Who Gets What? City Lobbying and State Transfers

This chapter uses a variety of panel data methods to estimate the returns to lobbying for individual municipalities. When cities start lobbying, they receive significantly more revenue from the state in the following year compared to other cities. But not all localities benefit equally. In particular, wealthy communities with higher median incomes tend to receive substantially more revenue after lobbying than less affluent municipalities. The chapter concludes by discussing some of the mechanisms that might be driving these results. While higher-income cities don’t spend more money on lobbying, they do spread their efforts across a greater number of bills, and they appear to be particularly savvy at using their lobbyists to advocate for shovel-ready projects that make attractive funding targets for state officials.

What is the benefit from publishing a working paper in a journal in terms of citations? Evidence from economics

Many papers in economics that are published in peer reviewed journals are initially released in widely circulated working paper series. This raises the question about the benefit of publishing in a peer-reviewed journal in terms of citations. Specifically, we address the question: to what extent does the stamp of approval obtained by publishing in a peer-reviewed journal lead to more subsequent citations for papers that are already available in working paper series? Our data set comprises about 28,000 working papers from four major working paper series in economics. Using panel data methods, we show that the publication in a peer reviewed journal results in around twice the number of yearly citations relative to working papers that never get published in a journal. Our results hold in several robustness checks.

The Impact of the COVID-19 Pandemic on the Tourism Economy

This chapter seeks to examine the impact of COVID-19 on the Turkish tourism economy. Towards this end, first of all, tourist arrivals and tourism income of Turkey were compared between pre-pandemic and within the pandemic period. Afterwards, to investigate whether COVID-19 leads to a decrease in stock prices of tourism firms, the return data of 11 firms listed in the Borsa Istanbul restaurants and hotels subsector for 10 months were examined by panel data methods. According to comparison of tourist arrivals and tourism income to the previous year, there was a vital decrease as 69% and 65% respectively in 2020. Moreover, coverage rate of foreign trade deficit of tourism income in Turkey decreased almost 80% in 2020. Overall, results of pooled OLS regression analysis revealed that COVID-19 cases and COVID-19-related deaths were decreasing monthly stock price returns. Based on these findings, it is recommended to policymakers to find better policies for a better tourism economy.

The Role of FDI on Exports Performance: Evidence from the Mexican States

This article examines manufacturing export determinants across Mexican states and regions from 2007 to 2015. Paying particular attention to the role of FDI, the analysis considers internal and external determinants of manufacturing exports under static and dynamic panel data methods. Several interesting results were obtained. First, the ratio of manufacturing to total GDP is the most consistent determinant of exports performance, regardless of the estimation method or specification employed. Second, static panel data estimations under GMM techniques suggest different sensitivity to FDI across regions, with the Mexico-U.S. border region observing the most substantial short-term effect of FDI on manufacturing exports. Finally, using dynamic panel data methods, we found significant persistence and similar long-term effects of FDI across most of the regions.

Modeling tourism demand: Theoretical and empirical considerations for future research

Although tourism literature is replete with tourism demand studies, numerous empirical and theoretical issues remain unresolved. The majority of the extant studies center around replication of well-established theory of tourism demand with applications of contemporary empirical techniques for different sets of tourist-originating and receiving countries. However, the application of empirical techniques, especially panel data methods that are utilized to model tourism demand, seems to be arbitrarily chosen without due consideration of theoretical and empirical ramifications. The purpose of this study is to present a critical review of the tourism demand literature. We assessed the theoretical and methodological inaccuracies when modeling tourism demand in general and when applying panel data models in particular. We provided a guide to application of panel data techniques when modeling tourism demand. The article ends with discussions of an agenda for future research to fill the gaps in the extant literature and advance tourism demand research.

From Data to Causes II: Comparing Approaches to Panel Data Analysis

This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.