log Noble families_d (per capita) - Log Noble families_o (per capita) as its instrumental variable. For the complete list of the controls we use in our specification see Table A1. We perform a Hausman test, where the null hypothesis is that OLS estimates are identical to the IV ones, and we do not find evidence of endogeneity. Standard errors are in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 6 Results of the first stage and IV FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 FIRST STAGE REDUCED FORM 2SLS Δlog Museums log Tourist flows log Tourist flows (1) (2) (3) Log Noble families_d (pc) - Log Noble families_o (pc) 0.318 0.073 (0.010) (0.026) (0.031) (0.047) Log Museums_d (pc) - Log Museums_o (pc) 0.229 (0.109) (0.086) Log Population_o 0.557 0.109 –0.018 (0.016) (0.052) (0.043) (0.038) (0.073) (0.054) Log Population_d –0.557 0.655 0.782 (0.016) (0.052) (0.046) (0.103) (0.073) (0.101) Log Distance 0.000 –0.654 –0.654 (0.011) (0.018) (0.055) (0.103) (0.035) (0.083) Log Regional Income_o (per capita) –2.889 –0.812 –0.151 (0.101) (0.279) (0.313) (0.230) (0.476) (0.366) Log Regional Income_d (per capita) 2.889 –1.496 –2.156 (0.101) (0.279) (0.292) (0.547) (0.476) (0.617) Log Education_o 1.042 2.540 2.302 (0.330) (0.857) (1.030) (0.480) (1.057) (0.331) Log Education_d –1.042 –5.803 –5.565 (0.330) (0.857) (1.107) (2.247) (1.057) (2.017) Log Foreign Tourists_o (per capita) 0.267 0.193 0.132 (0.023) (0.063) (0.072) (0.058) (0.076) (0.055) Log Foreign Tourists_d (per capita) –0.267 0.852 0.913 (0.023) (0.063) (0.065) (0.103) (0.076) (0.077) Controls yes yes yes Observations 380 380 380 R-squared 0.979 0.979 0.869 0.869 0.805 0.805 Notes: First-stage results using the instrumented variable Log Museums_d (per capita) - Log Museums_o (per capita) as dependent variable and the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as an independent variable. Reduced form results using the instrument (Log Noble families_d (per capita) - Log Noble families_o (per capita)) as a regressor. 2SLS results using logTourist flows as dependent variable, Log Museums_d (per capita) - Log Museums_o (per capita) as independent variable and Log Noble families_d (per capita) - Log Noble families_o (per capita) as its instrumental variable. For the complete list of the controls we use in our specification see Table A1. We perform a Hausman test, where the null hypothesis is that OLS estimates are identical to the IV ones, and we do not find evidence of endogeneity. Standard errors are in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Column 2 shows the estimates for the reduced form. The coefficient on the number of noble families is positive and significant when we use robust standard errors (it is almost significant, at 14%, when we cluster the standard errors) and equal to 0.073. The last column in Table 6 reports the results of the IV (2SLS). The coefficient Md-Mo is equal to 0.229 and is close to that of the OLS estimation without bilateral area dummies. These results confirm that museums help attracting tourists from other regions and retaining the local residents to go to other regions to consume art.18 When we introduce bilateral area fixed effects in the 2SLS regression, the first stage F-statistic is far below the rule of thumb of 10 (2.47 with 2 bilateral area dummies, 2.80 with 8 bilateral area dummies, and 4.51 with 24 bilateral area dummies), indicating that the instrument is too weak. The regression of the number of noble families on just the bilateral area fixed effects has an R-squared that is around 0.5, meaning that fixed effects explain most of the variation. For this reason, we cannot use bilateral area fixed effects in the IV specification. 5. Robustness checks We perform different robustness checks to make sure that our results do not depend on the particular specification we used. Like we did in the main regressions, we use both robust standard errors and two-way cluster-robust standard errors by region of origin and region of destination. We use four different specifications (see Tables A2 and A3 in the online Appendix): the first one (column 1) without bilateral macro-area dummies and the other three with, respectively, 3, 8, and 24 bilateral macro-area dummies (columns 2–4). Table 7 shows the main results of Tables A2 and A3 based on the specification with 24 bilateral macro-area dummies. Since the OLS estimates appear to be a conservative estimate of the effect of museums on tourist flows, the robustness checks are based on the OLS specifications. Table 7 Robustness checks: OLS estimates Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Notes: The variable of interest is LogMuseumsd(pc)−LogMuseumso(pc). In panel A, we show the robustness checks weighting for the population in the region of origin, controlling for the regional land area (both in the region of origin and in that of destination), for