Forty Days of Quarantine – What Have We Learned?

 “There are lies, damn lies, and COVID-19 statistics”
Johan Norberg

 

 

The term quarantine comes from a venetian word meaning “forty days” which was the number of days in which ships and people had to be isolated before being admitted to the Republic of Venice in Medieval times in order to be sure that they were not infected by deadly infectious diseases, such as the Plague, Cholera, Syphilis, or Yellow Fever.

In Bolivia we are nearing our forty days of “reverse quarantine” (keeping the healthy people locked up, in order to prevent them from getting infected by an external threat).

This reverse quarantine has bought us some time to get to understand the novel SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Corona Virus No. 2), and the COVID-19 disease it is causing. Initial data out of China and Italy was so scary that it was worth the high costs to buy us some time to formulate a strategy on how to deal with the threat, and prepare the population and the health system to manage it as well as possible. We have very little data from Bolivia, but since this is a global pandemic, we can learn a lot from other countries, despite extremely flawed data.

A few basic things have become clear:

  • The SARS-CoV-2 virus has infected people in pretty much every country and territory on the planet, so suppression and eradication of the virus is unfortunately no longer a realistic option (1);
  • Many people who catch the virus have no symptoms, which is why this virus has managed to spread so easily across the globe (2).
  • There is no previous immunity, nor any treatment available (3), so the virus will not go away until we have achieved herd immunity, either through vaccination, or by 60-70 percent of the population having been infected (4);
  • Although many potential vaccines have been developed in record time, they have to be tested for safety and efficacy, which means that vaccines at a global scale will not be available for at least another 12 months. By the time a safe and effective vaccine becomes available for billions of people, it may not be needed any more (5);
  • The Infection Fatality Rate (IFR) is likely to be somewhere between 0.1% and 10%, depending on the health of the population, the age composition of the population, the quality of the health care system, the policies enacted to confront the problem, and possibly the type of the virus that dominates, because there seem to be different strains circulating already (6). It is clear that men are more likely to die than women, older people are far more likely to die than younger people, and people with underlying health problems, especially hypertension, obesity and diabetes, are more likely to die (7).

Given these facts, it is clear that we are facing some difficult decisions. Short of simultaneously locking up everybody on the planet for many months, there is no way we can prevent that millions of people will die from COVID-19. In the absolute best case scenario (IFR like the common flu at 0.1% and 60% of the world population getting infected), we will see 4.2 million people die from this disease, and we should consider ourselves very lucky if that is the number we converge towards over the next 24 months. More likely, there will be at least 5 times more deaths than that, meaning at least 20 million people will die. So far, only about a quarter of a million have died, so the world is still in the very early stages of the pandemic (98% still to come).

 

How are we doing in Bolivia?

In Bolivia, we have barely started the process, because we locked down early and thoroughly. To date we have only 62 confirmed COVID-19 deaths, out of a minimum of 7,000 and a maximum of 800,000 to be expected. That is a frustratingly large range, and it is difficult to make wise decisions until we narrow down the likely path of this epidemic. The whole point of locking down is to obtain more information and figure out which end of the range is most likely, and thus which kind of policies are appropriate to get us through this pandemic.

In a normal year, about 24,100 people die from all causes in Bolivia. In the absolute best case scenario, this virus would kill off 6,600 old and frail people who would have died from other causes this year anyway, thus implying no excess mortality. Unfortunately, we already know that this best case scenario will not play out, because among the first to die of COVID-19 in Bolivia were a pregnant nurse, and several otherwise healthy people below 70 years of age.

 

How bad is it going to be?                                                                                       

We have waited for 40 days in order to rule out the worst case scenario, which would be a 10% IFR. The original data coming out of Wuhan suggested that 20% of infected people would need hospital care in order to survive, and that almost 4.9% of infected people died anyway. Also, of more than 1 million closed cases to date worldwide, 18% have died (8). However, recent antibody tests carried out in California (9), Germany (10), Denmark (11), and the Netherlands (12) suggest that many people have been infected without any symptoms, which means that the true number of infections is several times higher than the confirmed cases, implying that the IFRs are much lower than the official Case Fatality Rates (CFR = Deaths/Confirmed Cases) suggest.

In the USA, the New York State recently carried out random anti-body testing on 3,000 individuals to figure out how many people had really been infected, and they found that 13.9% of the population, or about 2.7 million people in the state, had already been infected at a time when “only” 19,453 COVID-19 deaths had been registered. This suggests an IFR in New York state of around 0.72%, or a little bit higher, since some of these infected people are still critically ill and more will unfortunately die (13).

Of course, New York is one of the richest places on the planet, so their IFR may not be relevant for Bolivia. Data from Peru is probably more relevant here, and fortunately Peru has somehow managed to carry out more than 300,000 tests, while Bolivia has only done around 6,000. In Peru, more than 37,000 people have been confirmed to have the virus, but only 2.8% of confirmed cases have died so far (8). However, Peru, like all other countries, has limited testing capacity, so in reality there will be many more infected, and thus the IFR will be a lot lower.

With the still very incomplete information available at the moment, I estimate that we will end up with an IFR of around 1% for Bolivia (meaning anywhere between 0.3% and 2%, given the still high uncertainty). If 60% of 11.6 million people get infected, and 1% of those die, we would end up with about 70 thousand COVID-19 deaths in Bolivia. The number could be lower if a vaccine becomes available before we reach herd immunity through infection, but I consider that unlikely (5). The good news is that more than 11 million Bolivians will not die from COVID-19.

 

Proportionate interventions

We are facing an undeniably difficult situation, like all other countries. What we definitely have to make sure is not to make things even worse than they have to be. 70,000 dead people is bad. But these people dying alone, shunned and isolated in designated COVID hospitals, without family, friends and funerals, seems much worse. If at the same time even more people are losing their livelihoods, their investments and their dreams due to quarantine, that would be awful. If children start dying from hunger because their parents are not allowed to work (14), it would be a full-blown disaster. If we lose our basic human rights and freedoms, and we cannot see and hug our loved ones for years (15), that is simply an unbearable thought.

Thus, we have to make sure our interventions are well thought through and based on the best evidence possible. We are lucky that our country got infected relatively late (first confirmed case on the 10th of March, 2020), and we managed to keep numbers low for the first couple of months through strict quarantine measures, which means we got the gift of time to enable us to learn from good and bad experiences in other countries, and from all the new scientific research that is coming out to help us understand our options better.

 

Flattening the curve is clearly necessary

I am not suggesting that we should flatten the curve so that our health care system does not get overwhelmed, because it got overwhelmed by the very first patient (16). But I do suggest we flatten the curve enough to make sure that we can physically, mentally and socially handle every diseased person in a dignified manner. If we do not spread our expected 70,000 deaths out as evenly as possible over at least a year, we will experience the horrors of dead bodies piling up in the streets, like we are seeing in Guayaquil in Ecuador (17). If we could spread our expected 70,000 deaths perfectly evenly over the next 12 months, we would have around 1,350 COVID-19 deaths per week. Hopefully some of these would have died from other causes anyway, but it is clear that we have to be prepared to increase our funeral capacity, because Bolivia is used to handle only about 1,300 deaths per week from all causes.

 

What does successful management of a global pandemic look like?

Ideally, we should have nipped this epidemic in the bud, like we managed to do with the first SARS outbreak in 2003, the MERS outbreak in 2012, the Ebola outbreak in 2014, and hopefully most future similar outbreaks. However, this time the world screwed up big time, and with millions of people being infected all over the globe, eradication is just not realistic anymore. A few rich island nations may be able to test, trace and isolate cases and keep it under control until a vaccine is available, but for most of the world’s countries, including Bolivia, that is just not a realistic aspiration.

My criteria for success are much less ambitious: If less than 0.6% of the population die from COVID-19 within the next 12 months, and if the unfortunate 0.6% die with loved-ones holding their hands, and family members and close friends get the opportunity to pay their respects and process their losses, and if the economy contracts less than 5% (a setback of less than 2 years), then I would consider that a successful management of an unescapable pandemic with no known cures available.

 

How do we successfully manage this epidemic?

The key is avoiding huge and unmanageable spikes in deaths. That will require carefully calibrated social distancing measures.

Some “easy” social distancing measures should be implemented by everybody at all times until this pandemic is over:

  • No kissing, hugging and handshaking, but try to be kind to everybody anyway;
  • No unnecessary gatherings of a lot of people, meaning no sports events, no concerts, no carnivals, no festivals, no graduation events, and no religious gatherings; but try to have fun in new creative ways;
  • Maintain a 2-metre distance from strangers, interact with as few different people as possible, and wear a mask if you have to be close to them;
  • Avoid touching surfaces that a lot of other people touches, and wash your hands thoroughly after touching a potentially infected surface;
  • Work and study from home as much as possible, and limit interactions to as few different people as possible.
  • When work from home is not possible, implement flexible working hours and staggered work schedules to reduce peak occupancy in public transportation systems and work places.

These simple measures substantially reduce infection rates, but they may not be enough. Even tougher measures may be necessary in certain locations if infections spike for some reason.

 

Monitoring of outbreaks

In order to know when tougher measures are necessary, we need extremely good monitoring of the epidemic. Ideally, we would have massive testing capacity like Iceland or South Korea, but Bolivia has the lowest testing capacity in South America at less than 1 in a thousand people (18), and we have to be realistic about what is actually feasible.

There are two alternative options that could provide us with valuable information in real time about how the pandemic is evolving:

  • A daily symptom tracker app on our phones, which could alert authorities to a local outbreak, and help individuals get the help they need. A simple such app exists in the UK, and much more elaborate apps are used in many places in Asia (19). It is trickier in Bolivia, as it requires high levels of trust in the government, and the population would need to perceive concrete benefits of using the app. For example, it could be linked to a generous donation of free phone minutes and Internet access, telemedicine consultations, free medicine delivery, and more. Such an app would be technically relatively easy to develop, but it would require serious thoughts on how to get a large share of the population to trust and use the app daily.
  • A less demanding option is to monitor weekly deaths from all causes at a sub-national (ideally municipal) level, so as to alert us if any region is beginning to spike, and would therefore need to implement stricter social distancing measures and receive more support from the central government. EuroMOMO would be a good model for this (20).

Both of these options would be much less expensive and damaging than shutting down the entire country for many months. A traffic light system could be designed to clearly communicate the current levels of restrictions in different parts of the country. Indeed, this should be part of the app mentioned above.

 

Sustainable transition towards a new Bolivia

Whatever systems we do implement, we have to make sure they can be sustained over time, because this is going to take at least a year to get through, and the world will look different on the other side. Families and firms will have to adapt to these new circumstances, and the government needs to support them through this transition. At the very least, the government has to make sure nobody starves to death (people should be able to request help through the app if they have urgent needs, and the government needs to develop the infrastructure needed to respond). The government also has to accelerate investments in absolutely crucial infrastructure, such as water, sanitation, electricity and Internet. In order to facilitate a more agile transition and response to the rapidly changing market conditions, now is a good time to eliminate the very strong rigidities in the Bolivian labor market, because many of the changes we will see are not going to be transitory. It would also be a good idea to make it much easier to close companies that have become unsustainable, so that people can spend their time and money on starting up new businesses, instead of spending months or years going through all the ridiculously difficult procedures to close a company.

