Ethnic inequalities challenging the Kuznets curve? Afro-descendants in the Peruvian labour market

Ethnic inequalities challenging the Kuznets curve? Afro-descendants in the Peruvian labour market

Lewis (1954) suggested that capital formation, which involves increments in inequality, drives economic growth. In addition, he postulated that, in the long-term, widening inequality could diminish depending on factors like limited supply of labour. In a similar approach, while analyzing structural transformation, Kuznets (1955) proposed that, when labour began to be transferred from agriculture into higher-productivity sectors, inequality would increase as a consequence of higher earnings; but later, as more people transitioned jobs, redistribution would generate a decrease in inequality. The graphical representation of the inequality trend that Kuznets describes has been known as the “Kuznets Curve”.

Lewis (1954) and Kuznets (1955) studies provided new insights into inequality; nevertheless, reality has proven that their theories cannot be generalized. In fact, since the 1980s inequality has gone up in many countries (Atkinson, 2015). Moreover, as Lewis (1954) and Kuznets (1955) theoretical frameworks were developed in a more homogenous European context, they fall short on describing how ethnic inequalities can affect the structural transformation process. In contrast, recent research highlights the importance of looking at inequality across different groups, including location, gender, and ethnicity (Atkinson, 2015). Addressing the impact of ethnic inequalities is particularly relevant for countries like Peru, in which researchers contend that ethnic differences are one of the main reasons behind persistent inequality (Oboler. & Callirgos, 2015). Moreover, ethnic inequalities in the labour market would present limitations for minority groups when trying to transition to higher productivity or better jobs (Galarza, Yamada, & Zelada, 2020). Therefore, focusing in the afro-peruvian community, this paper will answer the following research question: How do ethnic inequalities affect the labour-market in Peru?

In Peru there is evidence of structural transformation in terms of employment share (See Appendix). Nevertheless, despite there has been a 7.4 percentual point decrease in the Gini coefficient (Solt, 2020), between 1980 and 2020 the bottom 50%, top 1%, and top 10% income shares have all varied by less than 1.5%; moreover, the top 1% accumulates 20.1% of the national share, while the bottom 50% only accumulates 11.2% (See Figure 1). In a country where ethnic minorities are the most unprivileged, and where around 30% of the Peruvian population self-identifies as indigenous peoples or afro-peruvians (INEI, 2018), focusing on reducing ethnic inequalities is critical for development. Nonetheless, peruvian minorities, particularly afro descendants, have often been neglected from academic research (Galarza et al., 2020). Therefore, in order to analyze the impact of ethnic inequalities against afro- peruvians in the labour-market this paper will focus in three aspects: 1) immediate market limitations and structural inequalities in 2) education and 3) health. Education and health are considered in the analysis as they can affect job accessibility and work performance.

Figure 1: Income Shares in Peru

Source: Author’s elaboration with WID (n.d.) data

Analyzing racial discrimination in the peruvian labour force Galarza et al. (2020) performed a study where they sent fictitious CVs for different professional, technical, and low- qualification jobs. The CVs for each vacancy had similar human capabilities, but captured racial differences between white-european descendants and afro-peruvians by using different last names. Through their study they found that, despite the average calling of afro-peruvians was lower, there was not a statistical significant difference. Nevertheless, when isolating occupational categories, they found that in callings for technical and professional positions “white-european” had a positive significant difference against afro-peruvians. Moreover, they found that, for professional vacancies, afro-peruvians received 38% less calls than white-europeans. This evidence thus suggests that, only because of race, it is harder for afro-peruvians to access higher quality jobs.

 

Regarding education, McMillan, Rodrik, and Sepulveda (2017: 3) emphasize that for development to occur “structural transformation” needs to be accompanied by the accumulation of “fundamentals”, which he describes as individual skills and institutional capabilities. Considering skills as “fundamentals” evolves from Glaeser (2004) postulate that human capital involving training and education is a primary driver of long-term development. According to national census data, only 11.5% of the self-identified afro-peruvian population had access to education at the university level, while 22.1% of the non afro-peruvian did; similarly, at the higher non-university level there is a 3.5% gap in favor of non afro-peruvians (INEI, 2018) (See Figure 2).

 

Figure 2: Access to higher education in Peru

Source: Author’s elaboration with INEI (2018) data

 

Concerning health, Wilkinson and Pickett (2010) argue that, in societies with wider inequalities, the most underprivileged are the ones who suffer the larger consequences of depression, illnesses, and even obesity. Likewise, Deaton (2006) highlights social epidemiologist opinions which suggest that the principal determinant of health is not health care but socioeconomic status. Their postulates go in accordance with what afro-peruvians experience. Benavides, Sarmiento, Valdivia and Moreno (2013) analyzed the Peruvian national household survey (2006) and found that the majority of afro-peruvian children do not get their sicknesses treated at an official health facility, this partially due to four reasons: locations with an afro-peruvian majority typically 1) lack proper health infrastructure 2) lack enough supply of medicine 3) lack good quality of health services and 4) lack proper control of health workers. Moreover, afro-peruvians have less access to health insurance and experience higher mortality rates than non afro-peruvians (INEI, 2018).