the number of UNESCO World Heritage Sites (both in the region of origin and in that of destination), not using per capita values (both in the dependent variable and in the regressors), controlling for the number of international flight passengers (both in the region of origin and in that of destination) and, finally, using the number of museums in the region of origin and destination taken separately. In panel B, we show the robustness checks using different measures of museums. We also generate a composite index (the cultural index), that is, an aggregated measure of three different cultural goods (museums, theatrical performances, concerts). Finally, we show the estimates using all the different goods that enter the cultural index. We show all the results controlling for 24 bilateral area dummies (Northeast, Northwest, Center, South, Islands). For the complete list of the controls we use in our specification, see Table A1. The number of observations is 380. The only exception is when we control for international flight passengers (240 observations) because four regions do not have airports (Basilicata, Molise, Trentino Alto Adige, and Valle d’ Aosta) and are excluded given the log specification. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Table 7 Robustness checks: OLS estimates Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Log Tourist flows Panel A: Other specifications Weighted for the population in the region of origin 1.732 (0.279) (0.176) Controlling for the regional land area 1.921 (0.267) (0.088) Controlling for the UNESCO World Heritage Sites 1.468 (0.335) (0.134) Not using per capita values 0.703 (0.328) (0.441) Controlling for international flight passengers in the region of origin and destination 2.623 (0.603) (0.187) With museums in the region of origin and destination taken separately: Log Museums_d (per capita) 2.266 (0.446) (0.395) Log Museums_o (per capita) –1.392 (0.433) (0.267) Panel B: Other measures of culture Measure of museums taken from ‘museionline.it’ 0.539 (0.108) (0.086) A measure of museums’ quantity and quality taken from ‘tripadvisor.com’: 0.473 (0.070) (0.069) Cultural Index 0.371 (0.064) (0.064) Disaggregated Cultural Index Theatrical performances 1.152 (0.179) (0.153) Concerts 0.564 (0.106) (0.111) Museums 1.829 (0.259) (0.195) Notes: The variable of interest is LogMuseumsd(pc)−LogMuseumso(pc). In panel A, we show the robustness checks weighting for the population in the region of origin, controlling for the regional land area (both in the region of origin and in that of destination), for the number of UNESCO World Heritage Sites (both in the region of origin and in that of destination), not using per capita values (both in the dependent variable and in the regressors), controlling for the number of international flight passengers (both in the region of origin and in that of destination) and, finally, using the number of museums in the region of origin and destination taken separately. In panel B, we show the robustness checks using different measures of museums. We also generate a composite index (the cultural index), that is, an aggregated measure of three different cultural goods (museums, theatrical performances, concerts). Finally, we show the estimates using all the different goods that enter the cultural index. We show all the results controlling for 24 bilateral area dummies (Northeast, Northwest, Center, South, Islands). For the complete list of the controls we use in our specification, see Table A1. The number of observations is 380. The only exception is when we control for international flight passengers (240 observations) because four regions do not have airports (Basilicata, Molise, Trentino Alto Adige, and Valle d’ Aosta) and are excluded given the log specification. The standard errors are shown in parentheses. The left parenthesis shows robust standard errors, while the right shows two-way clustered standard errors using region of origin and destination as groups. Let’s start by discussing the results of Table A2 (its short version is panel A in Table 7). To be sure that our results are not biased by the different dimension of the regions, we estimate a weighted regression, weighting for population in the region of origin. Again, the coefficient on (Md-Mo) is significant and positive (its elasticities is between 0.461 without bilateral macro-area dummies and 1.732 with 24 bilateral macro-area dummies). Since regional land area is another important characteristic that might explain tourist flows, we control for it (both that in the region of destination and in that of origin). Results are very close to those of our main regression. We use a specification where we control for the number of UNESCO World Heritage Sites in the Italian regions (both in that of origin and in that of destination) in 2006 because they are a potential substitute