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.

IT and Management in America

The Census Bureau recently conducted a survey of management practices in over 30,000 plants across the US, the first large-scale survey of management in America. Analyzing these data reveals several striking results. First, more structured management practices are tightly linked to higher levels of IT intensity in terms of a higher expenditure on IT and more on-line sales. Likewise, more structured management is strongly linked with superior performance: establishments adopting more structured practices for performance monitoring, target setting and incentives enjoy greater productivity and profitability, higher rates of innovation and faster employment growth. Second, there is a substantial dispersion of management practices across the establishments. We find that 18% of establishments have adopted at least 75% of these more structured management practices, while 27% of establishments adopted less than 50% of these. Third, more structured management practices are more likely to be found in establishments that export, who are larger (or are part of bigger firms), and have more educated employees. Establishments in the South and Midwest have more structured practices on average than those in the Northeast and West. Finally, we find adoption of structured management practices has increased between 2005 and 2010 for surviving establishments, particularly for those practices involving data collection and analysis.

Business-Level Expectations and Uncertainty

The Census Bureau’s 2015 Management and Organizational Practices Survey (MOPS) utilized innovative methodology to collect five-point forecast distributions over own future shipments, employment, and capital and materials expenditures for 35,000 U.S. manufacturing plants. First and second moments of these plant-level forecast distributions covary strongly with first and second moments, respectively, of historical outcomes. The first moment of the distribution provides a measure of business’ expectations for future outcomes, while the second moment provides a measure of business’ subjective uncertainty over those outcomes. This subjective uncertainty measure correlates positively with financial risk measures. Drawing on the Annual Survey of Manufactures and the Census of Manufactures for the corresponding realizations, we find that subjective expectations are highly predictive of actual outcomes and, in fact, more predictive than statistical models fit to historical data. When respondents express greater subjective uncertainty about future outcomes at their plants, their forecasts are less accurate. However, managers supply overly precise forecast distributions in that implied confidence intervals for sales growth rates are much narrower than the distribution of actual outcomes. Finally, we develop evidence that greater use of predictive computing and structured management practices at the plant and a more decentralized decision-making process (across plants in the same firm) are associated with better forecast accuracy.

The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flowefficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.