Survey Dataset on the Socioeconomic Impact of Public Universities in Bangladesh

This dataset has information from a survey about peoples economic situations. The survey was done by talking to people in person at their homes and workplaces. These homes are near six universities in different parts of the region. The study wants to see how having a university nearby affects the people who live in these homes. It looks at things like how money they make what kind of work they do who lives in the home and how long they have lived there. The study is really about how universities relate to the situations of the households near them including things, like income and occupation and demographic attributes and how long people have lived in the area.

We talked to 484 people in person. Wrote down what they said. Later, we put this information into a computer so we could look at it. Each line is one person from a household. We did not use their names to keep them private. The information we collected includes the age of the person, in charge of the family how money the household makes each month in BDT what they do for work how long they have lived there and which university area they are connected to.

When we worked with the data, we took out the information that could identify people. We also made sure the income numbers were all in the format. The category that said "Local" was changed to a number, 25 years because that is what we thought it meant when someone has lived somewhere for a time. If there are spaces in the data that means the person did not answer the question or we did not have that information, about them.

The university has some information about the area like how many people can read and write in different regions. This information is in a file, and it gives us an idea of what is going on in each area rather than telling us about individual people. The university is looking at things like literacy rates to get a better understanding of the situation.

This dataset is intended for academic research, teaching, and exploratory analysis in fields such as socioeconomic studies, regional development, education policy, and data science. Users should consider potential limitations related to self-reported responses, interviewer recording bias, and non-random sampling when interpreting results.