Abstract: Abstract I propose a tractable model of “complexity aversion”. The key ingredient is “first order complexity aversion”: when people know they're making a mistake (because the situation is complex) they experience some dread, which is a utility loss proportional to the absolute value of the expected error. I show how complexity aversion leads to optimally simple mechanisms. I illustrate this in five examples complexity aversion makes a large difference. (i) If complexity aversion is high enough, the price of a good will be constant over time, even though the marginal cost might be variable, to avoid annoying the consumer with a complex price system. (ii) In the theory of optimal taxation, if complexity aversion is high enough, the optimal tax system is “simple”, e.g. just features a uniform tax rate rather than a different tax rate for each good, as recommended by the traditional Ramsey model. (iii) Whereas the traditional model predicts that contracts should be indexed aggregate factors (e.g. on inflation, GDP, or the stock market), with enough complexity aversion, contracts are non-indexed, “simple”. (iv) Complexity aversion leads to a model of a non-traditional (first order) cost of inflation, which in calibration is quite important: as different sources of income do not react equally to inflation, higher inflation leads to a more complex planning process. (v) This in turn changes optimal monetary policy, which will de facto target a zero inflation (or, more generally, zero deviation from the inflation target), to the exclusion of other goals, except in rare extreme circumstances such as an extreme recession. I finally discuss how using this model of complexity aversion will lead to a useful “behavioral mechanism design” theory, and more realistic—simpler—mechanisms.
Abstract: Wage employees who are laid-off may turn to entrepreneurship to generate income. Conventional wisdom suggests that these necessity entrepreneurs perform poorly because they lack entrepreneurial resources (e.g. financial, managerial, human, or social). In this paper we challenge this view, using data from matched employee-employer tax records that cover incorporated and unincorporated firms. We find that displaced workers subject to mass layoffs, who "aim low" and start unincorporated companies, perform better than matched voluntary entrepreneurs starting similar firms. However, necessity entrepreneurs who "aim high" and start incorporated companies perform worse than their voluntary counterparts. We show that exposure to entrepreneurial coworkers improves the performance of necessity entrepreneurs in incorporated firms, whereas their position in their previous employer’s hierarchy does not. Taken together, our results show that entrepreneurial success depends on the match between a founder’s existing resources and the resource requirements of different organizational forms.
Abstract: This study examines startups' management of source code repositories, distinguishing authentic developers (``code-producers''), from those inflating activity to mislead investors (``code-washers''). Using global blockchain startup and GitHub data, we find that code-producers and code-washers achieve greater fundraising success during hot markets than startups without repositories, indicating that investors struggle to evaluate open-source innovation accurately. However, while code-washers experience poorer outcomes post-fundraising, a portfolio of code-producers generates substantial long-term returns. Our study introduces the novel phenomenon of ``code-washing,'' offering insights into how early ventures navigate (or exploit) information asymmetries during the fundraising phase.
