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on Computational Economics |
| By: | João Amador; Paulo Barbosa; João Cortes |
| Abstract: | This paper studies firms’ distances to becoming successful exporters. The empirical exercise uses rich data on Portuguese firms and assumes that there are significant features distinguishing exporters from non-exporters. An array of machine learning models—Bayesian Additive Regression Tree (BART), Missingness Not at Random (BART-MIA), Random Forest, Logit Regression, and Neural Networks—are trained to predict firms’ export probability and to shed light on the critical factors driving the transition to successful export ventures. Neural Networks outperform the other models and remain highly accurate when export definitions and training and testing strategies are changed. We show that the most influential variables for prediction are labor productivity and the share of imports from the EU in total purchases. Additionally, firms at the median distance to sell in international markets operate with about twice the assets of the group in the decile more distance from exporting. Firms in the decile closest to the export market operate with around 12 times more assets than those in the decile more distant from exporting. |
| JEL: | C53 C55 L2 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202420 |
| By: | Chenyu Hou (Simon Fraser University) |
| Abstract: | Most empirical studies on expectation formation models share a common dynamic structure but impose different functional form restrictions. I propose a flexible non-parametric method that maintains this dynamic structure to estimate a model of expectation formation using Recurrent Neural Networks. Applying this approach to data on macroeconomic expectations from the Michigan Survey of Consumers and a rich set of signals, I document three novel findings: (1) agents’ expectations about the future economic condition have asymmetric and non-linear responses to signals; (2) agents’ attentions shift from signals about the current state to signals about the future as the economic condition deteriorates ; (3) the content of signals on economic conditions plays the most important role in creating the attention-shift. Double Machine Learning approach is used to obtain statistical inferences of these empirical findings. Finally, I show these stylized facts can be generated by a model with rational inattention, in which information endogenously becomes more valuable when economic status worsens. |
| Date: | 2023–04 |
| URL: | https://d.repec.org/n?u=RePEc:sfu:sfudps:dp23-13 |
| By: | Ashwin, Julian; Chhabra, Aditya; Rao, Vijayendra |
| Abstract: | Large Language Models (LLMs) are quickly becoming ubiquitous, but the implications for social science research are not yet well understood. This paper asks whether LLMs can help us analyse large-N qualitative data from open-ended interviews, with an application to transcripts of interviews with displaced Rohingya people in Cox’s Bazaar, Bangladesh. The analysis finds that a great deal of caution is needed in using LLMs to annotate text as there is a risk of introducing biases that can lead to misleading inferences. Here this refers to bias in the technical sense, that the errors that LLMs make in annotating interview transcripts are not random with respect to the characteristics of the interview subjects. Training simpler supervised models on high-quality human annotations with flexible coding leads to less measurement error and bias than LLM annotations. Therefore, given that some high quality annotations are necessary in order to asses whether an LLM introduces bias, this paper argues that it is probably preferable to train a bespoke model on these annotations than it is to use an LLM for annotation. |
| Date: | 2023–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10597 |
| By: | von der Heyde, Leah (LMU Munich); Haensch, Anna-Carolina; Wenz, Alexander (University of Mannheim) |
| Abstract: | [For the more comprehensive version of this work, please see https://arxiv.org/abs/2407.08563] The rise of large language models (LLMs) like GPT-3 has sparked interest in their potential for creating synthetic datasets, particularly in the realm of privacy research. This study critically evaluates the use of LLMs in generating synthetic public opinion data, pointing out the biases inherent in the data generation process. While LLMs, trained on vast internet datasets, can mimic societal attitudes and behaviors, their application in synthesizing data poses significant privacy and accuracy challenges. We investigate these issues using the case of vote choice prediction in the 2017 German federal elections. Employing GPT-3, we construct synthetic personas based on the German Longitudinal Election Study, prompting the LLM to predict voting behavior. Our analysis compares these LLM-generated predictions with actual survey data, focusing on the implications of using such synthetic data and the biases it may contain. The results demonstrate GPT-3’s propensity to inaccurately predict voter choices, with biases favoring certain political groups and more predictable voter profiles. This outcome raises critical questions about the reliability and ethical use of LLMs in generating synthetic data. |
| Date: | 2023–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:97r8s_v1 |
| By: | von der Heyde, Leah (LMU Munich); Haensch, Anna-Carolina; Wenz, Alexander (University of Mannheim) |
