# data[].attributes.body
COVID

# data[].attributes.body
Nisrine Benaboura
Ferdaous Benaboura

# data[].attributes.headline
Óscar Valdivia Pla e Iñaki Gabriel Pérez González

# data[].attributes.subject
Óscar Valdivia Pla e Iñaki Gabriel Pérez González

# data[].attributes.body
https://ourworldindata.org/grapher/human-rights-violations?country=CHN~FIN~MMR~IND~EGY~CAN

# data[].attributes.attachment_link.description
Human rights violations measured on a scoring system from 0 to 10 (where 10 is worst). This is an aggregated metric on that basis of multiple variables, including: press freedom, civil liberties, political freedoms, human trafficking, political prisoners, incarceration, religious persecution, torture and executions.

# data[].attributes.body
https://ourworldindata.org/grapher/suicide-vs-violent-deaths?country=~OWID_WRL

# data[].attributes.attachment_link.title
Suicide rate vs. death rate from violence (per 100,000)

# data[].attributes.attachment_link.description
Death rate from suicide versus death rate from violence or murder, measured per 100,000 individuals. Deaths from violence include the categories of interpersonal violence (homicide), conflict and terrorism, and police conflict and executions. A line of parity (where suicide rate equals violent deaths rate) is also shown.

# data[].attributes.headline
Parte de la población con tanstorno de uso de drogas

# data[].attributes.subject
Parte de la población con tanstorno de uso de drogas

# data[].attributes.body
https://ourworldindata.org/grapher/share-with-drug-use-disorders

# data[].attributes.attachment_link.readable_text
Drug dependence is defined by the International Classification of Diseases as the presence of three or more indicators of dependence for at least a month within the previous year. Drug dependency includes all illicit drugs.

Interactive visualization requires JavaScript

# data[].attributes.attachment_link.title
Share of population with drug use disorders

# data[].attributes.attachment_link.description
Drug dependence is defined by the International Classification of Diseases as the presence of three or more indicators of dependence for at least a month within the previous year. Drug dependency includes all illicit drugs. Interactive visualization requires JavaScript

# data[].attributes.headline
Gráfica de muertes por guerras y conflictos del mundo desde 1946

# data[].attributes.subject
Gráfica de muertes por guerras y conflictos del mundo desde 1946

# data[].attributes.body
https://ourworldindata.org/grapher/battle-related-deaths-in-state-based-conflicts-since-1946-by-world-region

# data[].attributes.attachment_link.description
Energy intensity is measured as primary energy consumption per unit of gross domestic product. This is measured in kilowatt-hours per 2011$ (PPP).

# data[].attributes.headline
Acceso a la salud (algunos países) (chart)

# data[].attributes.subject
Acceso a la salud (algunos países) (chart)

# data[].attributes.body
https://ourworldindata.org/grapher/healthcare-access-and-quality-index?tab=chart&time=earliest..2015&country=ESP~SDN~SYR~GBR~ZWE~VEN~VUT~NPL~MEX~IND~LUX~MLT~KAZ~JPN~ITA~JAM~GIN~GTM~ETH~DNK~CZE~COL

# data[].attributes.attachment_link.description
The Healthcare Access and Quality (HAQ) Index is measured on a scale from 0 (worst) to 100 (best) based on death rates from 32 causes of death that could be avoided by timely and effective medical care (also known as 'amenable mortality').

# data[].attributes.headline
La gráfica de abajo, el periodo de tiempo es de 1980 a 2007 e incluye países de todo el planeta

# data[].attributes.subject
La gráfica de abajo, el periodo de tiempo es de 1980 a 2007 e incluye países de todo el planeta

# data[].attributes.body
https://ourworldindata.org/grapher/military-expenditure-by-country-in-thousands-of-2000-us-dollars?time=1980..latest&country=AFG~ALB~AGO~AUS~AZE~BLR~BEL~BOL~TCD~CHL~CHN~CAN~BRA~COG~CIV~HRV~CUB~DEU~EGY~FIN~FRA~GRC~IND~IRN~IRQ~ISR~ITA~JPN~LAO~MEX~NPL~NGA~PRK~POL~RUS~SAU~SEN~ESP~GBR~USA~VNM

# data[].attributes.body
https://ourworldindata.org/grapher/literacy-rate-by-country

# data[].attributes.attachment_link.description
Literacy rate for the entire population, 2011 or latest data from CIA Factbook.

# data[].attributes.attachment_link.readable_text
The world has seen dramatic progress in poverty alleviation over the last few centuries, but particularly in very recent decades. In the 1800s almost everyone lived in extreme poverty. By 2000 this had fallen to just under one-third of the world population (29 percent). By 2015, this had fallen to one-in-ten people globally.

This chart can also be viewed as the absolute number of people living in extreme poverty by unticking the 'relative' box in the bottom-left of the chart.

The chart below shows the projected number of people in extreme poverty under the baseline (business-as-usual) scenario. This is based on the study presented in Nature by Crespo-Cuaresma et al. (2018). This work is also monitored/presented visually at the World Poverty Clock.

These estimates are presented from 2015 through to the end of the SDG period (2030/31). Clicking on any country will provide a time-series view of its projected trend over this period.

By 2030 it's projected that the largest number of people in extreme poverty will be in Nigeria, at nearly 100 million. Second highest is the Democratic Republic of Congo at 60 million.

In the chart below this data is also shown as the share of the population living in extreme poverty in the baseline (business-as-usual) scenario. This is derived using the above data on the number of people in extreme poverty and UN Population Prospects (2017) medium fertility projections.

Using the 'play' button in the timeline below we can see how this is projected to change from 2015 to 2030. By clicking on a country, you can view its time series over this period. In the 'chart' tab below you can also view the time-series for the OECD's Fragile States categories, or by region.

In the chart below we see the business-as-usual projections of extreme poverty aggregated by region. In the bottom-left of the chart you see a 'relative' tick-box; this will switch these figures to show their relative share of the total.

There are multiple key points here:

1. By 2030, it's projected that 446 million people will still live in extreme poverty. This is still a significant reduction from the 729 million in 2015 (approximately a 40 percent reduction).

2. The total number of people in extreme poverty is expected to decline significantly in Asia (by approximately 85 percent from 292 million down to 38 million).

3. The number of people in extreme poverty in Africa declines only slightly from 401 million in 2015 to 376 million in 2030 (a 6 percent reduction).

4. Most people in world living in extreme poverty will be in Africa. In 2015 Africa accounted for 55 percent of the global total; by 2030 this is expected to rise to 84 percent.

In the chart below we also show the business-as-usual projections of extreme poverty aggregated by the OECD States of Fragility 2018 assessment. Here you can view it in absolute numbers of people in extreme poverty, but also switch to 'relative' view by ticking the box in the bottom-left of the chart; this shows the percentage of people in extreme poverty who live in fragile states.

It's projected that by 2030, around 89 percent of people still living in extreme poverty will live in fragile (57 percent) or extremely fragile (32 percent) states.

The figures presented above are based on the baseline (business-as-usual) scenario. The chart below presents the results of other modelled scenarios. Crespo-Cuaresma et al. (2018) modelled multiple scenarios of extreme poverty based on the commonly used Shared Socioeconomic Pathways (SSP).

The projections of extreme poverty for 2030 under the five SSP scenarios are shown in the chart below. Using the "change country or region" button on the bottom-left of the chart you can view this data by country or region. Two scenarios showed higher global poverty numbers than the baseline:

1. a scenario based on low economic growth, educational attainment, high fertility rates and major climate change mitigation and adaptation challenges (this is shown in the next section);

2. a scenario based on worldwide polarization with high income countries exhibiting relatively high growth rates of income, while developing economies present low levels of education, high fertility and economic stagnation.

Two scenarios produced lower global numbers of extreme poverty:

1. a scenario based on low challenges for both climate change adaptation and mitigation resulting from income growth which does not rely heavily on natural resources and technological change, coupled with low fertility rate and high educational attainment.

2. a scenario with high economic growth (and therefore low adaptation challenges) coupled with high demand for fossil energy from developing economies, but with high global CO₂ emissions.

The future socioeconomic scenario (SSP3) which results in the greatest number of people in extreme poverty is one characterized by low economic growth coupled with low educational attainment levels and high population growth at the global level, which is exacerbated by high climate change mitigation and adaptation challenges.

In the chart below we present the estimated difference in the number of people in extreme poverty in SSP3 versus the baseline scenario. Here we see that for some countries - predominantly richer countries across Europe, North America and Oceania - this scenario would result in a decline of poverty headcounts. The magnitude of this decline is however several orders of magnitude lower than the increased poverty across most of the world's countries. This is particularly true of some of the world's most fragile countries including the Democratic Republic of Congo, Tanzania, Nigeria, Niger, and Afghanistan.

Overall, it's projected that this scenario of heightened climate vulnerability (combined with high fertility, low educational attainment and growth) would result in an additional 60 million people in extreme poverty in 2030.

The other scenario which results in higher numbers of extreme poverty relative to the baseline scenario is one of worldwide polarization. This is based on an assumption of high economic growth rates in today's high-income countries with low levels of education, high fertility and economic stagnation in low-to-middle income countries. We may envisage this scenario could evolve from domestic protectionism, reduced trade and lower international investment.

This would result in reductions in extreme poverty across most high-income countries (including South Africa, Brazil and Latin America). However, poverty numbers increase across today's lowest income and and fragile states, including the Democratic Republic of Congo, Nigeria, Tanzania, Niger, Uganda and Afghanistan.

Overall, it's projected that this scenario would result in an additional 50 million people in extreme poverty in 2030.

Does extreme poverty, as measured by the international poverty line of $1.90 per person per day provide a good indication of deprivation?

In the chart below we have plotted the poverty rate as defined by the international poverty line (y-axis) versus the poverty share as measured by multidimensional poverty. The Multidimensional Poverty Index (MPI), is derived by weighing ten indicators of deprivation in the context of education, health and living standards. Individuals are considered poor if deprived in at least one third of the weighted indicators.

Here we notice two key points:

1. there is an overall positive correlation that countries with higher extreme poverty rates based on income also have higher rates of multidimensional poverty.

2. there can be large differences in extreme poverty and multidimensional poverty rates. The grey line below is a line of parity; countries which lie above the line have higher rates of 'poverty' by the extreme poverty income measure; countries below the line have higher rates of 'poverty' by the multidimensional poverty measure. Here we see that most (but not all) countries have higher measures of poverty by the multidimensional metric; in some countries this can be many times higher than the international poverty line would suggest (although admittedly the extreme poverty line is acknowledged to be set at a very low level of income).

In the chart below we have visualized the latest statistics on undernourishment from the UN FAO. By region and at the global level, these estimates are available for the year 2017. At national levels, this extends only to the year 2016. National-level data is reported as "3-year averages"; for visualization we have taken the mid-year of these periods (e.g. "2014-2016" we have allocated to "2015").

In the chart below we show the data of some key regions (these can be added using the " Add country /region" button in bottom-left) in addition to the aggregate groupings of the OECD States of Fragility 2018 list. A map and timeline of national data can also be seen using the "map" tab below.

This data is shown as the share of people who are undernourished (top chart) and the total number (bottom chart). Here we see that despite overall progress in many countries over this period, there has been a global increase over the past 3 years. This increase has been dominated by a rise in undernourishment in Sub-Saharan Africa. In its 2017 and 2018 reports, The State of Food Security and Nutrition in the World, the UN FAO has suggested that both a rise in conflict and political instability, combined with the climatic impacts of the El Nino contributed to this increase.,

The chart below shows country programmable aid (CPA) received from all donors. The chart is based on data supplied by the OECD, extracted from the OECD Statistics on Development and official development assistance (ODA). The measure - Country Programmable Aid - is the portion of aid that donors programme at country or regional level (detailed explanation available at the link: https://data.oecd.org/oda/country-programmable-aid-cpa.htm). Data from 2000-2016 is based on estimated figures; those from 2017-2019 are projections based on countries' Forward Spending Plans.

Clicking on a given country shows its time series over time.

The charts below presents the data on humanitarian and food aid received from 2002 to 2016.

Clicking on a given country shows its time series over time.

In the chart below we show the total aid received directly (the sum of CPA, humanitarian and food aid) aggregated by the OECD States of Fragility 2018 groupings. This is shown from 2000 to 2016. By clicking on the 'Relative' box, this chart is displayed as the share of total aid received directly by each group.

In the charts below we have calculated total aid received (the sum of CPA, humanitarian and food aid) per capita, by combining OECD aid data provided with UN World Population Prospects (2017) estimates of population. We have also calculated this on a regional basis, for OECD fragility groupings, Least Developed countries (LDCs) and Small Island Developing States (SIDS). This is shown in map form with a timeline from 2000 to 2016 (and extension to 2019 based on Forward Spending Plans). Clicking on a country shows its time series form.

Alternatively time series are available by clicking the 'Chart' tab (or as shown in the second chart below).

In the third chart below we have created a scatterplot of aid received per capita versus GDP per capita. Both axes are currently on a log scale, but both (or one) can be changed to linear simply by clicking the 'Log' dial. The size of the bubble indicates the estimated number of people in extreme poverty (as defined by the international poverty line of $1.90 per person per day). Bubbles are coloured by World Bank income group; hovering over any of the income groups in the legend highlights all relevant countries in the chart. The timeline function is available from 2000 to 2016 at the bottom of the chart.

Countries defined as under-aided by the OECD are those which are are "underfunded" by need relative to others. This is assessed on the basis of multiple aid allocation models: the Egalitarian, Performance-based allocation (PBA), UNDP, and Collier-Dollar poverty allocation (CD) model. The top under-aided countries in a given year are defined here as those identified as underfunded on the basis of 3 or more of the allocation models.

In the charts below we show the top under-aided countries in any given year from 2006 to 2016 (this can be seen through time using the timeline in the first chart below). The second chart below shows the number of years a country has been identified as under-aided (according to at least 3 of the aid allocation models) over the period from 2006 to 2016. In 2016 all under-aided countries were in Africa, all are Least Developed Countries (LDCs), 1 is a Small Island Developing State (SIDS), 1 extremely fragile, 3 fragile, 1 not defined as fragile. Note that since all under-aided countries are in Africa, this map can be zoomed to show Africa only using the dial/cog button in the bottom right of the map chart.