 

As demonstrated, afro-peruvians experience discrimination when trying to access higher quality jobs. Moreover, a lower percentage of afro-peruvians are able to access universities or obtain non-university higher education. As well, afro-peruvians have higher mortality rates, less access to health insurance, and a majority of their children do not get treated in official health facilities. To conclude, afro descendants in the labour-market get heavily impacted by ethnic inequalities through direct racial discrimination when applying to jobs and through the long-term effects of deficient provision of education and health for their community.

by Cesar Gonzalo Davila Novoa

 

*Essay written for the "EKHT 42 – Explaining Growth and Inequality" course at Lund University

Bibliography

 

 

Atkinson, A. B. (2015). Inequality: What Can Be Done?

 

 

Benavides, M., Sarmiento, P., Valdivia, N. y Moreno, M. (2013). ¡Aquí estamos! Niñas, niños y adolescentes afroperuanos!

 

Benavides, M., Leon, J., Espezúa, L. & Wangeman, A. (2015). Estudio especializado sobre población afroperuana. Ministerio de Cultura y GRADE.

 

Deaton, A. (2006). The great escape: A review of robert fogel's the escape from hunger and premature death, 1700-2100. Journal of Economic Literature, 44(1), 106-114

 

Galarza, F., Yamada, G. & Zelada, C. (2015). Empleo y discriminación racial: afrodescendientes en Lima, Perú. Universidad del Pacífico.

INEI (2018). Censos Nacionales XII de Población y VII de Vivienda, 22 de octubre del 2017, Perú: Resultados Definitivos. Instituto Nacional de Estadistica e Informática.

 

Kuznets, S. (1955) ‘Economic Growth and Income Inequality’. The American Economic Review 65, 1: 1-28

 

Lewis, A. (1954) ‘Economic Development with Unlimited Supply of Labour’. The Manchester School 2 (2): 139-191.

 

McMillan, M., Rodrik, D., & Sepulveda, C. (2017). Structural change, fundamentals and growth: A framework and case studies (No. w23378). National Bureau of Economic Research.

 

Oboler, S. & Callirgos, J. (2015). El racismo peruano.

 

 

Pickett, K., & Wilkinson, R. (2010). The spirit level: Why equality is better for everyone. Penguin UK.

 

Solt, F. (2020). Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database.

 

WID (n.d.). World Inequality Database.

 

World Bank (n.d.). World Development Indicators.

The African Growth Miracle: Economic growth, structural transformation and growth sustainability in Kenya

The African Growth Miracle: Economic growth, structural transformation and growth sustainability in Kenya

    Back on year 2000, due to high poverty rates, lack of access to health and education, string of wars, and political instability, Africa was labeled by a cover on The Economist magazine as the “hopeless continent”. Particularly, the Sub-Saharan Africa (SSA) region faced stagnation with minimal economic growth, and living standards changing less from one generation to the other (Perkins, Radalet, Lindauer & Block, 2013: 8). Researchers like Amsden (1989), addressing Gershenkron’s (1963) backwardness theory, suggested that there could be a limit on the point of backwardness of a country that would determine if a country could successfully achieve substitution. In addition, growth theorists argue that convergence of underdeveloped countries -equalizing per capita output to that of developed countries- is dependent upon country specifics like institutions, policies, human capital and endowments. (Thorbecke & Ouyang, 2016: 239-240). Being the most underdeveloped region in the world, some thought that SSA could be too far behind to be able to recover.

    Nevertheless, during the past two decades, Africa has experienced sustained economic growth, surpassing the global average and experiencing steady rise of per capita income. (Page & Shimeles, 2015). Part of this growth was driven by an increase in commodity prices, foreign investment, better quality of governance, and the appearance of a middle class (Austin, 2016: 222; Thorbecke & Ouyang, 2016: 236). Moreover, Young (2012), determined that SSA living standards, measured through real household consumption, were growing at an annual rate of 3.4% to 3.7%, a rate that is 3.5 to 4 times the rate on international data sets. Given the nature of these unexpected results, the growth experienced in the African continent is referred to as the ‘African Growth Miracle’. Concerning this period, the following paper will investigate the regional context of SSA to then place special focus in Kenya. In particular, it will address the following research question:

 

How economic growth and structural transformation have evolved in Kenya during the ‘African Growth Miracle’ and, regarding long term perspectives, how could this growth be sustained?