to museums. Estimates are, again, very close to the main ones. We estimate a regression without per capita values controlling for the population in the region of origin and in the region of destination. The coefficient on (Md-Mo) is still positive and significant in all the specifications, but the first one without bilateral fixed effects (its elasticities is between 0.145 without bilateral macro-area dummies and 0.715 with 3 bilateral macro-area dummies). We also adopt a specification that includes the fraction of international flight passengers in the region of origin and destination as a proxy for efficient transports: the coefficient on (Md-Mo) is still positive and significant (its elasticities are between 0.733 with 8 bilateral macro-area dummies and 2.623 with 24 bilateral macro-area dummies). We consider the number of international passengers because the number of Italian passengers would clearly be endogenous. Finally, we use a specification with the number of museums (per capita) in the region of origin and in that of destination taken separately. Our results show that tourists tend to travel from regions with a significantly lower number of museums to those with a significantly larger number of museums. This is in line with our main results. In Table A3 (its shorter version is panel B in Table 7), we cope with the potential measurement error using two different measures of museums, and we also take into account the fact that museums are not the only typology of cultural goods considering other two additional important cultural goods: theatre performances and concerts. First, we take into account as an alternative measure of the number of museums provided by the web site ‘museionline.it’, a partnership between Microsoft and Adnkronos Culture, a news agency which collects and constantly updates information on over 3,000 museums in Italy. The coefficient on (Md-Mo) is statistically significant. Its elasticity is between 0.282 without bilateral macro-area dummies and 0.539 with 24 bilateral macro-area dummies. Then we use a measure of the (perceived) quality of the museums: the list of the top cultural attractions on the web site ‘tripadvisor.com’ at a regional level. The coefficient on (Md-Mo) is between 0.237 (without bilateral macro-area dummies) and 0.473 (with 24 bilateral macro-area dummies). Finally, we perform a robustness check using a composite index (the cultural index), that is, an aggregated measure of three different cultural goods: museums, theatre performances, and concerts. The index is constructed with a factor analysis and represents a weighted average of the three cultural measures, where the weights are based on the correlation structure of these variables. The difference in the supply of art between the region of destination and that of origin measured by the cultural index has a positive and significant effect on tourist flows, and its elasticity is between 0.260 (without bilateral macro-area dummies) and 0.371 (with 24 bilateral macro-area dummies). We also show the estimates with the three cultural goods taken separately. The difference in the supply of theatrical performances between the region of destination and that of origin increases tourist flows by an elasticity that is between 0.235 (without bilateral macro-area dummies) and 1.152 (with 24 bilateral macro-area dummies). The difference in the supply of concerts in the region of destination and that of origin has a positive and significant effect on tourist flows (the elasticities is between 0.392 and 0.564). 6. Conclusions This paper identifies a causal relationship between the number of museums and tourist flows. Based on bilateral tourist flows between Italian regions, cultural attractions are shown to have a positive and significant effect on domestic tourist flows. To address the potential endogeneity problem, we use a series of different identification strategies, and results are similar across all methods. These findings are consistent with the recent investments undertaken by several countries, like China, Saudi Arabia, Australia, Albania, Brazil, and Ukraine (see the Economist, 2013c), to increase the number of museums, in an effort to attract more and more tourists. In our analysis, we focus on a country which is characterised by a large supply of museums but with important differences across regions. Another advantage of Italy is that nobility has been abolished after World War II, adding credibility to the exclusion restriction of our instrument, the number of noble families residing in a region. As is often the case, improvements in the internal validity of an estimation come at the expense of the external validity of them. To judge the external validity of our findings, we have to consider the peculiarities of the country we have analysed and call for extending our methodology to other countries. Italy has