# data[].attributes.attachment_link.title
OECD Collaboration on 'Joining Forces to Leave No One Behind'

# data[].attributes.attachment_link.description
The world has seen dramatic progress in poverty alleviation over the last few centuries, but particularly in very recent decades. In the 1800s almost everyone lived in extreme poverty. By 2000 this had fallen to just under one-third of the world population (29 percent). By 2015, this had fallen to one-in-ten people globally.

# data[].attributes.body
https://ourworldindata.org/grapher/existence-of-nationally-representative-learning-assessment-at-the-end-of-primary-education

LO APRUEBO, PERO NO SE QUE SIGNIFICA. SI NO SABEIS EXPLICARLO, LO ELIMINAMOS LA PROXIMA SEMANA

# data[].attributes.attachment_link.title
Existence of nationally representative learning assessment at the end of primary education

# data[].attributes.attachment_link.description
Binary variable denoting whether there is a nationally representative learning assessment at the end of primary education, as per most recent survey in the period 2010-2015

# data[].attributes.attachment_link.readable_text
The world has seen dramatic progress in poverty alleviation over the last few centuries, but particularly in very recent decades. In the 1800s almost everyone lived in extreme poverty. By 2000 this had fallen to just under one-third of the world population (29 percent). By 2015, this had fallen to one-in-ten people globally.

This chart can also be viewed as the absolute number of people living in extreme poverty by unticking the 'relative' box in the bottom-left of the chart.

The chart below shows the projected number of people in extreme poverty under the baseline (business-as-usual) scenario. This is based on the study presented in Nature by Crespo-Cuaresma et al. (2018). This work is also monitored/presented visually at the World Poverty Clock.

These estimates are presented from 2015 through to the end of the SDG period (2030/31). Clicking on any country will provide a time-series view of its projected trend over this period.

By 2030 it's projected that the largest number of people in extreme poverty will be in Nigeria, at nearly 100 million. Second highest is the Democratic Republic of Congo at 60 million.

In the chart below this data is also shown as the share of the population living in extreme poverty in the baseline (business-as-usual) scenario. This is derived using the above data on the number of people in extreme poverty and UN Population Prospects (2017) medium fertility projections.

Using the 'play' button in the timeline below we can see how this is projected to change from 2015 to 2030. By clicking on a country, you can view its time series over this period. In the 'chart' tab below you can also view the time-series for the OECD's Fragile States categories, or by region.

In the chart below we see the business-as-usual projections of extreme poverty aggregated by region. In the bottom-left of the chart you see a 'relative' tick-box; this will switch these figures to show their relative share of the total.

There are multiple key points here:

1. By 2030, it's projected that 446 million people will still live in extreme poverty. This is still a significant reduction from the 729 million in 2015 (approximately a 40 percent reduction).

2. The total number of people in extreme poverty is expected to decline significantly in Asia (by approximately 85 percent from 292 million down to 38 million).

3. The number of people in extreme poverty in Africa declines only slightly from 401 million in 2015 to 376 million in 2030 (a 6 percent reduction).

4. Most people in world living in extreme poverty will be in Africa. In 2015 Africa accounted for 55 percent of the global total; by 2030 this is expected to rise to 84 percent.

In the chart below we also show the business-as-usual projections of extreme poverty aggregated by the OECD States of Fragility 2018 assessment. Here you can view it in absolute numbers of people in extreme poverty, but also switch to 'relative' view by ticking the box in the bottom-left of the chart; this shows the percentage of people in extreme poverty who live in fragile states.

It's projected that by 2030, around 89 percent of people still living in extreme poverty will live in fragile (57 percent) or extremely fragile (32 percent) states.

The figures presented above are based on the baseline (business-as-usual) scenario. The chart below presents the results of other modelled scenarios. Crespo-Cuaresma et al. (2018) modelled multiple scenarios of extreme poverty based on the commonly used Shared Socioeconomic Pathways (SSP).

The projections of extreme poverty for 2030 under the five SSP scenarios are shown in the chart below. Using the "change country or region" button on the bottom-left of the chart you can view this data by country or region. Two scenarios showed higher global poverty numbers than the baseline:

1. a scenario based on low economic growth, educational attainment, high fertility rates and major climate change mitigation and adaptation challenges (this is shown in the next section);

2. a scenario based on worldwide polarization with high income countries exhibiting relatively high growth rates of income, while developing economies present low levels of education, high fertility and economic stagnation.

Two scenarios produced lower global numbers of extreme poverty:

1. a scenario based on low challenges for both climate change adaptation and mitigation resulting from income growth which does not rely heavily on natural resources and technological change, coupled with low fertility rate and high educational attainment.

2. a scenario with high economic growth (and therefore low adaptation challenges) coupled with high demand for fossil energy from developing economies, but with high global CO₂ emissions.

The future socioeconomic scenario (SSP3) which results in the greatest number of people in extreme poverty is one characterized by low economic growth coupled with low educational attainment levels and high population growth at the global level, which is exacerbated by high climate change mitigation and adaptation challenges.

In the chart below we present the estimated difference in the number of people in extreme poverty in SSP3 versus the baseline scenario. Here we see that for some countries - predominantly richer countries across Europe, North America and Oceania - this scenario would result in a decline of poverty headcounts. The magnitude of this decline is however several orders of magnitude lower than the increased poverty across most of the world's countries. This is particularly true of some of the world's most fragile countries including the Democratic Republic of Congo, Tanzania, Nigeria, Niger, and Afghanistan.

Overall, it's projected that this scenario of heightened climate vulnerability (combined with high fertility, low educational attainment and growth) would result in an additional 60 million people in extreme poverty in 2030.

The other scenario which results in higher numbers of extreme poverty relative to the baseline scenario is one of worldwide polarization. This is based on an assumption of high economic growth rates in today's high-income countries with low levels of education, high fertility and economic stagnation in low-to-middle income countries. We may envisage this scenario could evolve from domestic protectionism, reduced trade and lower international investment.

This would result in reductions in extreme poverty across most high-income countries (including South Africa, Brazil and Latin America). However, poverty numbers increase across today's lowest income and and fragile states, including the Democratic Republic of Congo, Nigeria, Tanzania, Niger, Uganda and Afghanistan.

Overall, it's projected that this scenario would result in an additional 50 million people in extreme poverty in 2030.

Does extreme poverty, as measured by the international poverty line of $1.90 per person per day provide a good indication of deprivation?

In the chart below we have plotted the poverty rate as defined by the international poverty line (y-axis) versus the poverty share as measured by multidimensional poverty. The Multidimensional Poverty Index (MPI), is derived by weighing ten indicators of deprivation in the context of education, health and living standards. Individuals are considered poor if deprived in at least one third of the weighted indicators.

Here we notice two key points:

1. there is an overall positive correlation that countries with higher extreme poverty rates based on income also have higher rates of multidimensional poverty.

2. there can be large differences in extreme poverty and multidimensional poverty rates. The grey line below is a line of parity; countries which lie above the line have higher rates of 'poverty' by the extreme poverty income measure; countries below the line have higher rates of 'poverty' by the multidimensional poverty measure. Here we see that most (but not all) countries have higher measures of poverty by the multidimensional metric; in some countries this can be many times higher than the international poverty line would suggest (although admittedly the extreme poverty line is acknowledged to be set at a very low level of income).

In the chart below we have visualized the latest statistics on undernourishment from the UN FAO. By region and at the global level, these estimates are available for the year 2017. At national levels, this extends only to the year 2016. National-level data is reported as "3-year averages"; for visualization we have taken the mid-year of these periods (e.g. "2014-2016" we have allocated to "2015").

In the chart below we show the data of some key regions (these can be added using the " Add country /region" button in bottom-left) in addition to the aggregate groupings of the OECD States of Fragility 2018 list. A map and timeline of national data can also be seen using the "map" tab below.

This data is shown as the share of people who are undernourished (top chart) and the total number (bottom chart). Here we see that despite overall progress in many countries over this period, there has been a global increase over the past 3 years. This increase has been dominated by a rise in undernourishment in Sub-Saharan Africa. In its 2017 and 2018 reports, The State of Food Security and Nutrition in the World, the UN FAO has suggested that both a rise in conflict and political instability, combined with the climatic impacts of the El Nino contributed to this increase.,

The chart below shows country programmable aid (CPA) received from all donors. The chart is based on data supplied by the OECD, extracted from the OECD Statistics on Development and official development assistance (ODA). The measure - Country Programmable Aid - is the portion of aid that donors programme at country or regional level (detailed explanation available at the link: https://data.oecd.org/oda/country-programmable-aid-cpa.htm). Data from 2000-2016 is based on estimated figures; those from 2017-2019 are projections based on countries' Forward Spending Plans.

Clicking on a given country shows its time series over time.

The charts below presents the data on humanitarian and food aid received from 2002 to 2016.

Clicking on a given country shows its time series over time.

In the chart below we show the total aid received directly (the sum of CPA, humanitarian and food aid) aggregated by the OECD States of Fragility 2018 groupings. This is shown from 2000 to 2016. By clicking on the 'Relative' box, this chart is displayed as the share of total aid received directly by each group.

In the charts below we have calculated total aid received (the sum of CPA, humanitarian and food aid) per capita, by combining OECD aid data provided with UN World Population Prospects (2017) estimates of population. We have also calculated this on a regional basis, for OECD fragility groupings, Least Developed countries (LDCs) and Small Island Developing States (SIDS). This is shown in map form with a timeline from 2000 to 2016 (and extension to 2019 based on Forward Spending Plans). Clicking on a country shows its time series form.

Alternatively time series are available by clicking the 'Chart' tab (or as shown in the second chart below).

In the third chart below we have created a scatterplot of aid received per capita versus GDP per capita. Both axes are currently on a log scale, but both (or one) can be changed to linear simply by clicking the 'Log' dial. The size of the bubble indicates the estimated number of people in extreme poverty (as defined by the international poverty line of $1.90 per person per day). Bubbles are coloured by World Bank income group; hovering over any of the income groups in the legend highlights all relevant countries in the chart. The timeline function is available from 2000 to 2016 at the bottom of the chart.

Countries defined as under-aided by the OECD are those which are are "underfunded" by need relative to others. This is assessed on the basis of multiple aid allocation models: the Egalitarian, Performance-based allocation (PBA), UNDP, and Collier-Dollar poverty allocation (CD) model. The top under-aided countries in a given year are defined here as those identified as underfunded on the basis of 3 or more of the allocation models.

In the charts below we show the top under-aided countries in any given year from 2006 to 2016 (this can be seen through time using the timeline in the first chart below). The second chart below shows the number of years a country has been identified as under-aided (according to at least 3 of the aid allocation models) over the period from 2006 to 2016. In 2016 all under-aided countries were in Africa, all are Least Developed Countries (LDCs), 1 is a Small Island Developing State (SIDS), 1 extremely fragile, 3 fragile, 1 not defined as fragile. Note that since all under-aided countries are in Africa, this map can be zoomed to show Africa only using the dial/cog button in the bottom right of the map chart.

# data[].attributes.attachment_link.title
OECD Collaboration on 'Joining Forces to Leave No One Behind'

# data[].attributes.attachment_link.description
The world has seen dramatic progress in poverty alleviation over the last few centuries, but particularly in very recent decades. In the 1800s almost everyone lived in extreme poverty. By 2000 this had fallen to just under one-third of the world population (29 percent). By 2015, this had fallen to one-in-ten people globally.

# data[].attributes.attachment_link.title
Projected number of people in extreme poverty

# data[].attributes.attachment_link.description
Number of people projected to be living in extreme poverty through to 2030. Extreme poverty is defined by the international poverty line of $1.90 per day (2011, PPP-adjusted). These projections are based on a business-as-usual scenario of recent socioeconomic trends and medium future population scenarios.

# data[].attributes.attachment_link.title
Share of crimes which are alcohol-related

# data[].attributes.attachment_link.description
This includes two groups of criminal offenses: First, offenses in which the alcohol consumption is part of the crime such as driving with excess alcohol, liquor license violations, and drunkenness offenses. And second, all those crimes in which the consumption of alcohol is thought to have played a role of some kind in the committing of the offense - including assault, criminal damage, and other public order offenses.

# data[].attributes.attachment_link.readable_text
What is the daily number of confirmed deaths?

This chart shows the number of confirmed COVID-19 deaths per day.

Three points on confirmed death figures to keep in mind

All three points are true for all currently available international data sources on COVID-19 deaths:

The actual death toll from COVID-19 is likely to be higher than the number of confirmed deaths - this is due to limited testing and problems in the attribution of the cause of death. The difference between reported confirmed deaths and actual deaths varies by country.

How COVID-19 deaths are recorded may differ between countries (e.g., some countries may only count hospital deaths, whilst others also include deaths in homes).

The death figures on a given date do not necessarily show the number of new deaths on that day, but the deaths reported on that day. Since reporting can vary significantly from day to day - irrespectively of any actual variation of deaths - it is helpful to view the seven-day rolling average of the daily figures as we do in the chart here.

→ We provide more detail on these three points in the section ' Deaths from COVID-19: background '.

Three tips on how you can interact with this chart

Daily confirmed deaths per million people

Why adjust for the size of the population?

Differences in the population size between countries are often large, and the COVID-19 death count in more populous countries tends to be higher. Because of this it can be insightful to know how the number of confirmed deaths in a country compares to the number of people who live there, especially when comparing across countries.

For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the United States, with its population of 331 million. This difference in impact is clear when comparing deaths per million people of each country's population - in this example it would be roughly 3 deaths/million people in the US compared to a staggering 2,941 deaths/million people in Iceland.

Three tips on how to interact with this map

What is the cumulative number of confirmed deaths?

The previous charts looked at the number of confirmed deaths per day - this chart shows the cumulative number of confirmed deaths since the beginning of the COVID-19 pandemic.

Another tip on how you can interact with this chart

Cumulative confirmed deaths per million people

This chart shows the cumulative number of confirmed deaths per million people.

Weekly and biweekly deaths: where are confirmed deaths increasing or falling?

Why is it useful to look at weekly or biweekly changes in deaths?

For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day - but to the deaths reported on that day.

Since reporting can vary significantly from day to day - irrespectively of any actual variation of deaths - it is helpful to look at changes from week to week. This provides a slightly clearer picture of where the pandemic is accelerating, slowing, or in fact reducing.

