Why are the wealthiest so wealthy? 

Do they inherit wealth, or do they earn more? Do they save more? Do they make better returns on their investments? Professor Sergio Salgado answers these questions using detailed Norwegian data spanning two decades. At age 50, the excess wealth of the top 0.1% group relative to mid-wealth households is accounted for in about equal terms by higher saving rates (34%), higher initial wealth (32%), and higher returns (27%), while higher labor income (5%) and inheritances (1%) account for the small residual. There is significant heterogeneity among the wealthiest: one-fourth of them—dubbed the “New Money”—start with negative wealth but experience rapid wealth growth early in life. For this group, higher-than-average saving rates (46%), returns (34%), and labor earnings (16%) account for the majority of their wealth holdings by age 50. 

 

 

 

(Almost) 200 years of news-based economic sentiment 

New artificial intelligence algorithms have sparked a revolution in the analysis of textual data. Using 200 million pages of text from 13,000 U.S. local newspapers, Professor Jules van Binsbergen constructs a 170-year-long time series measure of economic sentiment at the country and state levels. The learning corpus includes roughly 1 billion newspaper articles and is 95 times larger than the total number of English-language Wikipedia entries combined. The new sentiment measure predicts economic fundamentals such as GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly used predictors, and materially predicts monetary policy decisions, particularly during recessions.

 

Does the minimum wage help low-income workers?

The minimum wage aims to provide a living wage and reduce poverty and inequality. Does it achieve those goals? To answer that question, Professor Thomas Winberry develops a model with worker heterogeneity, firm monopsony power, and putty-clay technology. According to the model, in the long run, a large increase in the minimum wage — on the order of $15 per hour — induces firms to substitute away from low-wage workers, reducing their employment and earnings. However, these workers benefit in the short run because firms cannot immediately shift production to less labor-intensive practices.

Why are OTC investors mostly trading against expert dealers?

Bonds, currencies, derivatives, and structured products are often traded in over-the-counter (OTC) markets. The most popular dealers in these markets are incredibly sophisticated, spending millions per year to acquire enough expertise (e.g., data, technology, and skills) so they have superior information when trading with their counterparties. Why would an unsophisticated OTC investor route its transactions to such an expert dealer, despite obvious adverse selection concerns? Professor Vincent Glode explains this conundrum with the help of a model.  When sophisticated dealers are involved in many negotiations, their resources are spread thinly, reducing the apparent advantage coming from their expertise. Thus, the popularity of a few dealers is what allows them to provide their counterparties with liquidity without imposing severe adverse selection costs.

 

Explaining asset concentration in OTC markets

Traders in OTC markets negotiate directly with each other rather than through a centralized exchange. How do OTC investors find counterparties? Professor Chaojun Wang introduces the first model in which investors direct their search for counterparties without observing prices, inventories, or any other investor characteristics. This search behavior gives rise to asset concentration, in line with the observed distribution of assets across investors in the interbank market.