The datasets were accessed through the CEIC Data Global Database and the Philippine Statistics Authority (PSA) Database. Given that the data was obtained from the Philippine Statistics Authority (PSA) rather than collected directly by our team, it is important to acknowledge that the PSA has already safeguarded data quality through data cleaning, consistency checks, and error detection.

For RQ#1, we extracted data on urbanity and total household income from all editions of the Family Income and Expenditure Survey. For RQ#2, we extracted data on consumer price index or CPI.

Preprocessing

Two databases were utilized as sources due to the need for comprehensive data coverage. In instances where one dataset lacked certain variables or characteristics, we would refer to the other dataset to ensure completeness. Furthermore, a cross-validation process was conducted, comparing and verifying the data from both sources to enhance data reliability and accuracy.
The following steps were done:

1. Rename labels across all sheets for uniformity
2. Sort the data by urbanity and then by region
3. Convert relevant columns to numeric types
4. Compute for the average household income per area type per year
5. Perform interpolation for missing values

Testing

Considering that all the variables are independent of each other, the researchers have decided to use a two-tailed t-test in order to test the hypothesis. Furthermore, the researchers have decided to use an alpha value of 0.95. If the p-value is less than 0.05, then we reject the null hypothesis. Otherwise, we accept the null hypothesis.

Conclusions

Urbanity and Average Income

The researchers used a t-test to compare between urbanity and total income. After calculating the p-value, the researchers have found the p-value to be approximately 0. Since this value is less than 0.05, we reject the null hypothesis and conclude that there is indeed a relationship between urbanity and the average household income for each region.

CPI and Income Disparity

The researchers also used a t-test to compare the income disparity and the CPI of the Philippines. Disparity was computed by taking the average income of the rural and urban regions then subtracting rural from urban. Upon performing a t-test, the researchers have found a p-value of ~0.25. Because this is higher than 0.05, we accept the null hypothesis and therefore conclude that there is no significant relationship between CPI and income disparity.

Discussion

Research Question 1

Our results suggest a significant difference in average household income between urban and rural areas in the Philippines, with urban areas consistently exhibiting higher income levels than rural areas. This disparity has been increasing over the years, highlighting a growing economic gap.

Higher income levels in urban areas, particularly in NCR, reflect greater access to economic opportunities, education, healthcare, and infrastructure. Conversely, rural areas, where income levels are significantly lower, often lack access or the means to avail of these essential services. This income disparity amplifies social inequalities, leading to a concentration of wealth and resources in urban areas, while rural areas remain underdeveloped.

This further highlights the concept of Imperial Manila—the centralization of economic and political power in the NCR— since our findings show that the region constantly has a 100% urbanity percentage. This urban-centric development model has led to an imbalance where Manila and other urban areas attract the majority of investments, jobs, and services, leaving other regions, particularly rural areas, underfunded and underdeveloped.

Research Question 2

Our graphs suggest that the CPI increases at a rate that is comparable to that of urban income levels, which imply that rural income levels are lagging behind. However, results from our significance tests indicate that there is no statistically significant relationship between CPI and income disparity, suggesting that there may be confounding factors involved.

With that knowledge, other factors or data that contribute to the income inequality between rural and urban areas may be considered and further explored. Aside from this, we might also need to consider politics and the ever-changing economic policies that affect both rural and urban areas. Truly, problems as complex as these should not be tied down to one factor alone but must be thoroughly evaluated with all the good candidate causes considered.

Limitations

The major limitation we encountered is that the data we used (FIES) is only recorded every 3 years. On top of that, there is also missing data in the FIES dataset for the year 2003. To resolve this, we performed interpolation in order to compensate for the missing values to achieve the most accurate results possible.

Conclusion

Our project analyzed the disparity in household income between urban and rural areas in the Philippines, and our results highlighted a significant and growing economic divide. Our findings also indicate that urban areas—especially the NCR— consistently have higher income levels, which reflects the centralization of economic opportunities and resources in urban areas.

One of the main goals of our study is to help local governments, especially those in rural areas, to better understand the needs of their constituents. We aim to assist leaders in governing their respective constituencies more effectively. The insights from our analysis could translate into meaningful policies that enhance community welfare and support the further development of these rural regions. This also includes better management and allocation of resources on key projects, across all regions in the Philippines.