Empirical Project 9 Credit-excluded households in a developing country

Learning objectives

In this project you will:

  • identify credit constrained and credit-excluded households using survey information (Part 9.1)
  • create dummy (indicator) variables (Part 9.1)
  • compare characteristics of successful borrowers, discouraged borrowers, credit constrained households, and credit-excluded households (Part 9.1)
  • explain why selection bias is an important issue (Part 9.1)
  • analyse the characteristics of loans obtained by successful borrowers (Part 9.2).

Key concepts

  • Concepts needed for this project: mean, standard deviation, range, percentile, correlation/correlation coefficient, confidence interval for the difference in means, dummy variable.
  • Concepts introduced in this project: selection bias.


CORE projects

This empirical project is related to material in:

  • Unit 9 of Economy, Society, and Public Policy
  • Unit 10 of The Economy.

Borrowing, either through formal or informal institutions, can help smooth consumption and also enable investment. However, the lenders cannot observe how hard the borrower is working to repay the loan, and cannot make the loan contract conditional on such effort. The relationship between the lender and the borrower, like that between the employer and the employer studied in Project 8, is called a principal–agent problem. Section 9.12 of Economy, Society, and Public Policy compares the labour market and the credit market.

principal–agent relationship
This is an asymmetrical relationship in which one party (the principal) benefits from some action or attribute of the other party (the agent) about which the principal’s information is not sufficient to enforce in a complete contract. See also: incomplete contract. Also known as: principal–agent problem.
credit constrained
A description of individuals who are able to borrow only on unfavourable terms. See also: credit excluded.
credit excluded
A description of individuals who are unable to borrow on any terms. See also: credit constrained.

Lenders can partially mitigate or eliminate the risk of default by requiring collateral, which can be repossessed and sold to repay the loan if the borrower defaults. People who are unable to provide this collateral often have to borrow under more unfavourable conditions (higher interest rates) or may be refused a loan entirely. We call the former group credit constrained, and the latter group credit excluded.

Since the ability to borrow depends on a person’s wealth, such credit constraints and exclusion contribute to inequality, and some opportunities for economic growth are not realized. For example, a hard-working person without any assets may be refused a loan to start a business, which could contribute both to raising their income and to economic activity.

To design policies that help credit markets function better, we first need to look at how widely borrowing conditions and available sources of finance differ according to household characteristics. Sometimes borrowers who are excluded from formal credit markets can still obtain loans through other lenders such as relatives or friends, so it is important to consider these sources when classifying people as credit constrained or excluded.

In countries where formal credit markets are still developing, informal arrangements are important ways for communities to share resources and pool risks. For example, in Ethiopia, households may be part of a social support network called an ‘iddir’, a group of people who regularly pay cash into a common pool that is shared among group members who need it.

We will be looking at data from an Ethiopian household survey (the Ethiopian Socioeconomic Survey) to investigate the borrowing conditions that different types of households face. Aside from credit constrained and credit-excluded households, we will also look at a third group of households that are ‘discouraged borrowers’, meaning that they did not apply for a loan because they thought they would be refused.

Working in Excel

Working in R

Working in Google Sheets

Working in Python