Prior to the onset of COVID-19 the World Bank, WHO, and UN had all been watching a concerning trend, the slowing of the rate at which people moved out of poverty. This trend was exacerbated during the onset on COVID-19 as economies slowed down and healthcare expenses for individual households skyrocketed worldwide. This created a new trend of negative poverty recovery, in other words, more people were pushed into poverty or deeper into extreme poverty. Estimates from the world bank show around half a billion people shoved into poverty, or deeper into poverty, in December 2021. The quote above comes from a 2020 estimate and discussion by the world bank, where they claimed 150 million are likely to be added into extreme poverty as a result of COVID-19. Current numbers are harder to pinpoint, but we can assume it is likely a very real issue globally.
First, we should define what tools are available to aid in the reduction of global poverty. These typically take the form of accelerated economic growth, agricultural growth, development of infrastructure, development of human resources, growth of employment, access to assets, and access to credit. While we don’t have time to unpack all of these, I would like to focus on what an increased access to credit does for individuals and economies facing poverty with a discussion using the Solow model to describe what we should expect to see on a economic level from an increased individual or group access to credit.
Microcredit is typically defined as the provision of small loans to underserved entrepreneurs and has been both celebrated and vilified as a development tool. It was the basis for the 2006 Nobel Peace Prize and has a wide following from policymakers, donors, and funders worldwide as an effective policy tool. However, there was a severe lack of evidence for some of microcredit’s biggest claim, that it could pull people out of poverty. Many theories of the impacts of microcredit ranged from poverty traps, general equilibrium effects, and credit market competition suggested that the expanding of credit to the impoverished need not be positive and may even be negative. Now for further discussion around microcredit using six randomized evaluations of microcredit complied by Abhijit Banerjee, Dean Karlan, and Jonathan Zinman to better understand the observed outcomes of microcredit.
Six randomized studies of microcredit from geographically different areas picking to serve different groups within each nation ranging from Bosnia, Ethiopia, India, Mexico, Mongolia, and Morocco. All except Bosnia have an annual household income of less than GDP per capita in the given year of each study. In each study loans were randomly offered to both a treatment group and control group, this could be an individual or a group, the key was how to have people take the access to credit, and this was from first screening the desire for credit. Some studies only offered loans to women within a certain age group while the study in Ethiopia had a random selection of households holding poverty status and a viable business plan, as these loans were mostly given to entrepreneurs with only some studies tracking how the funding was spent. Average loan sizes ranged from about $500 USD in Ethiopia to $1,800 USD in Bosnia. What is important is the proportion of the size of the loan compared to annual household income, this ranged from 118% in Ethiopia to 6% in Mexico.
Now to move to challenges researchers faced when conducting these surveys. They found three main issues that influenced the rate at which loans were chosen and the credit market as a whole. First being there is a modest demand for microcredit. Second being both groups viewed are very similar and given economic conditions in each nation we should expect to see low take-up rates of loans, which was observed to range 17 to 31 percent. The last challenge seen focus on the relationship between microcredit and traditional credit in terms of substitute or complementary good which may affect either form of credit from increases in demand as consumption of microcredits increases, however evidence from the study suggests both goods seem as substitutes, so as microcredit use increases we see a reduced demand for credit from other Monetary Financial Institutions.
We’ll briefly touch on all of the study’s findings then shift focus to one of them to analyze through the Solow model to give potential implications. Researchers grouped outcomes into groups, specifically: business activity (litmus test), Income, Consumption, and Social Indicators. The first finding focuses on micro entrepreneurial activity and we can think of this as starting a business or funding a current business. Since most surveys within this study focused on entrepreneurial activity as a prerequisite to loan consideration, we can use this outcome as a litmus test, or to show that the effect is measurable and exists regardless of noisy data. Here the study found modest effects on ownership, starts and closures so this suggests a partial passing of the litmus test. The second outcome group viewed was income, or more specifically household income and income composition. Here we see no change in household income but there is an interesting result of income composition, or the ratio of business income vs wage income. Due to increased investment into businesses, we see an offsetting of wage labor replaced by business income. A key observation within this group of observations is the growth of freedom of choice in the form of occupation and time spent rather than directly lifting people out of poverty. The third outcome group is consumption, specifically consumption expenditures which is a widely used proxy for standard of living. The results found no increase in total household expenditure and mixed effects in other metrics, but there was an interesting result in the composition of consumption. A robust finding is a decrease in the consumption of discretionary goods (temptation goods, recreation, or entertainment) it is unknown why this is but another observation found is no change in healthcare or education spending. The final outcome group of social indicators yields no change in the measured areas of child schooling and female empowerment. Each study measured these changes, yet no transformative effects on social indicators was observed. Now to transition to a widened discussion of the outcome group most relevant to my presentation, micro entrepreneurial activity.
Most studies measured changes to business investment, size, and profits with increased access to credit through microcredit. Many of the studies found positive results around these areas, specifically that the average effects when pooled across studies is economically significant. The study concludes that with pooling many surveys we can see some evidence of expanded credit access yielding positive changes to business activity in the form of business investment. Let’s now view how this finding may affect an overall economy if access to credit were expanded further through analysis with the Solow model.
Here we can see a production function graphed as a function of capital per worker with two savings functions graphed below and a standard linear depreciation of capital. We should expect the increased investment from expanded access of credit by microcredits to push this savings function upward which pushes capital per worker outward to a new equilibrium point and may also show some increase to GDP per worker depending on where the original point of equilibrium is located.
This increase to capital per worker and GDP per worker from increased access to credit from microcredit may yield growth in national level economies if the effects compound between many groups or individuals, we may see an increase to living conditions years into the future from repeated investment at a new higher equilibrium and a growing economy of a previously hurt or stagnant economy.
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