Assignment 1. Annotated Bibliography

Victor Tran

February 23

Problem Statement

Define the problem. What is the harm I am seeking to investigate and why is it significant within the context of my selected LMIC?

Cameroon’s large informal economy exploits the current rampant economic inequality that exists in Cameroon, causing unfair competition with formal businesses and discourages economic growth in rural regions, which is ultimately a result of the significant lack of resources in the rural regions such as access to high-quality infrastructure, job opportunities, and education.

In Cameroon, regions are very economically separate, with some areas such as the capital, Yaoundé, being very populated, urban cities where industries such as manufacturing are continuing to thrive, but like many LMIC’s, a large part of Cameroon is rural (B et al.). Because of this, the vast majority of Cameroon’s economy is based off of farms and other rural industries, therefore making the majority of Cameroon’s economy based on the informal economy, where Cameroon’s government has no oversight (Bougna & Nguimkeu). While informal business owners do not have to follow Cameroon’s regulations and are tax-free, they do not have access to essentials for expansions such as credit (Bougna & Nguimkeu). Additionally, informal economies are able to sell goods at lower prices, which negatively affects the profits of formal businesses (Rozos & Wrinkler).

The size of the informal economy is also an indirect indicator of the conditions of many regions in Cameroon. In general, employees of informal businesses are less educated and do not have the proper skills or training to compete for jobs in formal businesses (Bougna & Nguimkeu). This is in addition to rural-urban migration where those who work in informal sector in the rural side move to the urban side either temporarily or permanently in the search of a job that much higher pay than their current one (Todaro). In the urban sector, the informal economy also includes illegal businesses as well such as the production and selling of drugs or firearms. Residents are taken advantage of because of their lack of skills and education and end up working for less in illegitmate businesses.

Simply put, the informal economy is not a cause, but rather a consequence of existing poor conditions in LMIC’s such as Cameroon. Therefore, in order to reduce the level of the informal economy, it is essential to remove the root causes that force people to resort to working in informal businesses. This includes reducing economic inequality in regions of Cameroon which can be done through higher accessibility to education, job opportunities, and infrastructure.

Sources

Bougna Lonla, T., & Nguimkeu, P. (2018, July). Spatial and sectoral heterogeneity of occupational choice in Cameroon (Technical Report No. WPS8515). Washington D.C., US: World Bank Group

Bugna and Nguimkeu investigate the issue of formal and informal businesses, with one being taxed and regulated by the Cameroon government while the latter is in a gray area which is not taxed nor regulated by the government. Formal businesses have the benefit of access to credit, which is necessary for most businesses to expand. However, there are many constraints preventing informal businesses from becoming formal businesses such as entry costs and tax. Consequently, the companies do not have the resources to maximize production nor hire more labor. In order to reduce the unemployment rate and maximize economic productivity in regions of Cameroon where informal businesses are prevalent, the authors intend to identify the types of policies that could remove current constraints, increasing the overall transition rate of informal businesses formalizing.

The authors used a geospatial analysis of the General Enterprise Census and National Survey on Employment and the Informal Sector to map out the areas of informal and formal businesses and performed cross section testing to identify what types of areas were most profitable and in what industry. While they did not specifically mention how they mapped their census data spatially, one could assume that they either used a top-down or bottom-up approach. One factor that they saw had significant correlation with economic success is the distance to the nearest roads and the average road density of the cities the businesses reside in. They then used this data to form counterfactual samples to make predictions on what factors such as tax rates, government revenue, and job creation had on the profit of these businesses.

The researchers use data science strategies such as Ellison and Glaeser index to calculate the concentrations of industries within certain regions. From this, they observed that 60% of industries are weakly or strongly concentrated. They also used Krugman’s Specialization Index at the 4 digit NACE level to describe levels of specialty of each region for certain industries. OLS regression tests were used to identify the correlation between entrepreneurial income and certain characteristics such as age, education, and population density.

This paper relates to Amartya Sen’s idea of human development because it concerns removing the constraints that prevent informal businesses from formalizing, giving them access to credit and making them appear more reputable. If the constraints to formalize a business are removed, then entrepreneurs have access to credit to expand their business and provide jobs for other people. The work most closely follows sustainable development goal #8 regarding fair economic growth because it shows the distribution of informal and formal businesses across Cameroon and the difficulties that certain locations have with the transition from an informal to formal business.

B, E.-N., Léandre, B., & Saumik, P. (2010, November). Accounting for heterogeneity in growth incidence in Cameroon (Technical Report No. WPS5464). Washington D.C., USA: World Bank.