The maps shown here provide figures on weekly and biweekly deaths: one set shows the number of deaths per million people in the previous seven or fourteen days (the weekly or biweekly cumulative total); the other set shows the percentage change (growth rate) over these periods.

Global comparison: where are confirmed deaths increasing most rapidly?

Simply looking at the cumulative total or daily number of confirmed deaths does not allow us to understand or compare the speed at which these figures are rising.

The table here shows how long it has taken for the number of confirmed deaths to double in each country for which we have data. The table also shows both the cumulative total and daily new number of confirmed deaths, and how those numbers have changed over the last 14 days.

A tip on how to interact with this table

Confirmed deaths and cases: our data source

In this document, the many linked charts, our COVID-19 Data Explorer, and the Complete COVID-19 dataset we report and visualize the data on confirmed cases and deaths from Johns Hopkins University (JHU). We make the data used in our charts and tables downloadable as a complete and structured .csv, .xlsx and json file here on our GitHub site.

The Johns Hopkins University dashboard and dataset is maintained by a team at its Center for Systems Science and Engineering (CSSE). It has been publishing updates on confirmed cases and deaths for all countries since January 22, 2020. A feature on the JHU dashboard and dataset was published in The Lancet in early May 2020. This has allowed millions of people across the world to track the course and evolution of the pandemic.

JHU updates its data multiple times each day. This data is sourced from governments, national and subnational agencies across the world - a full list of data sources for each country is published on Johns Hopkins's GitHub site. It also makes its data publicly available there.

The attribution of deaths to specific causes can be challenging under any circumstances. Health problems are often connected, and multiplicative, meaning an underlying condition can often lead to complications which ultimately result in death.

This is also true in the case of COVID-19: the disease can lead to other health problems such as pneumonia and acute respiratory distress syndrome (ARDS).

So, how are deaths from COVID-19 recorded? What is and isn't included in these totals?

As is standard in death reporting, countries are asked to follow the 'cause of death' classifications from the WHO's International Classification of Diseases guidelines. However, countries also typically provide their own guidance to practitioners on how and when COVID-19 deaths should be recorded.

Let's take a look at two concrete examples of national guidance: the United States and the UK. Both provide very similar guidelines for medical practitioners on the completion of death certificates. Here is the US CDC's Vital Statistics Reporting Guidance; here is the UK Government guidance.

Both guidelines state that if the practitioner suspects that COVID-19 played a role in an individual's death it should be specified on the death certificate. In some cases, COVID-19 may be the underlying cause of death, having led to complications such as pneumonia or ARDS. Even when it's the underlying and not the direct cause, COVID-19 should be listed.

Although confirmed cases are reliant on a positive laboratory confirmation of the COVID-19 test, a laboratory diagnosis may not be required for it to be listed as the cause of death. In the UK guidelines, for example, it makes clear that practitioners should complete death certificates to the best of their knowledge, stating that "if before death the patient had symptoms typical of COVID-19 infection, but the test result has not been received, it would be satisfactory to give 'COVID-19' as the cause of death, and then share the test result when it becomes available. In the circumstances of there being no swab, it is satisfactory to apply clinical judgement."

This means a positive COVID-19 test result is not required for a death to be registered as COVID-19. In some circumstances, depending on national guidelines, medical practitioners can record COVID-19 deaths if they think the signs and symptoms point towards this as the underlying cause.

The US CDC guidelines also make this clear with an example: the death of an 86-year-old female with an unconfirmed case of COVID-19. It was reported that the woman had typical COVID-19 symptoms five days prior to suffering an ischemic stroke at home. Despite not being tested for COVID-19, the coroner determined that the likely underlying cause of death was COVID-19 given her symptoms and exposure to an infected individual.

Just as with confirmed cases, the number of deaths reported on a given day does not necessarily reflect the actual number of COVID-19 deaths on that day, or in the previous 24 hours. This is due to lags and delays in reporting.

Delays can occur for several reasons:

After a death certificate has been completed, inspection by post-mortem or laboratory testing may be required to verify the cause of death.

Death certificates are then either automatically or manually coded. It is often the case that COVID-19 deaths are always manually coded (e.g., this is the case in the USA).

There can be significant delays in this coding process, particularly when there is a large increase in the number of deaths (e.g., this averages 7 days in the US).

These figures are then collected in national registration statistics and reported to international sources.

The delay in reporting can be on the order of days and sometimes as long as a week or more. This means the number of deaths reported on a given day is not reflective of the actual number of deaths that occurred on that day.

What we know is the number of confirmed deaths due to COVID-19 to date. Limited testing and challenges in the attribution of the cause of death means that the number of confirmed deaths may not be an accurate count of the actual number of deaths from COVID-19.

In an ongoing outbreak the final outcomes - death or recovery - for all cases is not yet known. The time from symptom onset to death ranges from 2 to 8 weeks for COVID-19.This means that some people who are currently infected with COVID-19 will die at a later date. This needs to be kept in mind when comparing the current number of deaths with the current number of cases.

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 - the likelihood that someone who is infected with the disease will die from it.

We look into this question in more detail on our page about the mortality risk of COVID-19.

We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.

And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.

# data[].attributes.attachment_link.description
This chart shows the number of confirmed COVID-19 deaths per day. Three points on confirmed death figures to keep in mind All three points are true for all currently available international data sources on COVID-19 deaths: The actual death toll from COVID-19 is likely to be higher than the number of confirmed deaths - this is due to limited testing and problems in the attribution of the cause of death.

# data[].attributes.attachment_link.description
Our vaccination dataset uses the most recent official numbers from governments and health ministries worldwide. Population estimates for per-capita metrics are based on the United Nations World Population Prospects. Income groups are based on the World Bank classification. A full list of our country-specific sources is available at the bottom of this page, and we also answer frequently-asked questions there.

# data[].attributes.attachment_link.readable_text
Our interactive data visualizations that show the case fatality rate in each country are updated daily.

The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available.

The case fatality rate (CFR) is simply the number of confirmed deaths divided by the number of confirmed cases.

This chart here plots the CFR calculated in this way.

When the number of actual cases and deaths is not known - as is the case for COVID - one has to be careful in interpreting the CFR. In the sections that follow we explain what we can and cannot learn about the mortality risk based on the CFR.

Understanding the case fatality, crude mortality, and the infection fatality rate

In the media, it is often the "case fatality rate" that is talked about when the risk of death from COVID-19 is discussed.¹

This measure is sometimes also called case fatality risk or case fatality ratio. It is often abbreviated as CFR.

The CFR is not the same as the risk of death for an infected person - even though, unfortunately, journalists sometimes suggest that it is. It is relevant and important, but far from the whole story.

The CFR is easy to calculate. You take the number of people who have died from the disease, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%.

But it's important to note that it is the ratio between the number of confirmed deaths from the disease and the number of confirmed cases, not total cases. That means that it is not the same as - and, in fast-moving situations like COVID-19, probably not even very close to - the true risk for an infected person.

Another important metric, which should not be confused with the CFR, is the crude mortality rate.

The "crude mortality rate" is another very simple measure which, like the CFR, gives something that might sound like the answer to the question "if someone is infected, how likely are they to die?".

But, just as with CFR, it is actually very different.

The crude mortality rate - sometimes also called the crude death rate - measures the share among the entire population that have died from a particular disease. It's calculated by dividing the number of deaths from the disease by the total population. For instance, if there were 10 deaths in a population of 1,000, the crude mortality rate would be [10 / 1,000], or 1%.

Unfortunately, writers sometimes confuse case fatality rates and crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate for the death toll of the Spanish flu, by Johnson and Mueller (2002), is that the pandemic killed 50 million people.² That would have been 2.7% of the world population at the time. This means the crude mortality rate was 2.7%.

But 2.7% is often misreported as the case fatality rate - which is wrong, because not everyone in the world was infected with the virus that caused the Spanish flu. If the crude mortality rate really was 2.7%, then the case fatality rate was much higher - it would be the percentage of people who died after being diagnosed with the disease. [We looked at the global death count of the Spanish flu pandemic and others here.]

The key question for understanding the mortality risk of a disease is the following: if someone is infected with the disease how likely is it that they will die from it? The answer to that question is captured by the infection fatality rate, or IFR.

The IFR is the number of deaths from a disease divided by the total number of cases. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.³,⁴,⁵,⁶,⁷

To work out the IFR, we need two numbers: the total number of cases and the total number of deaths from the disease.

However, as we explain here, the total number of cases of COVID-19 is not known. One big reason for this is that not everyone with COVID-19 is tested.⁸,⁹

The total number of cases is not known, so the IFR cannot be simply calculated from observed data. But, researchers are able to estimate the total number of cases and use that to calculate the IFR - we get to this further below.

There is a straightforward question that most people would like answered. If someone is infected with COVID-19, how likely is that person to die?

This question is simple, but surprisingly hard to answer.

The key point is that the case fatality rate (CFR) - the most commonly discussed measure - is not the answer to the question.

The main reason why it does not answer that question is that the CFR relies on the number of confirmed cases, and many cases are not confirmed.

In order to understand what the case fatality rate can and cannot tell us about a disease outbreak such as COVID-19, it's important to understand why it is difficult to measure and interpret the numbers.

Sometimes commentators talk about the CFR as if it's a single, steady number: an unchanging fact about the disease.

But it's not a biological constant; instead, it reflects the situation in a particular context, at a particular time, in a particular population.

The probability that someone dies from a disease doesn't just depend on the disease itself, but also on the treatment they receive, and on the patient's own ability to recover from it.

This means that the CFR can decrease or increase over time, as responses change; and it can vary by location and by the characteristics of the infected population, such as age, or sex. For instance, older populations would expect to see a higher CFR from COVID-19 than younger ones.

The case fatality rate of COVID-19 is not constant. This was clear right from the start of the pandemic. You can see that in the chart below, first published in the Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), in February 2020.¹⁰

It shows the CFR for COVID-19 in several locations in China during the early stages of the outbreak, from the beginning of January to 20th February 2020.

You can see that in the earliest stages of the outbreak the CFR was much higher: 17.3% across China as a whole (in yellow) and greater than 20% in the center of the outbreak, in Wuhan (in blue).

But in the weeks that followed, the CFR declined, reaching as low as 0.7% for patients who first showed symptoms after February 1st. The WHO reported that that was because "the standard of care has evolved over the course of the outbreak".

You can also see that the CFR was different in different places. By 1st February, the CFR in Wuhan was still 5.8% while it was 0.7% across the rest of China.

This shows that what we said about the CFR generally - that it changes from time to time and place to place - is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it in a specific time and place - the CFR in Wuhan on 23rd February, or in Italy on 4th March - rather than as a single unchanging value.

Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 - Figure 4 in WHO (2020)¹¹

If the case fatality rate does not tell us the risk of death for someone infected with the disease, what does it tell us? And how does the CFR compare with the actual mortality risk?

There are two reasons why we would expect the CFR not to represent the real risk. One of them would tend to make the CFR an overestimate - the other would tend to make it an underestimate.
When there are people who have the disease but are not diagnosed, the CFR will overestimate the true risk of death. With COVID-19, we think there are many undiagnosed people.
As we saw above, in our discussion on the difference between total and confirmed cases (here), we do not know the number of total cases. Not everyone is tested for COVID-19, so the total number of cases is higher than the number of confirmed cases.

Whenever there are cases of the disease that are not counted, the probability of dying from the disease is lower than the reported case fatality rate. Remember our imaginary scenario with 10 deaths and 100 cases. The CFR in that example is 10% - but if there actually 500 cases (and we've simply missed 400 of them due to lack of testing), then the real risk (the IFR) is just 2%.
A second consideration is especially important in the early stages of an outbreak: When some people are currently sick and will die of the disease, but have not died yet, the CFR will underestimate the true risk of death.
Or to summarize in one sentence. If the number of total cases is higher than the number of confirmed cases, then the ratio between deaths and total cases is smaller than the ratio between deaths and confirmed cases.¹²

Importantly, this means that the number of tests carried out affects the CFR - you can only confirm a case by testing a patient. So when we compare the CFR between different countries, the differences do not only reflect rates of mortality, but also differences in the scale of testing efforts.

In ongoing outbreaks, people who are currently sick will eventually die from the disease. This means that they are currently counted as a case, but will only eventually be counted as a death too. With the COVID-19 outbreak, it can take several weeks for people to go from first symptoms to death.

This means that the CFR in the early stages is an underestimate of what it will be when the outbreak has run its course.

Case fatality rate of other diseases

Once again, we should stress what we discussed above. One has to understand the measurement challenges and the definitions to interpret estimates of the CFR for COVID-19, particularly those relating to an ongoing outbreak.

As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.¹³

The US seasonal flu has a case fatality rate of approximately 0.1% to 0.2%.¹⁴

Sources of data shown in the table:
SARS-CoV: Venkatesh, S. & Memish, Z.A. (‎2004)‎. SARS: the new challenge to international health and travel medicine. EMHJ - Eastern Mediterranean Health Journal, 10 (‎4-5)‎, 655-662, 2004.
SARS-CoV and MERS-CoV: Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). A novel coronavirus emerging in China-key questions for impact assessment. New England Journal of Medicine, 382(8), 692-694.
Seasonal flu: US Centers for Disease Control and Prevention (CDC). Influenza Burden, 2018-19.
Ebola: Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). Distinguishing epidemiological features of the 2013-2016 West Africa Ebola virus disease outbreak. Disaster Health, 3(3), 78-88.
Ebola: World Health Organization (2020). Ebola virus disease: Factsheet.

Disease Estimated case fatality rate (CFR)

SARS-CoV

10%
Venkatesh and Memish (‎2004)‎
Munster et al. (2020)

MERS-CoV

34%
Munster et al. (2020)

Seasonal flu (US)

0.1 to 0.2%
US CDC

Ebola

50%
40% in the 2013-16 outbreak

WHO (2020)
Shultz et al. (2016)

We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.

And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.

# data[].attributes.attachment_link.description
Our interactive data visualizations that show the case fatality rate in each country are updated daily. The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available.