Footnotes:

(1) Bolivia, as well as several other female-led countries, such as Taiwan, Hong Kong, New Zealand, Iceland, Norway, Finland and Germany, potentially could suppress and eliminate the virus, but that doesn’t help us much in this globalized world, if there are major male-led countries around us that fail to do that (e.g. United States, United Kingdom and Brazil).

(2) For example, 408 residents at a homeless shelter in New York was tested for the virus, and 36% of them was found to have the virus, but 87.7% of the people who had the virus, did not have any symptoms (https://jamanetwork.com/journals/jama/fullarticle/2765378?guestAccessKey=a5d28066-8f72-4633-a291-90b472754093&utm_source=silverchair&utm_medium=email&utm_campaign=article_alert-jama&utm_content=olf&utm_term=042720).

(3) Countries around the globe have been scrambling to buy mechanical ventilators, but it is not a treatment, it only provides life-support while the body’s own immune system battles the virus. A recent study of outcomes in 12 New York hospitals show that the vast majority of COVID-19 patients on ventilators die. Indeed, of those aged 65+, 97.2% of COVID-19 patients with an outcome by the end of the study, had died. For patients aged 18-65, 76.4% had died. See: Richardson S, Hirsch JS, Narasimhan M, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. Published online April 22, 2020. doi:10.1001/jama.2020.6775 ( https://jamanetwork.com/journals/jama/fullarticle/2765184)

(4) Some people are questioning whether we will even be able to achieve herd immunity, as some people who had previously been diagnosed and then cleared, have caught the virus a second time within a few months (https://www.reuters.com/article/us-health-coronavirus-who/who-says-looking-into-reports-of-some-covid-patients-testing-positive-again-idUSKCN21T0F1?il=0). See also this note about Corona virus immunity research at Columbia University: https://www.technologyreview.com/2020/04/27/1000569/how-long-are-people-immune-to-covid-19/?fbclid=IwAR3fkGPtqipyy_eieEBWaWOTeDnsdxkcb8BMpkYOXlaBW10OYaPs0CmUFVk.

(5) See this article for a discussion of what it takes to develop, test, produce and distribute a new vaccine: https://unherd.com/2020/04/when-we-get-the-covid-19-vaccine/?tl_inbound=1&tl_groups[0]=18743&tl_period_type=3

(6) The Chinese scientist who originally proposed the lock down of Wuhan, Dr. Li Lanjuan, has carried out ultra-deep sequencing of the RNA in different samples, and says that the SARS-CoV-2 virus mutates faster than previously thought, and that some strains are more infectious and more lethal than others ( https://www.scmp.com/news/china/science/article/3080771/coronavirus-mutations-affect-deadliness-strains-chinese-study).

(7) Richardson S, Hirsch JS, Narasimhan M, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. Published online April 22, 2020. doi:10.1001/jama.2020.6775 ( https://jamanetwork.com/journals/jama/fullarticle/2765184)

(8) See https://www.worldometers.info/coronavirus/.

(9) Researchers at Stanford conducted antibody tests on 3,300 volunteers (a non-random sample obtained through facebook ads) in Santa Clara, California, and found that  1.5% of the sample tested positive for the antibodies, suggesting that the real number of COVID-19 infection was 50-85 times higher than the official numbers by April 1st, 2020 ( https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.full.pdf). However, it should be noted that this was not a random sample. Volunteers who responded to the ad, might be people who had experienced COVID-19 symptoms, and were eager to find out if they have already had the virus.

(10) Researchers conducted anti-body tests on inhabitants of Gangelt, a German municipality near the border with the Netherlands, which was hard hit by covid-19 after a February carnival celebration. They found that 14% of the population had already been infected by late March (https://www.technologyreview.com/2020/04/09/999015/blood-tests-show-15-of-people-are-now-immune-to-covid-19-in-one-town-in-germany/).

(11) On 6-8 April 2020, Denmark tested 3,898 blood donations from asymptomatic people and found that 1.9% had COVID-19 antibodies (https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-rapid-risk-assessment-coronavirus-disease-2019-ninth-update-23-april-2020.pdf).

(12) Between the 6th and the 12th of April 2020, the Netherlands tested 4,194 blood donations and found that 3.4% had COVID-19 antibodies (https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-rapid-risk-assessment-coronavirus-disease-2019-ninth-update-23-april-2020.pdf).

(13) See Dr. John Campbell’s discussion of these results here: https://www.youtube.com/watch?v=ypsUIh41xUw

(14) Unfortunately this has already started happening in Bolivia. A 12-year-old girl in the municipality of Montero killed herself after being quarantined without food for several days with her mother and her 7 siblings (https://www.lostiempos.com/actualidad/pais/20200422/tragica-muerte-menor-enluta-familia-humilde-montero-piden-ayuda-entierro).

(15) While other countries help, or at least allow, their nationals to return home, Bolivia has closed its borders so tightly that many Bolivians have been stranded either at border crossings, or wherever they happened to be at the time of the lock down. Especially inhuman was the treatment of a group of Bolivians, including pregnant women and women with babies, who tried to get home from Chile late March (https://eldeber.com.bo/171695_bolivianos-en-la-frontera-con-chile-claman-por-volver-y-el-gobierno-les-responde-que-no). I was also horrified to read than an opposition mayor in Cochabamba was arrested in her home last week for playing loud music and drinking “chicha” (a fermented beverage made from corn) together with her closest family. Although she tested negative in the alcohol test made on the “scene of crime”, all eight people present were arrested, and the youngest child was sent to a center for homeless children ( https://erbol.com.bo/seguridad/alcaldesa-de-vinto-dice-que-s%C3%B3lo-%E2%80%9Cbrind%C3%B3%E2%80%9D-con-una-%E2%80%9Ctutuma-de-chicha%E2%80%9D).

(16) https://rpp.pe/mundo/actualidad/bolivia-covid-19-grupo-de-ciudadanos-bloquea-el-acceso-de-pacientes-con-coronavirus-a-hospitales

(17) https://www.nytimes.com/2020/04/23/world/americas/ecuador-deaths-coronavirus.html?smid=tw-share

(18) See https://ourworldindata.org/coronavirus.

(19) Here is the symptom app used in the UK: https://covid.joinzoe.com/. Google and Apple are also working to develop a contact tracing app that can alert you if you have been near a confirmed COVID-19 infected person (https://www.theverge.com/2020/4/10/21216715/apple-google-coronavirus-covid-19-contact-tracing-app-details-use), but that would have to be used in conjunction with extensive testing and it requires a very disciplined population for people to self-isolate for two weeks, just because their phone indicates they have passed by an infected person, so it does not seem ideal for Bolivia.

(20) EuroMOMO is a European mortality monitoring activity, aiming to detect and measure excess deaths related to seasonal influenza, pandemics and other public health threats ( https://www.euromomo.eu/graphs-and-maps/)

___

* SDSN Bolivia.

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Municipal Atlas of the SDGs in Bolivia” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

Education Inequality in Bolivia

While inequality indicators are usually calculated using monetary income or consumption data, they can be calculated on any variable of interest, thus informing us about other types of inequality in society. In this blog we present a new education inequality indicator to be included in our Municipal Atlas of the SDGs in Bolivia.

Using data from the 2012 Population Census we calculated years of education for every person. We then chose the age group 25-65 years, since that is the age group in which most people will have finished their education and are highly likely to be working and using their education. Having learned from our experience analyzing electricity consumption inequality, we calculated a series of different inequality measures in order to figure out which measure would be the best choice. Unlike what we found for electricity consumption inequality, all education inequality measures were highly correlated, so it didn’t matter much which one we use, so we decided to use the Gini coefficient of Years of Education.

The Gini coefficient of Years of Education ranges from 0.205 in Coipasa (Oruro) to 0.642 in Ocurí (Potosí). This is a range in which the Gini coefficient works well and correlates closely with other measures of inequality. La Paz has very low education inequality (Gini = 0.207), while Sucre has the highest level of education inequality (Gini = 0.321) of the 10 main cities.

Many smaller municipalities have much higher education inequality, and as can be seen in Figure 1, this has a lot to do with women having been denied the same education opportunities as men in the past. Very few municipalities have a Gender Parity Index close to 1 (meaning that men and women have the same level of education). In many small, rural municipalities women have only between half and two-thirds of the education of men, and this situation correlates with high education inequality.

Figure 1: Education inequality versus gender inequality in education, by municipality, 2012

Source: Authors’ elaboration based on data from the 2012 Population Census.

Although returns to education are low in Bolivia, (in terms of future income) and the quality is suspected to be low as well, Bolivians are studying more than ever, so education is clearly considered an important investment by the population. We consider this education inequality variable an important indicator to be included in the Municipal Atlas of the SDGs in Bolivia.

—-

* SDSN Bolivia.

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Municipal Atlas of the SDGs in Bolivia” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

Measuring Inequality at the Sub-National Level in Bolivia

The scale of inequality in the world is staggering. In 2018, the average inhabitant of Denmark, Norway, Sweden, Iceland, and Ireland earned more in two days than what the average inhabitant of Malawi and Burundi earned during an entire year [1].

However, the inequality within some countries is even bigger than the inequality between countries. As we show in the upcoming Municipal Atlas of the SDGs in Bolivia, there are larger differences in the Sustainable Development Index between municipalities within Bolivia than there are between all the countries in the world. This means that within Bolivia we find municipalities with levels of development similar to the most advanced countries in the World, but also municipalities similar to some of the least developed countries in the World.

In addition to this astounding inequality between municipalities within Bolivia, there are also huge inequalities within each municipality, and that is what we focus on in this blog.

Measuring inequality at the sub-national level is difficult, because it requires income/consumption data that is representative at the municipal level, and this is neither available from household surveys nor population censuses. We get around this problem by using data on electricity consumption by each household in Bolivia, under the assumption that electricity consumption in each household is a reliable predictor of general consumption/income/welfare in the household. The analysis was made possible due to a research project sponsored by the Centre for Social Research (CIS) of the Bolivian Vice-presidency [2].

The CIS project calculated Gini coefficients of electricity consumption to estimate the level of inequality within each municipality. Figure 1 shows two examples. To the left is the Lorenz curve and the Gini coefficient for a big urban municipality, Santa Cruz de la Sierra. To the right is the Lorenz curve and Gini coefficient for a poor rural municipality, Tinguipaya, with low electricity coverage and low electricity consumption for the ones that actually do have access to the electricity grid.

 

Figure 1: Examples of electricity consumption Lorenz curves, 2016

Source: Andersen, Branisa y Guzman (2019) [2].

 

According to these Lorenz curves, the big, urban municipality of Santa Cruz de la Sierra is clearly more equal in electricity consumption (and thus likely consumption in general) than the small, rural municipality of Tinguipaya, as the Lorenz curve of the former is much closer to the diagonal line of perfect equality.

Indeed, as can be seen in Figure 2, this is the general tendency across all municipalities in Bolivia. Urban municipalities tend to have lower levels of poverty (poverty being measured as extremely low electricity consumption) and lower levels of inequality (as measured by the Gini coefficient of electricity consumption).

 

Figure 2: Energy inequalty versus energy poverty in Bolivian municipalities, 2016

Source: Authors’ elaboration based on data from Andersen, Branisa y Guzman (2019).

