Topic: Reduced Inequalities

Some authors argue that if there is more diversity, more creativity arises, and more output is generated (for an extensive discussion on the issue revise Alesina & La Ferrara (2005)). In this short article, it’s shown that there is a negative relation between the Herfindahl Hirschman Index (HHI hereafter) of Ethnicity at the Municipality level (Instituto Nacional de Estadística, 2012) in 2012 and the Sustainable Development Municipality Index (Andersen et al., 2020) in 2020 respectively. For a discussion on the HHI revise Herfindahl (1950) and Hirschman (1945). The formula is the following:

$$HHI_j = \sum_{i=1}^4 S_{ij}^2$$

Where $S_i^2$ is the squared share of ethnic group i in each municipality j multiplied by 100. If there is a higher value of that index, then we have more concentration (less diversity). If it’s lower, there is less concentration (more diversity). By construction, the possible outcomes are only 4: Indigenous, Non Indigenous, Non Bolivian and Unspecified. Therefore, ideally, the minimum theoretical value can be 2,500 and the maximum, 10,000. To make an interpretation of this index, we shall note that when it is closer to 10,000, it means that only one ethnic group concentrates all the population. When it is closer to 2,500, there is more diversity (each of the 4 groups have similar populations). If we make a disaggregation of more groups, the indicator could become closer to 0. In a scatter plot, a negative slope means that more diversity is correlated with a higher dependent variable.

In figure 1 we can see that there is a negative relation between the Sustainable Development Municipality Index and our measurement of ethnic diversity. This means that there is more development, in general, in those municipalities where there is more diversity. However, if we separate by indigenous majoritarian or non-indigenous majoritarian municipalities, we see that this only holds for the indigenous municipalities.

In figure 2 we see that the negative relation is clearer when using NTL per capita in 2012. This means that the results are sensitive to what dependent variables are used. Here, the results hold also for non-indigenous municipalities. The main message here is that there is more illumination and, therefore, GDP in more diverse municipalities.

But why does this happen? We shall explore in more detail the components of the Sustainable Development Municipality Index. Maybe there are some processes going on in the components of that index. It’s important to point out that other controls could be added in both cases which could reduce the dispersion and make more patent the negative correlation between the two dependent variables we used in this short article. That’s a task for future studies. The main message of this blog entry is to point out that there are huge chances ethnic diversity also promote development. Conflict also may arise and if that happens, institutions to promote mutual understanding and peaceful ways to solve differences shall be enhanced. There are still gaps to close in Bolivia. Policies to decolonize Bolivia and promote ethnic equality still have many challenges ahead.

References

• Alesina, A., & La Ferrara, E. (2005). Ethnic Diversity and Economic Performance. Journal of Economic Literature, 43(3), 762–800. http://www.jstor.org/stable/4129475
• Andersen, L. E., Canelas, S., Gonzales, A., & Peñaranda, L. (2020). Atlas municipal de los Objetivos de Desarrollo Sostenible en Bolivia 2020. Universidad Privada Boliviana, SDSN Bolivia. www.sdsnbolivia.org/Atlas
• Herfindahl, O. C. (1950). Concentration in the Steel Industry.
• Hirschman, A. (1945). National Power and the Structure of Foreign Trade.
• Instituto Nacional de Estadística. (2012). Censo Nacional de Población y Vivienda 2012. https://redatam.org/cdr/descargas/censos/poblacion/CP2012BOL.zip

Variables Used:

Bolivia is a country rich in cultural diversity, with a population that speaks more than 36 different languages. Despite Spanish being the official language, there are many indigenous languages spoken throughout the country, such as Quechua, Aymara, Guarani, and others. In some municipalities, the non-Spanish speaking population is quite significant, with some areas having over 90% of the population speaking a language other than Spanish. This diversity is part of what makes Bolivia unique, but it also presents unique challenges for the country.

