First Look

Abstract—The terms “capitalism” and “socialism” continue to haunt our political and economic imaginations, but we rarely consider their interconnected early history. Even the 18th century had its “socialists,” but unlike those of the 19th, they paradoxically sought to make the world safe for “capitalists.” The word “socialists” was first used in Northern Italy as a term of contempt for the political economists and legal reformers Pietro Verri and Cesare Beccaria, author of the epochal On Crimes and Punishments. Yet the views and concerns of these first socialists, developed inside a pugnacious intellectual coterie dubbed the Academy of Fisticuffs, differ dramatically from those of the socialists that followed. I turn to Milan in the late 1700s to recover the Academy’s ideas and the policies they informed. At the core of their preoccupations lay the often lethal tension among states, markets, and human welfare in an era when the three were becoming increasingly intertwined. What distinguished these thinkers was their articulation of a secular basis for social organization, rooted in commerce, and their insistence that political economy trumped theology as the underpinning for peace and prosperity within and among nations. I argue that the Italian Enlightenment, no less than the Scottish, was central to the emergence of political economy and the project of creating market societies. By reconstructing ideas in their historical contexts, I address motivations and contingencies at the very foundations of modernity.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=54558

Abstract—Ethnic migrant inventors may differ from locals in terms of the knowledge they bring to host firms. We study the role of first-generation ethnic migrant inventors in cross-border transfer of knowledge previously locked within the cultural context of their home regions. Using a unique dataset of Chinese and Indian herbal patents filed in the United States, we find that an increase in the supply of first-generation ethnic migrant inventors increases the rate of codification of herbal knowledge at U.S. assignees by 4.5%. Our identification comes from an exogenous shock to the quota of H1B visas and from a list of entities exempted from the shock. We also find that ethnic migrant inventors are more likely to engage in reuse of their prior knowledge, whereas knowledge recombination is more likely to be pursued by teams comprising inventors from other ethnic backgrounds.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=54569

Abstract—A debt trap occurs when someone takes on a high-interest rate loan and is barely able to pay back the interest, and thus perpetually finds themselves in debt (often by refinancing). Studying such practices is important for understanding financial decision-making of households in dire circumstances as well as for setting appropriate consumer protection policies. We conduct a simple experiment in three sites in which we paid off high-interest moneylender debt of individuals. Most borrowers returned to debt within six weeks. One to two years after intervention, treatment individuals were borrowing at the same rate as control households.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=54587

Abstract—When workers make operational decisions, the firm's global knowledge and the workers’ domain-specific knowledge complement each other. Oftentimes workers have the final decision-making power. Two key decisions a firm makes when designing systems to support these workers are 1) what guidance to deliver and 2) what kind of training (if any) to provide. We examine these choices in the context of mobile money platforms—systems that allow users in developing economies to deposit, transfer, and withdraw money using their mobile phones. Mobile money has grown quickly, but high stockout rates of currency persist due to suboptimal inventory decisions made by contracted employees (called agents). In partnership with a Tanzanian mobile money operator, we perform a randomized controlled trial with 4,771 agents over eight weeks to examine how differing types of guidance and training impact the agents' inventory management. We find agents who are trained in person and receive an explicit, personalized, daily text message recommendation of how much electronic currency to stock are less likely to stock out. These agents are more likely to alter their electronic currency balance on a day (rebalance). In contrast, agents trained in person but who receive summary statistics of transaction volumes or agents who are notified about the program and not offered in-person training do not experience changes in stockouts or rebalances. We observe no evidence of learning or fatigue. Agent-level heterogeneity in the treatment effects shows that the agents who handle substantially more customer deposits than withdrawals benefit most from the intervention.

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Abstract—After decades of both deprioritizing shareholders' economic interests and low corporate profitability, Japan introduced the JPX400 in 2014. The index highlighted the country's "best-run" companies by annually selecting the 400 most profitable among Japan's large and liquid firms. Index-inclusion incentives led firms to increase ROE proportionally by 41%, though firms did not realize significant capital-market or product-market benefits from inclusion. Status incentives contributed to the observed performance improvement. Back-of-the-envelope estimates suggest that JPX400 inclusion incentives accounted for 16% (20%) of the growth in aggregate earnings (market capitalization) over our sample period. Stock indexes can transform longstanding behavior via nonpecuniary channels.

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Abstract—Machine learning process technologies usher new questions regarding their potential complementarity with existing human capital. Within the context of the U.S. Patent and Trademark Office examination process, our experimental framework investigates productivity differentials when workers with heterogeneous human capital interface with machine learning, relative to the older Boolean search technology. We randomly assign individuals with and without computer science and engineering (CS&E) knowledge bases to each process technology, a subset of whom are also randomly provided expert domain specific knowledge, and analyze their productivity as measured by accuracy and speed in identifying prior art relevant for patent claims adjudication. We find that, when provided with expert domain knowledge, productivity with machine learning technology is lower than Boolean technology, but these results are driven almost entirely by heterogeneous effects by those with and without computer science and engineering (CS&E) backgrounds. Specifically, tests of underlying mechanisms reveal that unlocking superior prediction from machine learning requires CS&E skills. Further, participants lacking these skills are able to compensate for more imprecise information from Boolean searches through superior reading and information sifting skills. Our study contributes to literature streams on artificial intelligence, endogenous technological change, and strategic management of the pace of technological substitution by providing insights on complementarities between technologies and horizontally differentiated human capital.

