Strengthen monetary-policy transmission through firm borrowing and investment

[agurgone]

Problem

Monetary policy appears to have weak effects on firm borrowing, investment, and inflation. The policy rate is transmitted to firm loan rates, but the downstream quantity margins are weak.

Current transmission path

1. The Taylor rule sets the central-bank policy rate.
2. Bank lending rates respond to the policy rate through the configured pass-through/markup mechanism.
3. Long-term firm loan rates are passed to the productivity-investment planner as effective_cost_rate.
4. Firm credit targets are then calculated mainly from liquidity gaps, debt service, overdrafts, input costs, and investment budgets.
5. Dividends and other cash-flow changes affect borrowing only with a lag, after the current period's credit decision.

Suspected bottlenecks

• Firms.compute_target_credit() does not make desired new borrowing sufficiently sensitive to its price.
• The long-term rate affects mainly productivity investment, not capital replacement, technical investment, or working-capital finance.
• Investment is frequently constrained by cash and configured investment caps before the interest-rate margin binds.
• silence_technical_investment: true further narrows the rate-sensitive investment channel.
• Lower investment may reduce demand but can also reduce future productivity and raise unit costs, so the sign of the inflation response needs to be made explicit.

Proposed investigation

Use a policy-rate shock and compare treatment/control paths for:

• firm short- and long-term borrowing rates
• interest paid on loans
• target and received short-/long-term credit
• effective investment cost rate
• desired, planned, and executed investment
• binding cash/credit/investment constraints
• production, excess demand, unit costs, PPI, and CPI

Acceptance criteria

• A policy-rate increase produces a measurable, documented response in at least one firm borrowing or investment margin.
• The response propagates to aggregate demand, production/capacity, and inflation with a theoretically interpretable sign and lag.
• The channel is validated across multiple seeds.
• Diagnostics identify whether the binding constraint is financing cost, cash availability, credit supply, or an investment cap.

Relevant code: macromodel/agents/firms/firms.py, macromodel/agents/firms/func/productivity_investment_planner.py, macromodel/agents/banks/func/interest_rates.py, and macromodel/country/country.py.