|
on Macroeconomics |
| By: | Galo Nuño; Philipp Renner; Simon Scheidegger |
| Abstract: | This paper studies monetary policy in a New Keynesian model with persistent supply shocks, that is, sustained increases in production costs due to factors such as wars or geopolitical fragmentation. First, we demonstrate that Taylor rules fail to stabilize long-term inflation due to endogenous shifts in the natural interest rate. Second, we analyze optimal policy responses under discretion and commitment. Under discretion, a systematic inflationary bias emerges when the shock impacts the economy. Under commitment, the optimal policy adopts a lean-against-the-wind approach without compensating for past inflation, implying that “bygones are bygones”. We further extend the model to incorporate the zero lower bound (ZLB) and show that the optimal policy supports preemptive easing. |
| Keywords: | deep learning, Markov switching model, cost-push shocks |
| JEL: | E32 E58 E63 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11463 |
| By: | Yannick Bury; Lars P. Feld; Ekkehard A. Köhler |
| Abstract: | With an outstandingly long data set of Swiss cantonal public finances, we study whether this Swiss subnational level runs sustainable fiscal policies. Going back to the year 1905, we test for stationarity of cantonal public debt, revenue and spending and for cointegration between cantonal revenues and expenditures. Based on time series properties, we estimate individual fiscal reaction functions for each canton and for the panel of cantons as a whole. Using second generation panel-modelling and thus accounting for heterogeneity in cantonal fiscal policy, structural breaks and cross-sectional dependence among the cantons, our results show that the cantons run sustainable policies. Moreover, our results provide evidence that fiscal rules explain part of the heterogeneity in cantonal fiscal reactions to increased debt. |
| Keywords: | fiscal sustainability, fiscal institutions, Swiss Cantons |
| JEL: | H62 H77 H72 C23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11453 |
| By: | Jesper Lindé; Mr. Patrick Schneider; Mrs. Nujin Suphaphiphat; Hou Wang |
| Abstract: | This paper analyzes the effectiveness of foreign exchange intervention (FXI) in mitigating economic and financial shocks in India by applying the Integrated Policy Framework (IPF). It highlights how FXI can be a complementary tool in mitigating the tradeoff between output and inflation, specifically under large economic shocks amid temporarily shallow FX markets. The paper indicates that while FXI can soften adverse impacts on domestic demand and output during severe risk-off shocks, its benefits under normal conditions with liquid FX markets are limited. |
| Keywords: | Integrated policy framework; foreign exchange intervention; risk-off shocks |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/236 |
| By: | Kristina Trajkovic (National Bank of Serbia) |
| Abstract: | Prevention of money laundering and other abuses in the digital assets sector is a major step in the preservation of financial system stability. Non-alignment of regulatory regimes in an environment of rapid market development creates a potential for abuse and illicit activities. Monitoring the market requires systematic analysis in order to define clear guidelines for mitigating identified risks. Regular implementation of risk assessment and the regulator’s supervisory function facilitate the identification of the riskiness of the entire digital assets sector. In addition to an overview of regulations and standards governing the prevention of money laundering, the paper looks into the risks to which the digital assets sector is exposed, including the conduct of supervision and, in this sense, implementation of the risk-based approach. |
| Keywords: | regulation, digital assets, virtual currency, supervision, money laundering, abuse |
| JEL: | E30 K20 K23 G18 |
| Date: | 2023–09 |
| URL: | https://d.repec.org/n?u=RePEc:nsb:bilten:17 |
| By: | Yulia Evsyukova; Felix Rusche; Wladislaw Mill |
| Abstract: | We assess the impact of discrimination on Black individuals’ job networks across the U.S. using a two-stage field experiment with 400+ fictitious LinkedIn profiles. In the first stage, we vary race via AI-generated images only and find that Black profiles’ connection requests are 13 percent less likely to be accepted. Based on users’ CVs, we find widespread discrimination across social groups. In the second stage, we exogenously endow Black and White profiles with the same networks and ask connected users for career advice. We find no evidence of direct discrimination in information provision. However, when taking into account differences in the composition and size of networks, Black profiles receive substantially fewer replies. Our findings suggest that gatekeeping is a key driver of Black-White disparities. |
| Keywords: | discrimination, job networks, labor markets, field experiment |
| JEL: | J71 J15 C93 J46 D85 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11433 |
| By: | Burgelman, Robert A. (Stanford U); Aaltonen, Pertti (?) |
| Abstract: | Our field study of new business development in a German-based global pharmaceutical company reveals that the emergence of co-selection bias in project-level state-gate resource allocation engendered a corporate-level innovation portfolio imbalance. We show how the corporate portfolio imbalance resulted from incoherent managerial activities in the multilevel resource allocation process (RAP) decision context and how this caused fizzling out of the proactively established incipient strategic context of the favored-for-growth business unit. Moreover, we identify strategic RAP exploitation challenges that explain why sequential exploitation capability and exploitation drive deficits caused an exploitation trap that limited strategic discretion and stymied top management strategic intent to maintain the company’s independence. Our integrated frameworks augment strategic management theory of corporate RAP and offer guidance for future research. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4191 |