 

This strong positive correlation between energy poverty and energy inequality seems suspicious, and perhaps even counter-intuitive. Consider this extreme hypothetical example: If all households in a municipality had zero electricity consumption, except the mayor, who had a moderate electricity consumption covering a few light bulbs and a refrigerator, then the Gini coefficient would be 0.99, suggesting extremely high inequality in the municipality, whereas common sense would suggest that the population is very equal in their extremely high level of poverty.

The Gini coefficient is by far the most widely used measure of inequality, and easy to interpret, so it was the logical metric to use. But these results made us wonder if the Gini coefficient tends to confound extreme poverty with extreme inequality.

Thus, in this blog we set out to explore whether there are alternative measures of inequality that might correspond better to intuition. There are surprisingly many different measures of inequality, so it became another long post.

There are four basic principles that one would expect from an inequality measure [3]:

  • Symmetry (or anonymity): If two people switch incomes, the index level should not change.
  • Population invariance (or replication invariance): If the population is replicated or “cloned” one or more times, the index level should not change.
  • Scale invariance (or mean independence): If all incomes are scaled up or down by a common factor (for example, doubled), the index level should not change.
  • The Pigou-Dalton Transfer Principle: If income is transferred from one person to another who is richer, the index level should increase. In other words, in the face of a regressive transfer, the index level must rise.

It can be shown that the Gini coefficient satisfies these four basic principles, as does several other inequality measures, such as the Atkinson Index, the Theil entropy measure, and the Theil mean log deviation measure. These inequality measures are all called strongly Lorenz-consistent [4].

Some frequently used inequality measures are only weakly Lorenz-consistent, however, as they do not fully comply with the fourth principle. When a poorer person makes a transfer to a richer person, the inequality indicator need not rise, but at least it should not fall [4]. The Palma ratio (the income of the 10% richest divided by the income of the 40% poorest) and other Kuznets ratios (X% richest/Y% poorest) are only weakly Lorenz-consistent.

Other inequality measures are plainly Lorenz-inconsistent. This is the case for quantile ratios (p90/p10, p75/p25, etc.) and the Variance of Logarithms. For both of these measures it is possible that a regressive transfer from a poorer person to a richer person would cause a fall in the inequality measure, which is clearly counter intuitive [4]. The Absolute Gini proposed by Jason Hickel [5] is even worse, as it also violates the third principle of scale invariance.

From the theoretical arguments mentioned above, we would expect all the Lorenz-consistent measures to yield pretty much the same results as the Gini coefficient, so we decided to also include weakly Lorenz-consistent and Lorenz-inconsistent measures in our comparisons.

However, we quickly ran into an important problem: Many inequality measures cannot handle zero values (e.g. Theil, Atkinson, percentile ratios). Some algorithms circumvent this problem simply by ignoring zero values. But we do not consider this a reasonable strategy, since the lack of access to electricity is one of the fundamental problems we want to highlight rather than ignore. An alternative way around this problem is simply to add a small value, for example 1 kWh per year, to all observations, which does not change overall patterns, but will make all computational algorithms work [6].

During the following sections, we will compare many different inequality measures with the Gini coefficient. All inequality measures are calculated on household level data on annual electricity consumption + 1 kWh for a selection of 25 municipalities that span the whole range shown in Figure 2. For each of the alternative inequality measures we discuss the intuition behind the measure, its advantages and disadvantages, and show its relationship to the Gini coefficient.

 

Atkinson Indices

The Atkinson Index, A(e), is actually a whole class of inequality measures, differentiated by a parameter, e, that measures the degree of inequality aversion. When e = 0, there is no aversion to inequality, and A(0) = 0. When e = ∞, there is infinite aversion to inequality and A(∞) = 1. The Atkinson Index thus varies between 0 and 1, like the Gini coefficient, which facilitates interpretation. The Atkinson Index has the advantage of being sub-group decomposable, which the Gini coefficient is not.

The Stata package ineqdeco (created by Stephen P. Jenkins at London School of Economics) calculates the Atkinson Index for three different parameters: 0.5, 1 and 2. Figure 3 shows how the Gini coefficient (of annual electricity consumption + 1 kWh) compares to these three Atkinson Indices.

 

Figure 3: Comparing the Gini coefficient with three Atkinson Indices for a sample of 25 municipalities

Source: Authors’ elaboration.

 

We see that for moderate inequality aversion (e = 0.5), the Atkinson Index behaves very similarly to the Gini coefficient. For stronger inequality aversion (e = 1), the Atkinson index increases at all levels, but especially for the ones that had high Gini coefficients, thus exaggerating the counter-intuitive results of the Gini coefficient. For very high inequality aversion (e = 2), the Atkinson Index is very close to maximum for all our municipalities, and thus do not provide any useful information about differences in inequality.

In conclusion, the A(0.5) index seems the most useful of the three, but it behaves very much like the Gini coefficient and thus does not solve our initial problem of counter-intuitive results.

 

Generalized Entropy Indices (including mean log deviation and Theil index)

The Generalized Entropy Index, GE(α), is another class of inequality measures. As with the Atkinson Indices, the GE Indices involve a parameter, α, that can shift the sensitivity to different parts of the distribution. For lower values of α, GE is more sensitive to differences in the lower tail of the distribution, and for higher values GE is more sensitive to differences that affect the upper tail. The most common values of α used are 0, 1 and 2 [7].

The GE Index has several other inequality metrics as special cases. For example, GE(0) is the mean log deviation (also sometimes called Theil’s L), GE(1) is the Theil index, or Theil’s T, and GE(2) is half the squared Coefficient of Variation.

The values of GE indices can vary between 0 and ∞, with zero representing an equal distribution and higher value representing a higher level of inequality.

Like the Atkinson Index, the GE Index is decomposable, which is an advantage over the Gini coefficient. However, it is not bounded above, and the interpretation is not at all intuitive.

The Stata package ineqdeco calculates several different GE Indices. Figure 4 shows how the Gini coefficient (of annual electricity consumption + 1 kWh) compares to the three most common ones.

 

Figure 4: Comparing the Gini coefficient with the three most common
Generalized Entropy Indices for a sample of 25 municipalities

Source: Authors’ elaboration.

 

In all three cases the GE indices agree with the Gini coefficient that the poorest municipalities with the highest Gini coefficients have the highest levels of inequality. The GE(0) index moderates the relationship slightly, whereas the GE(1) exaggerates it, and the GE(2) exaggerates it even more. Thus, none of the three most commonly used GE indices suggests that poorer municipalities would be more equal.

However, α can also take on negative values, making it more sensitive to differences in the lower end of the distribution. Figure 5 shows that the GE(-1) index is completely different from the Gini coefficient. One municipality that stands out with extremely high inequality (GE(-1) = 250) is Santa Cruz de la Sierra (Bolivia’s most populous municipality, home to both extremely rich and extremely poor households), whereas the poorer municipalities in our sample have the lowest levels of inequality according to this measure (still high, though, as the scale is completely different for this GE measure).

 

Figure 5: Comparing the Gini coefficient with the GE(-1) index for a sample of 25 municipalities

Source: Authors’ elaboration.

 

This rarely used inequality measure, GE(-1), potentially fits better with common intuition about which municipalities are more unequal. We will explore it in more detail further below.

 

Income shares, Kuznets ratios and Palma ratio

Thomas Piketty’s best-selling book “Capital in the Twenty-First Century” made the use of percentile shares popular for analyzing inequality. Piketty and collaborators focused on top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.).

A related concept is the Kuznets ratio, which compares the income of the top X% with the bottom Y% of the population. A special case of this is the Palma ratio (the income of the 10% richest divided by the income of the 40% poorest) (8). The top-percentage shares are a special case of Kuznets ratio, as it is the top X% divided by the bottom 100%. So all these measures can be called Kuznets ratios.

Kuznets ratios are weakly Lorenz-consistent, since progressive or regressive transfers within each group analyzed will not affect the various indicators.

Ben Jann of the University of Bern created a convenient Stata command, pshare, to calculate the share of income received by any group along the income distribution [9]. The default is the income shares of the five quintiles (20% poorest to 20% richest). However, due to the high level of inequality in electricity consumption indicated by the Gini coefficient, we find it important to disaggregate the top quintile, and see the share of electricity consumed by the top 10%, top 5% and top 1% of households in each municipality.

In addition, in order to calculate the Palma ratio, we use the 40% and 90% cut-offs.

Figure 6 shows how the Gini coefficient (of annual electricity consumption + 1 kWh) compares to the following three Kuznets ratios: Top 5% of electricity consumption share; top 1% share; and the Palma ratio.

 

Figure 6: Comparing the Gini coefficient with three Kuznets ratios

Source: Authors’ elaboration.

 

The first two measures, which focus on the top end of the distribution, confirms -and even exaggerates- the finding that the poorest municipalities are the most unequal.

However, the Palma ratio shows a completely different pattern. The extreme outlier in our small sample is Uyuni, with a Palma ratio of 777, followed by Ixiamas, Machacamarca and Patacamaya. In contrast, all the mayor cities of Bolivia have very low levels of inequality by this measure. There is nothing about these results that seems even remotely related to intuition.

In the range of Gini coefficients between 0.4 and 0.63, there seem to be a positive relationship with the Palma ratio, but for higher Gini’s the relationship becomes completely random and unrelated to common sense.

 

Other inequality measures

There are several standard measures of dispersion reported by statistical packages, but which do not have fancy names nor particularly intuitive interpretations, and thus are not widely used in the inequality literature.

The first three indicators reported by the Stata command inequal (developed by Edward Whitehouse of OECD in Paris), are the Relative Mean Deviation (RMD), the Coefficient of Variation (CV) and the Standard Deviation of Logs (SDL). Figure 7 plots these inequality measures against the Gini for our sample of 25 municipalities.

 

Figure 7: Comparing RMD, CV, and SDL to the Gini coefficient

Source: Authors’ elaboration.

 

The first of these measures, RMD, are closely related to the Gini coefficient; the second one, CV, exaggerates the relationship between inequality and poverty, but the third paints a different picture that might possibly correspond better to intuition. According to the bland indicator called “Standard Deviation of logs”, Santa Cruz de la Sierra has high inequality, while Poroma (one of the poorest municipalities in Bolivia) has low inequality, which seem to correspond to intuition.

The same inequal Stata command reports the Mehran, Piesch and Kakwani measures of inequality. They are not well known, nor widely used, and since they all correlate strongly with the Gini coefficient (see Figure 8), we don´t think they provide much additional insights into inequality within Bolivian municipalities.

 

Figure 8: Comparing Mehran, Piesch and Kakwani to the Gini coefficient

Source: Authors’ elaboration.

 

Conclusions

In this blog we have investigated the properties of 16 alternative measures to the Gini coefficient of inequality. Most of them agree with the Gini coefficient that poor, rural municipalities are more unequal than rich, urban municipalities.

However, we found two inequality measure that provide different, yet plausible patterns of inequality. Those are the Standard Deviation of logs and the Generalized Entropy Index with a parameter of -1 (strong emphasis on the lower end of the distribution).

Since the results seemed intuitive for our sample of 25 municipalities, we decided to crunch the numbers for all 339 municipalities. Figure 9 shows how Figure 2 would look if we use GE(-1) instead of the Gini coefficient. And Figure 10 shows the same, but using the Standard Deviation of logs measure instead.

 

Figure 9: Comparing poverty and inequality using GE(-1) for all municipalities in Bolivia

Source: Authors’ elaboration.
 

