Egg prices, once a steady and affordable staple, have seen dramatic increases, particularly since 2020. This shift from stability to sharp price surges could be attributed to factors such as avian flu outbreaks, rising production costs, and supply chain disruptions. But what exactly drove this surge?
In this analysis, we first uncovered the key trends and price fluctuations over time, before diving into a deeper analysis to pinpoint the true drivers of egg prices. Spoiler alert: it wasn’t just inflation.
Egg prices fluctuated between $0.68 and $4.95 per dozen, with an average price of around $1.38. Prices were relatively stable before 2005, but post-2005, there was a gradual rise, punctuated by occasional surges. These peaks were especially notable after 2020, driven by unforeseen events and pressures in the economy.
Notably, a 3-month moving average revealed a long-term upward trend, with spikes occurring in 1985, 2005, 2015, and most recently in 2020 and beyond. This volatility suggested that inflation, supply chain disruptions, and demand shifts were key contributors. The median price was around $1.15, but with outliers reaching upwards of $3.00–$5.00, it was clear that recent years introduced substantial price unpredictability.
Looking ahead, we explored various forecasting methods to predict the future trajectory of egg prices. Using models such as the Drift Method, Time Series Linear Model (TSLM), Exponential Smoothing (ETS), and ARIMA, we sought to understand how prices might continue to evolve.
The Drift Method provided the most reliable short-term forecasts, while the TSLM highlighted a gradual upward trend in prices.
ARIMA(0,1,1) emerged as the best-fitting model, suggesting that egg prices were likely to continue their upward march, with potential price hikes reaching $6.25 in the near future. However, this model also pointed to increasing uncertainty, making it clear that predicting egg prices was no easy task.
While these models gave us an outlook, they only told part of the story. To truly understand why egg prices were behaving this way, we needed to go deeper—looking at factors beyond simple trends and seasonal adjustments.
As we explored the economic forces at play, we wanted to understand why egg prices were fluctuating. It wasn’t enough to look at simple descriptive statistics or basic models; we needed to investigate the real underlying factors that were influencing prices.
Was it inflation? Was it the cost of feed or energy? Were supply-side factors like bird flu or weather disruptions affecting prices? Or was there something deeper—perhaps a relationship between disposable income and egg prices?
To uncover the root causes, we performed a regression analysis using a broad set of variables from both the demand and supply sides of the market. Here’s how we approached it:
To uncover the true drivers behind egg price fluctuations, we collected a comprehensive dataset that included both supply-side and demand-side factors, as well as macroeconomic indicators. By casting a wide net, we aimed to let the data guide us toward the most significant predictors of egg prices. Our dataset included variables such as:
Weather Data: Temperature, which can influence both supply (e.g., laying conditions) and demand (e.g., changes in consumption patterns during extreme weather).
Supply-Side Shocks: Notably, bird flu outbreaks, which have the potential to disrupt egg production and affect supply.
Feed Input Costs: Prices for corn and soybeans, which are primary ingredients in poultry feed and directly impact egg production costs.
Energy Costs: Diesel, natural gas, propane, and gasoline prices, which influence transportation and production costs for egg farmers.
Demand-Side Indicators: Unemployment rate, population growth, and personal income, all of which can affect consumer purchasing behavior.
Inflation Indicators: The Consumer Price Index (CPI), inflation rate, and Real Disposable Personal Income (RDPI), which are key drivers of purchasing power and cost-of-living adjustments.
This diverse set of variables allowed us to explore the broader economic trends and pinpoint which factors might have been contributing to the rising prices of eggs.
With our dataset in hand, we ran several Ordinary Least Squares (OLS) regressions and correlation tests to evaluate the individual effects of each factor on egg prices. While some factors appeared to have an impact, others did not show strong or consistent relationships. Here’s what we found:
Bird Flu, Corn Prices, and Soybean Prices: While bird flu outbreaks did lead to short-term disruptions, they didn’t consistently affect egg prices across the entire period we analyzed. Similarly, the prices of corn and soybeans showed weak correlations with egg prices, especially when considering long-term trends.
Energy Prices (Diesel, Propane, Natural Gas, Gasoline): Despite expectations, energy prices did not emerge as strong predictors of egg price fluctuations. While they might influence production and transportation costs, they did not have as large an impact as other variables in our model.