| Abstract: | UPDATED VERSION AT https://arxiv.org/abs/2407.08563 The recent development of large language models (LLMs) has spurred discussions about whether LLM-generated “synthetic samples” could complement or replace traditional surveys, considering their training data potentially reflects attitudes and behaviors prevalent in the population. A number of mostly US-based studies have prompted LLMs to mimic survey respondents, finding that the responses closely match the survey data. However, several contextual factors related to the relationship between the respective target population and LLM training data might affect the generalizability of such findings. In this study, we investigate the extent to which LLMs can estimate public opinion in Germany, using the example of vote choice as outcome of interest. To generate a synthetic sample of eligible voters in Germany, we create personas matching the individual characteristics of the 2017 German Longitudinal Election Study respondents. Prompting GPT-3 with each persona, we ask the LLM to predict each respondents’ vote choice in the 2017 German federal elections and compare these predictions to the survey-based estimates on the aggregate and subgroup levels. We find that GPT-3 does not predict citizens’ vote choice accurately, exhibiting a bias towards the Green and Left parties, and making better predictions for more “typical” voter subgroups. While the language model is able to capture broad-brush tendencies tied to partisanship, it tends to miss out on the multifaceted factors that sway individual voter choices. Furthermore, our results suggest that GPT-3 might not be reliable for estimating nuanced, subgroup-specific political attitudes. By examining the prediction of voting behavior using LLMs in a new context, our study contributes to the growing body of research about the conditions under which LLMs can be leveraged for studying public opinion. The findings point to disparities in opinion representation in LLMs and underscore the limitation of applying them for public opinion estimation without accounting for the biases in their training data. |
| Date: | 2023–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:8je9g_v1 |
| By: | Dirk Bergemann (Yale University); Alessandro Bonatti (Massachusetts Institute of Technology); Alex Smolin (Toulouse School of Economics) |
| Abstract: | We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints. Users with similar aggregate value-scale characteristics choose similar levels of fine-tuning and token consumption. The optimal mechanism can be implemented through menus of two-part tariffs, with higher markups for more intensive users. Our results rationalize observed industry practices such as tiered pricing based on model customization and usage levels. |
| Date: | 2025–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2425 |
| By: | Churchill, Alexander; Pichika, Shamitha; Xu, Chengxin (Seattle University); Liu, Ying (Rutgers University) |
| Abstract: | Text annotation, the practice of labeling text following a predetermined scheme, is essential to qualitative researcher in public policy. Despite its importance utility, text annotation for policy research faces challenges of high labor and time costs, particularly when the size of the qualitative data is enormous. Recent Developments in large language models (LLMs), specifically models with generative pre-trained transformers (GPTs), shows a potential approach that may alleviate the burden of manual annotation coding. In this report, we test if Open AI’s GPT3.5 and GPT-4 models can be employed for text annotation tasks and measure the results of different prompting strategies against manual annotation. Using email messages collected from a national corresponding experiment in the U.S. nursing home market as an example, on average, we demonstrate 86.25% percentage agreement between GPT and human annotations. We also show that GPT models possess context-based limitations. Our report ends with suggestions, guidance, and reflections for readers who are interested in using GPT models for text annotation. |
| Date: | 2024–01–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:6fpgj_v1 |
| By: | Reiss, Michael V.; Roggenkamp, Hauke |
| Abstract: | We examine the reproducibility and robustness of the central claims from Robertson et al. (2023) who investigate the impact of negative language on online news consumption by analyzing over 12, 448 randomized controlled trials on upworthy.com. Applying "lexical" sentiment analyses, the authors make two central claims: first, they find that headlines with negative words significantly increase click-through rates (CTR). Second, they find that positive words in a headline reduce a news headline's CTR. Our reproducibility efforts include two different techniques: using the same data and procedures described in the study, we successfully reproduce the two claims through a blind computational approach, with only minor and inconsequential discrepancies. When using the authors' codes, we reproduce the two claims with identical numerical results. Examining the robustness of the authors' claims in a pre-registered third step, we validate and apply a "semantic" sentiment analysis using two large language models to re-compute their independent variables describing negativity and positivity. While we find support for the negativity bias, we do not find semantic (in contrast to lexical) positivity to reduce online news consumption. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:i4rdps:199 |
| By: | Friedrich Geiecke; Xavier Jaravel |