The authors describe the large improvements of Cameroon’s economy in the past few decades while specifying the levels of inequality in such growth and how there is a disparity in the current economic status of many regions in Cameroon. In the early 2000’s, Cameroon created policies that were intended to decrease the level of inequality among regions, but a decade after the policies, the Gini index has only dropped by a couple of points. The policies that were put in place were not as effective as intended in improving economic inequality. The author’s intent of this research paper was to use household statistics to create counterfactual analysis and identify which attributes were effective in eliminating economic inequality in regions of Cameroon.

The authors use the census provided by the National survey office. In particular, they looked at the 1996, 2001, and 2007 census. They used several geospatial data science techniques in order to parse this data on a subnational level. While the authors do focus on certain regions as part of their regions, they also divide the country into deciles based on expenditures per adult. Through comparing the deciles, the author portrays how significant the difference between the top 10% and the bottom 10% really is, with the top decile making 12 times as much as the bottom decile. They used the Gini index to identify the levels of inequality in the parts: Cameroon as a whole, the urban side of Cameroon, and the rural side of Cameroon. In addition to the Gini index. They also performed re-centered influence regressions and Oaxaca-Blinder type decomposition into quantiles of the data to identify which factors are most prevalent for inequality within certain income distributions.

In regard to certain regions of Cameroon, the authors used the Watts Poverty Gap Index and mapped it to certain regions in Cameroon. They split Cameroon into 12 areas and saw that the poverty index was greatest in the northern areas such as North and Far North Cameroon and southern areas to be more equal. This research paper is related to the sustainable development goal #8 and #10 since their work is related to the economic progress of Cameroon and the levels of inequality in economic growth Cameroon based on region. By identifying and addressing the areas of vast economy inequality, they will be able to identify deficiencies in parts of Cameroon and aiding regions effectively

This is related to Amartya Sen’s definition of human development since if there is a level of inequality, then people are most likely not reaching their potential output either because they do not the capabilities to do so or they do not have the opportunities to utilize their capabilities. Through understanding the reasons for this economic inequality, aid groups or politicians would be able to identify which type of aid or policies would be needed to properly address the issues.

Rozo, S., & Wrinkler, H. (2019, October). Is informality good for business? The impacts of IDP inflows on formal firms (Technical Report No. WPS9035). Washington D.C., USA: World Bank.

This study involves the impact of internally displaced people (IDP) on the formal business economy. Generally, IDP are forced out of their region of origin instead of leaving out of their own free will. The authors specifically focus on IDP who leave due to violence such as political instability or gangs. Columbia has a large percentage of the population as IDP, so the authors use it for their study. The goal of authors’ research is to identify how IDP affect the performance of formal businesses, including productivity, prices, input demands, etc.

The authors used two main sources for geospatial data. First was Columbia’s annual manufacturing survey which provides information such as sales, wages, employment, capital, input prices and output for companies with at least ten employees. They also used the Registry of Violence which provides direct information about internally displaced people, such as their original municipality, the date they left, new municipality, and socioeconomic status. This provided the researchers with information about which regions were being migrated to and from and the number of these migrations.

These two sources of data were then analyzed and displayed geospatially as rasterized data. The authors used an intensity index to identify which regions had lost most of their people as IDP. In Columbia, the west and the upper south regions had the highest intensity of lost IDP. They also used a pressure index which was defined as the ratio of forcefully displaced people in a region and the average municipality population. This could be used as an indirect metric for congestion of public goods and services from IDP. They ran regressions based on predicted inflow of IDP and individual aspects of formal businesses such as productivity, prices, and input demands. They saw that areas which were the destination had a significant loss of profit and production and chalk it up to the idea that IDP are a source of labor for informal businesses, which are able to produce and sell goods at cheaper prices. While this certainly not beneficial for formal businesses, IDP are also being given the short end of the stick as well since they are being exploited for cheap labor since they are generally not able to work in the formal sector due to lack of paperwork or skills.

This research deals in particular with sustainable development goals relating to economy and equality since it is studying the effects of IDP on the performance of formal businesses and discusses IDP in general as people displaced forcibly from the municipality of origin. This also relates to Amartya Sen’s definition of human development because businesses are not reaching their full potential due to IDP, which means people will have less opportunities for jobs. Additionally, IDP are not able to obtain formal jobs and have to rely on informal businesses which can be in the legal-gray area or exploitative. Countries with a significant number of IDP need to address the causes of IDP such as violence in the regions of origin. Through this, there will be higher economic growth and the number of IDP will decrease.

