Knowledge that Transforms

Organizational learning (OL) is a critical organizational capability. Yet designing management control (MC) that effectively supports OL is challenging, as learning outcomes are often intangible and unfold over extended time horizons. Employing situational strength theory, we examine combinations of MC practices that affect OL contingent on environmental uncertainty. Drawing on a survey of 210 senior business unit controllers, we use fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify multiple combinations of MC practices that achieve high OL in different uncertainty contexts. In a subsequent step, we examine whether the situational strength of certain combinations of MC practices is associated with OL. Results show that in a low-uncertainty environment, the combined use of diverse performance measures and social controls, as well as performance pay and a mechanistic administrative structure, have a synergetic effect on OL. In a high-uncertainty context, the combined use of measure diversity and deliberate formal strategic planning is beneficial for achieving OL. Our results suggest that the effectiveness of MC practices for OL depends on aligning MC practices with the internal as well as the external context.

We collect 2,848 instrumental variable (IV) regressions from 391 papers published in top economics and finance journals and show that estimated IV coefficients are often much larger than their OLS counterparts, even when theory suggests they should be smaller. We derive meta-regression estimators that distinguish measurement error bias from omitted variable bias and simultaneity bias; applying our estimators to a set of 1,040 comparable regressions from 139 papers, we find that measurement error shrinks reported coefficients by about two to four times. Measurement error remains quantitatively important even after accounting for weak instruments, publication bias, instrument invalidity, and heterogeneous treatment effects. Our results suggest that measurement error is a severe, pervasive, and understated source of bias in economics and finance.

Prior studies on cryptocurrency momentum overlook liquidation risk and other real-world frictions and inadequately assess its profitability. We evaluate time-series and cross-sectional momentum addressing these issues. When portfolios are marked to market daily, many cross-sectional momentum portfolios are liquidated, and those with statistically significant mean returns often earn negative profits. The mean return is insufficient for testing profitability in the cryptocurrency market, where returns are heavily skewed and fat-tailed. Evidence of time-series momentum is strong, whereas evidence of cross-sectional momentum is almost nonexistent. Investor overconfidence and the disposition effect appear to be important drivers of cryptocurrencies’ price dynamics.

Corporations make substantial progress toward their Zero Routine Flaring commitments globally, particularly in Africa where preexisting environmental standards are weak. Moreover, committers’ flaring reductions on the continent are driven primarily by operational improvements rather than offloading assets and are concentrated among committers facing greater stakeholder pressure. Although flaring is higher for assets continuously operated by, awarded to, or divested to noncommitters, corporate actions to uphold commitments are associated with a conservatively estimated 10 percent net reduction in flaring in Africa without a corresponding reduction in oil production or local economic activity in extraction communities. Our findings suggest that environmental commitments are backed by corporate policies akin to establishing a uniform cost of pollution across the firm’s global operations and therefore may alleviate the problems posed by globally fragmented regulation. Data Availability: All data except for Enverus oil and gas are publicly available. Enverus is available for purchase on a subscription basis.