an internationally renowned cultural heritage and represents a clear outlier in terms of wealth of cultural supply. As a consequence, Italians may have developed a preference for cultural tourism, generating estimates that are larger than for a random citizen in a random country. For this reason, it is important to replicate our study in other countries that experience art patronage. Since art patronage tended to arise wherever a royal or imperial system dominated a society, our instrument could be appropriate for those countries that were ruled by an aristocracy before the nineteenth century: among others, France, Germany, the UK, Spain, the Netherlands, Denmark, Sweden, Belgium, and Austria. Another limitation of the study is that the cultural supply coming from museums has been approximated by their sheer number. Better data on the characteristics of museums, including their detailed exhibitions, special events, the price of the admission ticket, the marketing (including the online one), as well as their capacity and visibility, would allow for a more detailed analysis of how museums shape tourism. An avenue of future research is to understand how digital technologies are changing the demand for and the consumption of museums.19 The ‘MuseiD-Italia’ project, a digital library of Italy’s most important museum collections, started in 2012, allows users to browse the art collections online, and this could either crowd out real visits or, instead, promote additional ones. Supplementary material Supplementary material (the Appendix) is available online at the OUP website. Footnotes 1 Jeffrey Johnson, the founding director of China Megacities Lab at Columbia University (New York City), called this unprecedented museum building boom the ‘museumification’ of China (Johnson and Florence, 2012). 2 Dante Alighieri and Francesco Petrarca were probably the first ones to use this expression in their poetic works: ‘del bel paese là dove ’l sì sona’ (Alighieri Dante, 1993, verse 80) and ‘il bel paese Ch’Appennin parte e ’l mar circonda e l’Alpe’ (Petrarca Francesco, 2015, verses 13–14). 3 Without normalising, the arrows would tend to be thicker whenever the size of the region of destination is larger. Since larger regions tend to have more museums, this could generate a spurious correlation between the number of tourists and the number of museums. One obtains similar figures when dividing by the area of the regions of destination. There is no need to divide by the population of origin because each map focuses on only one region of origin. 4 Despite the universally recognised importance of culture as a source of attraction for tourism, data on cultural tourism are still very limited. Information on the relevance of cultural tourism is scattered and indirect, and often based on ad hoc surveys. 5 See for example settler mortality in Acemoglu et al. (2012), the literacy rate at the end of the nineteenth century and past political institutions in Tabellini (2010), and the presence of a bishop before the year 1000 and foundation by Etruscans in Guiso et al. (2016). 6 Note that here each museum is treated symmetrically no matter the importance, but that later we will use different sources to check robustness. 7 In our study, we focus on domestic tourism and control for the number of foreign ones. The reason is that for foreign tourism in our bilateral strategy, we would not know the number of museums in the country of origin and also would not have a measure of the number of the corresponding noble families. Furthermore, using just domestic tourist flows does not rise concerns in terms of selection. Italy is an extraordinary country in terms of wealth of cultural heritage and, for this reason, could attract a special typology of international tourist with strong preferences for cultural attractions, thus generating a problem of selection. 8 There are 52 = 25 combination available, and we drop one dummy variable from the regressions. 9 To divide regions into broad geographic areas (North, South, Centre, etc.), we follow the Italian National Institute of Statistics—ISTAT classification). 10 The population of the region of origin represents the potential demand for tourism. The population of the region of destination is likely to influence its attractiveness as well, at least through visits to friends and relatives. The budget constraint of tourists depends on the level of income in the region of origin (thus we control for the per capita regional income) and possibly also on its distribution as measured by the regional Gini index. We also include two other socio-demographic variables of the region of origin in the model: the level of education, measured by the percentage of people with at least a middle school diploma, and the demographic dependency ratio, equal to the ratio between the population aged 65 or over and the population aged 20–64. The level of education is expected to be positively correlated with tourism, while the demographic dependency ratio has an a priori ambiguous effect on tourist flows (traveling for business being more likely for prime-age individuals, with pilgrimages being more frequently associated with the elderly). 