| Abstract: | Large language models can conduct interviews at speed and at scale. Friedrich Geiecke and Xavier Jaravel present a new open source platform to support this innovative form of qualitative research. |
| Keywords: | Technological change |
| Date: | 2025–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:695 |
| By: | Matthews, Ben |
| Abstract: | The paper demonstrates simulating Victimization Divides (Hunter and Tseloni, 2016) using an example from the Crime Survey for England and Wales. The simulation method is based on King et al. (2000). |
| Date: | 2024–02–02 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:k8j9e_v1 |
| By: | Lofgren, Hans; Cicowiez, Martín; Mele, Gianluca |
| Abstract: | Over nine years of violence and conflict have profoundly altered the Republic of Yemen’s economy. The war has shattered the country’s already fragile socioeconomic equilibria, affecting nearly every facet of life. Since the onset of the conflict, economic diagnostics have focused on descriptions of the deteriorating macro-fiscal and poverty conditions, lack of food security, and loss of capital accumulation. However, relatively little attention has gone toward the development of a forward-looking vision for the country, rooted in Yemen’s current economic structure. This paper helps to fill this gap by presenting and analyzing a set of scenarios for Yemen’s economy up to 2030. The analysis is based on a new version of the Sustainable Development Goal Simulation model, a dynamic computable general equilibrium (CGE) model, which is applied to a new social accounting matrix (SAM) for Yemen. The new social accounting matrix has the virtue of consolidating sparce and often inconsistent Yemeni data from multiple sources (the World Bank, the International Monetary Fund, and the United Nations system) into a coherent framework that reflects the basic structure of the economy, both at the macro and sectoral levels. The simulation analysis is built around three broad scenarios spanning 2022 through 2030. The results suggest that if the conflict subsides, governance is strengthened, and the donor community provides crucial aid, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great. |
| Date: | 2023–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10599 |
| By: | KINA, MEHMET FUAT |
| Abstract: | This study analyzes Turkey's political landscape by harnessing Computational Social Science techniques to parse extensive data about public ideologies from the Politus database. Unlike existing theoretical framework that considers ideologies of political elites and cadres, this study examines public ideologies in a contentious political manner. It distills eight most prevalent ideologies down to the city level and employs unsupervised machine learning models. The Principal Component Analysis delineates two fundamental axes, the traditional left-right political spectrum and a separate spectrum of secular-religious inclination, namely political and cultural dimensions. Then, the Cluster Analysis reveals three distinct groups: left-leaning and religiously inclined, right-leaning and religiously inclined, and those with centrist views with a pronounced secular focus. The outcomes provide valuable insights into the political and cultural axes within political society, offering a clearer understanding of the most recent ideological and political climate in Turkey. |
| Date: | 2024–05–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:kp7s2_v1 |
| By: | Von Arnim, Rudiger; Tröster, Bernhard; Raza, Werner |
| Abstract: | Is the harmonization of different food standards between countries in the context of free trade agreements exclusively positive, or is there a risk of high social costs for consumers? The harmonization of different national regulations plays an important role in contemporary trade policy. In this paper, the costs and benefits of harmonizing food safety regulations between the EU and the USA are estimated, using the ÖFSE Global Trade Model. The results show that the cost savings for companies due to the harmonization of regulatory standards are significantly lower than the associated negative effects on public health. Trade policy impact assessments must therefore take into account the social costs of regulation. |
| Abstract: | Ist die Angleichung unterschiedlicher Lebensmittelstandards zwischen Ländern im Kontext von Freihandelsabkommen ausschließlich positiv zu bewerten, oder besteht hier die Gefahr von Qualitätsverlusten mit hohen sozialen Kosten für die betroffene Bevölkerung? Die Angleichung unterschiedlicher nationaler Regulierungen spielt in der zeitgenössischen Handelspolitik eine wichtige Rolle. In diesem Working Paper werden die Kosten und Nutzen der Angleichung von Lebensmittelsicherheitsvorschriften zwischen der EU und den USA mithilfe des "ÖFSE Global Trade Model" exemplarisch abgeschätzt. Die Ergebnisse zeigen, dass die Kostenersparnisse für Unternehmen aufgrund der Angleichung regulatorischer Standards deutlich geringer ausfallen als die damit einhergehenden negativen Effekte auf die öffentliche Gesundheit. Die handelspolitische Folgenabschätzung muss daher die gesellschaftlichen Kosten regulatorischer Qualitätsverluste systematisch berücksichtigen. |
| Keywords: | non-tariff barriers, TTIP, CETA, free-trade agreements, global markets |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:hbsfof:311845 |
| By: | Esteban Rossi-Hansberg; Jialing Zhang |