Figure 10: Comparing poverty and inequality using Standard Deviation of logs for all municipalities in Bolivia

Source: Authors’ elaboration.

 

It is interesting to note that the two measures GE(-1) and Standard Deviation of logs agree on many of the most unequal municipalities (e.g. Rurrenabaque, Reyes, Cobija, Santa Cruz de la Sierra, Puerto Suarez), while they completely disagree with the Gini coefficient about the high level of inequality in small, poor, completely rural municipalities. Both are in reasonable agreement concerning the relative rankings of the large urban municipalities, with all agreeing that Cochabamba is the most equal of the 10 capital cities (+ El Alto) and Cobija the most unequal. The correlation between the two is 0.83, which is high, but not so high as to be redundant.

Of the 17 different ways to measure inequality that we have tried out in this blog, GE(-1) and Standard Deviation of logs seem to correspond best to intuition about inequality within Bolivian municipalities. The Gini coefficient, and all the other measures that correlate strongly with the Gini coefficient, do not seem to correspond to an intuitive perception of inequality, at least not for very poor municipalities.

It is clear that the optimal choice of inequality measures depends greatly on the distribution of the data, and that the Gini coefficient is not automatically the best choice.

After having processed millions of data points for 300+ municipalities in 17 different ways, we conclude that the best indicators to include in our Municipal Atlas of the SDGs in Bolivia under SDG 10 are: 1) Inequality Index 1: Standard Deviation of log household electricity consumption, and 2) Inequality Index 2: Generalized Entropy (α = -1) of household electricity consumption.

We do recognize, however, that we have engaged in what could be called “method-mining” in order to obtain results that correspond better to our intuition and expectations. Thus, in order not to give too much weight to these electricity-consumption-based inequality measures, we recommend to include other measures of inequality under SDG 10. Next week we will explore within-municipality inequality measures based on education levels.

 

Notes

[1] World Development Indicators, Gross National Income per capita, Atlas method, 2018. https://data.worldbank.org/indicator/NY.GNP.PCAP.CD

[2] Andersen, L. E., B. Branisa & F. Calderón (2019) “Estimaciones del PIB per cápita y de la actividad económica a nivel municipal en Bolivia en base a datos de consumo de electricidad.” Investigación ganadora presentada al Centro de Investigaciones Sociales (CIS) de la Vicepresidencia del Estado Plurinacional de Bolivia. Mayo.

[3] In the latest Human Development Report 2019, which analyzes inequality, James Foster and Nora Lustig provide a useful overview to help decide between alternative inequality measures. See Spotlight 3.2, pp. 136-138.

[4] See Francisco Ferreira’s blog “In defense of the Gini coefficient”: https://blogs.worldbank.org/developmenttalk/defense-gini-coefficient.

[5] See https://www.jasonhickel.org/blog/2018/12/13/what-max-roser-gets-wrong-about-inequality.

[6] We also considered the possibility of adding more than 1 kWh per year to each observation, because all households receive a transfers from the sun amounting to at least a couple of lightbulbs for 12 hours per day, and perhaps a whole lot more. In cold regions, the sun also provides free heat, but in hot regions, that may not be perceived as a benefit, but rather as a disservice. Thus, it is virtually impossible to take into account these varying contributions from the sun, and any values we might chose would be arbitrary. Researchers face the same dilemma when calculating income inequality, or wealth inequality, as there are so many public services or environmental services that contribute to the wellbeing of families, but are extremely difficult to quantify and include in each family’s income/wealth. It is major research problem that we do not pretend to be able to solve here, so in this blog we will just add 1 kWh to each observation in order to overcome the technical problem of zeros and be able to actually compare all the different inequality statistics.

[7] See http://siteresources.worldbank.org/PGLP/Resources/PMch6.pdf.

[8] See https://www.cgdev.org/blog/palma-vs-gini-measuring-post-2015-inequality.

[9] See http://repec.sowi.unibe.ch/files/wp13/jann-2015-pshare.pdf.

—-

* SDSN Bolivia.

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Atlas of the SDGs in Bolivia at the municipal level” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

What is the collective transportation situation in Bolivia?

The world population has been increasing greatly, growing from 2.6 billion in 1950 to 7.3 billion by 2015. The world population is estimated to reach 8.5 billion by 2030 [1]. Due to rural-urban migration, this growth is accompanied by an exponential population increase in cities. In 1960 the population residing in cities was 1.0 billion (33.6% of the total population), however, in 2018 that number had increased to 4.2 billion (55.3%) [2]. This rapid urbanization is putting pressure on the fresh water supply, wastewater treatment, living environment, and public health.

Traffic jams and air contamination are important problems that afflict cities, causing not only a loss of efficiency in economic activities as a result of time wasted stuck in traffic, but also serious environmental and health consequences.

The Sustainable Development Goal (SDG) number 11 (Sustainable Cities and Communities) aims to make cities and human settlements inclusive, safe, resilient and sustainable. To achieve this, cities must invest in public transportation. Specifically, Target 11.2 seeks to provide access to safe, affordable, accessible and sustainable transport systems for all by expanding public transportation.

For this reason, in the Municipal Atlas of the SDGs in Bolivia, we include an innovative indicator that measures the supply of collective transportation in each municipality.

The data was obtained from the National Statistical Institute (INE), based on the Municipal Tax Administration Registry Office (RUAT). The data includes the car fleet by municipality and vehicle class between 2003 and 2017.

During the construction of the indicator, we decided to consider only vehicles that are destined for collective transport, that is, that have the capacity to transport 10 or more passengers. That is why only the categories bus, microbus and minibus were included in the construction of the indicator. The analysis considers a potential seating capacity of 54 for buses, 29 for microbuses and 14 for minibuses [3].

After estimating the number of collective transport seats, we divided the result by the population of the respective municipality to have the number of available seats per thousand inhabitants. This analysis allows us to identify the municipalities with the greatest collective transport capacity.

In addition to the above mentioned privately owned buses, we also added the seats available in Mi Teleférico (cable cars operating in La Paz and El Alto as a system of public transportation), as well as in the PumaKatari and ChikiTiti buses (La Paz), and Wayna Bus (El Alto).

In the case of Mi Teleférico, the analysis was different because it has interurban lines between La Paz and El Alto. That is why we consider the number of cabins of each line and the location of each station to determine the seats available in each municipality. For example, for the Red line, with 109 cabins and three stations, we determined that a third of the cabins belong to El Alto and two-thirds to La Paz since the line has only one station in El Alto and two in La Paz. The following table shows the distribution of cabins in both cities according to each station’s location:

 

Table 1: Station and cabin distribution by Mi Teleférico line

Source: Author’s elaboration based on data from Mi Teleférico.

 

The analysis allowed us to approximate the total number of seats available per thousand inhabitants of Mi Teleférico in both La Paz and El Alto, considering that each cabin has a maximum transport capacity of ten passengers.

The estimates of seats available for public transport provided by the PumaKatari, ChikiTiti and Wayna Bus were made in the same way as those based on RUAT data. All the existing vehicles in the municipality were considered: 179 PumaKatari buses and 30 ChikiTitis in La Paz and 60 Wayna Buses for El Alto. Likewise, the maximum transport potential of each of these vehicles was also used (61, 55 and 82, respectively) [4] [5].

The following graph shows the results for the ten municipalities with the largest number of seats available per thousand inhabitants, by type of collective transport. The municipality of Achocalla in La Paz is the one with the most available seats per 1000 inhabitants. Most collective transportation seats available come from minibuses.

 

Graph 1: Top 10 municipalities by number of available seats, by type of collective transport

Source: Author’s elaboration based on SDG 11 results for the Municipal Atlas of the SDGs in Bolivia.

 

Finally, we use the methodology proposed by the SDSN office in Paris to estimate the thresholds of this indicator. It uses the average +/- half the Standard Deviation to define the ranges in which a municipality is considered to have reached the goal, is close to reaching it, still has significant challenges, or has great challenges. In Bolivia we consider that a municipality has reached the target if there are at least 93 seats available per thousand inhabitants. Despite the fact that this figure seems low, the results indicate that only 12% of Bolivian municipalities have managed to reach this goal, 6% are close to reaching it, while the remaining 82% still have significant/great challenges to reach this goal. Although initiatives such as Mi Teleférico, PumaKatari, ChikiTiti and Wayna Bus have helped increase public transportation supply, there is still a long way to go, especially considering the trends of population growth and migration to cities.

Notes

[1] https://www.un.org/es/sections/issues-depth/population/index.html

[2] https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS

[3] El RUAT define a los autobuses como ómnibuses.

[4]  https://www.paginasiete.bo/sociedad/2016/1/17/altenos-evaluan-wayna-bus-destacan-tarifa-critican-ruta-83673.html

[5] https://amn.bo/2019/07/22/el-chikititi-unico-en-su-tipo-y-esta-hecho-a-la-medida-de-la-paz/

______

* Alejandra Gonzales Rocabado, Assitant Manager, SDSN Bolivia

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Atlas of the SDGs in Bolivia at the municipal level” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

Let’s reuse our waste

Today, climate change and waste generation are fairly addressed issues, not just by environmentalists, but by the general public. However, little is known about their relationship and the nexus between the two issues.

First, waste is a global problem. The generation of municipal solid waste that the world generates according to the latest World Bank report, What a Waste 2.0 (2018), reaches 2,010 million tons annually. This means more than 10 times the amount that was produced a century ago. In addition, estimates from the same report claim that these figures will increase by 70% by 2050, given population growth and changes in the consumption patterns that occur as a result of economic growth.

On the other hand, the main source of greenhouse gases remains to be the energy sector. According to the World Resource Institute’s climate analysis indicators in 2017, much of the total anthropogenic greenhouse gas comes from some sort of energy consumption, covering 72% of total emissions, of which 31% comes from electricity and heat generation, 15% of the transport sector, 12.4% of the manufacture and construction sectors, 5.2% of fugitive emissions and 8.4% from other fuel combustion. The remaining 28% greenhouse gas emissions come from agriculture (11%), from the change in land use and forestry (6%), from industrial processes (6%), from bunker fuel (2%), and 3% from waste or solid waste.

In other words, to the surprise of many, the waste problem is not only found in soil pollution, the drowning of oceans, lakes and rivers, the negative impact on biodiversity, the significant effects on people’s health since the gases they emanate affect the respiratory tract and have carcinogenic effects, among others conditions. But also, the solid waste decomposition emits greenhouse gases, like methane and carbon dioxide.

However, this biogas can be captured and used as fuel and/or electricity. Thus, using certain waste treatment methods, such as anaerobic digestion, it is possible not only to reduce the polluting organic matter but by producing energy through biogas, there is also a reduction in greenhouse gases.

These reductions are due, on one hand, to the capture and combustion of methane and carbon dioxide contained in biogas, and on the other, to the replacement of fossil fuels used to generate the same amount of electricity that will be generated from biogas.

In the case of Bolivia, although the main source of carbon dioxide emissions comes from deforestation, accounting for more than 80% of the total emissions, more than 11,500,000 tons of carbon dioxide are annually produced by diesel, gasoline, electricity and petroleum liquid gas consumption, representing approximately 2 tons of carbon dioxide per person every year. These figures are high compared to El Salvador or Paraguay, who both emit approximately one ton per person annually (6,305,000 tons and 4,122,000 tons respectively).