# data[].attributes.headline
Las 11 industrias que mas dinero genera en el mundo

# data[].attributes.subject
Las 11 industrias que mas dinero genera en el mundo

# data[].attributes.body
https://www.rankia.com/blog/bolsa-al-dia/3534358-top-11-ranking-industrias-que-mas-dinero-mueven-mundo-como-invertir-ellas#farmac%C3%A9uticas

# data[].attributes.attachment_link.readable_text
¿Alguna vez te has preguntado qué empresas/industrias generaban más ingresos? En el siguiente artículo vamos a estudiar qué industrias generan más ingresos y veremos también diferentes formas para invertir en ellas.

Entre todas estas industrias hay muchas que no son legales, por lo que las cifras de volumen de negocio que mueven expuestas en este post son estimaciones. También hay que fijarse en que es cada cifra, y no en la cifra en sí. Al no haber demasiada información específica, algunos datos que se dan son beneficios, otros ventas, etc.

Una vez hechas estas aclaraciones vamos a ver cuáles son las 11 industrias que más dinero mueven en el mundo

Drogas

Según la ONU esta industria mueve unos 300.000 millones de dólares al año y se estima que desde 2015 provoca más de medio millón de muertes. Si fuera un país, según el FMI estaría en aproximadamente el puesto 42, entre Egipto y Chile.

Desde 2017 cotiza el primer ETF que invierte en marihuana:

Prostitución

Con un movimiento de unos 108.000 millones de dólares anuales, sigue siendo ilegal en la gran mayoría de países. Es muy complicado poder calcular el volumen de esta actividad pero los expertos han llegado a calcular que en un país como el Reino Unido podría aportar unos 3.700 millones de euros anuales.

Banca

Tras la crisis económica de 2008, esta industria mueve hasta unos 115.000 millones de dólares anuales, aunque algunas entidades guardan en sus cámaras acorazadas hasta 800 billones en activos. Estos son los ocho mayores bancos del mundo según su capitalización en miles de millones de dólares son:

También podemos encontrar ciertos ETFs especializados en el sector:

Armamento

En 2003 el gasto global superó los 1.9 Trillones de dólares según el SIPRI. Con siete de las primeras diez compañías de origen Estadounidense podemos comprobar que las diez mayores empresas armamentísticas del mundo ordenadas por ventas son:
ETFs Aeroespacial y Defensa:
Pornografía

Aunque sea complicado saber la cifra exacta por el gran número de empresas no reguladas en internet, se estima que en esta industria se movieron unos 100.000 millones de dólares en todo el mundo y aunque no sea algo común, podemos adquirir acciones de algunas empresas productoras de pornografía como Beate Uhse o DNXcorp.

Alcohol

La capitalización de las 5 mayores empresas dedicadas a la producción o venta de bebidas alcohólicas del mundo supera los 227.000 millones de dólares anuales en los países desarrollados. Las 10 mayores empresas ordenadas por capitalización bursátil en millones de dólares son:

ETFs de la industria de bebidas alcohólicas:

Casinos y apuestas

En 2009 los ingresos brutos sólo en EEUU fueron de 33.000 millones de dólares y tras una gran expansión en nuestro mercado hemos podido conocerlas mejor. Las cinco mayores empresas del sector ordenadas por capitalización bursátil en millones son:
ETF de la industria del juego:
Petróleo

Con más de 2.5 billones de dólares en ventas a principios de la década pasada, los beneficios netos de las 6 petroleras más grandes fueron cercanos a los 156.000 millones de dólares en 2008. La mayor empresa petrolera del mundo, Saudi Aramco, salió a cotizar a finales de 2019.

:

ETF´s de la industria del petróleo:

Tráfico de personas

En el año 2015 el Servicio Jesuita a Migrantes estimó que esta industria mueve unos 35.000 billones de euros anuales que supone casi tanto dinero como el comercio de drogas y armas. Si encima sumáramos el tráfico de migrantes, la cantidad ascendería según la INTERPOL a 39.000 billones de euros al año.

Las rutas más famosas serían las de Myanmar, China, y Camboya hasta Tailandia; la de Rusia a los Emiratos del Golfo; la de Filipinas y Colombia a Japón; o la que va de Brasil, Paraguay, Colombia y Nigeria hacia España.

Farmacéuticas

Se calcula que esta industria mueve unos 700.000 millones de dólares anuales. Con un negocio en constante desarrollo encontramos que sus cinco mayores empresas ordenadas por capitalización bursátil son:
ETF sobre empresas farmacéuticas:
Falsificaciones

Se estima que la industria de las falsificaciones, sobre todo tecnológicas, mueven alrededor de 500.000 millones de euros al año en el mundo y ciertos estudios como el realizado por Euromonitor le otorgan nada más ni nada menos que el 10% del comercio mundial. Sólo la Policía y la Guardia Civil incautaron en España 9 millones de objetos falsificados en 2011.

La marca más afectada según la OCDE sería Nike seguida de las marcas: Rolex, Ray Ban, Louis Vuitton, Adidas, Apple y Samsung.

¿Cómo invertimos en dichas industrias?

Como podemos ver, la mayoría de las empresas listadas cotizan en bolsa y por si fuera poco, también tenemos la posibilidad de participar en sus beneficios a través de ETF´s especializados en los distintos sectores.

¿Qué rentabilidad han tenido estos sectores en bolsa?

Los sectores que aparentaban ser más estables y con rendimientos más elevados eran los pertenecientes a la industria armamentística, de juego y farmacéuticas.

La rentabilidad del sector bancario ha sido negativa durante un prolongado periodo de tiempo debido a los grandes cambios y transiciones que están afectando al sector.

Los ETFs que hemos visto de Defensa y Seguridad están todos en positivo en los últimos 3 y 5 años con una media de rentabilidad del 10% anual.

El ETF sobre bebidas Alcohólicas (PowerSharesDynamic Food & Beverage) ha obtenido una rentabilidad positiva en los últimos 3 y 5 años.

El petróleo, sabemos que no lo ha hecho muy bien en los últimos años y se ha depreciado fuertemente, arrastrando así la rentabilidad de ETFS como el United States Oil Fund (USO), que ha sufrido fuertes pérdidas en los últimos años.

Sobre la industria farmacéutica hemos visto que lo ha hecho muy bien durante los últimos años y parece que la tendencia va a seguir así. Muchos factores como el envejecimiento de la población lo respaldan, además parece que el coronavirus ha sido u n catalizador.

Para invertir en estos sectores, como comentábamos, hace falta un bróker que permita negociar ETFs o bien acciones internacionales, y si es a un precio competitivo, mejor, ya que el mercado internacional, por lo general es bastante más caro en cuanto a comisiones de intermediación.

# data[].attributes.attachment_link.title
TOP 11 ranking industrias que más dinero mueven en el mundo y cómo invertir en ellas

# data[].attributes.attachment_link.description
¿Alguna vez te has preguntado qué empresas/industrias generaban más ingresos? En el siguiente artículo vamos a estudiar qué industrias generan más ingresos y veremos también diferentes formas para invertir en ellas. Entre todas estas industrias hay muchas que no son legales, por lo que las cifras de volumen de negocio que mueven expuestas en este post son estimaciones.

# data[].attributes.headline
Gráfica de muertes por conflictos mundiales desde 1400.

# data[].attributes.subject
Gráfica de muertes por conflictos mundiales desde 1400.

# data[].attributes.body
https://ourworldindata.org/grapher/world-conflict-deaths-various-sources?country=~number+of+deaths

# data[].attributes.body
https://ourworldindata.org/life-expectancy

# data[].attributes.attachment_link.description
The levelized cost of electricity (LCOE) from onshore wind, given in 2016 USD per kilowatt-hour (kWh). This is given as the annual weighted average across in the world, in addition to select countries where data is available.

# data[].attributes.body
https://ourworldindata.org/higher-poverty-global-line

# data[].attributes.attachment_link.readable_text
Abstract: The extremely low poverty line that the UN relies on has the advantage that it draws the attention to the very poorest people in the world. It has the disadvantage that it ignores what is happening to the incomes of the 90% of the world population who live above the extreme poverty threshold.
The global poverty line that the UN relies on is based on the national poverty lines in the world's poorest countries. In this article I ask what global poverty looks like if we rely on the notions of poverty that are common in the world's rich countries - like Denmark, the US, or Germany. Based on the evidence I ask what our aspirations for the future of global poverty reduction might be.
In every country of the world there are people living in poverty. Even in the world's richest countries the poorest people often live in poor housing and struggle to afford basic goods and services like heating, transport, and healthy food for themselves and their family.

Those who are in monetary poverty also have much poorer living conditions more broadly. Even in a rich and relatively equal country like Denmark middle-aged men who are among the poorest 20% of the population die on average 9 years earlier than those among the richest 20%.¹ In Denmark a person who lives on less than $30 per day is considered poor, and it is the declared goal of the country to reduce poverty relative to this threshold.²

Countries that are much poorer than Denmark also have the goal to reduce poverty. The United Nations declared the objective of ending 'extreme poverty' to be the number 1 goal of the global Sustainable Development Goals. According to the UN a person is considered to live in extreme poverty when he or she is living on less than $1.90 per day, this is called the International Poverty Line. According to the latest global statistics about one in ten people live in extreme poverty globally.³

If we know that poverty is a large problem even in high income countries like Denmark where the poverty line is set at around $30 a day, why should we use an International Poverty Line that is so extremely low to measure poverty globally?

It is the reality of our extremely unequal world - in which every tenth person lives in extreme poverty - that makes such an extremely low poverty line necessary. Without having an extremely low poverty line we would not be aware of the fact that a large share of the world lives in such extreme poverty. The UN's global poverty line is valuable because it draws attention to the reality of extreme poverty in our world.

In a world where the majority still lives on very low incomes it would be wrong if the UN decided to measure global poverty solely by a poverty line as high as the poverty line of Denmark. It would mean that the global statistics gloss over the extremely large and important income differences among the poorest billions in the world. It would mean that the difference between those who live on only $1 per day and those who have an income that is more than 20-times higher would be entirely disregarded. They would all be considered poor, and the reality that some of them are much poorer than others would be hidden.

Slightly higher global poverty lines - such as the poverty line of $3.10 per day that Kate Raworth relies on in her 'Doughnut' framework, or the poverty line of $7.40 per day that anthropologist Jason Hickel uses in his work, or Bob Allen's absolute poverty line based on minimal nutritional requirements - all have the same value.⁴ These low poverty lines allow us to understand the material living conditions of the poorest people in the world and have been successful in drawing attention to the terrible depths of poverty experienced by a large share of the world's population. The only way to achieve these goals is to rely on extremely low poverty lines.

Indeed, there is an argument for using an even lower poverty line. To understand what is happening to the very poorest in the world, we need to look even lower than $1.90. This is because one of the biggest failures of development is that over the last decades the incomes of the very poorest people have not risen. A big part of the reason for why this issue doesn't get discussed enough is that the International Poverty Line we rely on is too high to see this fact.

Yet, only measuring global poverty relative to such extremely low poverty lines has its own large downside.

By focusing on an income threshold that is lower than the incomes of 90% of the global population we are ignoring what is happening to the majority of the world's population. This matters. The majority of the world do not live in extreme poverty anymore, but billions are nevertheless living in great poverty still.

The obvious solution to the problem that the majority of the world is not considered by the International Poverty Line is to use an additional poverty line. This is not a new idea. One poverty researcher who has made the argument for an additional higher global poverty line based on the notions of poverty in rich countries is Lant Pritchett - you find it in his short, yet widely-cited essay 'The case for a high global poverty line' from eight years ago.⁵

The definition of poverty differs between countries. Poorer countries set much lower poverty lines than richer countries.⁶ This means that if we were to simply rely on national poverty definitions for a global measure of poverty we would end up with a measurement framework in which where a person happens to live would determine whether they are poor or not: If we would count as poor those who are defined nationally as poor we would end up counting a person who lives on $20 per day as poor in a rich country, while at the same time counting a person who lives on $2 as not-poor when they happen to live in a very poor country.

One way out of this problem is to set global poverty lines based on the national definitions, but to apply them globally. This is how the UN decided to define the International Poverty Line. In order to ground this global poverty line on something more than the views of global poverty researchers, it is based on the existing definitions of poverty adopted in countries around the world at the national level, but to avoid the problem outlined above they apply the national poverty lines globally. As we explain here in some detail, the $1.90 per day poverty line is set to reflect the national poverty lines adopted in the world's poorest countries.⁷ Applying this poverty line globally means that a person who lives on less than $1.90 per day is considered extremely poor no matter where they live.

In recent years the World Bank has applied this same methodology to countries in the middle-income bracket, those countries with a GNI per capita between $1000 and $12,500. Based on the poverty lines in these countries they have set additional global poverty lines at $3.20 and $5.50 per day, which are now directly available via the World Bank statistics.⁸

What I want to do here is to see what a global poverty line would be if we rely on the notion of poverty in rich countries - countries like Denmark, the US, or Germany.⁹ That is what Pritchett suggested eight years ago: "Since the origin of the [International Poverty Line] was just to adopt as a global lower bound the poverty lines used by the poorest countries, it symmetrically makes sense to say that the global upper bound poverty line is based on the poverty line used in rich countries."

The definition of poverty is certainly not an easy ethical question and thoughtful people disagree about it in ways that have meaningful consequences for our understanding of the world. There are also interesting proposals for hybrid poverty lines that combine absolute and weakly-relative measures; see Ravallion (2019) for a recent proposal.¹⁰ And I would also recommend Tony Atkinson's last book ' Measuring Poverty around the World ' for an excellent recent overview of the topic.

→ To understand how it is possible to compare poverty levels and living standards across countries you need to know the basics of global poverty measurement. You find a summary of the basics in the following fold-out box.

The basics of global poverty measurement

Throughout this article - and in global income and expenditure data generally - the statisticians who produce these figures are careful to make these numbers as comparable as possible.

First, many poorer people rely on subsistence farming and do not have a monetary income. To take this into account and make a fair comparison of their living standards, the statisticians that produce these figures estimate the monetary value of their home production and add it to their income/expenditure.

