This study examines the socio-political landscape of the ancient city of Amastris (modern Amasra) through the lens of its road infrastructure, with a particular focus on the construction and significance of Aquila’s roads. Situated in the challenging terrain of northern Anatolia’s Küre Mountains, Amastris served as a vital maritime hub, linking diverse inland and coastal communities within Paphlagonia. Employing a multidisciplinary approach that integrates ancient literary analysis, archaeological evidence, and geospatial modeling, this paper reconstructs the network of primary and secondary Roman roads emanating from Amastris. The research highlights the dual role of these roads in fostering territorial coherence and enhancing regional connectivity, supporting both local autonomy and imperial governance. Key findings demonstrate that Aquila’s roads were not merely infrastructural projects but strategic undertakings that blended private investment with public utility. These projects reflect the intricate interplay between individual agency and state interests in Roman provincial administration. Furthermore, the study explores the broader cultural and economic impacts of road construction on Amastris, illustrating how connectivity shaped civic identity, social integration, and territorial integrity. The paper concludes that Aquila’s road-building initiatives were instrumental in sustaining Amastris’s strategic significance and functionality within the Roman Empire. By examining the dynamic relationship between local and imperial priorities, this study offers insights into how infrastructure functioned as a nexus of governance, economic development, and regional integration in ancient Anatolia.
Using the census data from 2000-2015 and a pseudo-event study design, we estimate the motherhood penalty in China and explore its association with declining fertility. We find that one-third of working women leave their jobs in the year when they give birth, and the penalty persists for over eight years. The motherhood penalty increases significantly across almost all provinces during this period, and provinces with larger increases in the penalty experience greater declines in fertility rates. Using a mover-based design, we find that the rising motherhood penalty has caused a significant decline in the total fertility rate.
We investigate how exposure to the One-Child Policy (OCP) during early adulthood affects marriage and fertility in China. Exploring fertility penalties across provinces over time and the different implementations by ethnicity, we show that the OCP significantly increases the unmarried rate among the Han ethnicity but not among the minorities. The OCP increases Han-minority marriages in regions where Han-minority couples are allowed for an additional child, but the impact is smaller in other regions. Finally, the deadweight loss caused by lower fertility accounts for 10 percent of annual household incomes, and policy-induced fewer marriages contribute to 30 percent of the fertility decline.
Order type plays an important role in algorithmic trading and is a key factor of price impact. In this paper, we propose a new framework for studying the discrete price change process, which focuses on the impacts of aggressive orders (market orders and aggressive limit orders) and cancellations. The price change process is driven by states and events of best quotes, and we define the event-based price change as the "natural price change" (NPC). Under the framework, we propose a heteroscedastic linear econometric model for the NPC to explore the impact of different types of orders on the price dynamics. To verify the usability of our model and explore the driving factors of price dynamics, we conduct a thorough empirical analysis for 786 large-tick stocks traded on the Shenzhen Stock Exchange. Empirical results statistically demonstrate that aggressive orders can introduce stronger impact on the NPC than cancellations. Meanwhile, splitting a big order into several small orders can lead to a larger NPC. Our framework can also be applied for the prediction of price change.