| Abstract: | We use high-resolution spatial data to build a novel global annual gridded GDP dataset at 1°, 0.5°, and 0.25° resolutions from 2012 onward. Our random forest model trained on local and national GDP achieves an R² above 0.92 for GDP levels and above 0.62 for annual changes in regions left out of the training sample. By incorporating diverse indicators beyond population and nighttime lights, our estimates offer more precise subnational GDP measurements for analyzing economic shocks, local policies, and regional disparities. We evaluate the precision of our estimates with a sample case of COVID-19’s impact on local GDP in China. |
| JEL: | E0 F0 R0 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33458 |
| By: | Chase, Sarah K.; Sachdeva, Sonya; Wood, Spencer A (University of Washington); Lawler, Joshua J |
| Abstract: | 1. Addressing social and ecological values is a central aim of democratic environmental management and policymaking, especially during deliberative and participatory processes. Agencies responsible for managing public lands would benefit from a deepened understanding of how various publics’ value those lands. 2. Federal land management agencies receive millions of written comments from the public on proposed management actions annually, providing a unique source of insights into how the public assigns value to public lands. To date, little attention has been directed towards methods for analyzing the public’s comments to understand their expressed values, in part because the volume of comments often makes manual analysis unworkable. 3. This study introduces and applies a novel computational approach to inferring values in written text by using natural language processing and a method that combines a lexicon with semantic embedding models. We developed embedding models for four types of values that are expressed in public comments. We then fit models to 409, 241 public comments on actions proposed by the United States Forest Service from 2011 to 2020 and regulated by the Natural Environmental Policy Act. 4. The embedding model generally outperformed the lexicon word-count, particularly for value types with shorter lexicons, and, like human evaluators, the embedding models performed better for more evident values and were less reliable for more abstract or latent values. 5. By applying the resulting model, we furthered our understanding of how the public values National Forest lands in the United States. We observed that aesthetic and moral values were expressed more often in comments for projects that received more public interest, as gauged by the number of comments a project received and in comments for projects addressing recreational management. |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:f4pgy_v1 |
| By: | Muggleton, Naomi; Rahal, Charles; Reeves, Aaron |
| Abstract: | The Covid-19 pandemic brought unprecedented changes to business ownership in the UK which affects a generation of entrepreneurs and their employees. Nonetheless, the impact remains poorly understood. This is because research on capital accumulation has typically lacked high-quality, individualized, population-level data. We overcome these barriers to examine who benefits from economic crises through a computationally orientated lens of firm creation. Leveraging a comprehensive cache of administrative data on every UK firm and all nine million people running them, combined with probabilistic algorithms, we conduct individual-level analyzis to understand who became Covid entrepreneurs. Using these techniques, we explore characteristics of entrepreneurs—such as age, gender, region, business experience, and industry—which potentially predict Covid entrepreneurship. By employing an automated time series model selection procedure to generate counterfactuals, we show that Covid entrepreneurs were typically aged 35–49 (40.4%), men (73.1%), and had previously held roles in existing firms (59.4%). For most industries, growth was disproportionately concentrated around London. It was therefore existing corporate elites who were most able to capitalize on the Covid crisis and not, as some hypothesized, young entrepreneurs who were setting up their first businesses. In this respect, the pandemic will likely impact future wealth inequalities. Our work offers methodological guidance for future policymakers during economic crises and highlights the long-term consequences for capital and wealth inequality. |
| Keywords: | big data; computational social science; inequality; economic sociology |
| JEL: | L81 R14 J01 J1 |
| Date: | 2025–05–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127228 |
| By: | Pérez-Sebastián, Fidel; Serrano Quintero, Rafael; Steinbuks, Jevgenijs |
| Abstract: | How does the misallocation of complementary public capital affect the spatial organization of economic activity To answer this question, this paper endogenizes the government's decision to invest in the transport and electricity networks. A novel multi-sector quantitative spatial equilibrium model incorporates the quality of the electric power and the road transportation infrastructure networks, which determine sectoral productivities and trade costs. Simulation results for the Brazilian economy point to significant welfare gains from reallocating infrastructure investment. Spatial and fiscal complementarities in heterogeneous infrastructure provision determine a sizeable part of those gains. Misallocation of both infrastructure investments is positively associated with local political support for the incumbent authority. |
| Date: | 2023–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10650 |
| By: | Robayo, Monica; Cabrera, Maynor Vinicio |