Second, price changes over time (inflation) and price differences across countries are both taken into account: all measures are adjusted for differences in purchasing power.¹¹ To this end incomes and expenditures are expressed in so-called international dollars. This is a hypothetical currency that results from the price adjustments across time and place. An international dollar is defined as having the same purchasing power as one US-$ in the US. This means no matter where in the world a person is living on int.-$30, they can buy the goods and services that cost $30 in the US. None of these adjustments are ever going to be perfect, but in a world where price differences are large it is important to attempt to account for these differences as well as possible, and this is what these adjustments do.¹²

Throughout this text I'm always adjusting incomes for price changes over time and price differences between countries in this way. All dollar values discussed here are presented in int.-$; the UN does the same for the $1.90 poverty line. Sometimes I leave out 'international' as it is awkward to repeat it all the time; but every time I mention any $ amount in this text I'm referring to international-$ and not US-$.¹³

An additional higher poverty line of $30 per day

Pritchett made his proposal based on data and prices a decade ago and so it is necessary to update his calculations. But I want to go beyond Pritchett's approach and additionally provide a number of other relevant comparisons to inform our understanding of who is considered poor in a rich country.

By following this idea I find that a poverty of 30 international-$ per day corresponds to the notion of poverty in a rich country. In the following section I consider a long number of benchmarks that made me arrive at this poverty line. Here is the short summary of these comparisons:

The range of possible higher poverty lines based on richer countries is wide, as the list of benchmarks suggests. At the lower end I believe that it might be as low as $25 per day, and on the higher end it might be as high as $40 or $50 per day.

Just as someone who lives on less than $1.90 per day is defined as extremely poor, a person who lives on less than $30 a day could be considered moderately poor.

A reality check for any poverty line you might want to consider is to ask yourself what you think about living on less than that poverty line yourself. I lived on less than $30 per day before and would consider myself poor if I'd fall back on that income level again.

In the following box you find the sources and calculations of the benchmarks that led me to my $30 per poverty line proposal.

Who is considered poor in rich countries? Poverty lines and other relevant benchmarks

Poverty lines in European countries

As mentioned before most European countries set their poverty line at 60% of the median income in the country. In his original proposal Pritchett was relying on this 60% of median cut-off.

Calculating the poverty line for European countries therefore means that we look up their median income and then multiply it by 0.6. This is less straightforward than it might first appear. The reason for that is that there are many different income concepts. You quickly realize that it is not easy to define a person's income if you ask yourself what your own income is. Do you take government transfers into account or not? Do you take your partner's income into account and divide it by two? How do you take into account that you have a child for which you need to pay? It is possible to take these and many other aspects into account and arrive at useful statistics, but various sensible ways of addressing such questions lead to many different income metrics. As such, in comparing different poverty thresholds across countries we have to take care to avoid mixing different income concepts as much as possible.

One important difference is how incomes are adjusted for the size of the household: whether the total household income is simply divided by the number of people (including children) - 'per capita' income - or whether some adjustment is made to account for the fact that larger households, and particularly households with children, face lower costs per person - known as 'equivalised' income. Whereas EU countries, like other rich countries, use equivalised income to measure poverty, the UN's measurement of global poverty is based on a global dataset of per capita incomes. This dataset is called PovcalNet, and it is this that we must use in order to make comparisons of poverty measures in different countries according to the same income concept.

In this dataset we find the median income for countries around the world and we can take that median income and then apply the logic on which the European poverty lines are based. In the extensive footnote here you find more details and the full calculations.¹⁴

As high-income European countries I'm referring to those European countries, which according to the Eurostat statistics had a higher income in 2019 than the European average. These are the following countries: Finland, Netherlands, Belgium, Sweden, Germany, France, Iceland, Switzerland, Norway, Luxembourg, Denmark, Austria, Ireland, and the UK.

These are the poverty lines for daily income in a number of high-income European countries (based on 60% of the median incomes from PovcalNet):

Sweden: $29.40 per da

Norway: $37.80 per day

Austria: $31 per day

UK: $25.04 per day

Switzerland: $35.82 per day

Germany: $28.35 per day

France: $27.28 per day

Luxembourg: $43.86 per day

Finland: $27.22 per day

Iceland: $31.64 per day

Ireland: $24.68 per day

Netherlands: $28.6 per day

Belgium: $26.92 per day

Denmark: $29.06 per day

The span of poverty lines in these countries ranges from $25 (for the UK and Ireland) up to $38 (for Norway); in the small country of Luxembourg the poverty line is higher.

The poverty line in the US

UnlikeEuropean countries, the US does not set the poverty line in a relative way. Instead the US poverty line dates back to the work of Mollie Orshansky, an economist working for the Social Security Administration in the early 1960s. Since then it has been of course revised for price changes, but otherwise it remained unchanged.

The US poverty line is very often criticised as being too low. Those that criticize the US poverty line in that way therefore suggest that the severity of poverty in the US is understated in the statistics.

How high is the poverty line in the US? In 2020 the poverty threshold for a single person under 65 was 35 international-$ per day.¹⁵

Now the problem with comparing this poverty line with the global statistics is again that the income concept is different. The US crucially relies on an equivalence scale for adjusting the income cutoff depending on the household size.

An alternative is to apply the same concept that the Europeans are using for their poverty line determination. If the US would use the 60% of median income definition of poverty their poverty line would be int.-$32.8 per day¹⁶ Very close to the one-person poverty line based on Orshansky's work.

A somewhat comparable poverty line based on these two approaches therefore falls into the range of around $33 to $35 per day. Within the range of poverty lines in European countries.

Survey results - Below which income do you consider a person poor?

The UN and Pritchett rely on the existing poverty lines in low-income and high-income countries respectively to derive their poverty lines. We can follow other approaches too.

An obvious one is to ask what people out there believe: Who is considered poor in a high-income country by people in high-income countries?

For the regular poverty report of the German government, a survey is conducted that asks Germans below which income level they consider someone as poor. The latest data is from the year 2015.¹⁷

The mean answer given by the German population for a cutoff below which a person is considered poor was 947€ per month. In international dollars per day this corresponds to an income of int.-$37.58.¹⁸

Universal Basic Income

Universal Basic Income (UBI) is a political idea that is becoming rapidly more popular.

A large UBI study in Germany - called 'Mein Grundeinkommen' - sets this income at €1200. In international-$ this corresponds to an income of int.-$48.19 per day.¹⁹

Social security in Germany

Germany pays basic social care for its citizens. This social security payment is referred to as 'Hartz-IV'.

How much a person receives depends on the particular circumstances of the individual, but we can look at the average payment. In 2018 a single person received on average 783 Euro per month. That corresponds to int.-$30.78 per day.²⁰

The Roslings' suggest a cutoff of $32 per day

Anna Rosling-Rönnlund, Ola Rosling and Hans Rosling challenged the old dichotomy between developed and developing countries in their bestselling book ' Factfulness'. They argue that the old dichotomy corresponds to a view of the world that was accurate half a century ago when a few countries were relatively well-off, but most countries were living in very poor conditions.Today, they say, people around the world live on a large spectrum. To reflect this spectrum they proposed 4 income levels.

The first cut-off corresponds to the international poverty line (rounded to int.-$2 per day). The next income cutoff they set at $8 per day, the following one at $16 and the highest one at int.-$32 per day.

Kahneman's and Deaton's study of income and emotional well-being

Nobel laureates Daniel Kahneman and Angus Deaton published a famous study on the link between life satisfaction and income.²¹

The authors find that higher incomes go together with higher self-reported life satisfaction, but for people's self-reported emotional well-being this is only true up to a certain point: the study finds that above $75,000 further increases in income do not correspond with improvements in people's emotional well-being - a finding that is often cited to argue that additional economic growth does not improve people's lives in high-income countries.²²

Again, the income concept is not the same as that in PovcalNet, and so comparisons with the global data are not directly possible. But we can ask what the daily income at which emotional well-being supposedly levels off corresponds to: $75,000 per year are int.-$205 per day.

It is certainly worth considering whether an income up to which emotional well-being increases could be taken as the basis for a definition of poverty. A US company reacted to the research finding of Kahneman and Deaton by using it to set the minimum wage in their company: everyone in that company gets paid that salary.

For the discussion of global poverty however it might be considered as an even higher poverty line, but for any practical purpose in the world today the income cutoff would be too high as only a very small fraction of the world lives on more than $75,000 per year.

We have seen that 10% of the world live in extreme povertyas defined by the UN. How large is the share of the world that lives in moderate poverty?

The latest global data tells us that 85% of the world population live on less than $30 per day. These are 6.5 billion people.

Relying on a higher poverty line of $45 per day you find that 92% live in poverty, and using a lower poverty line of $20 per day you find that 78% live in poverty. No matter which of these poverty lines you might want to choose, at least three-quarters of the world live in poverty.

All of this data refers to pre-pandemic times. The global recession has certainly increased the share below any of these cutoff points. As soon as the new data is available you will find it on Our World in Data.

The chart shows where in the world people are poor. If we would only rely on the UN's extreme poverty line we would conclude that barely anyone lives in poverty in high-income countries. Relying on higher poverty lines, this data here shows that even in high-income countries there is a significant share of the population that lives in poverty. No country, not even the high-income countries, has eliminated poverty. There are no 'developed countries' - there is work to do for all.

But just as clear from this data is the fact that in many world regions the large majority of people are very poor. In Sub-Saharan Africa about 40% of the population lives on less than $1.90 per day as the chart shows. In all regions outside of high-income countries more than 85% of all people live in moderate poverty.

Two centuries ago the global income distribution was very different. Back then almost everyone in the world was living in extreme poverty. Those places in which few people live in moderate poverty today only left poverty behind in the very recent past.

Denmark is one of those places. The reason why the majority of people in Denmark is not living in poverty is that the economic inequality is low and the average income high.

The fact that the inequality is low you can see on the map. It shows an inequality measure called the Gini coefficient (explained here) which makes clear that Denmark is among the least unequal countries in the world.

The reason that the average income in Denmark is high is due to the fact that average incomes have increased steadily for the last two centuries; this long-term development is called economic growth. As the historical data shows the average incomes in Denmark are today more than 20-times higher than in the past.

You can add any other country to this chart. By adding one of those countries in which the majority lives in poverty - like Ethiopia - you see just how large the differences in average incomes are.

GDP per capita is by far the most widely used measure of average income and is yet another income concept from the two I mentioned so far.²³ It is a more comprehensive measure of incomes and crucially takes into account government expenditures. For these and other reasons (mentioned in the long footnote) you will find that dividing GDP per capita by 365 days will let you arrive at a higher value than the income that is determined in household income surveys.²⁴

The income of every person depends on two factors, the average income in the country they live in and the position that particular person has in that country's income distribution. This chart here shows the average income in countries around the world. The height of each bar represents the average daily income in a country, the width of each country corresponds to the country's population size. I have ordered the countries by income: from the poorest country on the very left (South Sudan where the average person lives on $1.12 per day) to the richest country on the very right (Luxembourg with an average of $86 per day).

After two centuries of economic growth the average income in Denmark is now $57 per day today. You find the country far to the right in this chart, which tells you that only very few countries in the world have such high average incomes. The fact that the average income is far higher than the poverty line tells us that the existing poverty in Denmark discussed at the beginning of this post is to a large extent the consequence of inequality.

What this chart makes very clear is how low the average incomes in many countries in the world are. The huge majority of the world live in countries where the average income is much lower than the poverty threshold in rich countries. 82% of the world population live in countries where the mean income is less than $20 per day.

And where incomes are low, living standards generally are poor. As the last chart below shows, a child that is born into a poorer country must not just expect to live on a very low income, but also faces a much higher risk of not staying alive at all.

As I have said before, people are not poor because of who they are, but because of where they are. This is why economic growth is so important to leave poverty behind. By far the most important difference between those people who are not living in poverty and those who do is the average income in the country that they live in - this single factor matters more for a person's income than all other factors taken together. The increase of average income in a country is called economic growth and for global poverty to decrease substantially economic growth for the poorest billions of people is necessary.

→ See my previous article: The economies that are home to the poorest billions of people need to grow if we want global poverty to decline.

The world today is far from the 'end of poverty' relative to any poverty definition. After two centuries of unprecedented progress against the very worst poverty it is still the case that every tenth person lives on less than $1.90 per day.

As the world has not even ended extreme poverty it is therefore right to focus much of our attention on this very low poverty cutoff; ending extreme poverty surely is a global goal of great importance.

Yet at the same time we should consider what our aspirations for the future are. In the past our ancestors did not know that it was possible for a society to leave widespread poverty behind. Today we are in a different situation. We know from the reality of today's rich countries that widespread poverty is not inevitable. Because we know that poverty relative to such higher cutoffs is not inevitable I believe it would be wrong to limit our ambitions to eradicating poverty based on the definition of poverty in the very poorest countries.

What I take away from this discussion are three insights: First, we have seen from countries like Denmark that it is possible to reduce poverty for an entire population relative to a poverty line of about $30 per day. Second, we have seen that these countries were extremely poor in the past and were able to reduce poverty over the course of the last few generations. And third we have seen that the huge majority of the world is still living in great poverty, by any standard. What this suggests to me is that the history of global poverty reduction has only just begun.

Continue reading Our World in Data: My colleague Hannah Ritchie has just published a series of posts on the drivers of deforestation and how to bring humanity's long history of deforestation to an end. You find her work here.

Acknowledgement: I would like to thank Joe Hasell for his thoughtful comments on draft versions of this article.

Endnotes

1. Brønnum-Hansen H, Foverskov E, Andersen I. Income inequality in life expectancy and disability-free life expectancy in Denmark. J Epidemiol Community Health 2021;75:145-150. https://jech.bmj.com/content/75/2/145

2. For the moment it is important to note that this $30 per day poverty line is defined in international-$ and therefore comparable with the 'International Poverty Line' discussed in the following section. Much more details about how to compare incomes across countries, the income concept here, and the definition of this poverty line follows further below in this text.

3. Before the pandemic 9% lived below this poverty line. Due to the global recession the share in extreme poverty is expected to increase this year to over 10%.

4. Kate Raworth (2017) - A Doughnut for the Anthropocene: humanity's compass in the 21st century. In The Lancet Planetary Health. Volume 1, Issue 2, E48-E49, May 01, 2017. Open Access DOI: https://doi.org/10.1016/S2542-5196(17)30028-1 You find the metrics that the Doughnut relies on in the Appendix here.
Jason Hickel - Could you live on $1.90 a day? That's the international poverty line and here.
Allen, Robert C.(201). - Absolute Poverty: When Necessity Displaces Desire. American Economic Review, 107 (12): 3690-3721.DOI: 10.1257/aer.20161080

5. Lant Pritchett (2013) - Monitoring progress on poverty: the case for a high global poverty line. Online here https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/8440.pdf

6. Jolliffe and Prydz (2016)

7. Specifically, the line is set at the average national poverty line amongst 15 particular low-income countries. As Jolliffe and Prydz (2016) demonstrate however, this is also the average poverty line found among the poorest quarter of countries with available data, and also among countries falling into the World Bank's low-income category.

