The region known as Sub Saharan Africa (SSA) contains some of the poorest countries in the world, known for its economic failure and astounding poverty rates. In recent years, the dynamic of the economy, particularly in the labor force, has generated the first ever recorded positive economic growth rates in this area. The analysis, “The Changing Structure of Africa’s Economies”, performed by Xinshen Diao, Kenneth Harttgen, and Margaret McMillan, seeks to provide evidence that Sub Saharan Africa is beginning to indicate a shift towards the development track that many successful economic countries followed on their way to prosperity. The authors claim that most of the economic progress comes from a structural change of the labor force: a shift from the agricultural sector, to the manufacturing and service sectors. This shift contributed to overall labor productivity growth, and allowed Africa to experience its “strongest growth in four decades” (Diao et al 20).
Basis and Assumptions:
To adequately analyze the economic trends of rural Africa, the authors chose to utilize two data sets: the Groningen Growth and Development Center (GGDC), and the Demographic and Health Surveys (DHS). Between these, there are varying numbers of observed countries, but the eight overlapping countries are specifically targeted for the data analysis. These countries include some of the lowest income African countries such as Ethiopia, Nigeria, and Tanzania. These are compared to some of the highest income African countries such as Botswana, South Africa, and Mauritius, as well as data groups from Latin American, Asian, and highly developed countries (such as the United States). The purpose behind using developed countries’ data was “to study the evolution of the distribution of employment between sectors across levels of income experienced in Africa and how it compares with the patterns seen historically in other regions over the course of development” (Diao et al 12).
For decades, poverty-stricken areas of Africa have largely focused simply on agricultural labor: providing the basic food for the people to survive. The authors believe that a shift in the labor sectors is leading to a decrease in the magnitude of the labor productivity gap, and an increased prosperity level overall. The heart of the paper focuses on the changes in the “level of employment shares” in each labor sector, corresponding to the “levels of income” (Diao et al 6). The logic behind comparing the levels of the structural sections of labor provides the observer the ability to map out trends of shifts based on previously developed nations.
Several assumptions related to the accuracy and availability of the data collected by both the GGDC and the DHS surveys must be made to observe and compare the economic growth in Africa. The authors point out that employment data, informal labor sector knowledge, and measurements of human capital (well-being, education, etc.) are all taken at the level of detail and availability that the surveys provide. Unfortunately, as seen with many studies throughout research, data which comes from poverty stricken countries is not always reliable, accurate/without error. There are instances where the authors of this NBER analysis exclude an entire group of data, justifying their actions as if generalizing groups will “avoid confounding the results” of the data (Diao et al 28). However, the assumptions in the analysis are justified, as the authors took care to check each set of data, inquire the survey agencies regarding errors they found, and base their analysis of trends on data in which two or more benchmark surveys were always provided.
“The Changing Structure of Africa’s Economies” is based on the hypothesis that structural change in the labor sector distribution has a positive effect on economic growth. The author’s stand by their premise by stating that in developed/prosperous countries, there are very few people who are involved in the agricultural labor sector. Reallocating labor in rural areas into sectors such as manufacturing can have a huge increase in labor productivity, thus “allowing aggregate productivity to catch up…[causing] rapid growth rates” (Diao et al 11). In fact, empirical evidence suggests that according to a “GGDC sample, annual labor productivity grew by an (unweighted) average of 2.82 percent, and structural change contributed an (unweighted) average of 1.13 percentage points to overall labor productivity growth. Put differently, from 2000 to 2010, structural change accounted for 40 percent of Africa’s annual labor productivity growth” (Diao et al 24).As observed in Table 1, for the majority of African countries the labor sector with the lowest productivity is agriculture: with a maximum value of 4.37.