| By: | Yu, Shuo; Falco, Nicola; Patel, Nivedita; Wu, Yuxin; Wainwright, Haruko |
| Abstract: | In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing. |
| Keywords: | Earth Sciences, Geoinformatics, Climate Action, Zero Hunger, carbon use efficiency, yield, climate change, machine learning, remote sensing, CESD-Sustainable Agriculture, Meteorology & Atmospheric Sciences |
| Date: | 2023–06–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:agrebk:qt0tw2k914 |
| By: | Amilcar Velez |
| Abstract: | This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving the performance of these estimators in applications. DML is an estimation method suited to economic models where the parameter of interest depends on unknown nuisance functions that must be estimated. It requires weaker conditions than previous methods while still ensuring standard asymptotic properties. Existing theoretical results do not distinguish between two alternative versions of DML estimators, DML1 and DML2. Under a new asymptotic framework, this paper demonstrates that DML2 asymptotically dominates DML1 in terms of bias and mean squared error, formalizing a previous conjecture based on simulation results regarding their relative performance. Additionally, this paper provides guidance for improving the performance of DML2 in applications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.01864 |
| By: | Peng Zhu; Yuante Li; Yifan Hu; Sheng Xiang; Qinyuan Liu; Dawei Cheng; Yuqi Liang |
| Abstract: | As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been widely used but often struggle to capture the intricate nonlinear dynamics of markets, particularly in the flexible selection and effective utilization of key historical information. Recently, methods like Graph Neural Networks and Reinforcement Learning have shown promise in stock prediction but require high data quality and quantity, and they tend to exhibit instability when dealing with data sparsity and noise. Moreover, the training and inference processes for these models are typically complex and computationally expensive, limiting their broad deployment in practical applications. Existing approaches also generally struggle to capture unobservable latent market states effectively, such as market sentiment and expectations, microstructural factors, and participant behavior patterns, leading to an inadequate understanding of market dynamics and subsequently impact prediction accuracy. To address these challenges, this paper proposes a stock prediction model, MCI-GRU, based on a multi-head cross-attention mechanism and an improved GRU. First, we enhance the GRU model by replacing the reset gate with an attention mechanism, thereby increasing the model's flexibility in selecting and utilizing historical information. Second, we design a multi-head cross-attention mechanism for learning unobservable latent market state representations, which are further enriched through interactions with both temporal features and cross-sectional features. Finally, extensive experiments on four main stock markets show that the proposed method outperforms SOTA techniques across multiple metrics. Additionally, its successful application in real-world fund management operations confirms its effectiveness and practicality. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20679 |
| By: | Mikhail Freer; Hassan Nosratabadi |
| Abstract: | In a two-stage model of choice a decision maker first shortlists a given menu and then applies her preferences. We show that a sizeable class of these models run into significant issues in terms of identification of preferences (welfare-relevance) and thus cannot be used for welfare analysis. We classify these models by their revealed preference principles and expose the principle that we deem to be the root of their identification issue. Taking our analysis to an experimental data, we observe that half of the alternatives that are revealed preferred to another under rational choice are left revealed preferred to nothing for any member of this class of models. Furthermore, the welfare-relevance of the specific models established in the literature are much worse. The model with the highest welfare-relevance produces a revealed preference relation with the average density of 2% (1 out of 45 possible comparisons revealed), while rational choice does 63% (28 out of 45 possible comparisons). We argue that the issue is not an inherent feature of two-stage models, and rather lies in the approach with which the first stage is modelled in the literature. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08263 |
| By: | Lykke E. Andersen (SDSN Bolivia); Marcelo Delajara (Anker Research Institute); Agnes Medinaceli (SDSN Bolivia); Richard Anker (Anker Research Institute); Martha Anker (Anker Research Institute) |
| Abstract: | This report provides updated estimates of family living expenses and living wages for rural areas and small and medium size towns of Lagunes, Vallée du Bandama and Lacs districts of Cote D’Ivoire. The update for June 2024 takes into account inflation and changes in payroll deductions since the original Anker living wage study carried out in July 2023 (Nguessan, et al., 2023). |