8. The study on which these thresholds rely is Jolliffe, D., Prydz, E.B. Estimating international poverty lines from comparable national thresholds. J Econ Inequal 14, 185-198 (2016). https://doi.org/10.1007/s10888-016-9327-5 The researchers also report an average high-income country poverty line of $21.70 per day.
High income countries in the World Bank framework are however relatively poor compared to the countries that I'm focusing on here - the cutoff for a high-income country according to the World Bank is $12,536, about a quarter of the GNI of Germany and only a fifth of the US. Accordingly, the poverty cutoff is much lower than in those countries. Here you find the World Bank income classification cutoffs.

9. The range of incomes considered 'middle' and 'high' income countries according to the World Bank are very low relative to rich countries. High-income economies are those with a GNI per capita of $12,536 or more. The range of middle-income economies begins at a GNI per capita of $1,036. In this post I want to rely on countries like Denmark; higher income countries by any standard.

10. Martin Ravallion (2019) - On Measuring Global Poverty. NBER Working Paper 26211. DOI 10.3386/w26211

11. This is possible by relying on the work of the International Comparison Project, which monitors the prices of goods and services around the world.

12. Angus Deaton and Alan Heston (2010) discuss the methods behind such price adjustments and many of the difficulties and limitations involved.

Deaton, A., and Heston, A. 2010. "Understanding PPPs and PPP-Based National Accounts." American Economic Journal: Macroeconomics 2 (4): 1-35. A working paper version is available online here.

13. Keep in mind that in the special case of the US the US-$ equals the international-$.

14. The European reference incomes are national median equivalised disposable income after social transfers.

The disposable household income including all income from work (employee wages and self-employment earnings), private income from investment and property, transfers between households, and all social transfers received in cash including old-age pensions.

Eurostat applies an equivalisation factor calculated according to the OECD-modified scale first proposed in 1994. The UN/World Bank is not.

This is according to Eurostat here, where you also find the relevant data. (If the link should break, search on Google for 'Distribution of income by quantiles - EU-SILC and ECHP surveys'.)

There are various ways of bringing the national poverty lines with reference to the national median equivalised disposable income after social transfers in line with the income/expenditure concept used in PovcalNet.

Joliffe and Prydz follow a different approach and their paper is very relevant for anyone interested in this question here. One alternative to the approach I'm following in this article would be to start from the poverty lines they estimated (based on the poverty headcount ratio) and apply the growth rate of the median income since the publication of their study. Yet another possibility would of course be to repeat their analysis with the up-to-date data. I am not following either of these approaches because I believe for a wide audience they are less transparent that the approach here - which is simply: I rely on the same dataset so that I rely on the same income concept, then look up the median income and multiply it by 0.6.

This is the reference: Jolliffe & Prydz (2016). Estimating international poverty lines from comparable national thresholds. The Journal of Economic Inequality, 14(2), 185-198.

---

The following are the relevant calculations. All of them are based on PovcalNet data:

Germany's median monthly income in 2017 was $1417.29 according to PovcalNet.
60% of the median expressed in daily income/consumption is 0.6*$1417.29=$850.374/30=$28.35 per day

Sweden's median monthly income in 2017 was $1469 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1469)/30=$29.38 per day

Norway's median monthly income in 2017 was $1890 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1890)/30=$37.8 per day

Austria's median monthly income in 2017 was $1534 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1534)/30=$30.68 per day

In the UK the median monthly income in 2017 was $1252 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1252)/30=$25.04 per day

France's median monthly income in 2017 was $1364 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1364)/30=$27.28 per day

Switzerland's median monthly income in 2017 was $1791 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1791)/30=$35.82 per day

Spain's median monthly income in 2017 was $982 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*982)/30=$19.64 per day

Iceland's median monthly income in 2017 was $1582 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1582)/30=$31.64 per day

Luxembourg's median monthly income in 2017 was $2193 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*2193)/30=$43.86 per day

Netherland's median monthly income in 2017 was $1430 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1430)/30=$28.6 per day

Belgium's median monthly income in 2017 was $1346 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1346)/30=$26.92 per day

Denmark's median monthly income in 2017 was $1453 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1453)/30=$29.06 per day

Ireland's median monthly income in 2017 was $1234 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1234)/30=$24.68 per day

Finland's median monthly income in 2017 was $1361 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1361)/30=$27.22 per day

15. According to the " Annual Update of the HHS Poverty Guidelines " the poverty line in the US is an annual income of US$12,760. [And 12,760/365.25=$34.95.]

16. The US median monthly income in 2017 was $1640 according to PovcalNet.
60% of the median expressed in daily income/consumption is (0.6*1640)/30=$32.8 per day

17. These reports are called 'Armuts- und Reichtumsbericht der Bundesregierung' online at armuts-und-reichtumsbericht.de

The latest survey was produced by aproxima and published in 2016. It is published as Wahrnehmung von Armut und Reichtum in Deutschland, Ergebnisse der repräsentativen Bevölkerungsbefragung „ARB-Survey 2015", Berlin: Bundesministerium für Arbeit und Soziales (Hrsg.).

18. The 2011 PPP conversion factor for private consumption (LCU per international $) for Germany in 2015 is 0.84 according to the World Bank here.

This means the perceived poverty threshold corresponds to €947/0.84=int.-$1,127.38 per month or int.-$37.58.

19. The 2011 PPP conversion factor for private consumption (LCU per international $) for Germany in 2017 is 0.83 according to the World Bank here.

This means the UBI corresponds to 1200/0.83=int.-$1,445.78 per month or int.-$48.19.

20. That's €783/0.834=int.-$938.85 per month. Or int.-$938.85/30.5=int.-$30.78 per day.

21. Kahneman and Deaton (2010) - High income improves evaluation of life but not emotional well-being. Published in the Proceedings of the National Academy of Sciences. https://www.pnas.org/content/107/38/16489

22. Kahneman and Deaton analyze two different concepts self-reported satisfaction:

- Emotional well-being refers to the "emotional quality of an individual's everyday experience - the frequency and intensity of experiences of joy, stress, sadness, anger, and affection that make one's life pleasant or unpleasant."

- Life evaluation refers to the thoughts that people have about their life when they think about it.

The authors find that higher incomes go together with higher self-reported life satisfaction in both metrics. What they emphasize is that at very high incomes this is not true anymore - emotional well-being does not increase over around $75,000. Evaluation of life however continues to increase even at incomes over $75,000.

23. The two previous ones were income/expenditure as determined in household surveys and equivalized disposable income after social transfers.

24. There is generally a gap between GDP per capita and the averages found in both income surveys and expenditure surveys. But the reasons for the gap are different depending on which we are comparing.

GDP includes many items that are typically not measured in household income surveys, such as an imputed rental value of owner-occupied housing, the retained earnings of firms and taxes on production such as VAT. The gap is even larger when GDP is compared to surveys of household consumption - the latter concept excluding both investment expenditure and government expenditure on public services such as education and health.

Other aggregates beyond GDP are available in the national accounts that are more comparable to the concepts applied in household income and consumption surveys. However, important differences still remain even here. For example, in addition to imputed rents, imputations for the value of certain financial services, such as bank accounts, are included in aggregate household consumption measured in national accounts, with no equivalent for these items recorded in the survey data. In many countries the consumption of 'nonprofit institutions serving households' (NPISH) is included as part of household consumption within national accounts, but not within household surveys.

On top of these conceptual differences are a range of mismeasurement problems that affect both sets of data. On this topic see Deaton (2005), and Pinkovskiy and Sala-i-Martin (2016).

Deaton, Angus. 2005. "Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)." The Review of Economics and Statistics 87 (1): 1-1.

Pinkovskiy, Maxim, and Xavier Sala-i-Martin. 2016. "Lights, Camera... Income! Illuminating the Nation"

Reuse our work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.

All of our charts can be embedded in any site.

# data[].attributes.attachment_link.title
Global poverty in an unequal world: Who is considered poor in a rich country? And what does this mean for our understanding of global poverty?

# data[].attributes.attachment_link.description
Abstract: The extremely low poverty line that the UN relies on has the advantage that it draws the attention to the very poorest people in the world.

# data[].attributes.attachment_link.title
Number of teachers across education levels

# data[].attributes.attachment_link.description
Total number of teachers in public and private education institutions. Teachers are persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance.

# data[].attributes.attachment_caption
Share of primary energy from renewable sources

# data[].attributes.attachment_link.title
Share of primary energy from renewable sources

# data[].attributes.attachment_link.description
Renewable energy sources includes hydropower, solar, wind, geothermal, bioenergy, wave and tidal. It does not include traditional biofuels, which can be a key energy source especially in lower-income settings.

# data[].attributes.attachment_link.description
Total solar energy generation, measured in terawatt-hours (TWh) per year.

# data[].attributes.body
https://elordenmundial.com/wp-content/uploads/2019/12/Mundo-Econom%C3%ADa-Desarrollo-Pobreza-Poblaci%C3%B3n-del-mundo-que-vive-en-la-pobreza.png

# data[].attributes.body
https://www.google.com/amp/s/es.statista.com/grafico/amp/23034/numero-de-medicos-por-cada-1000-habitantes-en-paises-seleccionados/

# data[].attributes.attachment_caption
Global primary energy consumption by source

# data[].attributes.attachment_link.title
Global primary energy consumption by source

# data[].attributes.attachment_link.description
Primary energy is calculated based on the 'substitution method' which takes account of the inefficiencies in fossil fuel production by converting non-fossil energy into the energy inputs required if they had the same conversion losses as fossil fuels.

# data[].attributes.attachment_link.readable_text
What is the daily number of confirmed cases?

This chart shows the number of confirmed COVID-19 cases per day.

What is important to note about these case figures?

The reported case figures on a given date do not necessarily show the number of new cases on that day - this is due to delays in reporting.

The actual number of cases is likely to be much higher than the number of confirmed cases - this is due to limited testing. In a separate post we discuss how models of COVID-19 help us estimate the actual number of cases.

→ We provide more detail on these points in the section ' Cases of COVID-19: background '.

Five quick reminders on how to interact with this chart

Daily confirmed cases per million people

Differences in the population size between different countries are often large - it is insightful to compare the number of confirmed cases per million people.

Keep in mind that in countries that do very little testing the actual number of cases can be much higher than the number of confirmed cases shown here.

Three tips on how to interact with this map

What is the cumulative number of confirmed cases?

The previous charts looked at the number of confirmed cases per day - this chart shows the cumulative number of confirmed cases since the beginning of the COVID-19 pandemic.

Cumulative confirmed cases per million people

This chart shows the cumulative number of confirmed cases per million people.

Weekly and biweekly cases: where are confirmed cases increasing or falling?

Why is it useful to look at weekly or biweekly changes in confirmed cases?

For all global data sources on the pandemic, daily data does not necessarily refer to the number of new confirmed cases on that day - but to the cases reported on that day.

Since reporting can vary significantly from day to day - irrespectively of any actual variation of cases - it is helpful to look at changes from week to week. This provides a slightly clearer picture of where the pandemic is accelerating, slowing, or in fact reducing.

The maps shown here provide figures on weekly and biweekly confirmed cases: one set shows the number of confirmed cases per million people in the previous seven (or fourteen) days (the weekly or biweekly cumulative total); the other set shows the percentage change (growth rate) over these periods.

About this data

Khare, S., et al (2021) GISAID's Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406

Elbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID's innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258

Shu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101

In this document, the many linked charts, our COVID-19 Data Explorer, and the Complete COVID-19 dataset, we report and visualize the data on confirmed cases and deaths from the World Health Organization (WHO). We make the data in our charts and tables downloadable as complete and structured CSV, XLSX, and JSON files on GitHub.

The WHO has published updates on confirmed cases and deaths on its dashboard for all countries since 31 December 2019. From 31 December 2019 to 21 March 2020, this data was sourced through official communications under the International Health Regulations (IHR, 2005), complemented by publications on official ministries of health websites and social media accounts. Since 22 March 2020, the data has been compiled through WHO region-specific dashboards or direct reporting to WHO.

The WHO updates its data once per week.

In epidemiology, individuals who meet the case definition of a disease are often categorized on three different levels.

These definitions are often specific to the particular disease, but generally have some clear and overlapping criteria.

Cases of COVID-19 - as with other diseases - are broadly defined under a three-level system: suspected, probable and confirmed cases.

1. Suspected case
A suspected case is someone who shows clinical signs and symptoms of having COVID-19, but has not been laboratory-tested.

2. Probable case
A suspected case with an epidemiological link to a confirmed case. This means someone who is showing symptoms of COVID-19 and has either been in close contact with a positive case, or is in a particularly COVID-affected area.

3. Confirmed case
A confirmed case is "a person with laboratory confirmation of COVID-19 infection" as the World Health Organization (WHO) explains.

Typically, for a case to be confirmed, a person must have a positive result from laboratory tests. This is true regardless of whether they have shown symptoms of COVID-19 or not.

This means that the number of confirmed cases is lower than the number of probable cases, which is in turn lower than the number of suspected cases. The gap between these figures is partially explained by limited testing for the disease.

We have three levels of case definition: suspected, probable and confirmed cases. What is measured and reported by governments and international organizations?

International organizations - namely the WHO and European CDC - report case figures submitted by national governments. Wherever possible they aim to report cases, for two key reasons:

1. They have a higher degree of certainty because they have laboratory confirmation;

2. They help to provide standardised comparisons between countries.

However, international bodies can only provide figures as submitted by national governments and reporting institutions. Countries can define slightly different criteria for how cases are defined and reported. Some countries have, over the course of the outbreak, changed their reporting methodologies to also include probable cases.

One example of this is the United States. Until 14 th April 2020 the US CDC provided daily reports on the number of confirmed cases. However, as of 14 th April, it now provides a single figure of cases: the sum of confirmed and probable cases.