Table 1: Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies
By improving the labor productivity growth, the overall growth rate of the economy increases, poverty rates decrease, and human capital increases by creating skilled workers. The manufacturing sector in Africa may never compare to the manufacturing industry in a previously developed country, but it has been shown that African areas who devote their resources into building human capital to provide skilled manufacturing works generate higher levels of income to raise the poverty headcount ratio at drastic rates. Although some of these labor sectors are specialized and do not have the capacity to bear all structural change, there is a positive observable correlation due to structural transformation ASSUMING the transfer of labor ends up in a more profitable sector. Transfer of labor to a less profitable sector can lead to recession of economic growth. Because of this risk, it is often profitable to look within sectors to make infrastructure changes. Related articles, such as a USDA Economic Research Service report written by Keith Fuglie and Nicholas Rada, indicates that although a transfer in internal-sector productivity may be useful, doubling agricultural research can also boost Total Factor Productivity (which compares total outputs to total inputs in a country) growth rates by over 4%. According to the GGDC data, the initial benchmark revealed low-income countries exhibited an approximate 70% of their labor force was dedicated to agriculture, a number with declined by 9.3% by the time the most recent data was observed (Diao et al 24). This led to an increase of over one and a half percentage points in labor productivity and in some cases, positive country economic growth rates. Studies in the GGDC data, and the DHS data (categorized by gender, education, age, etc.) showed similar improving results with the decrease in agricultural labor – with the greatest difference being observed with females in rural areas. Figure 1 demonstrates the decline in the GDP level per capita of Agriculture, while contrasting with the increase of GDP wealth per capita in other labor sectors.
Figure 1: Employment shares by labor sector, increasing/decreasing with income: Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies
Daio, Harttgen, and McMillan chose to analyze two data sets to expand research on Africa’s upcoming economy that had previously never been approached. What they found was that distinct patterns were found in the structural trends that can be compared and adjusted by observing the development paths of previously poverty stricken and underdeveloped nations. In fact, at “lower levels of income, countries that pull themselves out of poverty also exhibit positive structural change” as a critical part of their economic development. Now, not every sector of expansion that prospered in a different country (ex. Latin America) will work in rural Africa, but similar growth concepts, such as the “importance of investing in human capital and infrastructure…can raise productivity levels” and improve a country’s overall state of well-being (Diao et al 32). The authors summarize their findings with 5 stylized facts listed below, which begin to outline growth patterns based on structural shift. The largest implication of this paper however, is to open the door for more empirical analysis and research in sub Saharan Africa, as the authors state that until this point, “economic data to undertake such analysis has been largely unreliable or nonexistent for most African countries” (Diao et al 12).
- “First, when the patterns of employment in Africa are compared to the patterns observed in other regions across levels of development, the pattern among our sample follows that seen in other regions for agriculture and services—that is, the agricultural employment share is decreasing in income, while the services employment share is increasing in income.”
- “Second, when the levels of employment shares are compared to the levels observed in other countries, the levels of employment shares in agriculture and services approximate the levels observed in other countries at similar levels of income.”
- “Third, all of this holds for industry and manufacturing in the eight low-income African countries.”
- “Fourth, in Botswana, Mauritius, and South Africa, the patterns in industry are similar but the levels differ, and in the case of manufacturing, the relationship between income and employment shares follows more of an upward sloping line than an inverted U-shape.”
- “Fifth, Africa is still, by far, one of the poorest regions of the world.”
- “Finally, structural change continues to remain a potent source of labor productivity growth in much of SSA.”
Stylized Facts Courtesy of Daio, Harttgen, and McMillan, “The Changing Structure of Africa’s Economies”.
Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies (Working paper No. 23021). Retrieved April 19, 2017, from National Bureau of Economic Research website: http://www.nber.org.proxy-um.researchport.umd.edu/papers/w22872.pdf
JEL No. O11,O4,O55
Fuglie, K., & Rada, N. (2013, May 6). Research Raises Agricultural Productivity in Sub-Saharan Africa. Retrieved April 24, 2017, from https://www.ers.usda.gov/amber-waves/2013/may/research-raises-agricultural-productivity-in-sub-saharan-africa/