From another perspective, according to reports from the Bolivian Ministry of Environment and Water, by 2016 Bolivia generated approximately 2 million tons of solid waste per year, the equivalent of 5,400 tons per day, a figure that is 20% higher in comparison to 2010. In addition, according to data from the 2012 Population and Housing Census, just under 60% of households dispose of waste in a container or through the public collection service. the remainder is removed in alternative ways, such as burning, throwing it into the river or some wasteland or by recycling it.

Likewise, it is necessary to mention that not all the waste generated can be used for the generation of biogas. In the case of Bolivia, according to the last Diagnostics of Solid Waste Management in Bolivia, published by the Vice-Ministry of Potable Water and Basic Sanitation, approximately 60% of the total waste generated is organic matter and paper/cardboard which could be used for the generation of biogas.

Considering that the consumption of domestic electricity in Bolivia was 3,217,315,730 kWh for the year 2016 and that the energy potential can be 550 kWh per ton of incinerated waste, Bolivia could produce approximately 15 to 20% of its electricity consumption by generating it from its solid waste. Figures similar to those of Denmark, one of the world leaders in biogas electricity production.

While there are several factors that need to be considered technically, economically, and as management issues, this alternative, as a way to reduce waste and greenhouse gas emissions, is one of the most beneficial applications in economic, social and environmental terms.

Latin American countries such as Argentina, Brazil, and Mexico, have already considered this solution. Bolivia, despite the conditions it offers and although it does not generate large greenhouse gas emissions by energy production, should also consider this measure taking into account the latest waste problems such as the case of Alpacoma.

 

* Alejandra Gonzáles, Assitant Manager, SDSN Bolivia

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Atlas of the SDGs in Bolivia at the municipal level” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia

Tackling the inherent contradictions in SDG 5 on Gender Equality

While the icon for SDG 5 says “Gender Equality”, the full text of the goal on the United Nations Sustainable Development Goal site is actually “Goal 5: Achieve gender equality and empower all women and girls.” This means that the gender equality goal is inherently biased towards women from the outset. This would be fine if women were overwhelmingly disadvantaged in all areas of life everywhere.

However, in this blog we will show that this is not necessarily so, and that decisions will have to be made on what we consider gender equality for the purpose of the upcoming Municipal Atlas of the SDGs in Bolivia.

For example, concerning the most basic question of having a roof over your head and a place to call home, we know from the latest population census in Bolivia (2012), that men are almost twice as likely to be homeless as women. In Santa Cruz de la Sierra, the municipality with most homeless people in Bolivia, men are almost 5 times more likely to be homeless than women, while in El Alto, the municipality with the second largest number of homeless people, men and women are almost equally likely to be homeless.

What is arguably worse than being homeless? Being incarcerated. In Bolivia men are about 10 times more likely to be incarcerated than women[1].

What is arguably worse than being incarcerated? Being so desperate that you prefer to kill yourself rather than to continue living in this world. Male suicide rates in Bolivia are almost 3 times higher than female suicide rates[2].

What is arguably worse than killing yourself? Being killed by somebody else against your will. According to police records in Bolivia, men are about 4 times more likely to be murdered than women.

Being homeless, incarcerated, suicidal, or murdered are all extremely bad situations, but fortunately they are rare in Bolivia.

How about gender equality in more everyday situations, such as going to school or being healthy?

Gender parity in school attendance is a widely used gender equality indicator, and it can easily be calculated at the municipal level in Bolivia as well. According to data from the Ministry of Education in Bolivia, there are about 95 girls in school for every 100 boys, so there is a slight under-representation of girls, but that may be at least partly explained by the better performance of girls in school. Boys are more than twice as likely to fail a school year and having to repeat. In 2017, 50,671 boys failed a school year, whereas this was only the case for 23,428 girls. The same year, 58,834 boys were repeating a school year, while this was the case for only 29,417 girls. By the end of undergraduate studies, women dominate graduating classes and are substantially over-represented among those graduating with honours.

It is well-known that women are healthier and live longer than men almost everywhere, and Bolivia is no exception. Unfortunately, we do not have gender disaggregated data at the municipal level on life expectancy. Indeed, the only gender-disaggregated health indicator which we have available at the municipal level is HIV incidence. Using data provided by the Programa Nacional ITS/VIH/SIDA y Hepatitis Virales, we found that men are twice as likely as women to contract HIV.

Work is where women seem to be at a systematic disadvantage. According to data from the latest population census (2012), the labour market participation rate is lower for women than for men in almost every Bolivian municipality, and earnings data from household surveys systematically show that women earn less than men per hour worked, even with the same levels of education.

Analysis of potential gender equality indicators

The SDG Index methodology that our Municipal Atlas of the SDGs in Bolivia uses requires that there is a clear unidirectional goal. This means that gender parity cannot be approached from both sides, but has to be defined so that once women are at least as well of as men, then the gender equality goal is achieved and the municipality will turn green for that indicator.

Below we are showing gender parity maps for all the indicators for which we managed to obtain gender disaggregated data at the municipal level. They are all constructed so that if the value is less than one, then women are at a disadvantage compared to men, whereas if it is larger than 1, then women are better off than men. Municipalities where women are equal or better off than men are painted green, whereas municipalities where women are substantially worse off than men are painted red. This is an unusually long blog post, as we have a lot of data to present and discuss.

School attendance

Figure 1 shows the gender parity index for primary school attendance in 2017. It is measured as the number of girls enrolled in primary school divided by the number of boys enrolled in primary school in each municipality. It would have been better to use the gender parity index for primary net enrolment rates, but unfortunately we do not have reliable estimates of the total number of primary school aged boys and girls in each municipality, so the rates cannot be estimated with sufficient precision. The gender parity index for school attendance is a good proxy, if the number of boys and girls in the municipality is approximately equal. At primary school level that seems to be a reasonable assumption, at least for municipalities with many children.

Figure 1: Gender parity index for primary school attendance, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

At the national level, the gender parity index for primary school attendance is 0.94, so there are about 6% fewer girls in primary school than boys. However, the values do not seem to have anything to do with poverty, rurality, or any other possible explanatory factor we can think of. Indeed, the poorest municipalities in the Bolivian highlands seem to have slightly better gender parity indices. We suspect that most of the variation in Figure 1 is random. For example, the two municipalities with the lowest index (Esmeralda with 0.5) and the highest index (Cruz de Machacamarca with 1.75) are neighbours within the same province in Oruro. The first has 6 girls and 12 boys enrolled in primary school, whereas the second has 7 girls and 4 boys enrolled. With so few children in each municipality, even these extreme values are well within random variation in the number of boys and girls present in the municipality.

In addition, the fewer girls in primary school at the national level is not necessarily due to girls not getting into primary school or not staying in primary school, but may rather be due to boys being 50% more likely to repeat a year of primary school (see further below). Thus, there may simply be more boys in primary school because they are more likely to repeat a school year.

Given the above two arguments, we conclude that it is not worth including this indicator among the SDG5 indicators in our Municipal Atlas of the SDGs in Bolivia. The indicator provides hardly any reliable information about where to target interventions to ensure that all girls complete primary school.

Figure 2 shows the same indicator, but at secondary school level. At this level there seem to be a bit more real information and a bit less random noise in the indicator. At least the gender parity index of secondary school attendance is negatively related with poverty, suggesting that girls are more disadvantaged in poorer municipalities, as would be expected. In the map we see a red cluster in northern Potosí, which is an area of extremely high poverty.

However, there are other important problems with this indicator. First, the gender differences in repetition rates are even higher at the secondary school level, with boys being 137% more likely to repeat a school year than girls. Thus, a gender parity value below 1 does not necessarily reflect that girls are more likely to drop out of secondary school than boys, but rather that boys have to repeat school years more often than girls (see further below). Second, when the gender parity index is above 1, the municipalities are coloured green, and are considered to have achieved the goal of gender equality. But in reality, rather than reflecting a happy outcome of gender equality, this situation may instead reflect young men dropping out of school early in order to work, especially in mining, construction or agriculture, which demands low-skilled, but strong workers. On average, the drop-out rates for boys are about 60% higher than the drop-out rates for girls at the secondary school level (see further below).

Given the abovementioned problems, we do not recommend this indicator for inclusion in the Municipal Atlas either.

Figure 2: Gender parity index for secondary school attendance, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

 Drop-out rates

Figures 3 and 4 show the gender parity indices for drop-out rates in primary and secondary school, respectively. Drop-out rates are calculated from individual level administrative data and are therefore both precise and up-to-date. Both maps are overwhelmingly green, indicating that girls are less likely to drop out of school than boys in the vast majority of municipalities. Municipalities where girls are more likely to drop out than boys stand out clearly, and can therefore be the subject of targeted interventions.

Figure 3: Gender parity index for drop-out rates in primary school, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

The correlation between gender parity in drop-out rates at primary and secondary levels is low (r = 0.07), which means that there may be different underlying causes. It is therefore difficult to choose between the two. However, since drop-out rates generally are very low at primary school level, we think that the gender parity index for drop-out rates in secondary school is the best choice, and recommend that it is included among one of the SDG5 indicators for the Municipal Atlas.

Figure 4: Gender parity index for drop-out rates in secondary school, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

 Repetition rates

Figures 5 and 6 show the gender parity indices for repetition rates in primary and secondary school, respectively. Repetition rates are also calculated from individual level administrative data and are therefore both precise and up-to-date. Both maps are overwhelmingly green, indicating that girls are less likely to repeat a school year than boys in the vast majority of municipalities, especially at the secondary level. Indeed, at the secondary level, there are only 13 municipalities out of 339 with a parity index in repetition rates below 1.

Figure 5: Gender parity index for repetition rates in primary school, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

Figure 6: Gender parity index for repetition rates in secondary school, 2017

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

Given that boys in Bolivia are almost everywhere more likely than girls to fail a school year, and thus either having to repeat the year or drop out of school, it makes little sense to have a goal of empowering women and girls in the Bolivian education system. Boys are clearly struggling more than girls, and attention should rather focus on how to reduce the systematically higher failure rates of boys. We therefore recommend not to include the two gender parity indices of repetition rates in the Municipal Atlas.

Students with disabilities

Figures 7 and 8 show the gender parity index for enrolment of children with disabilities in the regular school system, at the primary and secondary level, respectively. At the national level this parity index is 0.71 at the primary level and 0.80 at the secondary level, indicating 20 – 29% less handicapped girls enrolled in the regular school system. This may suggest that girls with disabilities are facing more obstacles to getting into school, although there may be alternative explanations as well. For example, girls may be more likely to be enrolled in special schools for children with disabilities, but the Ministry of Education do not report statistics from these schools.

Figure 7: Gender parity index for enrolment of children with disabilities in primary school, 2016

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

The main problem with these two indicators is that in most municipalities there are few children with disabilities at all, so just random variation in the gender of the children with handicaps can make this indicator fluctuate wildly. Consider two neighbouring municipalities in Chuquisaca: Sopachuy had 1 boy and 2 girls with disabilities enrolled in primary school in 2016, resulting in a parity index of 2.0. In contrast, Villa Alcalá had 2 boys and 1 girl with disabilities, resulting in a parity index of 0.5. The first is coloured green, while the second is coloured orange, but clearly the differences could easily be explained by random variation.

Figure 8: Gender parity index for enrolment of children with disabilities in secondary school, 2016

Source: Ministerio de Educación: Sistema de Estadísticas e Indicadores Educativos
(http://seie.minedu.gob.bo/).