| Abstract: | Bulgaria still ranks among the EU countries with the highest levels of poverty and inequality. Before 2023, Bulgaria's Social Assistance / Monthly Social Allowance scheme had limited coverage, strict eligibility criteria, and limited impact on poverty reduction. Additionally, it was not adjusted or linked to inflation. The Bulgarian government introduced a reform in 2022 aimed to increase the scope and access of individuals to social support by increasing the basis for determining the differentiated minimum income threshold (now 30 percent of the relative poverty line) and the parameters linked with age, health condition, and social status, affecting the social programs anchored to it, such as the Monthly Social Allowance and the heating allowance. This paper assesses this reform's potential ex-ante poverty and distributional impacts, relying on a comprehensive tax/benefit system assessment called the Commitment to Equity and microsimulation techniques. The changes in the legal basis for determining access to social assistance introduced with the reform are expected to create some relief from the indexation of the benefits over time. They will now be tied to the evolution of the relative poverty line and, therefore, linked to the evolution of median income. The results of the policy simulations show that the combined effect of the changes in the Monthly Social Allowance and the heating allowance contributes to a slight reduction in the poverty gap but not enough to move a sizable share of people out of poverty, as shown by the negligible impact on the at-risk-of-poverty rate. Inequality is barely affected. Compared with a Bulgarian food basket, the results show that eligibility thresholds are still restrictive. These results suggest further scope for improvement in the design of these programs, including anchoring them to an absolute poverty line or basic consumption basket. |
| Date: | 2024–06–24 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10817 |
| By: | Bou Habib, Chadi |
| Abstract: | This paper investigates the similarities between the economy of 1912 Mount Lebanon on the eve of the famine of 1916 and the economy of 2004 Lebanon that set the stage for the major economic and social crisis of 2019. A simple general equilibrium simulation shows that, as long as the Lebanese economy remains reliant on foreign inflows, crises will persist, with different manifestations. Regardless of the period considered, foreign inflows increase domestic prices and induce real appreciation. Low productive capacities and insufficient job creation lead to high emigration. Emigration increases the reliance on foreign inflows, which in turn increase domestic prices and reduce competitiveness, hence triggering further emigration and further reliance on foreign inflows. Income and prices increase, but exports decline, and growth remains volatile. The interruption of the flows of capital and goods and the impossibility to migrate due to the First World War drove Lebanon into starvation in 1916. The interruption of inflows of capital in 2019 led to a major crisis and massive outmigration, as predicted through the simulations based on the structure of the Lebanese economy in 2004. The simulations effectively capture the impact of external shocks on the Lebanese economy and closely align with the actual changes in economic variables during 2005 to 2020. |
| Date: | 2024–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10688 |
| By: | Sara Riscado; Antonio F. Amores; Henrique Basso; Johannes Simeon Bischl; Paola De Agostini; Silvia De Poli; Emanuele Dicarlo; Maria Flevotomou; Maximilian Freier; Sofia Maier; Esteban Garcia-Miralles; Myroslav Pidkuyko; Mattia Ricci |
| Abstract: | Following the inflation surge in the aftermath of the pandemic crisis, governments adopted a large array of fiscal measures to cushion its impact on households and firms. In the euro area, discretionary fiscal measures are estimated to amount to around 2% of GDP, in both 2022 and 2023. In the analysis of the impact of inflation and related fiscal measures the distributional dimension is particularly relevant, since the sudden and strong increase in prices affected families differently depending on their position in the income distribution. Furthermore, the evaluation of the cost of fiscal measures and their targeting is fundamental to improve the efficiency and effectiveness of policy interventions. Using a microsimulation approach, this paper uncovers the aggregate and distributional impact of high consumer inflation, as well as the impact of the government measures aimed at supporting households and containing prices. This analysis is carried out for 2022 and includes Germany, France, Italy, Spain, Portugal and Greece, which together proxy for the euro area. Our work confirms that the purchasing power and welfare of lower-income households was more severely affected by the 2022 inflation surge than that of high-income households. Fiscal measures contributed significantly to closing the inflation gap, though with country differences. However, most fiscal measures were not particularly targeted to low-income households, implying a low cost-effectiveness in protecting the poorest in some countries. |
| JEL: | E31 D31 H12 H50 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202424 |
| By: | Giulia Aliprandi (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, EU Tax - EU Tax Observatory); Kane Borders (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, EU Tax - EU Tax Observatory) |