Suspected case figures are usually not reported. The European CDC notes that suspected cases should not be reported at the European level (although countries may record this information for national records) but are used to understand who should be tested for the disease.

The number of confirmed cases reported by any institution - including the WHO, the ECDC, Johns Hopkins and others - on a given day does not represent the actual number of new cases on that date. This is because of the long reporting chain that exists between a new case and its inclusion in national or international statistics.

The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:

1. Doctor or laboratory diagnoses a COVID-19 case based on testing or combination of symptoms and epidemiological probability (such as a close family member testing positive).

2. Doctor or laboratory submits a report to the health department of the city or local district.

3. Health department receives the report and records each individual case in the reporting system, including patient information.

4. The ministry or another governmental organization brings this data together and publishes the latest figures.

5. International data bodies such as the WHO or the ECDC can then collate statistics from hundreds of such national accounts.

This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases on that specific date.

To understand the scale of the COVID-19 outbreak, and respond appropriately, we would want to know how many people are infected by COVID-19. We would want to know the actual number ofcases.

However, the actual number of COVID-19 cases is not known. When media outlets claim to report the 'number of cases' they are not being precise and omit to say that it is the number of confirmed cases they speak about.

The actual number of cases is not known, not by us at Our World in Data, nor by any other research, governmental or reporting institution.

The number of confirmed cases is lower than the number of actual cases because not everyone is tested. Not all cases have a "laboratory confirmation"; testing is what makes the difference between the number of confirmed and actual cases.

All countries have been struggling to test a large number of cases, which means that not every person that should have been tested has been tested.

Since an understanding of testing for COVID-19 is crucial for an interpretation of the reported numbers of confirmed cases we have looked into the testing for COVID-19 in more detail.

You find our work on testing here. In a separate post we discuss how models of COVID-19 help us estimate the actual number of cases.

We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.

And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.

# data[].attributes.attachment_link.description
This page has a large number of charts on the pandemic. In the box below you can select any country you are interested in - or several, if you want to compare countries.

# data[].attributes.headline
Dinero que genera la empresa de Cocacola comparado con lo que se. mueve en las industrias ilegales

# data[].attributes.subject
Dinero que genera la empresa de Cocacola comparado con lo que se. mueve en las industrias ilegales

# data[].attributes.attachment_link.readable_text
Internacional
Luis CanoSEGUIR
Nutrición Guardar
El mapa de la peligrosidad en el mundo en 2021, realizado a partir de las recomendaciones de viaje del Ministerio de Asuntos Exteriores de España, ha cambiado notablemente con la pandemia. Aunque el cierre de fronteras y las limitaciones de movimientos han frenado el terrorismo y los conflictos internacionales, la crisis económica causada por las restricciones ha multiplicado la delincuencia en numerosos países, especialmente en Iberoamérica, aumentando la inseguridad.

Exteriores establece una relación, país por país, de recomendaciones de viaje para los españoles antes de salir al extranjero. Estos consejos son actualizados de forma continua en función de los acontecimientos. A partir de la literalidad de estos avisos, indirectamente se desprende un índice oficioso de peligrosidad mundial, representado en este mapa interactivo a partir de colores. Pincha sobre cada país en el mapa para conocer las recomendaciones, al margen de las restricciones de viaje impuestas para el control de la pandemia de coronavirus.

De los dos centenares de países analizados, una cuarentena ha empeorado su situación -Myanmar de forma más drástica que ninguno-, y tan solo una quincena han vivido ligeras mejoras en su seguridad. El resto mantiene la misma calificación previa a la pandemia.
-Negro: "Se desaconseja el viaje bajo cualquier circunstancia". Nueve países.
Rango de máximo peligro
-Rojo: "Se desaconseja a no ser por extrema necesidad". 16 países.
La lista de países más peligrosos del mundo se mantiene en nueve, los mismos que a finales de 2019, antes de la pandemia. Entra Myanmar, volátil desde el golpe de Estado del pasado febrero, y permanecen en el máximo nivel de peligrosidad Afganistán, Siria, Libia, Yemen, Somalia, Eritrea, República Centroafricana, Papúa Nueva Guinea. El joven Estado de Sudán del Sur deja el máximo nivel de peligrosidad.

Venezuela es el país americano con la mayor calificación de peligrosidad, instalada en la alta inseguridad desde hace ya un lustro. Nicaragua y Bolivia abandonaron recientemente este nivel de riesgo, además de Egipto y Liberia. Por el contrario, entran este año Palestina, por la situación de Gaza; Etiopía, con estallidos de violencia étnica y política en varias regiones del país; y Uganda. Los otros países con nivel rojo, aquellos que Exteriores desaconseja el viaje salvo por razones de extrema necesidad, son: Haití, Irak, Corea del Norte, Bangladés, Malí, Níger, Nigeria, Burkina Faso, Burundi, Chad, Etiopía y República Democrática del Congo.
-Marrón: "Se recomienda viajar con extrema precaución y abstenerse de hacerlo por determinadas zonas". 31 países.
Con zonas a evitar
-Naranja: "Se recomienda viajar con precaución y abstenerse de hacerlo por determinadas zonas". 52 países.
México se une a la lista de países americanos que forman parte del nivel de extrema precaución y con zonas a evitar. En este estadio permanecen Colombia, Nicaragua, Guatemala, Honduras y El Salvador. Entran también Angola, Costa de Marfil, Filipinas, Sri Lanka, Sudán y Trinidad y Tobago. Este nivel de peligrosidad está copado por países africanos (Kenia, Camerún, República del Congo y Sudáfrica, entre otros), pero con hueco también para asiáticos como Indonesia y Tailandia. Israel también forma parte de este rango. Se mantienen en la lista Turquía, Argelia y Mozambique.

El nivel más numeroso es, a su vez, el más heterogéneo, ya que la peligrosidad se restringe a zonas concretas y no a todo el país. Así, por ejemplo, Estados Unidos está por la violencia en algunos barrios de sus grandes ciudades, Corea del Sur por su frontera con el régimen de Pyongyang, y Rusia por su agitado Cáucaso.

La mayoría de los 52 miembros de la lista están ahí por la criminalidad en determinadas áreas. Así ocurre con países de Iberoamérica como Argentina, novedad en este estadio tras empeorar su seguridad, Brasil, Perú o Ecuador. También hay estados europeos, como Ucrania, Serbia, Bielorrusia, Chipre, Bosnia y Herzegovina, Armenia y Georgia. Otros países que se han incorporado a la lista son Marruecos y Costa Rica.
-Ámbar: "Se recomienda extremar la precaución durante el viaje". Veinte países.
Precaución
-Amarillo: "Se recomienda viajar con precaución". Cuarenta países.
El nivel que mayor número de países ha incorporado debido al empeoramiento de la seguridad, principalmente por el aumento de la delincuencia derivado de la crisis económica por las restricciones de la pandemia. A diferencia del anterior estadio, no incluye zonas a evitar, pero la precaución extrema es generalizada en todo el país, principalmente por la criminalidad. Chile, Cuba, Paraguay y República Dominicana se incorporan a este nivel tras el aumento de riesgo. La lista está formada por veinte países, principalmente africanos, como Cabo Verde, Guinea Ecuatorial, Madagascar o Ghana. No hay ningún europeo.

Países con delincuencia alta sin ser alarmante. África tiene aquí su suelo, con países como Botsuana. También Iberoamérica, con Uruguay o Bolivia. Hay representación europea, como Eslovaquia, Lituania y Montenegro.
-Azul: "No hay restricciones específicas relativas a viajes a este país". 41 países.
Sin restricciones

Canadá, Japón, Australia, Nueva Zelanda, Samoa y 32 países europeos forman el grupo con menor riesgo. Los pequeños estados de Qatar, Brunéi, Singapur y Taiwán también participan de este rango sin representantes africanos ni iberoamericanos.

No obstante, Exteriores remarca en cada información de país: "Se recuerda que en estos momentos ninguna región del mundo y ningún país están a salvo de posibles actos terroristas".
Ver los comentariosTemas
Seguridad mundial en 2021 Fuente: Elaboración propia a partir de las recomendaciones del Ministerio de Asuntos Exteriores

# data[].attributes.attachment_link.title
El mapa de los países más peligrosos del mundo en 2021

# data[].attributes.attachment_link.description
Internacional Luis CanoSEGUIR Nutrición Guardar El mapa de la peligrosidad en el mundo en 2021, realizado a partir de las recomendaciones de viaje del Ministerio de Asuntos Exteriores de España, ha cambiado notablemente con la pandemia.

# data[].attributes.headline
Dinero que geners en el tráfico de animales

# data[].attributes.subject
Dinero que geners en el tráfico de animales

# data[].attributes.attachment_link.title
Share of women, between 15 and 19 years old, with no education

# data[].attributes.attachment_link.description
An interactive visualization from Our World in Data.

# data[].attributes.body
1- Serán (2) por sesión.
2- Cada semana se preparan (3) grupos, (por orden del sorteo)
- Al grupo que no presente (sin justificación oficial) se le resta 1 punto.a
3- Cada 🤬 del grupo debe estar preparado para hacer toda la presentación.

# data[].attributes.attachment_caption
Países del mundo donde más agua se extrae por habitante

# data[].attributes.attachment_link.readable_text
Último dato disponible desde el año 2000

Agua extraída por habitante

Agua extraída

Azerbaiján

Canadá

Chile

Estados Unidos

Estonia

Finlandia

Guyana

Irak

Irán

Kazajistán

Kirguizistán

Nueva-Zelanda

Puerto Rico

Surinam

Tadjikistán

Timor-Leste

Tukmenistán

Uruguay

Uzbekistán

FAO

# data[].attributes.attachment_link.title
Países del mundo donde más agua se extrae por habitante

# data[].attributes.attachment_link.description
Agua extraída por habitante Agua extraída Azerbaiján Canadá Chile Estados Unidos Estonia Finlandia Guyana Irak Irán Kazajistán Kirguizistán Nueva-Zelanda Puerto Rico Surinam Tadjikistán Timor-Leste Tukmenistán Uruguay Uzbekistán FAO

# data[].attributes.body
https://images.app.goo.gl/SKa4H45LPxtSC9JC6

# data[].attributes.body
https://es.statista.com/grafico/amp/25820/paises-con-enfrentamientos-armados-registrados/

# data[].attributes.attachment_link.readable_text
En plena recuperación de la pandemia de COVID-19, una gran parte del planeta sigue inmersa en algún tipo de conflicto armado. Tal y como muestra este gráfico elaborado con datos del Proyecto de Localización y Datos de Conflictos Armados (ACLED, por sus siglas en inglés), la mayoría de los conflictos entre fuerzas estatales y/o rebeldes a nivel mundial se encuentran en África, Medio Oriente y Asia.

Este tipo de violencia también está presente en al menos cinco países latinoamericanos. En lo que va de 2021, ACLED registró cuatro enfrentamientos armados entre fuerzas de seguridad de México. Mientras tanto, en Colombia, la mayoría de los combates se atribuyeron a choques entre fuerzas militares y grupos guerrilleros, sumando 76 batallas entre enero y septiembre.

El 21 de septiembre de cada año, se conmemora el Día Internacional de la Paz. Establecida por la Asamblea General de las Naciones Unidas en 1981, la jornada pretende fortalecer los ideales de paz, haciendo un llamado al cese al fuego y la no violencia.

Descripción

Este gráfico muestra los países en los que se registraron enfrentamientos armados entre fuerzas estatales y/o rebeldes en 2021.

# data[].attributes.attachment_link.title
Infografía: ¿Qué países registran enfrentamientos armados en 2021?

# data[].attributes.attachment_link.description
En plena recuperación de la pandemia de COVID-19, una gran parte del planeta sigue inmersa en algún tipo de conflicto armado. Tal y como muestra este gráfico elaborado con datos del Proyecto de Localización y Datos de Conflictos Armados (ACLED, por sus siglas en inglés), la mayoría de los conflictos entre fuerzas estatales y/o rebeldes a nivel mundial se encuentran en África, Medio Oriente y Asia.

# data[].attributes.headline
Wikipedia: Incluye mapa y tabla de guerras tanto actuales como pasadas.

# data[].attributes.subject
Wikipedia: Incluye mapa y tabla de guerras tanto actuales como pasadas.

# data[].attributes.body
https://es.m.wikipedia.org/wiki/Anexo:Guerras_y_conflictos_actuales

# data[].attributes.attachment_link.readable_text
La siguiente es una lista de los conflictos en curso que tienen lugar alrededor del mundo en la actualidad y que siguen causando muertes violentas.

Esta lista tiene el único propósito de identificar los conflictos actuales y el número de muertes asociadas a cada conflicto. Los números de víctimas mortales incluyen las muertes de civiles y militares. Aquellos conflictos que ya no producen muertes violentas no son enumerados aquí.

Tampoco se cuentan los muertos que no son adscritos a alguno de los conflictos actuales en curso, sino que, por lo general forman parte de la estadística criminal como la tasa de asesinatos. Dicha estadística cabe resaltarla pues arroja algunas veces cifras iguales o incluso superiores a la contabilización de muertes por conflicto armado .

# data[].attributes.attachment_link.title
Anexo:Guerras y conflictos actuales - Wikipedia, la enciclopedia libre

# data[].attributes.attachment_link.description
La siguiente es una lista de los conflictos en curso que tienen lugar alrededor del mundo en la actualidad y que siguen causando muertes violentas. Esta lista tiene el único propósito de identificar los conflictos actuales y el número de muertes asociadas a cada conflicto.

# data[].attributes.attachment_link.readable_text
Haz clic aquí si no vuelves a acceder en pocos segundos.

Imágenes
Los países que más CO2 generan en el mundo por habitante - Mapas ...

elordenmundial.com

Las imágenes pueden estar sujetas a derechos de autor.

# data[].attributes.attachment_link.description
Haz clic aquí si no vuelves a acceder en pocos segundos. Imágenes Los países que más CO2 generan en el mundo por habitante - Mapas ... elordenmundial.com Las imágenes pueden estar sujetas a derechos de autor.

# data[].attributes.body
https://images.app.goo.gl/4MxzvYt1pcmhUQHQA

# data[].attributes.attachment_link.title
Workplace closures during the COVID-19 pandemic

# data[].attributes.attachment_link.description
There may be sub-national or regional differences in policies on workplace closures. The policy categories shown may not apply at all sub-national levels. A country is coded as 'required closures' if at least some sub-national regions have required closures.