These indicators only make sense in populous municipalities with a large number of children with disabilities. In Santa Cruz de la Sierra, for example, there are 171 boys and 94 girls with disabilities enrolled in regular primary schools, resulting in a parity index of 0.55. At the secondary school level there are 127 boys and 83 girls enrolled, resulting in a parity index of. 0.65. These results suggest that girls with disabilities face much bigger obstacles than boys with disabilities in the regular education system in that municipality. But about half of all students with disabilities are enrolled in special schools, and these might favour girls.

Given both the high level of random noise in the data, and the lack of information from all the specialized schools for children with disabilities, we conclude that these two indicators do not contribute sufficient information to be include in the Municipal Atlas of the SDGs in Bolivia.

Literacy rates

Figure 9 shows the gender parity index for adult literacy rates, according to the 2012 population census. Women are almost everywhere less likely to know how to read and write than men, and the gender differences are particularly large in certain areas in, and close to, northern Potosí.

This is a historical problem, and the country has been systematically battling adult literacy education during the last several decades. The remaining illiterate groups consist mainly of women aged 60 or more.

The information for this indicator is quite old, and may have improved substantially since 2012. Indeed, UNESCO officially declared Bolivia free of illiteracy already in 2008, when more than 96% of the population achieved literacy[3]. Thus, literacy seem to be too easy a target to be included in the Municipal Atlas of the SDGs in Bolivia, and we recommend not including the gender parity index for adult literacy rates either, but rather some harder education related parity indices.

Figure 9: Gender parity index for adult literacy rates, 2012

Source: Censo Nacional de Población y Vivienda, 2012.

Education of adult population

A more relevant gender parity index in the area of adult education levels is the ratio of the average number of years of study for women compared to years of study of men in each municipality.

Figure 10 shows that women have lower education levels almost everywhere in Bolivia, except for a few municipalities in the department of Santa Cruz. This is also a historical problem, and one that is more difficult to remedy than adult illiteracy, as people who have been out of the formal education system for decades are unlikely to enter it again.

Figure 10: Gender parity index for years of education for adults, 2012

Source: Censo Nacional de Población y Vivienda, 2012.

A more relevant, and more policy sensitive, indicator would be the gender parity index for years of education of young people entering the labour market. Figure 11 shows the gender parity index for years of education for 25-35 year olds. It shows more gender equality than the previous indicator, and, importantly, it is an indicator that can change significantly before 2030, when a completely new generation will be in that age group. We recommend that this indicator is included among the SDG5 indicators in the Municipal Atlas of the SDGs in Bolivia.

Figure 11: Gender parity index for years of education for 25-35 Year Old’s, 2012

Source: Censo Nacional de Población y Vivienda, 2012.

Another relevant indicator that can be calculated from the population census is the gender parity index for English speaking rates in adults. This is calculated as the percentage of adult women who speak English divided by the percentage of adult men who speak English (or at least claim to speak English).

Figure 12: Gender parity index for English speaking rates in adults, 2012

Source: Censo Nacional de Población y Vivienda, 2012.

Speaking English facilitates access to sophisticated information and participation in international processes, and it should be promoted for both men and women. However, it is particularly important to make sure that women are not left behind at these higher levels of participation, so we recommend including this indicator in the Municipal Atlas of the SDGs in Bolivia.

Labour market participation

The area where women are most systematically left behind is in labour market participation. Historically, many women have stayed at home in order to raise children and run the household. However, it is important to facilitate and encourage women’s participation in the labour market for several reasons. First, it contributes to higher economic growth of the country, although part of the higher growth is due to their work at home not being counted in GDP. Second, it empowers women to earn their own income, rather than having to always ask their partner or parents for money. Third, participating in the labour market creates more professional and social relations and interactions, which also helps empower women.

Figure 13: Gender parity index for labour market participation rates, 2012

Source: Censo Nacional de Población y Vivienda, 2012.

Paradoxically, female labour market participation rates tend to be higher in poorer municipalities.  Still, we think a key component of gender equality is to make sure that men and women are about equally likely to participate in the labour market, although a small difference should be accepted to allow women some time off for maternity. We recommend including this indicator in the Municipal Atlas of the SDGs in Bolivia, but rather than making perfect parity the goal, we think the goal should be the average of the five municipalities with the highest values, e.g. 0.95.

Health indicators

The only municipal level health indicators that we have obtained at a sex disaggregated level is HIV prevalence. Figure 14 shows the gender parity index for HIV incidence, averaging the years 2014 to 2017 in order to reduce random variation in this fortunately infrequent disease. Most municipalities are green, indicating that women are less likely to get infected by HIV. Indeed, at the national level, the parity index is 2.2, indicating that men are more than twice as likely to get HIV than women.

Figure 14: Gender parity index for HIV incidence, 2014-2017

Source: Programa Nacional ITS/VIH/SIDA y Hepatitis Virales 2014-2017.

Most of the red municipalities on the map are caused by a single woman and zero men with HIV in each municipality, which is hardly a systemic problem.

One municipality that clearly stands out in the detailed data provided by the National STI/HIV/AIDS and Viral Hepatitis Program, is Puerto Villarroel in the Department of Cochabamba. Not only because it has one of the highest HIV incidences in the country and ranks 11th in terms of absolute numbers that measure new cases between 2014 and 2017; but because particularly girls have been hit the hardest. Girls aged 15 to 19 account for only 6.3% of new HIV cases in the rest of the country, but in Puerto Villarroel, they account for 45.1% of cases. This is clearly a case of very young girls who are dangerously exposed to life-threatening diseases, and in turn generating dangerous transmission hotspots.

This problem, and similar problems in other municipalities, require specific interventions. Unfortunately, the low number of cases recorded in some municipalities generates the same problem perceived in the case of the education of handicapped girls. Only one woman with HIV can paint the map red, when in fact it is not a significant problem. Thus, we suggest not to include this indicator in the Municipal Atlas of the SDGs in Bolivia.

Multidimensional poverty

We were able to calculate municipal level Multidimensional Poverty Indices separately for female and male headed households, using a methodology developed by INESAD at the request of, and in collaboration with, Swisscontact, for the project Inclusive Markets, financed by the Swedish Cooperation (ASDI) and the Swiss Cooperation (COSUDE) in 2017. The methodology includes 9 different indications of lack of voice, choice and resources:

  • At least one illiterate person in the household
  • At least one person aged 6+ without personal identification documents
  • No telephone or cell phone in the household
  • At least one child birth took place outside a health facility within the last 5 years
  • At least one teenage pregnancy in the household within the last 5 years
  • At least one child between 6 and 19 who is not studying
  • No potable water on the property
  • No electricity
  • No basic sanitation services.

Figure 15: Gender parity index for Multidimensional Poverty, 2012 (measured by the gender of the head of household)

Source: Censo Nacional de Población y Vivienda, 2012.

Although the data is a bit old, from the 2012 Population Census, the calculations are very robust, and clearly point out the municipalities where female-headed households are much worse off than male-headed households. We recommend that this indicator is included in the Municipal Atlas of the SDGs in Bolivia.

Conclusions

Having analysed 15 different gender disaggregated indicators in detail, we conclude that the most useful ones for the Municipal Atlas of the SDGs in Bolivia are the following:

  • The Gender Parity Index for Dropout Rates, 2017.
  • The Gender Parity Index for Years of Education for 25-35 year olds, 2012.
  • The Gender Parity Index for English-Speaking Adults, 2012
  • The Gender Parity Index for Labor Market Participation Rates, 2012.
  • The Gender Parity Index for Multidimensional Poverty, 2012 (measured by the gender of the head of the household).

 

[1] https://www.redalyc.org/jatsRepo/112/11244805004/html/index.html .

[2] https://ourworldindata.org/suicide .

[3] https://elpais.com/internacional/2008/12/21/actualidad/1229814001_850215.html


* Lykke E. Andersen, Ph.D., Executive Director OF SDSN Bolivia.

** Line Munk, SDSN Bolivia

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Atlas of the SDGs in Bolivia at the municipal level” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

Playing with fire in Santa Cruz

Currently there is a lot of speculation about the causes of the forest fires in the Amazon region. A Bolivian congressman, speculated that they probably originated in Brazil, while Brazil’s president Jair Bolsonaro claimed that NGOs have set fires to the forest to hurt his government. Many people in Bolivia have blamed the Supreme Decree 3973 of the 9th of July 2019, which authorizes controlled fires for agricultural purposes in Santa Cruz and Beni. The new meat export agreement signed with China is also a suspect.

In this blog we will present some empirical evidence to help improve the quality of speculation. For that purpose, we have analysed all the fires detected in the Department of Santa Cruz in Bolivia by NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) sensor between the 1st of January 2016 and the 28th of August 2019.

Most readers will surely be shocked by the sheer number of fires detected in the department: About 100,000 per year! In Table 1 we have cross-tabulated all detected fires[1] by year and type of area in which they were detected, according to the Departmental Land Use Plan (PLUS). About 59% were in areas designated for agropastoral use, but an astonishing 41% of all fires were in areas which were designated for forestry activities or protected areas, neither of which are supposed to have caught fire.

Table 1: Number of fires detected, by year, and type of land use, 2016-2019

Source: Authors’ calculations based on data from NASA’s VIIRS sensor and the Departmental Land Use Plan (PLUS).

In this table 2019 does not look much different from previous years, but that is because we are still early in the burning season. If we graph the accumulated number of fires by date of the year, we see that this year clearly stands out (see Figure 1). 2016 was the previous worst year ever in Bolivia for both number of fires, extent of land burned, and area deforested, but 2019 looks set to beat those records easily. Just the first 28 days of this month saw more than 83 thousand fires in Santa Cruz. This is more than double the 36,591 fires detected during the same period in 2016.

Figure 1: Fires detected in Santa Cruz, Bolivia, by NASA satellites, 2016-2019

Source: Authors’ calculations based on data from NASA’s VIIRS sensor.

Usually, fires take place either on already deforested land, or very close to already deforested land, but this year fires have ventured farther away. In 2016, for example, only 16% of all fires in Santa Cruz took place more than 1 kilometre from areas that were already deforested by the end of the previous year. But so far in 2019, 37% of fires in Santa Cruz been observed more than 1 kilometre away from already deforested areas (see Figure 2).

Figure 2: Location of fires in Santa Cruz, by distance to already deforested areas, 2016-2019

Source: Authors’ calculations based on data from NASA’s VIIRS sensor, and the Hansen Global Forest Change data set version 1.5.

Burning of natural pastures is an ancient practice, which ranchers use to encourage the growth of fresh and tender grass for the cattle. However, we tabulated the number of fires in Santa Cruz by tree cover density in the year 2000, and found that so far this year, about 45% of fires have taken place in areas that used to have high tree cover like the “Cerrado Chiquitano” and “Cerrado Chaqueño” (between 61 and 90%) and 31% in areas that used to have very dense tree cover, more similar to the “Chiquitano Forest” (91-100%).

Figure 3: Location of Bolivian fires, 2016-2019, by tree cover the year 2000

Source: Authors’ calculations based on data from NASA’s VIIRS sensor, and the Hansen Global Forest Change data set version 1.5,
where tree cover corresponds to canopy closure of vegetation taller than 5m.