 

    The economic growth in SSA has not been accompanied by the expected structural transformation trend of going from agriculture to manufacturing and then services, instead there has been a direct transition from agriculture to services (Austin, 2016). In fact, the value-added contribution of manufacturing to GDP in SSA has experienced continuous decline within years 1990 to 2013 (Austin, 2016: 208). With regards to employment share in the region, among years 2000 and 2010, there has been a 10% decrease in agriculture, a 2% increase in manufacturing, and an 8% increase in services (McMillan & Harttgen, 2014)

    Concerning Kenya, the country has achieved annual growth rates ranging from 4.56% to 8.41% between years 2010 and 2019; in 2019 economic growth averaged 5.7% making Kenya one of the fastest growing economies in SSA (World Bank, n.d.). On the same period, according to World Bank Data, the value added annual percentage growth for the service sector averaged 6.26% versus 5.87% in industry and 4.64% in agriculture. The growth of the service sector, which accounts for nearly half of the country’s GDP (see Figure 1), was mainly driven by important contributions from the telecommunications and mobile-based financial services (World Bank Group, 2018: iii).

Figure 1: GDP growth and Value-added by sector (Constant 2010 US$ - Millions).

Source: Authors’s elaboration with World Bank Data

    The employment shares by sector in Kenya followed a similar pattern to the one seen in the rest of SSA. The industrial sector experienced a general decrease going from 13.1% on 1999 to 7.3% on 2019, the service and agricultural sector employment rates almost mirror each other during the same period of time (see Figure 2); from years 2005 to 2020, the agriculture sector experienced a 7.3% decrease while the service sector increased by 6.54% (World Bank, n.d.). The mirroring curve of the agriculture and service sectors, combined with the industrial sector share decrease, are an indication that most of the labor force transitioned directly from agriculture to services.

 

Figure 2: Employment Share by Sector (%).

Source: Authors’s elaboration with World Bank Data

 

    Despite the changes on employment share and the impact of the service sector, Rodrik (2016) argues that the actual contribution of structural transformation to growth in Kenya has been minimal; correlational analysis suggests that sectoral productivity has really not been affected by change in employment, as workers have migrated from agriculture to a service sector where productivity is not much higher. (see Figure 3)

 

Figure 3: Correlation Between Sectorial Productivity and Change in Employment Shares in Kenya.

Source: Rodrik (2016: 12) An African Growth Miracle.

*Note: Size of circle represents employment share in 1990 **Note: β denotes coeff. Of independent variable in regression equation: ln (p/P) = α + β ΔEmp. Share

    In order for rapid and sustainable growth to occur in SSA, structural transformation should be accompanied with productivity increase. Unfortunately, the direct transition from agriculture to services poses some challenges. Services require relatively high skills and years of education to act as productivity escalators; in contrast, in manufacturing a farmer could increase his/her productivity with just manual dexterity and experience (Rodrik, 2016: 16). In Kenya, the direct transition of an unskilled agricultural labor force into the service sector, has not allowed the country to activate the mentioned productivity escalators. Reverting this deficiency would require a great effort to train the service sector laborers up to a point where they can take better advantage of the potential productivity gains.

    As an alternative, and despite that currently it is not an engine of growth, investment in the industrial sector could provide a faster productivity increase and the stable and formal jobs needed for growth sustainability (Thorbecke & Ouyang, 2016: 261). Nonetheless, deficiencies in infrastructure, corruption and excessive regulation, raise the costs of production making the business environment less competitive (Golub S. & Hayat F., 2014: 148). For this reason, it is critical that industrialization prospects are not only complemented with education but also with investments in infrastructure, like transportation and electricity supply (Austin, 2016: 225). Another option is to raise productivity through an agricultural revolution, but the difficulty is that many of the soils in SSA have low fertility or can be easily eroded (Austin, 2016: 211).

    In summary, SSA continuous unexpected growth during the last two decades, has been characterized by a direct structural transformation from agriculture to the service sector. Kenya’s transformation has followed the same pattern, with important contributions from telecommunications and financial services. However, the expansion of the service sector has not translated into higher productivity. The unique growth pattern of Kenya, may not need to follow the traditional convergence trend (first going from agriculture to manufacturing and then services). Nevertheless, in order to achieve sustainable growth, there should be an increase in productivity. This could be potentially achieved in different ways, by training the unskilled labor force in the service sector, investing in revitalizing the industrial sector or pursuing an agricultural revolution.

*Essay written for the "EKHM 61 – Development of Emerging Economies" course at Lund University

 

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