One intriguing finding in Bolivia is that municipalities with large non-Spanish speaking populations, who are primarily indigenous, tend to have lower values for UN SDG 10 - which is focused on reducing inequality within and between countries. A scatter plot showing this trend uses the value of UN SDG 10 on the y-axis and the percentage of non-Spanish speakers in a municipality on the x-axis. The scatter plot clearly demonstrates an inverse relationship between the two variables, with municipalities with higher percentages of non-Spanish-speaking populations having lower values for UN SDG 10. This suggests that there may be more inequality in these areas due to linguistic barriers or discrimination.

When we contrast SDG 10 values for the municipalities with the highest and lowest percentages of non-Spanish speakers, the contrast is even more pronounced.

The geographical distribution of the non-Spanish speaking population can be seen from the map below.

The map on the left uses a color gradient to depict non-Spanish speaking populations across Bolivia’s municipalities, with a higher level of non-Spanish speakers represented by the lighter shades. Similarly, the map on the right shows the literacy rate in each municipality using a color gradient, with darker shades indicating lower levels of literacy.

It is apparent that the areas with large non-Spanish speaking populations and low literacy rates are concentrated in the central region of the country. This suggests that there may be systemic challenges in the education system that are preventing individuals from acquiring the skills and knowledge they need to succeed.

Furthermore, this overlap between non-Spanish speaking populations and low literacy rates could also contribute to the challenges faced by these communities in achieving UN SDG 10. Limited access to education can make it difficult for individuals to acquire the skills and knowledge needed to improve their economic and social well-being, perpetuating inequality and exclusion.

To gain a better understanding of the impact of language barriers on healthcare outcomes, a bar chart comparing infant mortality rates is used. The chart compares the top 10 municipalities with the highest percentage of non-Spanish speakers against the bottom 10 municipalities, based on infant mortality rate.


The bar chart shows that the average infant mortality rate is higher in the municipalities with the highest percentage of non-Spanish speakers than it is in the municipalities without. This indicates that individuals in non-Spanish-speaking communities face additional difficulties in obtaining and receiving high-quality healthcare and that language barriers may be contributing to disparities in healthcare outcomes.

These findings are particularly concerning given the importance of reducing infant mortality as a key objective of UN SDG 3. Improving access to quality healthcare services is essential for achieving this goal, and language barriers may be contributing to the challenges faced by individuals in non-Spanish-speaking communities.

To gain a better understanding of the impact of language barriers on educational outcomes, a bar chart comparing the Gini coefficient of years of education is used.

The Gini coefficient is a measure of inequality. A higher Gini coefficient of years of education indicates greater inequality in access to education, with some individuals having significantly more years of education than others. This can result in disparities in employment opportunities, income, and social mobility, among other factors.

A Gini coefficient of 0 represents perfect equality, where everyone has the same level of education, while a coefficient of 1 indicates perfect inequality, where one person has all the education and everyone else has none. The municipalities with a High % of non-Spanish speakers have a much great Gini coefficient, 0.549, than the ones with a low % of non-Spanish speakers, 0.34.

Similarly, the graph shows that municipalities with the highest percentage of non-Spanish speakers have a higher average Gini coefficient of years of education compared to the municipalities that have a lower percentage of non-Spanish speakers. This indicates that language barriers may be contributing to disparities in access to education and educational outcomes, with individuals in non-Spanish-speaking communities facing additional challenges in accessing and succeeding in education.

These findings are particularly concerning given the importance of reducing educational inequality as a key objective of UN SDG 4, which is about quality education. Improving access to quality education is essential for achieving this goal, and language barriers may be contributing to the challenges faced by individuals in non-Spanish-speaking communities.

In conclusion, Bolivia’s population of non-Spanish speakers faces difficulties in achieving a number of UN Sustainable Development Goals, including income inequality, access to and quality of education, and disparities in healthcare. To increase universal access to high-quality healthcare, education and all human rights outcomes, it is important to consider language.

In order for Bolivia to achieve the UN Sustainable Development Goals, policymakers and stakeholders must work to build a more diverse and equitable society that benefits all citizens, regardless of their ethnicity or first language.