| Keywords: | Living costs, living wages, Anker Methodology, Cote D’Ivoire |
| JEL: | J30 J50 J80 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iad:glliwa:240446 |
| By: | Tiantian Mao; Gilles Stupfler; Fan Yang |
| Abstract: | We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and wealth when measuring risk. In this paper, we derive the first- and second-order asymptotic expansions for the generalized shortfall risk measure. Our asymptotic results can be viewed as unifying theory for, among others, distortion risk measures and utility-based shortfall risk measures. They also provide a blueprint for the estimation of these measures at extreme levels, and we illustrate this principle by constructing and studying a quantile-based estimator in a special case. The accuracy of the asymptotic expansions and of the estimator is assessed on several numerical examples. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07212 |
| By: | Kim, Jinhwan (Stanford U); Shi, Terrence Tianshuo (Harvard U); Verdi, Rodrigo S. (MIT) |
| Abstract: | We examine how the efficiency of the judicial system impacts corporate innovation. To do so, we exploit a pilot program introduced by the U.S. Congress in 2011, which allowed judges with expertise(as opposed to randomly selected judges) to preside over more patent cases to facilitate efficient ruling. We find firms headquartered in counties subject to the Patent Pilot Program increase patent-based innovation by 5.2% to 6.2%, relative to firms in counties not under the program. Our results are concentrated among firms with high legal costs and uncertainty: firms that engage in innovation with “fuzzy boundaries†, that have high litigation risk, and that are more resource-constrained. However, we also find non-random assignment has an adverse impact on firms more likely to be assigned to judges that are favorably-biased towards non-practicing entities (NPEs) or “patent trolls†, who engage in frequent, frivolous litigation. Taken together, our findings underscore the important role of judicial efficiency in helping firms better allocate their resources towards innovation investment, but also indicate that judicial efficiency programs can exacerbate the negative effects of judicial biases in certain contexts. |
| JEL: | J24 K11 K42 M41 O31 O38 O39 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4161 |
| By: | Viên, Trần Thị Xuân; Giao, Ha Nam Khanh; Thành, Tạ Văn |
| Abstract: | This article examines the impacts of smart tourism applications, destination imagery, and tourist satisfaction on the likelihood of tourists returning to Nha Trang. The authors implemented a mixed methods research approach, which included expert interviews, focus group discussions, and Confirmatory Factor Analysis within Structural Equation Modeling (CB – SEM). The study results reveal that smart tourism applications directly and positively influence their perception of the destination image. This enhanced destination image significantly boosts tourist satisfaction and positively correlates with their intention to revisit. From these findings, the authors suggest several managerial strategies: (1) to augment the visibility and development of smart tourism applications to boost Nha Trang’s tourism; (2) to enhance tourist satisfaction by crafting an impressive and memorable image of the destination through the effective preservation and utilization of the region’s natural assets and cultural heritage. Additionally, it is imperative for government authorities to rigorously manage the pricing of tourism services to maintain quality and deliver value to tourism. |
| Date: | 2024–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:6zasc |
| By: | Philipp Denter; Boris Ginzburg |
| Abstract: | Political agents often aim to influence elections through troll farms -- organisations that disseminate messages emulating genuine information. We study the behaviour of a troll farm that faces a heterogeneous electorate of partially informed voters, and aims to achieve a desired political outcome by targeting each type of voter with a specific distribution of messages. We show that such tactics are more effective when voters are otherwise well-informed, for example, when the media is of high quality. At the same time, increased polarisation, as well as deviations from Bayesian rationality, can reduce the negative effect of troll farms and restore efficiency of electoral outcomes. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.03241 |
| By: | Nordin, Martin (Lund University); Stanfors, Maria (Lund University) |
| Abstract: | Women have made substantial gains in education and outperform men regarding educational attainment across the OECD, but the consequences of this reversal of the gender gap in education (RGE) have not been well researched. We address the association between the RGE and partnering, employment, and support for a right-wing populist party in Sweden. We explore the differential impacts of women's educational advancements versus men's lagging by using cross-sectional register data and within-areal age variation in RGE. Results show that RGE is negatively associated with partnering and employment prospects among individuals with a low level of education. Results suggest that men's educational disadvantage may contribute to growing support for right-wing populist parties and that shifting gender gaps in education may foster frustration in various areas of life and anti-egalitarian values. |
| Keywords: | gender gap in education, partnership, employment, political opinion |
| JEL: | J12 I24 Z13 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17437 |