11 These bounds are now often computed in empirical work. For example, this approach has been used by Bellows and Miguel (2009) in their study on the impact of the Sierra Leone civil war on individuals who have been victimised in terms of their postwar socio-economic status, their political mobilization, and engagement, by Nunn and Wantchekon (2011) in their paper on the impact of slave trade on mistrust in Africa and by Adhvaryu et al. (2014) in their paper on the effect of cocoa price shocks at birth on adult mental health outcomes. 12 We generated two area dummies: North, which includes the region of Liguria, Lombardia, Piemonte, Valle d’Aosta, Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, Veneto, Lazio, Marche, Toscana, and Umbria, and South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, and Sicilia. 13 We generated three area dummies: North, which includes the region of Liguria, Lombardia, Piemonte, Valle d’Aosta, Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, and Veneto; Center, which includes the region of Lazio, Marche, Toscana, and Umbria; and South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, and Sicilia. 14 We generated five area dummies: Northwestern, which includes the region of Liguria, Lombardia, Piemonte, and Valle d’Aosta; Northeastern, which includes the region of Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige, and Veneto; Central, which includes the region of Lazio, Marche, Toscana, and Umbria; South, which includes the region of Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia; and Islands, which include the region of Sardegna and Sicilia. There are 52=25 combination available, and we drop one dummy variable from the regressions. 15 We also run the regressions using a Poisson estimator, as suggested by Silva and Tenreyro (2006): under heteroscedasticity, the parameters of log-linearised models estimated by OLS might lead to biased estimates of the true elasticities. The estimated effect of the difference in the number of museums is positive and significant at the 1% level (the coefficient on Md-Mo is equal to around 0.29 without bilateral fixed effects and increases up to 0.89 with bilateral fixed effects). 16 Our preferred specification is the one that uses the largest number of bilateral area dummies. The specification in first differences between destination and origin that we use relies on the assumption that adding a museum in the region of destination has the same effect as reducing the number of the museum in the region of origin. For this reason, we also regressed tourist flows on the number of museums in destination and in origin separately and then test the assumption that the coefficients sum up to zero or, in another words, are symmetric. We find that the two coefficients taken separately are not significantly different from zero (the p-value is equal to 0.21 with robust standard errors and to 0.13 with clustered standard errors). 17 One reason to favour this cut-off is that researchers typically focus their data collection efforts (or their choice of regression controls) on the controls they believe ex ante are the most important (Angrist and Pischke, 2010). 18 While without bilateral macro-areas a Hausman test rejects the hypothesis that there is endogeneity, the instrument varies too little within macro-areas to run the IV using such dummies. 19 For a discussion on how new technologies are shaping cultural consumption, see Borowiecki and Navarrete (2017, 2016) and Borowiecki et al. (2016). Acknowledgements Special thanks go to Giovanni Mastrobuoni for his valuable suggestions and constant encouragement. We are grateful to Bruce Weinberg, Francesc Ortega, Orley Ashenfelter, Eugene Smolensky, Mika Kortelainen, and Andrea Vindigni for their useful comments. We also thank all the participants at the seminars at the Industrial Relations Section at Princeton University, at the Department of Economics at Queens College CUNY (New York), at the Department of Economics at the University of Essex, and at the Collegio Carlo Alberto (Moncalieri, Italy). Finally, we gratefully acknowledge the comments by the participants at the 69th annual conference of the International Institute of Public Finance (Palermo, Italy), the 5th Applied Economics Workshop (Petralia Sottana, Italy), and the 4th European Workshop on Applied Cultural Economics (Aydin, Turkey). References Acemoglu D. , Johnson S. , Robinson J. A. 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Published: Sep 23, 2017

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