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Abstract—We document evidence that firms systematically increase specialized, locally targeted advertising following the firm being taken to trial in that given location—precisely following initiation of the suit. In particular, we use legal actions brought against publicly traded firms over the 20-year sample period that progress to trial from 1995–2014. In terms of magnitude, the increase is sizable: targeted local advertising increases by 23% (t=4.39) following the suit. Moreover, firms concentrate these strategic increases in locations where the returns on their advertising dollars are largest: in smaller, more concentrated advertising markets where fewer competitor firms are advertising. They focus their advertisement spikes specifically toward jury trials and, in fact, specifically toward the most likely jury pool. Lastly, we document that these advertising spikes are associated with verdicts, increasing the probability of a favorable outcome.

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Abstract—Behavioral biases that cause errors in decision-making are often blamed on cognitive limitations. We show that biases can also arise, or be exacerbated, because agents are motivated to make errors. In three experiments involving nearly 3,200 participants, agents motivated to be selfish make simple computational errors and respond to the salience of information known to them, and agents motivated to believe they are high ability update on entirely uninformative signals. When we remove self-serving motives, agents appear completely (or much more) rational. Biases due to motivated errors survive standard debiasing interventions including providing experience, ensuring attention, and simplifying decisions.

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Abstract—During the period 2007–2016, S&P 500 firms distributed to shareholders more than $4.2 trillion via stock buybacks and $2.8 trillion via dividends—$7 trillion in total. These shareholder payouts amounted to over 96% of the firms' net income. Academics, corporate lawyers, asset managers, and politicians point to such shareholder-payout figures as compelling evidence that "short-termism" and "quarterly capitalism" are impairing firms' ability to invest, innovate, and provide good wages. We explain why S&P 500 shareholder-payout figures provide a misleadingly incomplete picture of corporate capital flows and the financial capacity of U.S. public firms. Most importantly, they fail to account for offsetting equity issuances by firms. In particular, we explain the importance of accounting for indirect equity issuances, which constitute the majority of total equity issuances in public firms. In addition, S&P 500 firms are not representative of public firms generally as they tend to be older and return more cash to shareholders. We show that, taking into account issuances, net shareholder payouts by all U.S. public firms during the period 2007–2016 were in fact only about $3.33 trillion, 41% of their net income. We also explain that net income is a poor proxy for the amount of capital potentially available for investment, as R&D and other future-oriented expenditures are already deducted in computing it. Our analysis and data can help explain why investment has been increasing and cash balances have been ballooning even though S&P 500 firms appear to be paying out all of their profits to shareholders. In short, S&P 500 shareholder-payout figures are not indicative of actual capital flows in public firms and thus cannot provide much basis for the claim that short-termism is starving public firms of needed capital.

Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=52111

Abstract—Evidence suggests that consumers seek to become more expert about hedonic products to enhance their enjoyment of future consumption occasions. Current approaches to becoming an expert center on cultivating an analytic mindset. In the present research the authors explore the benefit to enthusiasts of moving beyond analytics to cultivate a holistic style of processing. In the taste context the authors define holistic processing as non-verbal, imagery based, and involving narrative processing. The authors conduct qualitative interviews with taste experts (Master Sommeliers) to operationalize the holistic approach to hedonic learning, and then test it against traditional analytic methods in a series of experiments across a range of hedonic products. The results suggest that hedonic learning follows a sequence of stages whose order matters and that the holistic stage is facilitated by attending to experience as a narrative event and by employing visual imagery. The results of this multi-method investigation have implications for both managers and academics interested in how consumers learn to become expert in hedonic product categories.

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Abstract—We document a strong and robust positive relationship between real rates and the contemporaneous valuation of volatile stocks, which we contend measures the economy’s risk appetite. Our novel proxy for risk appetite explains 41% of the variation in the one-year real rate since 1970, while the valuation of the aggregate stock market explains just 1%. In addition, the real rate forecasts returns on volatile stocks, confirming our interpretation that changes in risk appetite drive the real rate. Increases in our measure of risk appetite are followed by a boom in investment and output.

Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=54559

Paritosh Desai joined Target.com in 2013 as VP of Business Intelligence, Analytics & Testing to explore how the retailer could use its relatively small but thriving e-commerce arm to drive sales and win customers. The case explores the technological and organizational challenges Desai faced and the trade offs he considered in his four-year journey to develop the larger retail business into a data science organization.

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A team of investors at Omidyar Network explore two different investment possibilities in the budding financial inclusion space using their investment framework to consider capital alternatives available for both investments, each of which carries highly divergent financial and impact potential.

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Cowen Inc.’s broker-dealer, Cowen and Company, LLC, boasted a number of analysts who had made prescient stock calls on the basis of creative data analysis. Now Cowen Inc. had opened a new subsidiary, Kyber, which would attempt to monetize new data science products. Robert Fagin, head of research, had to consider what Kyber’s risks were, even as the unit uncovered the possibility for new revenue streams. How could Cowen continue to exploit its competitive advantage with data?

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As of early 2018, five U.S. technology companies—Google, Apple, Facebook, Amazon, and Microsoft—were among the largest companies in the world. Similarly, three Chinese technology firms—Baidu, Alibaba, and Tencent, or BAT—had emerged as global players due in part to the protection of China’s “Great Firewall,” which made it more difficult for foreign companies to compete in Chinese markets. As these companies continued to scale by branching into new businesses, such as voice AI and self-driving vehicles, they also faced new and challenging questions about user privacy. The European Union had recently passed the General Data Protection Regulation, a comprehensive set of consumer data protection laws that would require technology companies to make significant changes to their operating model. Meanwhile, social media giant Facebook was facing allegations that Cambridge Analytica, a political data firm, had accessed information on tens of millions of Facebook users without their consent, prompting calls for big technology firms to be more strictly regulated. How would the five U.S. companies and BAT respond to these concerns? And, looking forward, in what ways would these big companies compete with one another, and which would come out ahead?

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