Aghion and coauthors developed an endogenous growth model examining how taxation and corruption jointly influence innovation and economic performance. Taxes can reduce entrepreneurial incentives by lowering profits but also fund essential public goods such as infrastructure, education, and rule-of-law institutions that support innovation. 

The model predicts an inverted U-shaped relationship between taxation and economic growth. Moderate taxation supports growth by funding public goods, while excessive taxation discourages innovation. Corruption shifts this relationship downward by diverting public resources. 

Using Longitudinal Business Database data combined with measures of corruption derived from convictions of public officials, researchers found that taxation supports economic growth in low-corruption environments but has limited or negative effects in high-corruption environments. Calibration results suggest that eliminating corruption could produce welfare gains exceeding 20 percent in consumption-equivalent terms. 

This study examines inflation measurement bias associated with establishment closures, known as outlet bias. When establishments exit markets, statistical agencies often estimate price changes using surviving establishments, which may underestimate productivity growth if new firms introduce higher-quality or lower-cost products. 

Using LBD data covering the entire private nonfarm sector from 1983 to 2013, researchers developed a model estimating growth lost due to this measurement limitation. The authors estimated that approximately 0.5 percentage points of annual productivity growth—more than one-fifth of measured productivity growth—is not captured in official statistics. 

The study by Aghion, et al., also found that missing growth is concentrated in industries such as retail trade, restaurants, and hotels. Comparisons with sector-specific research using detailed price and quantity data suggest the estimates may be conservative, reinforcing the importance of accounting for creative destruction when measuring productivity growth. 

This study examines how organizational structure influences firm resilience during economic crises. Researchers compared centralized decision-making models with decentralized organizational structures that delegate authority to local plant managers. 

Using international management survey data and confidential Census manufacturing data from MOPS and other FSRDC data assets, researchers analyzed firm performance during the Great Recession of 2008–2009. Measures of economic turbulence included export declines, product churn, and stock market volatility. 

Results show that firms with more decentralized management structures prior to the recession performed better in industries experiencing greater economic turbulence. Decentralization allowed firms to leverage local information and adapt more effectively to changing market conditions. The study concludes that decentralized management structures can provide strategic advantages during periods of economic disruption, though optimal organizational structure varies by economic conditions.