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on Tourism Economics |
| By: | Giuseppe Di Giacomo; Benjamin Lerch |
| Abstract: | We explore how unexpected temporary increases in the demand for low-skill service jobs in the tourism industry impact educational choices in Italy. We identify exogenous variation in the demand for service jobs using positive shocks to the tourism industry caused by terrorist attacks in foreign destinations that compete with Italy for tourists. We find that an exogenous increase in tourism decreases college enrollment and completion in the year after the shock. The decline in enrollment is driven by fewer students choosing fields related to humanities and social sciences. Both men and women respond by reducing college enrollment and completion. The effect for men is temporary, while it is more persistent for women. These effects are likely driven by higher opportunity costs of college education, as we find that tourism shocks increase the demand for labor in service jobs, raising local employment in the tourism industry as well as labor force participation. Using an IV approach, we show that the average annual increase in tourist arrivals between 2010 and 2019 (21, 000 tourists) increases tourism employment growth by 16 percent, while reducing college enrollment and completion growth by 9 percent. |
| Keywords: | service jobs, tourism, education, college enrollment, field of study |
| JEL: | I25 J24 L83 Z32 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12383 |
| By: | Giuseppe Di Giacomo |
| Abstract: | This paper studies the long-run effects of foreign tourism on local labor markets and economic development in advanced economies, using Italy as a case study. To isolate plausibly exogenous variation in tourist arrivals, I construct a shift-share measure that interacts changes in outbound tourism by country of origin with historical destination preferences across Italian locations. Higher exposure to tourism reduces employment and labor-force participation rates. It also induces structural transformation by expanding employment rates in hospitality and entertainment while contracting them in manufacturing and in non-tourism-related services. Average per-capita and labor income decline, whereas property income increases. Estimates in log-levels indicate that tourism raises local population, shrinks the manufacturing sector, and expands tourism-related services. Evidence on underlying mechanisms points to two main channels. First, tourism alters the composition of the local labor supply. Population growth is driven by young, low-skilled non-Italians, limiting the ability of productive, non-tourism firms to benefit from agglomeration forces. Second, rising land costs crowd out non-touristic activities. Consistent with this, nearly all net firm entry is accounted for by tourism-related establishments. Overall, results suggest that, in advanced economies, tourism may hinder long-run development by reallocating resources from more to less productive sectors. |
| Keywords: | tourism, structural transformation, local economic shocks, Dutch disease |
| JEL: | D31 E24 J21 L60 L83 O14 O18 R11 Z32 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12434 |
| By: | Harrison Katz |
| Abstract: | Understanding how the composition of guest origin markets evolves over time is critical for destination marketing organizations, hospitality businesses, and tourism planners. We develop and apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to forecast the compositional dynamics of guest origin market shares using proprietary Airbnb booking data spanning 2017--2024 across four major destination regions. Our analysis reveals substantial pandemic-induced structural breaks in origin composition, with heterogeneous recovery patterns across markets. The BDARMA framework achieves the lowest average forecast error across all destination regions, outperforming standard benchmarks including na\"ive forecasts, exponential smoothing, and SARIMA on log-ratio transformed data. For EMEA destinations, BDARMA achieves 23% lower forecast error than naive methods, with statistically significant improvements. By modeling compositions directly on the simplex with a Dirichlet likelihood and incorporating seasonal variation in both mean and precision parameters, our approach produces coherent forecasts that respect the unit-sum constraint while capturing complex temporal dependencies. The methodology provides destination stakeholders with probabilistic forecasts of source market shares, enabling more informed strategic planning for marketing resource allocation, infrastructure investment, and crisis response. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18358 |