While there may be a few fires caused by a fluke accident (such as a burning cigarette or a camp fire left by an irresponsible tourist), it is safe to suppose that by far the most likely culprits are the hundreds of thousands of farmers intentionally setting fire to the vegetation in Santa Cruz every year to prepare for the next agricultural season. The dramatic increase in fires just this last month suggests that the Supreme Decree and the meat export opportunities to China have indeed encouraged farmers to clear land more aggressively this year.

Notes

[1] We excluded all low-confidence observations from the analysis, as these might reflect glares from metal roofs or cars rather than actual fires.

——————–

* Lykke E. Andersen, Ph.D., Executive Director, SDSN Bolivia.

** Juan Carlos Ledezma, Scientific Manager, Conservation International Bolivia

*** Eduardo Forno, Director Ejecutivo, Conservation International Bolivia

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Municipal Atlas of the SDGs in Bolivia” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

Deforestation and wildfires in Bolivia

Each year, towards the end of the dry season, Bolivian farmers burn forest and other vegetation to prepare for the next agricultural season. Slash-and-burn agriculture is a traditional farming technique that begins by cutting down the trees and woody plants in an area. The downed vegetation is then left to dry, and before the rainy season starts, the debris is burned, resulting in a nutrient-rich layer of ash which makes the soil fertile, and also temporarily eliminates weed and pest species. After some years of cultivation, nutrient contents fall and weed infestation increases, and the plot is left to grow into forest again. This practice works well on a small scale, but can be disastrous at a larger scale, and can accidentally cause out-of-control wildfires.

This is what Bolivia is suffering right now. Forest fires are ravaging more than 500,000 hectares in the Santa Cruz department, which is why the department has declared a state of emergency due to out-of-control wildfires. Particularly affected are the municipalities of Roboré, El Trigal, Pampa Grande, San Ignacio de Velasco and San Rafael who also declared themselves in a state of municipal emergency.

Due to the dry weather and strong winds, fires expand far beyond the plots on which fire was intended. Currently, human settlements and protected areas are also affected by forest fires, which are completely overwhelming the limited response capacity of the Departmental Emergency Operations Center (COED).

As we have seen repeatedly in the news lately, even rich countries with massive resources find it almost impossible to fight forest fires (1). Therefore, it is unrealistic to expect that a few Bolivian helicopters, appointed to help put out the fire, will make a difference. Similarly, after more than a week of fires, it is difficult to know whether even the American Supertanker will have a significant effect. If the government of France couldn’t even protect their treasured Notre Dame in the middle of a wealthy city, the natural and human made treasures of the Chiquitania are facing long odds.

The current situation should not come as a surprise to anybody, though. Bolivia has been burning forests more and more aggressively for decades to expand the agricultural frontier. While intact Amazon rainforest is quite fire resistant, this is not the case for the fragmented forests at the drier areas at the border of the rainforest. If hundreds of thousands of hectares of transitional forests are intentionally burned, not only are huge amounts of CO2 emitted into the atmosphere, but the habitat of the biodiversity that resides there is systematically destroyed. Moreover, as has been the case with other fires around the world, it is only a matter of time before fires come out of control and cause huge human, economic and cultural losses.

The question arises whether the benefit that the Bolivian society receives from the soy beans and livestock in Santa Cruz takes into account the costs of the enormous loss of biodiversity and environmental services. Not only would they have to be enough to pay the direct costs of the Supertanker rental, the countless hours of helicopter flights, the lost cattle heads, etc., but also the indirect costs that will arise after the fires, when those same regions experience stronger flooding and droughts due to the lack of vegetation cover to regulate the water cycle.

To reflect this threat, we are including several indicators related to deforestation in our Municipal Atlas of the SDGs in Bolivia. We have updated all the indicators to include information from 2018.

Figure 1 shows how annual deforestation has increased in Bolivia from an average of about 150,000 hectares per year during the 1990s to almost 300,000 hectares per year during 2016-2018. These are averages over several years, since there is a lot of random variation from year to year, mostly due to climatic variations and the dynamics of land use, and these random variations obscure the overall trend. For example, 2016 represented the highest level of deforestation in Bolivia ever, with more than 417,000 hectares deforested, but the number fell to about 263,000 hectares in 2017 and 215,000 in 2018. But in 2019, it looks destined to increase again due to out-of-control wildfires. Thus, we believe that the averages over several years provide a better idea of the general trend.

Figure 1: Average annual deforestation in Bolivia, 1990-2018 (hectares/year)

Source: Authors’ estimation, based on the sources cited in the footnote (2).

In the rest of the article we will focus on data for the last three years (averaging 2016, 2017 and 2018) and explore in more detail which municipalities recently recorded high deforestation levels.

We present the data in three different ways:

  1. Absolute levels of deforestation (hectares);
  2. Deforestation rates (annual deforestation as a percent of forest in 2015), and
  3. Deforestation per capita (m2 deforested per inhabitant per year).

In each of the cases we present the 25 municipalities with the highest deforestation between 2016 and 2018.

The blue column in Table 1 shows the 25 municipalities with the highest levels of deforestation in absolute terms (hectares/year). These 25 municipalities are responsible for 80% of total deforestation in Bolivia between 2016 and 2018. Of these, 16 are located in the department of Santa Cruz, the rest comprise municipalities in the following departments: 4 in Beni, 3 in La Paz, 1 in Cochabamba and 1 in Tarija.

The yellow column shows the 25 municipalities with the highest rates of deforestation (% of the existing forest in 2015). 23 of the 25 municipalities with the highest rates of deforestation are located in the department of Santa Cruz, while the remaining two are located in the departments of Cochabamba and Pando.

Finally, the red column shows the 25 municipalities with the highest rate of deforestation per capita (m2/person/year). Again, we see that most of the municipalities on this list are located in the department of Santa Cruz, but there are also some in Beni, Pando and La Paz.

Table 1: The 25 Bolivian municipalities with the highest levels of deforestation between 2016 and 2018, according to the three deforestation  indicators.

Source: Elaborated by the authors.

Table 1 shows three different ways to measure the intensity of deforestation. High levels of deforestation in absolute terms can be justified if the municipality has a large population. However, if a municipality is on the list of municipalities that most deforest in all three columns, deforestation is certainly considered to be high in that municipality from any point of view. This means that in such municipalities the environmental impact will be great in the short term.

In total, 50 different municipalities have made it to one of the Top 25 lists of deforestation in Table 1 above, and 7 made it to all three lists. These 7 municipalities with high deforestation, by any measure, are all located in the department of Santa Cruz and they are the following:

  • San José de Chiquitos
  • Pailón
  • Santa Rosa de Sara
  • Cabezas
  • San Javier
  • Cuatro Cañadas
  • El Puente

Map 1 shows in how many, and in which dimensions, each municipality in Bolivia have made it to one of the Top 25 lists above. Each dimension is represented by one of the primary colours (blue, yellow, red), but if a municipality appears on more than one list, it is coloured in the composite colour that arises from mixing the two primary colours. There are a lot of purple coloured municipalities, for example, which implies that they are both on the red list (high per capita deforestation) and the blue list (high absolute levels of deforestation). The 7 municipalities that made it to all three lists are coloured in black.

Map 1: Municipalities listed on one or more of the three lists of municipalities that deforest the most in Bolivia, 2016-2018.

Source: Developed by the authors based on information from Table 1 above.

Three of the municipalities that are currently in a fire-induced emergency (Roboré, El Trigal, and Pampa Grande) were not among the worst deforesters themselves during 2016-2018, but they are located close to some of the worst deforesters, and, unfortunately for them, fire does not respect borders.

—————————————————-

*The Sustainable Development Solutions Network – Bolivia

* Conservation International – Bolivia

The Sustainable Development Solutions Network (SDSN) in Bolivia is currently preparing a Municipal Atlas of the SDGs in Bolivia. It is a significant collaborative effort involving many different institutions. The viewpoints expressed in the blog are the responsibility of the authors and may not reflect the position of their institutions. Readers are encouraged to provide feedback to the overall coordinator of the Atlas, Dr Lykke E. Andersen, Executive Director of SDSN Bolivia at: Lykke.E.Andersen@sdsnbolivia.org.

————————————

  1. For example, the Camp Fire in California last year destroyed about 60,000 hectares, but since it was close to densely populated areas, it was the most costly (USD 16.5 billion) and deadly (at least 86 persons died) in California’s history. The same year, British Columbia in Canada lost more than 3 million hectares to wildfires, breaking the record of the previous year, where 1.2 million hectares burned, and 65 thousand people were forced from their homes. This year, at least 3 million hectares of forest has burned in Siberia, causing massive CO2 emissions, but fortunately few human casualties.
  2. Data sources: Data for 1990-2010 are from SERNAP & CI (2013) Deforestación y regeneración de bosques en Bolivia y en sus Áreas Protegidas Nacionales para los periodos 1990-2000 y 2000-2010. La Paz: Servicio Nacional de Áreas Protegidas, Museo de Historia Natural Noel Kempff Mercado y Conservación Internacional – Bolivia. Data for 2011-2015 are from the Bolivian Ministry of Environment and Water (Sala de Observación – OTCA, Dirección General de Desarrollo Forestal y Autoridad de Bosques y Tierras 2017). Finally, data for 2016 to 2018 are from the Hansen Global Forest Change data set version 1.5.
    Note: Only municipalities with more than 0.1 km2 of forest have been included in this analysis.

Inputs for the National Policy for Integral Urban Development

Bolivia is currently in the process of developing a National Policy for Integral Urban Development, which is very important given the rapid and unorganized process of urbanization that the country is experiencing. Although our Municipal Atlas of the SDGs in Bolivia is not yet ready, we already have interesting data that can serve as inputs for the elaboration of this policy.

What the data tells us is that the urbanization process can help us move faster and more efficiently towards the Sustainable Development Goals, as cities provide more benefits for the population, and at a lower cost, due to significant economies of scale and agglomeration. However, certain problems are more prevalent in cities than in rural areas, especially in regard to health. Finally, because of the large number of migrants arriving to metropolitan areas each year, municipal governments have difficulties providing all basic services in a timely manner, which means that there are large numbers of people with unsatisfied basic needs in the cities. In this article we show some data that supports these conclusions.

In the graphs below we have divided the 339 municipalities of Bolivia into four groups:

  • RRR: Totally rural (172 municipalities)
  • RRU: Predominantly rural (91 municipalities)
  • UUR: Predominantly urban (51 municipalities)
  • ZMC: Metropolitan areas and departmental capitals (25 municipalities).

Figure 1 shows that urban municipalities (UUR and ZMC) receive fewer transfers from the central government per person compared to rural municipalities (RRR and RRU), and that urban municipalities invest less per person than rural municipalities.

Figure 1: Public resources available, by type of municipality


Source: Own elaboration based on data from the Municipal Atlas of the SDGs in Bolivia,
provided by the Ministry of Economy and Finance.

Still, urban municipalities are more successful in reducing poverty and inequality. No matter how we measure poverty, results tend to be much better for urban municipalities, and especially for municipalities that are departmental capitals or that belong to one of the three metropolitan areas of the country. Figure 2 shows poverty levels according to three different ways of measurement (1), and also shows the average level of inequality in electricity consumption (proxy of general household consumption) between households within each municipality. 

Figure 2: Poverty and inequality levels, by type of municipality

Source: Own elaboration based on data from the Municipal Atlas of the SDGs in Bolivia,
from different sources. See details in note (1).