| Abstract: | Multinational enterprises have risen to become dominant forces in the global economy, accompanied by a troubling trend of aggressive tax avoidance. In 2022 alone, an estimated $1 trillion in profits was shifted to tax havens by multinationals, amounting to 35% of all profits booked outside their headquarters countries (Alstadsæter et al., 2023). Despite tax avoidance being a major public concern, the specific practices employed by individual companies have remained largely opaque to the public due to a lack of transparency and public disclosure obligations. Comprehensive transparency measures promote informed policymaking, accountability, public trust, and sustainable development globally. This report examines the current landscape of corporate tax transparency and evaluates how emerging transparency measures could shape future developments in this critical area. We focus on corporate tax transparency measures via Country-by-Country Reporting (CbCR), where multinationals disclose detailed financial and tax-related information for each country of operation. We collected the publicly available CbCR reports and compiled them into a single database: the Public CbCR Database. This new data source highlights that large multinationals, particularly from Western Europe, are leading the way as primary publishers of such reports. Overall, the large multinationals publishing public CbCR account for less than 2% of large companies, and less than 5% of global revenues and global profits. Despite the small numbers, our research reveals an upward trend in voluntary CbCR disclosures, signalling increasing tax transparency practices. However, significant gaps remain, as U.S. multinationals and firms from major economies like China and Russia have only a few CbCR disclosures available. The European Union (EU) made an important step in furthering corporate tax transparency by adopting a mandatory CbCR directive that started applying this year in many EU countries. Our simulations reveal the impact this directive will have. Nearly one-third of large U.S. MNEs will be compelled to publish more disaggregated financial information than ever before publicly available. The increased disclosure from these U.S. corporate giants, who have historically been opaque, could be a breakthrough in tax transparency. However, the directive has serious limitations, as the requirements for geographical disaggregations are largely insufficient to truly evaluate the activity of multinationals. Broader adoption and enhancement of corporate tax transparency initiatives are crucial, we suggest several ways to improve the directive going forward. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:pseptp:halshs-04947447 |
| By: | Yan, Xiaoqin; Bao, Honglin; Leppard, Tom; Davis, Andrew |
| Abstract: | This paper investigates the cultural ties in American sociology defined by the shared usage of cultural symbols across schools. Cultural symbols are operationalized as research focuses from the dissertations of a school’s graduates. We construct a unique pairwise dataset including 6, 441 school pairs across 114 schools, detailing their dyadic relationships (e.g., geographical co-residence) and cultural proximity inferred from dissertations. We build a socio-cultural network where a school sends a tie to another when their proximity is sufficiently high. We design computational linguistic methods to identify gatekeeping symbols co-used by reciprocally connected schools within the same cultural niche. Our findings reveal two major school clusters and their research trajectories, with one representing dominant trends in relatively esoteric areas like sociology of culture, economic life, organizations, and politics and the other a more explicit focus on social problems. We further discern key determinants that shape cultural convergence and distinction, including school prestige, geographical co-residence, and institutional classification. In sum, our study proposes a pipeline for measuring cultural ties across schools and understanding the factors that influence the development of duality between schools and schools of thought. |
| Date: | 2024–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:qvyj8_v1 |
| By: | Floreani, Vincent Arthur; Rama, Martin G. |
| Abstract: | Disasters are frequent and clearly harmful in developing countries, but precisely estimating their overall cost and distributional impact is challenging. This paper proposes a microsimulation approach to do so rapidly, borrowing concepts from both poverty analysis and urban economics. Because housing prices reflect the present value of a specific bundle of living conditions, local earnings opportunities, and local access to services, their change in the aftermath of a disaster can be interpreted as a measure of the welfare cost incurred by households. A hedonic pricing function is used to estimate such changes based on the destruction experienced by the dwellings themselves, but also on the overall destruction suffered by their surrounding areas. The first element captures the damage from worse living conditions, whereas the second captures the loss from diminished earnings opportunities and access to services. The proposed approach is illustrated by estimating the cost of the 2015 Gorkha earthquakes in Nepal. Overall, the estimated impact is comparable to that from the official assessment. But its spatial distribution is significantly different due to the pivotal influence of neighborhood effects. |
| Date: | 2024–01–12 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10668 |