Gachassin, M., Najman, B., & Raballand, G. (2010, February). The Impact of roads on poverty reduction: A case study of Cameroon (Technical Report No. WPS5209). Washington D.C., USA: World Bank.

The authors of this research study analyzed whether improvements in infrastructure, in particular roads, would be helpful in decreasing the poverty gap found in much of Cameroon. They saw that in rural areas, the poverty rate and distance to the nearest tarred roads were much higher than if they were looking at an urban area in Cameroon. Rural areas in general have agriculture as the number one source of jobs. However, it is one of the lowest paying jobs as well, so residents of rural areas do not have the opportunities for high-paying jobs to increase their income. The authors want to know if improving road availability will indirectly reduce poverty in Cameroon since they will be able to work and find necessities in areas farther away.

Their main source of data was the 2001 national household survey. In this survey, all parts of Cameroon were stratified into 612 clusters where 11,000 households were sampled. From this, they split the populations based on regions, specifically urban (defined as having a population of at least 50,000), and rural (defined by having a population of at most 10,000). They further divided rural regions into rural savannah, rural high plateau, and rural forest and used Yaoundé and Douala as their samples for urban regions.

The authors use several data science techniques to further their research. They used a three-stage-least-square regression, decomposing labor and the Welfare Ratio into equations to estimate the impacts of road improvement/creation on increased consumption of expenditure. Through this, they are able to identify whether certain factors are determinants for poverty. The authors do this using binary variables, comparing if one variable is a determinant while another is not. For example, whether an agriculture-based job versus a non-agriculture-based job is a determinant. In order to recognize bias, specifically selection bias, they calculated the inverse Mills ratio when testing the binary variables. For most variables, they saw that the inverse Mills ratio was insignificant but on others, such as when they tested agriculture as a binary variable, they found selection bias to be signficant.

This is related to Amartya Sen’s definition of human development because roads are required for access to many necessities such as healthcare, education, and jobs. Without roads, people do not have the ability to go to school or get jobs because the distance to travel on an unpaved road is too tiresome or long. By identifying the impact of a lack of infrastructure on accessibility of important resources, proper targeted planning can be done to reduce inequality by enhancing infrastructure in these impacted areas.

Todaro, M. P. (1969). A model of labor migration and urban unemployment in less developed countries. The American Economic Review, 59(1), 138-148.

In rural areas where jobs are mostly agriculture-based, income is low. This encourages rural workers to consider moving to urban areas in search of a job that has substantially higher pay. However, there is a delicate risk and reward analysis that must take place. Todaro suggests that it may be simple to move to the urban area, but finding a job is not guaranteed and could take a long time, burning money during the process. Todaro uses a series of models to identify the relationships between variables which may impact the rural-urban migration patterns. By creating these models, Todaro hopes that they will be used in order to identify and put in place effective policies that will reduce levels of unemployment in both the rural and urban section of the economy.

The main mathematical concept that Todaro uses is the rural-urban wages differential which is the difference in average wages between rural and urban areas. Since this article was written in the 1969, neither the advanced geospatial data science techniques, nor the technology to process the datasets were readily available. Instead, Todaro briefly discusses past studies and incorporates his analysis of said studies into his models. The first model he creates was used to identify the change in urban labor supply based on rural-urban migration. He then creates individual models for each of the variables includes in the first model, such as the expected urban income for someone with no labor skills and the expected rural income for someone with no labor skills.

This study involves sustainable development goal #8, decent work and economic growth, and #10 reduced inequalities. The author’s intent is to identify how policies can be effective in reducing unemployment and underemployment, which will sustain long-term economic growth as well. Additionally, the rural sector should increase labor productivity and be an asset towards economic growth, increasing economic equality between the rural and urban sector.

This relates to Amartya Sen’s definition of human development because people are seizing the opportunities to utilize their capabilities via migration for the sake of a better paying job. However, there is risk involved in going for these opportunities; therefore, models would be helpful to identify probabilities of finding higher paying jobs in the urban cities or the other way around. This would be a significant key in minimalizing risks of unemployment and underemployment. Additionally, this study could be helpful in a more time-restrained situation such as seasonal rural-urban worker migration where farmers will move to the cities for only several months during agricultural off-seasons and then return back to the rural side. Overall, basic economic inequality would be able to reduce the need for most risky rural-urban migration.