Unemployment and Population Growth: These demand-side factors showed marginal relationships with egg prices. However, the effects were often non-significant or weak, suggesting that other economic forces may have been at play.
This initial exploration made it clear that individual factors, while important, didn’t tell the full story. We needed to delve deeper into the interactions between these variables to uncover the true drivers behind egg prices.
Realizing that many of the individual variables had weak effects, we hypothesized that the relationships between these factors could be more complex, driven by interactions between variables. We introduced several interaction terms to explore potential synergies that could explain the price dynamics better:
Corn and Soybean Prices: We tested interaction terms like corn_outbreak, soy_outbreak, and corn_soy to capture how simultaneous disruptions in both feed ingredients might have affected egg prices.
Inflation and Disposable Income: We examined interactions such as cpi_fed, rdpi_fed, and unrate_rdpi, focusing on how inflation and disposable income together might have impacted consumer purchasing power and, subsequently, egg prices.
Energy and Environmental Factors: Variables like diesel_corn, propane_soy, and gas_temp helped assess how the costs of energy interacted with feed prices or temperature extremes.
Although most of these interactions didn’t significantly improve our model’s performance, they helped refine our focus and led us to eliminate less impactful variables.
In addition to interactions, we created composite variables that could capture broader economic trends more effectively:
CPI to RDPI Ratio: The ratio of the Consumer Price Index (CPI) to Real Disposable Personal Income (RDPI), which reflects overall purchasing power.
Per Capita Disposable Income: The amount of disposable income available per person, which could directly influence demand for eggs.
By developing these composite variables, we hoped to better capture the broader economic context in which egg prices were fluctuating.
As we refined our model, we encountered a significant issue: multicollinearity. Our model was showing an almost perfect R-squared value (greater than 0.999), suggesting that something was amiss. We ran a Variance Inflation Factor (VIF) check and found that several variables had high VIF scores, which indicated high correlation between them.
For example:
The correlation between cpi_rdpi and cpi_value was extremely high (0.9883), leading to inflated standard errors and unreliable regression coefficients.
To address this, we removed the variables with the highest VIFs, which helped resolve the multicollinearity issue. This adjustment also improved the reliability of the regression coefficients, making the model’s insights more trustworthy.
Once we removed the problematic variables, our final regression model revealed some surprising findings about the factors that truly drive egg prices:
CPI (Consumer Price Index) showed a positive relationship with egg prices, meaning that as inflation rose, so did egg prices.
Corn Prices had a negative relationship with egg prices—when corn became more expensive, the cost of poultry feed rose, pushing egg prices higher.
RDPI per capita (Real Disposable Personal Income) was another significant predictor—higher disposable income per person was correlated with higher egg prices, suggesting that as people had more money to spend, they were more willing to pay higher prices for eggs.
On the other hand, bird flu outbreaks and energy prices like diesel, propane, and natural gas didn’t have a significant impact on egg prices. After adjusting for multicollinearity and refining our model, these supply-side factors were found to have less influence than expected.
In conclusion, the rising cost of eggs is far from a simple case of inflation alone. While factors like the Consumer Price Index (CPI) and Real Disposable Personal Income (RDPI) per capita do play a crucial role, it’s clear that egg prices are influenced by a complex interplay of economic forces. Our analysis revealed that corn prices and inflationary pressures were significant contributors, while factors such as bird flu outbreaks and energy costs were less impactful than initially expected.
Looking ahead, it’s essential for both consumers and producers to stay attuned to these economic dynamics, as shifts in inflation, feed costs, and disposable income continue to shape the market. While predicting egg prices with complete accuracy remains a challenge, understanding these underlying drivers offers valuable insight into the forces at play in the poultry market.
As we continue to refine forecasting models and incorporate new data, we can expect egg prices to remain volatile, with the potential for future surges driven by a range of economic variables. The key takeaway? Egg prices may be subject to many forces, but they aren’t entirely out of our control—at least not when we understand the drivers beneath the surface.
Stay tuned for Part 4 of this series, where we’ll dive into egg price cluster analysis, providing a final layer of insight into the patterns behind egg price fluctuations.