Second phase of the baseline: Micro-Enterprise Profits

The second component of the second phase of the baseline is a study of micro-enterprise profits and is a little more complicated to explain because it is not standard in the literature. In fact, I am only aware of work by De Mel et al. dealing with this issue. I do not know of any work in Africa.

Any study looking at the outcomes of a business grant program must deal with the issue of measuring profits as it is one of the direct implications of a program on a person’s economic status. That few people have looked into this in detail is surprising to me.

This project came out of my time living in northern Uganda last year and assisting on designing an evaluation of an NGO that does very small scale grant programs. I noticed that most people made some small but serious mistakes in calculating their profits (not including transport and communication being the most popular). Then, in June of this year, I met with some people from a micro-finance group in Kampala and was shocked to learn that they give their clients no training. Not surprisingly, the majority of clients have to refinance their loans, and so get trapped in a cycle of debt.

Before getting to the research design, there are a number of research questions we want to address with this component. A short list includes: do people calculate their profits correctly, and if not, what are the mistakes they are making? Do these mistakes have long-run implications for the business? Are there better ways of eliciting correct profit data? Do the mistakes correlate with other variables that are easy to measure? How are the NUSAF businesses different than businesses in the communities? Do too many businesses in one area adversely affect profits, as one might expect? How much time do people spend on their businesses, and is there a household constraint to this time? How does this time constraint affect women? Do women make similar profits in similar industries as men, or less? What are the returns to business in the north, and how do they compare to loan interest rates? Do people have an innate business skill, like Yunnus believes, or do they need help? If they need help, what is the most cost effective way to address these needs? Are new businesses more prone to these mistakes than older businesses?

This is an incredibly long list of questions, but I think we can answer them using a research design similar to that of De Mel et al. mentioned above.

First, we will draw from three populations. I have data on the old and newly funded NUSAF groups and can identify different types of businesses from each of these. We will then conduct a community census of businesses in a number of areas where the NUSAF groups are. This will be done with a short (5 minute) questionnaire of about 1000 businesses to ensure a large enough sample (which also helps with generalization beyond NUSAF).

From both of these groups we will randomly select 400 businesses to conduct a detailed questionnaire that I have adapted from De mel et al. This is still in draft form; I will update on the progress of the pre-testing.

From these we will randomly select 200 businesses to give bookkeeping tools to. This will prove to be a difficulty as many people are illiterate. I will be looking into a method to get the household/community involved to help with increasing compliance.

It is possible that bookkeeping tools are an intervention in themselves, and so 50 businesses from this group will be randomly selected, along with 50 random businesses without bookkeeping tools, to be given a detailed follow-up. An enumerator will visit these selected businesses every other day for 30 days to collect daily business interaction records. This will then constitute our most accurate measure of profits, against which we will make all comparisons.

There are of course some potential difficulties. The NUSAF projects are group projects, so separating out assets and profits for each person will be difficult. Also, business involvement may be erratic, so the detailed follow-up may run into a lot of “no shows”. Identifying “shadow labor” (implied labor availability of household member, like the kids) of households could also be difficult. There is also a need to balance cost with dispersion to increase the generalizability of the findings. Finally, we probably can’t make strong claims on women’s involvement as we cannot identify individual preferences and social traditions that may complicate the results. This is an especially big problem as women likely engage in more informal work, and so this could confuse the data.

Besides pre-testing the instrument, I also want to think about possible very low cost interventions to assist businesses. Last year I came up with the idea of a low cost radio program to teach basic profit calculations. I am now leaning towards getting businesses to use the bookkeeping tools more. Both of these could then be compared to larger interventions, such as multi-day business skills training. Within this is the possibility of using the cross-cutting design to increase assistance to the groups, as well as compliance with the bookkeeping tools.

An outstanding question for me is how to connect the mistakes people make with profits with the outcomes of the business. I still do not have a strong enough design to make this clear as omitted variables and bad proxies of outcome could be important.