Staking: Economics, Models, and Reward Design

Staking is a foundational mechanism in cryptoeconomics. It simultaneously secures the network, reduces circulating supply, and generates income for participants. But behind the simplicity of “lock tokens — earn rewards” lies a complex economy where inflation, real yield, and sell pressure determine the token’s fate.

What Is Staking

Staking is locking tokens in a smart contract to earn rewards and/or participate in network operations. A staker temporarily gives up liquidity in exchange for income.

In tokenomics, staking plays a dual role:

1. Supply mechanism — staking rewards are a channel for emitting new tokens (the reward model in supply models)
2. Demand mechanism — staking creates a reason to buy and hold the token, working as a demand model

Two Types of Staking

Consensus staking is part of the blockchain architecture. Protocol staking is a tokenomist’s tool for shaping user behavior.

Staking Landscape (as of early 2026)

Rates change frequently — check current data before modeling.

APR and APY: Real Yield

Formulas

At APR = 10% with daily compounding: APY = (1 + 0.10/365)^365 - 1 = 10.52%. The difference is small at moderate rates, but at APR = 100%, APY reaches 171.5%.

Nominal vs Real Yield

Nominal APR is what the interface displays. Real yield accounts for token inflation:

Example. A network with 12% APR and 8% inflation:

• Nominal yield: 12%
• Real yield: 12% - 8% = 4%
• Non-stakers lose: -8% (their share is diluted by inflation)

Real Yield: Income from Fees

Protocols with real yield distribute not inflationary tokens but fees earned by the protocol:

Example. A protocol with $50M annual revenue, 50% to stakers, 100M tokens staked at $5:

Real_yield = ($50M × 0.5) / ($5 × 100M) = 5% — pure income without inflation.

Staking Economics: Design Parameters

1. Target Staking Percentage

What share of circulating supply should be staked? Typical values for PoS networks: 40–70%. Note: Ethereum currently sits at ~30%, well below this range — partly due to its late PoS transition (September 2022) and high DeFi opportunity cost.

2. Dynamic Reward Rate

Many networks use an adaptive model: the rate rises when staking is below target and drops when above.

At S_target = 50%, S_actual = 30%, k = 1, and APR_target = 8%:

APR = 8% × (50/30) = 13.3% — elevated rate to attract stakers.

At S_actual = 70%:

APR = 8% × (50/70) = 5.7% — reduced rate to encourage unstaking.

3. Lock-up Period

The time tokens are locked. Longer lock-up reduces sell pressure but limits liquidity.

4. Slashing — Penalties

Slashing is the confiscation of part of staked tokens for violations: double-signing a block, extended downtime, incorrect validation. Penalty size: 0.01% (minor violations) to 100% (network attack).

Slashing creates an economic incentive for honest behavior and is a key element of mechanism design.

Liquid Staking

Liquid staking is a technology that allows earning staking rewards without losing liquidity. The staker receives a derivative token (LST — Liquid Staking Token) that can be used in DeFi.

How It Works

1. User deposits 1 ETH into a liquid staking protocol (e.g., Lido)
2. The protocol stakes ETH on the network and issues 1 stETH (or rETH from Rocket Pool, cbETH from Coinbase)
3. The LST accrues rewards (rebase or exchange rate appreciation)
4. User uses the LST as collateral, liquidity, or trades it
5. On withdrawal — exchange the LST back for ETH + accumulated rewards

Liquid staking extends beyond Ethereum: JitoSOL and mSOL on Solana (Jito adds 0.5–1% MEV yield), stATOM via Stride on Cosmos, vDOT via Bifrost on Polkadot.

Economic Effects

Impact on Tokenomics

Liquid staking fundamentally changes the model: staked tokens don’t leave circulation. This means staking no longer reduces circulating supply. For a tokenomist, this means you cannot rely on staking as a sell pressure reduction mechanism if LST protocols dominate.

Restaking

Restaking extends the staking model by allowing already-staked assets to secure additional services (Actively Validated Services, or AVS) — oracles, bridges, data availability layers — without requiring separate collateral.

How It Works

1. User stakes ETH (natively or via an LST like stETH)
2. The restaking protocol (e.g., EigenLayer) re-delegates that stake to one or more AVSs
3. AVSs pay rewards for the additional security
4. In return, the restaker accepts dual slashing risk — violations on any secured AVS can slash the original stake

EigenLayer and the Restaking Market

EigenLayer dominates the restaking market with >$15B TVL and ~4.3M ETH (93.9% market share as of early 2026). ether.fi, the largest Liquid Restaking Token (LRT) provider, holds ~2.1M ETH (6.0% of the staking market), making it the third-largest staking provider overall.

LRT protocols (ether.fi’s eETH, Renzo’s ezETH, Puffer’s pufETH) add another composability layer: users receive a liquid token representing their restaked position, usable across DeFi — creating a three-layer yield stack: base staking + restaking rewards + DeFi yield.

