What drives differences in management practices?
Partnering with the US Census Bureau, we implement a new survey of “structured” management practices in two waves of 35,000 manufacturing plants in 2010 and 2015. We find an enormous dispersion of management practices across plants, with 40 percent of this variation across plants within the same firm. Management practices account for more than 20 percent of the variation in productivity, a similar, or greater, percentage as that accounted for by R&D, ICT, or human capital. We find evidence of two key drivers to improve management. The business environment, as measured by right-to-work laws, boosts incentive management practices. Learning spillovers, as measured by the arrival of large “Million Dollar Plants” in the country, increase the management scores of incumbents.
Turbulence, Firm Decentralization and Growth in Bad Times
What is the optimal form of firm organization during “bad times”? The greater turbulence following macro shocks may benefit decentralized firms because the value of local information increases (the “localist” view). On the other hand, the need to make tough decisions may favor centralized firms (the “centralist” view). Using two large micro datasets on decentralization in firms in ten OECD countries (WMS) and US establishments (MOPS administrative data), we find that firms that delegated more power from the central headquarters to local plant managers prior to the Great Recession outperformed their centralized counterparts in sectors that were hardest hit by the subsequent crisis (as measured by export growth and product durability). Results based on measures of turbulence based on product churn and stock market volatility provide further support to the localist view. This conclusion is robust to alternative explanations such as managerial fears of bankruptcy and changing coordination costs. Although decentralization will be suboptimal in many environments, it does appear to be beneficial for the average firm during bad times.
Data in Action: Data-Driven Decision Making in U.S. Manufacturing
Manufacturing in America has become significantly more data-intensive. We investigate the adoption, performance effects and organizational complementarities of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for 2005 and 2010, we observe the extent to which manufacturing firms track and use data to guide decision making, as well as their investments in information technology (IT) and the use of other structured management practices. Examining a representative sample of over 18,000 plans, we find that adoption of DDD is earlier and more prevalent among larger, older plants belonging to multi-unit firms. Smaller single-establishment firms adopt later but have a higher correlation with performance than similar non-adopters. Using a fixed-effects estimator, we find the average value-added for later DDD adopters to be 3% greater than non-adopters, controlling for other inputs to production. This effect is distinct from that associated with IT and other structured management practices and is concentrated among single-unit firms. Performance improves after plants adopt DDD, but not before – consistent with a causal relationship. However, DDD-related performance differentials decrease over time for early and late adopters, consistent with firm learning and development of organizational complementarities. Formal complementarity tests suggest that DDD and high levels of IT capital reinforce each other, as do DDD and skilled workers. For some industries, the benefits of DDD adoption appear to be greater for plants that delegate some decision making to frontline workers.