Endogenous Production Networks Under Supply Chain Uncertainty

This paper studies how firms’ optimal technique choices under productivity uncertainty endogenously shape the structure of production networks and aggregate macroeconomic outcomes. Each sector chooses input shares (production techniques) before observing sector-specific TFP realizations. Techniques are selected to maximize a risk-adjusted expected log GDP measure — expected log GDP minus a risk-aversion-scaled variance term — with endogenous productivity shifters that favor balanced use of inputs. When uncertainty about sector TFP rises, firms shift toward suppliers with lower expected productivity but lower variance — a “flight to safety” in input sourcing. The key aggregation result is that the contribution of each sector to aggregate welfare depends on its endogenous Domar weight (expenditure share times adjustment factor), which itself responds to changes in beliefs. The paper establishes propositions characterizing how Domar weights respond to changes in mean (μ) and variance (Σ) of TFP beliefs: higher mean raises a sector’s Domar weight; higher variance lowers it when inputs are gross substitutes, but can lower it even with complementary inputs through belief adjustment. A basic calibration to 37 US BEA sectors (1948–2020) finds that the flexible-network economy has expected log GDP 2.1% higher than a fixed-network alternative. During the Great Recession, elevated uncertainty caused firms to shift toward safer, lower-productivity suppliers, reducing expected log GDP by 0.25% but reducing GDP variance by 2.4% and improving actual realized GDP outcomes by 2.7% relative to a “no uncertainty” benchmark.

Kristoffer Nimark | Macro Paper Warehouse

Endogenous Production Networks Under Supply Chain Uncertainty