24 Years of Food Prices in the Philippines

There's a specific kind of sticker shock you get at the palengke. You remember paying a certain price for a kilo of rice, and then one day it's different — not dramatically, but noticeably. Multiply that small shift by 24 years and you get something worth studying.

I found the World Food Programme's price monitoring dataset for the Philippines, covering commodity prices from 2000 through 2023. It's an incredible resource — thousands of monthly price observations across markets and regions. I wanted to see the long arc of food affordability in the country, not just the crisis moments that make the news.

What I Built

A time series analysis of Philippine food commodity prices spanning over two decades. The project tracks rice (multiple varieties), corn, sugar, cooking oil, fish, and other staples. I built trend lines, year-over-year change calculations, and regional price comparisons — all in Python with pandas and matplotlib.

Why This Project

Food is the single biggest expense for most Filipino households. When prices climb even a few pesos per kilo, millions of families feel it. I wanted to move past anecdotes and look at the actual trajectory — which commodities spiked the most, whether prices ever came back down after a crisis, and how different regions experienced these shifts.

How I Put It Together

The WFP dataset was surprisingly messy. Price observations didn't arrive at regular intervals for every commodity in every market. Some months were missing entirely for certain regions. I used time series resampling to standardize everything to monthly frequency and applied forward-fill (ffill()) to handle gaps where a market simply didn't report that month.

Forward-fill was the right call here because food prices don't jump to zero when unreported — they stay roughly where they were. For longer gaps of 3+ months, I flagged those periods rather than filling them, so the charts wouldn't misrepresent data availability.

Annualizing the data for trend analysis used resample('Y').mean(), which smoothed out seasonal fluctuations and made it easier to spot structural shifts versus temporary spikes.

What the Data Showed

The 2008 global food crisis showed up like a wall in the charts. Rice prices jumped sharply that year, and here's the thing — they never fully came back down. The crisis created a new baseline. Prices stabilized at a higher level and then continued climbing from there.

Regional disparities were striking too. The same kilo of rice could cost significantly more in remote areas compared to major trading hubs. Transportation costs, market access, and local supply chains all play into this, but seeing the numbers side by side made the gap feel concrete.

Some patterns I didn't expect:

• Cooking oil prices were more volatile than rice, with sharper spikes and drops
• Fish prices varied the most by region — coastal areas consistently paid less
• Corn prices tracked rice closely but with a slight lag, suggesting substitution effects
• The COVID period (2020-2021) caused a brief dip in some commodities before a strong rebound

Looking Back

Twenty-four years of data tells you something that a single year's snapshot never could. Food in the Philippines has gotten steadily more expensive, and the relief periods after price shocks keep getting shorter. The 2008 spike established a new floor. The pandemic created another one.

What surprised me most was how persistent regional price gaps are. They don't close over time — if anything, they've widened for some commodities. That says something about infrastructure and market efficiency that goes beyond agriculture policy alone.