arXiv subjects

Jangho Yang

Publications and source records attributed to Jangho Yang.

6 recordsLinked to original sources

arXiv2025

"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data

Using monthly Patreon earnings, we quantify how platform attention algorithms shape earnings concentration across creator economies. Patreon is a tool for creators to monetize additional content from loyal subscribers but offers little native distribution, so its earnings proxy well for the attention creators capture on external platforms (Instagram, Twitch, YouTube, Twitter/X, Facebook, and ``Patreon-only''). Fitting power-law tails to test for a highly unequal earnings distribution, we have three key findings. First, across years and platforms the earnings tail and distribution exhibits a Pareto exponent around $\alpha \approx 2$, closer to concentrated capital income than to labor income and consistent with a compounding, ``rich-get-richer'' dynamic (Barabasi and Albert 1999). Second, when algorithms tilt more attention toward the top, the gains are drawn disproportionately from the creator ``middle class''. Third, over time, creator inequality across social media platforms converge toward similarly heavy-tailed (and increasingly concentrated) distributions, plausibly as algorithmic recommendations rises in importance relative to user-filtered content via the social graph. While our Patreon-sourced data represents a small subset of total creator earnings on these platforms, it provides unique insight into the cross-platform algorithmic effects on earnings concentration.

econ.GN↗

arXiv2025

The Attribution Crisis in LLM Search Results

Web-enabled LLMs frequently answer queries without crediting the web pages they consume, creating an "attribution gap" - the difference between relevant URLs read and those actually cited. Drawing on approximately 14,000 real-world LMArena conversation logs with search-enabled LLM systems, we document three exploitation patterns: 1) No Search: 34% of Google Gemini and 24% of OpenAI GPT-4o responses are generated without explicitly fetching any online content; 2) No citation: Gemini provides no clickable citation source in 92% of answers; 3) High-volume, low-credit: Perplexity's Sonar visits approximately 10 relevant pages per query but cites only three to four. A negative binomial hurdle model shows that the average query answered by Gemini or Sonar leaves about 3 relevant websites uncited, whereas GPT-4o's tiny uncited gap is best explained by its selective log disclosures rather than by better attribution. Citation efficiency - extra citations provided per additional relevant web page visited - varies widely across models, from 0.19 to 0.45 on identical queries, underscoring that retrieval design, not technical limits, shapes ecosystem impact. We recommend a transparent LLM search architecture based on standardized telemetry and full disclosure of search traces and citation logs.

cs.DL↗

arXiv2022

Innovation in times of Covid-19

Did the Covid-19 pandemic have an impact on innovation? Past economic disruptions, anecdotal evidence, and the previous literature suggest a decline with substantial differences between industries. We leverage USPTO patent application data to investigate and quantify the disturbance. We assess differences by field of technology (at the CPC subclass level) as well as the impact of direct and indirect relevance for the management of the pandemic. Direct Covid-19 relevance is identified from a keyword search of the patent application fulltexts; indirect Covid-19 relevance is derived from past CPC subclass to subclass citation patterns. We find that direct Covid-19 relevance is associated with a strong boost to the growth of the number of patent applications in the first year of the pandemic at the same order of magnitude (in percentage points) as the percentage of patents referencing Covid-19. We find no effect for indirect Covid-19 relevance, indicating a focus on applied research at the expense of more basic research. Fields of technology (CPC mainsections) have an additional significant impact, with, e.g., mainsections A (human necessities) and C (chemistry, metallurgy) having a strong performance.

econ.GN↗

arXiv2020

Growth, development, and structural change at the firm-level: The example of the PR China

Understanding the microeconomic details of technological catch-up processes offers great potential for informing both innovation economics and development policy. We study the economic transition of the PR China from an agrarian country to a high-tech economy as one example for such a case. It is clear from past literature that rapidly rising productivity levels played a crucial role. However, the distribution of labor productivity in Chinese firms has not been comprehensively investigated and it remains an open question if this can be used to guide economic development. We analyze labor productivity and the dynamic change of labor productivity in firm-level data for the years 1998-2013 from the Chinese Industrial Enterprise Database. We demonstrate that both variables are conveniently modeled as L\'evy alpha-stable distributions, provide parameter estimates and analyze dynamic changes to this distribution. We find that the productivity gains were not due to super-star firms, but due to a systematic shift of the entire distribution with otherwise mostly unchanged characteristics. We also found an emerging right-skew in the distribution of labor productivity change. While there are significant differences between the 31 provinces and autonomous regions of the P.R. China, we also show that there are systematic relations between micro-level and province-level variables. We conclude with some implications of these findings for development policy.

econ.GN↗

arXiv2020

Levels of structural change: An analysis of China's development push 1998-2014

We investigate structural change in the PR China during a period of particularly rapid growth 1998-2014. For this, we utilize sectoral data from the World Input-Output Database and firm-level data from the Chinese Industrial Enterprise Database. Starting with correlation laws known from the literature (Fabricant's laws), we investigate which empirical regularities hold at the sectoral level and show that many of these correlations cannot be recovered at the firm level. For a more detailed analysis, we propose a multi-level framework, which is validated with empirically. For this, we perform a robust regression, since various input variables at the firm-level as well as the residuals of exploratory OLS regressions are found to be heavy-tailed. We conclude that Fabricant's laws and other regularities are primarily characteristics of the sectoral level which rely on aspects like infrastructure, technology level, innovation capabilities, and the knowledge base of the relevant labor force. We illustrate our analysis by showing the development of some of the larger sectors in detail and offer some policy implications in the context of development economics, evolutionary economics, and industrial organization.

econ.GN↗

arXiv2019

Measuring productivity dispersion: a parametric approach using the L\'{e}vy alpha-stable distribution

It is well-known that value added per worker is extremely heterogeneous among firms, but relatively little has been done to characterize this heterogeneity more precisely. Here we show that the distribution of value-added per worker exhibits heavy tails, a very large support, and consistently features a proportion of negative values, which prevents log transformation. We propose to model the distribution of value added per worker using the four parameter L\'evy stable distribution, a natural candidate deriving from the Generalised Central Limit Theorem, and we show that it is a better fit than key alternatives. Fitting a distribution allows us to capture dispersion through the tail exponent and scale parameters separately. We show that these parametric measures of dispersion are at least as useful as interquantile ratios, through case studies on the evolution of dispersion in recent years and the correlation between dispersion and intangible capital intensity.

econ.GN↗