In cities, the population is better educated, especially women. This, together with economies of scale and agglomeration, makes the population more productive, which means that they can pay more taxes and thus contribute to the fiscal sustainability of their municipalities. In contrast, the 172 completely rural municipalities, on average, do not even manage to raise 1% of their income locally. More than 99% of municipal income consists of transfers from the central government, indicating acute financial unsustainability (see Figure 3). Even in metropolitan areas and departmental capitals, local tax revenues only reach 21%, indicating a strong dependence on the central government.

Figure 3: Schooling and local tax collection, by type of municipality

 Source: Own elaboration based on data from the Municipal Atlas of the SDGs in Bolivia,
the 2012 Population and Housing Census and the Ministry of Economy and Finance.

Not everything is automatically better and easier in cities. Even though chronic malnutrition in children and anemia in women are lower in cities, there are a number of other health problems. The clearest example is HIV, with a much higher incidence in metropolitan areas and departmental capital (see Figure 4). In fact, more than three-quarters of all HIV cases diagnosed between 2014 and 2017 were concentrated in only 5 municipalities: Santa Cruz de la Sierra, La Paz, Cochabamba, El Alto and Oruro (2). The rate of homicides registered by the police is also a lot higher in metropolitan areas, although the low rates in rural areas are likely to be due to incomplete registration.

Obesity problems also tend to be higher in urban areas, especially in intermediate cities (see Figure 4). In metropolitan areas and departmental capitals, the obesity problem is smaller than in other urban areas, probably due to better education and higher incomes.

Figure 4: Health problems, by type of municipality

 Source: Own elaboration based on data from the Municipal Atlas of the SDGs in Bolivia,
from various sources.

There are few indicators that are worse in cities, but in absolute terms, big cities concentrate many problems. For example, the percentage of young males between 15-24 years of age who are not in employment, education or training (NEET) is relatively low in Santa Cruz de la Sierra (9.6%, compared to the national average of 11.3%), but still Santa Cruz de la Sierra is the municipality with most male NEETs in the country, followed by La Paz, El Alto and Cochabamba (see Figure 5).

Figure 5: Number of male NEETs in Bolivia, by municipality, 2012

Source: Own elaboration based on data from the Municipal Atlas of the SDGs in Bolivia,
calculated with data from the 2012 Population and Housing Census.

This chart is typical. Although Bolivia’s four large cities tend to have the best indicators in terms of human well-being, they also concentrate the most people with deficiencies in the same aspects.

This is mainly due to the large number of migrants arriving to these cities each year. Figure 6 shows the 25 municipalities that are growing the fastest in Bolivia, according to the increase in the number of residential electric meters (with positive consumption) in each municipality. Santa Cruz de la Sierra and El Alto each add about 11,000 new families annually, who all need potable water, sanitation, electricity, health, education and various other public services. 

Figure 6: Population dynamics, 2013-2016 (Annual increase in the number of residential electric meters with positive consumption)

Source: Andersen, Branisa & Calderón (2019).

However, as we saw in Figure 1, public investment is directed more towards the places of origin of these migrants than to the places of destination, which means that the host municipalities are always lagging behind in terms of provision of basic services.

It also causes an under-utilization of many of the investments made in rural areas. Figure 7 shows a striking example. Analyzing data from all electricity meters in Bolivia, Andersen, Branisa and Calderón (2019) calculated the percentage of meters that had consumption of 0 kWh in May 2016 (the last month analyzed). In many municipalities of the Bolivian Altiplano, more than 20% of households with electricity service, are not using it regularly.

Figure 7: Proportion of residential electricity meters with zero electricity consumption during May 2016, by municipality

Source: Andersen, Branisa & Calderón (2019).

In conclusion, urbanization constitutes a remarkable opportunity to improve the living conditions of the population. In Bolivia we have the advantage that there are several different cities that attract people (Santa Cruz de la Sierra, El Alto, La Paz and Cochabamba), instead of just one mega-city. It would be ideal to develop more centers of attraction, and Figure 6 above shows some municipalities with potential.

However, for cities to handle the large number of migrants, they need more resources. Resource allocation should take into account migration patterns, to ensure that migrants quickly get access to basic public services at their destinations.

In cities, because of the high population density, public health need more attention. This includes good water and sanitation services, good hygiene practices, access to reproductive health, civic education to live well, road safety education for drivers and pedestrians to reduce accident rates, green areas and public spaces for mental and physical health, public transportation systems to reduce the need for private cars, and much more.

Notes

(1) The first measure of poverty is the Unsatisfied Basic Needs Index calculated by the National Statistics Institute based on the 2012 Population and Housing Census data. The second is a Multidimensional Poverty Index also calculated with Census data, but including more dimensions of deprivation (see description), and the third is a more up-to-date measurement of poverty based on electricity consumption in all homes in Bolivia, according to their electricity meters (see description).

(2) See here.

————————————————————————–

 

* Lykke E. Andersen, Ph.D., Executive Director, SDSN Bolivia.

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Municipal Atlas of the SDGs in Bolivia” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.

The influence of language on human and economic development in Bolivia

Languages are the main communication tool of the human species, but the impact they have on the interaction between people and the development of societies is often ignored. Currently, 7,111 languages are spoken worldwide, of which 1,058 are spoken in the Americas, that is, 15% of the world’s languages. On average, each native language in the Americas is spoken by only about one thousand people. However, Quechua is the most spoken native language in the Latin American and Caribbean region (LAC), with almost 7.7 million speakers. Aymara is the sixth most widely spoken native language in the LAC region, with 1.7 million speakers (1). Quechua and Aymara are by far the most spoken native languages in Bolivia.

In 2009, Bolivia established 36 official native languages besides Spanish. According to Ethnologue’s Expanded Graded Intergenerational Scale, thirteen of these languages are threatened or shifting (2); twelve are moribund, nearly extinct or dormant (3); two have 8 and 83 speakers respectively (4); and one did not register any speaker during the 2012 Population Census (5).

While Spanish is the integrating national language, used in the cities, in media, and for almost all interaction with the government, more than 2.5 million people in Bolivia spoke a different language than Spanish as his/her main language (according to the 2012 Population Census). Most of these spoke either Quechua (1.4 million) or Aymara (0.9 million), but a minority spoke 63 other languages, including several foreign languages.

Out of curiosity, we made a map of the most spoken language in each of the 339 Bolivian municipalities, excluding Spanish (6). Impressively, 16 different languages appear on the map, including three foreign languages. Quechua is by far the dominant non-Spanish language, spoken in 177 municipalities. This is followed by Aymara in 111 municipalities, which are all grouped together in a tight cluster in the Bolivian Highlands. The third most spoken non-Spanish language at a municipal level is Portuguese, dominating 19 municipalities along the border with Brazil. The fourth is Guaraní, which is spoken in 12 municipalities in the Chaco region close to Paraguay. The fifth is German, dominating in 6 municipalities, but it is a particular dialect spoken by a large number of Mennonites living in Bolivia. The sixth is Cavineño, dominating just three municipalities in the Bolivian lowlands. The remaining 11 languages are all concentrated in just one or two municipalities each.

Map 1: Most spoken language in each Bolivian municipality, excluding Spanish, 2012

Source: Authors’ elaboration based on the 2012 Population Census.

The municipality with the most different languages spoken every day is Santa Cruz de la Sierra, where at least 49 different languages are spoken fluently (main language). This is followed by La Paz with 43 different first languages and Cochabamba with 42. This is of course due to a large number of diverse people dwelling in the main cities of Bolivia.

Language diversity is great. The problem arises if some of these people do not speak the main, integrating language, since they will, to a large extent, be excluded from participation in public life, except at a very local level. People who do not speak Spanish in Bolivia will have great trouble in school, trouble receiving basic services from the government, and trouble obtaining information about what is happening in Bolivia and beyond.

Therefore, for the upcoming Municipal Atlas of the SDGs in Bolivia, we think it is important to include an indicator showing the percentage of the population (3 years or older) who do not speak Spanish (7). This is an indicator of inequality of opportunity, which is why we have included it under SDG target 10.2 which is about promoting social, economic and political inclusion.

In 2012, on average about 9.7% of the Bolivian population (aged 3+) did not speak Spanish, but there is substantial variation between municipalities, ranging from a few percent in the main cities to more than half in several municipalities in Cochabamba and Potosí (see Map 2).

Map 2: Percentage of population (aged 3+) who does not speak Spanish, 2012


Source:
Authors’ elaboration based on the 2012 Population Census.

Figure 1 shows a positive correlation between the share of people who do not speak Spanish in each municipality, and the level of Extreme Energy Poverty. While speaking Spanish does not assure low poverty, not speaking Spanish virtually guarantees very high levels of energy poverty.

 

Figure 1: Relationship between language exclusion and extreme energy poverty

Source: Authors’ elaboration based on information from the 2012 Population Census and Andersen, Branisa and Calderón (2019).

 

While speaking Spanish is important for integration into national processes, speaking English facilitates representation and voice in global institutions (target 10.6) as well as collaboration on and access to global science, technology and innovation (target 17.6). We, therefore, suggest to include the percentage of the population aged 18 or more who speak English as another indicator in the Municipal Atlas of the SDGs in Bolivia. As shown in Figure 2, this indicator is inversely related to Extreme Energy Poverty at the municipal level.

Figure 2: Relationship between foreign language skills and Extreme Energy Poverty

Source: Authors’ elaboration based on information from the 2012 Population Census and Andersen, Branisa and Calderón (2019).

 

Another language indicator that is frequently used as an explanatory variable in poverty studies in Bolivia is mother tongue (language in which you learned to speak). This is often used as a proxy for “indigenousness”, but it clearly covers many more aspects than the simple ability to communicate (e.g. culture and location). We feel that the two indicators proposed above (Percentage of population who does not speak Spanish, and Share of adult population who speaks English) constitute more precise indicators of exclusion and inclusion, with a clear path of causality.

Notes

(1) See Ethnologue

(2) Aymara, Araona, Bésiro, Cavineño, Chimán, Mojeño-Trinitario, Mojeño-Ignaciano, Mosetén, Sirionó, Tacana, Yaminawa, Yuki and Yurakaré.

(3) Baure, Canichana, Cayubaba, Itonama, Leco, Machajuyai-Kallawaya, Machineri, Maropa, Movima, Pacawara, Tapiete and Toromona.

(4) Moré, Uru-Puquina.

(5) Guarasu’we (presumed dead).

(6) We used data from the 2012 Population Census to calculate the municipal level indicators presented in this article. Specifically, people are asked what languages they speak, and we use their first answer to determine the language they speak in on a daily basis.

(7) To determine whether people speak Spanish, we use not only the language in which they learned to speak, but also the first two languages they mention that they speak. This interpretation is generous, and does not necessarily mean that people can write an essay without errors, or interpret a complex text, but just that they can probably make themselves understood when interacting with doctors, teachers, bureaucrats and other people you need to communicate with to obtain public services.

————————————————————————–

 

* Lykke E. Andersen, Ph.D., Executive Director, SDSN Bolivia.

** Lily Peñaranda, M.Sc., Chief Development Manager, SDSN Bolivia.

The viewpoints expressed in the blog are the responsibility of the authors and do not necessarily reflect the position of their institutions. These posts are part of the project “Municipal Atlas of the SDGs in Bolivia” that is currently carried out by the Sustainable Development Solutions Network (SDSN) in Bolivia.