Impact on Tokenomics

Restaking changes the staking economy in several ways:

• Additional yield layer — 3.8–6% APY on top of base staking, creating stronger lock-up incentives
• Dual slashing risk — a slash on any AVS can reduce the original stake, increasing the risk profile
• Supply effects — restaked tokens are locked more deeply (multiple commitments), but LRTs keep them circulating
• Security budget — AVSs can bootstrap security without their own validator set, reducing launch costs

Staking Economy Simulation

A model with dynamic APR shows: starting at 30% staked, APR increases to ~13% to attract stakers. As it approaches the 50% target, the rate drops to 8%. Reward inflation runs at 4–5% annually, but fee income adds 3–5% real yield.

Simulation results (parameters: 100M supply, target staking 50%, APR 8%, fees $5M/year):

Key observations:

• APR decreases from 13.3% to 8% as staking approaches the 50% target — the dynamic model works
• Inflation is stable at 4% — because staking and APR are balanced
• Total Real Yield = 8.3% with fees in steady state. Without fees ($0 revenue), Real Yield = APR - Inflation ≈ 8.2% - 4% = ~4.2% — pure transfer from non-stakers
• Total supply grows 22% over 5 years — the cost of inflationary staking

import numpy as np
import matplotlib.pyplot as plt

TOTAL_SUPPLY_0 = 100_000_000
STAKING_TARGET = 0.50
APR_TARGET = 0.08
SENSITIVITY = 1.0
FEE_REVENUE_YEAR = 5_000_000
SHARE_TO_STAKERS = 0.50
TOKEN_PRICE = 1.0
MONTHS = 60

months = np.arange(MONTHS)
supply = np.zeros(MONTHS)
staked_pct = np.zeros(MONTHS)
apr = np.zeros(MONTHS)
real_yield = np.zeros(MONTHS)
inflation = np.zeros(MONTHS)

supply[0] = TOTAL_SUPPLY_0
staked_pct[0] = 0.30

for m in range(MONTHS):
    staked = supply[m] * staked_pct[m]
    apr[m] = min(APR_TARGET * (STAKING_TARGET / max(staked_pct[m], 0.01)) ** SENSITIVITY, 0.30)
    monthly_emission = staked * (apr[m] / 12)
    inflation[m] = (monthly_emission * 12) / supply[m]
    fee_yield = (FEE_REVENUE_YEAR * SHARE_TO_STAKERS) / (TOKEN_PRICE * staked) if staked > 0 else 0
    real_yield[m] = apr[m] - inflation[m] + fee_yield
    if m < MONTHS - 1:
        supply[m + 1] = supply[m] + monthly_emission
        delta = (STAKING_TARGET - staked_pct[m]) * 0.05
        staked_pct[m + 1] = np.clip(staked_pct[m] + delta, 0.05, 0.90)

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
axes[0, 0].plot(months, apr * 100, color="#3b82f6", linewidth=2)
axes[0, 0].set_title("Dynamic APR"); axes[0, 0].set_ylabel("APR, %"); axes[0, 0].grid(True, alpha=0.3)
axes[0, 1].plot(months, staked_pct * 100, color="#10b981", linewidth=2)
axes[0, 1].axhline(y=50, color="#ef4444", linestyle="--", alpha=0.5, label="Target: 50%")
axes[0, 1].set_title("Staking percentage"); axes[0, 1].legend(); axes[0, 1].grid(True, alpha=0.3)
axes[1, 0].plot(months, real_yield * 100, color="#8b5cf6", linewidth=2, label="Real yield")
axes[1, 0].plot(months, inflation * 100, color="#ef4444", linewidth=2, linestyle="--", label="Inflation")
axes[1, 0].set_title("Real yield vs inflation"); axes[1, 0].legend(); axes[1, 0].grid(True, alpha=0.3)
axes[1, 1].plot(months, supply / 1e6, color="#f59e0b", linewidth=2)
axes[1, 1].set_title("Total supply"); axes[1, 1].set_ylabel("Tokens, M"); axes[1, 1].grid(True, alpha=0.3)
plt.tight_layout(); plt.show()

Common Mistakes

1. APR above 100% without a revenue source

Triple-digit APR attracts attention, but if the only source is emission, it’s hyperinflation. Real yield is negative, and price drops faster than quantity grows.

2. Not accounting for liquid staking

If 80% of stake goes through LST protocols, staking doesn’t reduce circulating supply. Models built on the assumption “staked = locked” will produce incorrect price forecasts.

3. Same APR for all lock periods

A 1-month staker and a 12-month staker bear different risk. Without rate differentiation, everyone chooses the minimum lock, and the mechanism fails.

4. Slashing too mild or absent

Without penalties, there are no economic incentives for honest behavior. Validators can attack the network without losses. Minimum slashing: 0.1% for downtime, 5%+ for double-signing.

5. Treasury rewards without limits

A fixed reward pool runs out. If staking rewards come from the treasury (not emission), the pool empties in 2–3 years and APR drops to zero. Solution: combine emission and fees.

Staking design checklist