# data[].attributes.headline
Porcentaje de vacunados contra algunas enfermedades(chart)

# data[].attributes.subject
Porcentaje de vacunados contra algunas enfermedades(chart)

# data[].attributes.body
https://ourworldindata.org/vaccination

# data[].attributes.body
https://ourworldindata.org/causes-of-death

# data[].attributes.attachment_link.readable_text
What are people dying from?

The chart shows what people died from globally, in 2019. Each box represents one cause, and its size is proportional to the number of deaths it caused.

The most common causes of death globally - shown in blue - were from 'non-communicable diseases'.

This includes heart disease, cancer, and chronic respiratory diseases. They tend to develop gradually over time and aren't infectious themselves.¹

Heart diseases were the most common cause, responsible for a third of all deaths globally. Cancers were in second, causing almost one-in-five deaths. Taken together, heart diseases and cancers are the cause of every second death.

In red are infectious diseases, which are responsible for around 1-in-7 deaths. These include pneumonia, diarrheal diseases, tuberculosis, HIV/AIDS, and malaria.

A smaller share - around 4% - was from neonatal and maternal deaths. A similar share was from accidents.

Violent deaths were less common, with 1.3% dying from suicide and less than 1% dying from interpersonal violence such as homicide or battle deaths.

In this article, we cover this in more detail:

Every child's death is a tragedy. Globally, the scale of child mortality is immense: five million children under five die yearly. That's around 14,000 each day.

In the chart, you can see what they die from. The size of each box corresponds to the number of children under five years old who die from each cause.

The most common causes for young children are different from the leading causes across the entire population - which was shown in the previous key insight.

Infectious diseases were most common - they kill an estimated 2.2 million children annually. They include pneumonia, diarrheal diseases, malaria, and meningitis.

Next were birth disorders, such as preterm birth, neonatal asphyxia (suffocation), and trauma, which caused an estimated 1.9 million child deaths.

Several other causes, such as heart abnormalities and malnutrition, were responsible for around 100,000 deaths each.

These figures are astonishing because many of these causes are preventable. With vaccination, basic medication, rehydration treatment, nutrition supplementation, and neonatal healthcare, a large share of child deaths could be prevented.

In this article, we cover this in more detail:

What people die from has changed dramatically over time.

In the chart, you can see long-run historical trends in death rates in France.² This is shown across age groups (on the y-axis) between 1925 and 1999 (on the x-axis). The colors represent the death rates.

As you can see, the rates of some causes of death rise exponentially with age; the shades are much darker along the vertical axis.

Take respiratory diseases as an example. In 1999, people aged 40 to 44 had a death rate 24 times higher than those aged 20 to 24. For those aged 60 to 64, it was almost 440 times higher.

In contrast, external causes, which include accidents, violence, falls, and suicides, tend to rise more slowly with age. The shades get darker slowly along the vertical axis.³

Causes of death have also changed over time.

The effects of major events - such as World War Two and the AIDS epidemic - are visible on the chart. They led to large surges in death rates from external causes and infectious diseases.

Another example is that death rates from infectious diseases and respiratory diseases have declined over time - especially from the mid-20th century onwards, with the rise of antibiotics, vaccines, and public healthcare.⁴

Some causes of death are far more common in some parts of the world than others.

In poorer countries in Africa and Asia - where clean water, sanitation, and access to healthcare are lacking - people are much more likely to die from infectious diseases, maternal, neonatal, and nutritional causes.

However, in wealthier countries, people are much more likely to die from 'non-communicable diseases' instead, which include heart diseases and cancers.

This is because of two related points.

First, infectious diseases are much more common in poorer countries, and treatment is often lacking.

Second, deaths from infectious diseases were much more common in wealthier countries in the past. As these causes of death are reduced or eliminated, people tend to live longer and die from other causes instead.⁵

Therefore, the data needs to be ' age-standardized ' to see how causes of death vary between countries, among people of the same age. In the chart, you can see an age-standardized comparison of these causes of death.

As you can see, countries with higher death rates from communicable diseases also tend to have higher death rates from non-communicable diseases.

This results from poorer healthcare, income, and living standards, which affect the chances of surviving many kinds of diseases.

You can also move the slider to see how they have shifted over time. Annual death rates have been reduced over time for both categories - but they have dropped faster for communicable diseases.

The most common way to track and understand causes of death is to rely on data from death certificates - where doctors describe the chain of events that led to each person's death and the disease or injury that caused it.

These are registered in Vital Registration systems and shared with the World Health Organization annually.

However, in many countries, this data is lacking. You can see this in the chart.

This is because of several factors - a lack of doctors, nurses, medical records, and hospitals, and a poorly functioning Vital Registration system.

Instead, our understanding of causes of death in poor countries often comes from other studies, such as 'verbal autopsies', which are not conducted regularly.

To improve our understanding of causes of death worldwide, we need better-functioning Vital Registration systems, medical records, and training for doctors and nurses to collect data where it's lacking.

In this article, we cover this in more detail:

In the data we present on causes of death, we show each death as caused by a single disease, event, or injury.

However, people have often been exposed to various risk factors earlier in life, affecting their chances of premature death.

These risk factors can include behaviors - such as smoking and low exercise - and environmental factors, such as air pollution, unsafe water, and pathogens.

Researchers try to estimate the number of deaths caused by each risk factor, using available data.

For example, researchers estimate the number of deaths that could be prevented if no one smoked, by using estimates of the risk caused by smoking, and the levels of smoking in the population.⁶

In the chart, you can see the estimated number of deaths caused by a range of risk factors.⁷

The chart shows that risk factors such as high blood pressure, smoking, and air pollution are each estimated to cause millions of deaths yearly.

These risk factors are not exclusive: people can be exposed to multiple risk factors, and some are related to each other. Therefore, the numbers do not sum up to the total number of deaths.

In this article, we cover this in more detail:

Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser (2023) - "Causes of Death" Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/causes-of-death' [Online Resource]

BibTeX citation

@article{owid-causes-of-death, author = {Saloni Dattani and Fiona Spooner and Hannah Ritchie and Max Roser}, title = {Causes of Death}, journal = {Our World in Data}, year = {2023}, note = {https://ourworldindata.org/causes-of-death} }

# data[].attributes.attachment_link.description
To find ways to save lives, it's essential to know what people are dying from. Explore global data and research on causes of death.

# data[].attributes.attachment_link.description
Total combined number of deaths from conflict (civil conflict and war between states) and terrorism.

# data[].attributes.body
https://ourworldindata.org/grapher/child-mortality-igme

# data[].attributes.body
https://www.smarttravel.news/wp-content/uploads/2020/08/How-Much-Major-Automakers-Make-Every-Single-Second-1200.png

# data[].attributes.body
http://www.clinicaserralta.com/wp-content/uploads/obesidad-hombres-1024x495.jpg

# data[].attributes.body
https://images.app.goo.gl/a9y9yfGCwzeBuCrK8

# data[].attributes.body
https://images.app.goo.gl/ivjiQbLRJVwUtDfb7

# data[].attributes.body
https://images.app.goo.gl/9M6dHSRrLiNAAbqW9

# data[].attributes.body
https://ourworldindata.org/trust-and-gdp

# data[].attributes.attachment_link.readable_text
[The visualization below and much more on this topic can be found in our entry on Trust]
In a much cited article, Arrow (1972)¹ says that "Virtually every commercial transaction has within itself an element of trust, certainly any transaction conducted over a period of time."

The extent to which trust is linked to economic development has been the subject of many academic papers in the economics literature on growth (see Guiso et al. 2006², Algan and Cahuc 2010³, and the references therein). A common way to get a first-order approximation of this relationship is to estimate the correlations between trust and GDP per capita. This visualization provides evidence of this correlation, by plotting trust estimates from the World Value Survey against GDP per capita. Each dot on this scatter-plot corresponds to a different country. You can learn more about measures of national income in our entry on GDP data.

As it can be seen, there is a very strong positive relationship. Most academic studies find that this relationship remains after controlling for further characteristics. And similar results can also be obtained by looking at other measures of economic outcomes. Looking at outcomes across individuals, Guiso et al. (2006), for instance, report that trust has a positive and statistically significant correlation with the probability of becoming an entrepreneur, even after controlling for education, age and individual income. Their results also hold if religious affiliation of the respondents' ancestors is used as a proxy for trust - they thus argue that, since ancestors' religion correlates with respondents' trust attitudes, this instrumental variable approach can be taken as evidence that the estimated relationship goes in the suggested direction (i.e. that trust leads to entrepreneurship, rather than the other way around).

Other studies using instrumental variables have also found similarly large effects. Algan and Cahuc (2010)⁴ predict that, according to their estimates, African countries would have a five-fold increase in GDP per capita if they had the same level of inherited social attitudes as Sweden, after controlling for lagged GDP per capita, contemporaneous political environment and time-invariant country characteristics.

Algan and Cahuc (2010) show that inherited trust of descendants of US immigrants is significantly influenced by the country of origin and the timing of arrival of their forebears. This is their instrumental variable: the inherited trust of descendants of US immigrants is used as a time-varying measure of inherited trust in the country of origin. This approach allows the authors to control for country fixed effects and interpret the effect of trust on growth causally. You can read a summary of their findings and approach in a voxeu.org article written by the researchers.

Nunn and Wantchekon (2011)⁵ provide evidence to explain mistrust in Africa: they show that current differences in trust levels within Africa can be traced back to the transatlantic and Indian Ocean slave trades. More specifically, they show that individuals whose ancestors were heavily raided during the slave trade are less trusting today - and using a variety of different econometric strategies, they claim that this relationship is causal.

Trust vs. GDP per capita, 2014 (or latest available data) ⁶

# data[].attributes.attachment_link.description
The visualization below and much more on this topic can be found in our entry on Trust] In a much cited article, Arrow (1972)1 says that "Virtually every commercial transaction has within itself an element of trust, certainly any transaction conducted over a period of time."

# data[].attributes.headline
fELICIDAD, BIENESTAR, MEJORES PAÍSES PARA VIVIR CALIDAD DE VIDA

# data[].attributes.subject
fELICIDAD, BIENESTAR, MEJORES PAÍSES PARA VIVIR CALIDAD DE VIDA

# data[].attributes.attachment_link.readable_text
Mental health is an essential part of people's lives and society. Poor mental health affects our well-being, our ability to work, and our relationships with friends, family, and community.

Mental health conditions are not uncommon. Hundreds of millions suffer from them yearly, and many more do over their lifetimes. It's estimated that 1 in 3 women and 1 in 5 men will experience major depression in their lives. Other conditions, such as schizophrenia and bipolar disorder, are less common but still have a large impact on people's lives.

Mental illnesses are treatable, and the impact they have can be reduced. Despite this, treatment is often lacking or poor quality, and many feel uncomfortable sharing their symptoms with healthcare professionals or people they know. This also makes it difficult to estimate the actual prevalence of mental illnesses.

To support them, it's essential to have good data to understand these conditions - how, when, and why they occur, how many people are affected by them, and how they can be treated effectively and safely.

On this page, we show data on the prevalence of mental illnesses and their burden, and people's attitudes toward mental health.

Research & Writing

Depression

Interactive charts on Mental Health

Cite this work

Our articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:

Saloni Dattani, Lucas Rodés-Guirao, Hannah Ritchie and Max Roser (2023) - "Mental Health". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/mental-health' [Online Resource]

BibTeX citation

@article{owidmentalhealth, author = {Saloni Dattani and Lucas Rodés-Guirao and Hannah Ritchie and Max Roser}, title = {Mental Health}, journal = {Our World in Data}, year = {2023}, note = {https://ourworldindata.org/mental-health} }

Reuse this work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.

All of our charts can be embedded in any site.

# data[].attributes.attachment_link.description
Mental health has a significant impact on people's lives and wellbeing. To support people with mental illnesses, we need better data to understand them.

# data[].attributes.attachment_link.readable_text
¿Qué hace feliz a un país? Es una pregunta que numerosos estudios y organismos han tratado de responder durante las últimas décadas. Los parámetros empleados para elegir las clasificaciones son variados, y quedan lejos de la perfección. El más celebrado, el World Happiness Report, parte del PIB y computa otros parámetros (desde acceso a la salud y a la educación hasta elementos más abstractos, como la autorealización personal) para listar aquellos estados donde las personas son más felices.

Y más infelices.

Como vimos en su día, la metodología no está exenta de problemas, así como su recepción e interpretación pública. La naturaleza de la pregunta (¿ qué es la felicidad?) provoca que los caminos para llegar a ella siempre sean objeto de disputa. ¿Es el bienestar material, poseer dinero, una buena infraestructura pública, una red estatal que cubra las necesidades de sus ciudadanos? Sí. ¿O es la forma de vida, la alimentación, el ocio, la socialización, el clima? Según otros, también.

En todo caso, estos últimos ponderables son difíciles de calcular, y dependen mucho del grado de preferencia cultural (dónde ha nacido, en qué ambientes se ha movido, a qué está acostumbrado) de cada uno. De ahí que el World Happiness Report perviva como una buena herramienta, si bien imperfecta, para entender qué regiones del mundo ofrecen mayor bienestar objetivo a sus habitantes. Y de ahí que Visual Capitalist lo haya empleado para dibujar el Mapa Global de la Felicidad.

Se trata de un repaso exhaustivo que va más allá del abanico de sospechosos habituales (los países del centro y del norte de Europa) y que identifica los focos felices en cada continente, además de los más infelices (que suelen correlacionar con los puntos más pobres y desiguales de cada región). Finlandia está por encima de todos (pese a sus pecularidades); Canadá triunfa en América del Norte; Chile en América del Sur (aunque el 26º global); Israel en Oriente Medio; Taiwan en Asia; Australia en Oceanía; y Mauricio (57º global) en África.

Por media continental, Europa es el más satisfecho con su estatus (6,3 de media, sólo Ucrania y Albania están por debajo del 5); América del Norte el segundo (6,2, pero con el país menos feliz, Haití); América del Sur el tercero (6,3, Venezuela el que más ha caído); Asia (5,4); y Oriente Medio (5,3, pero con Siria, Afganistán y Yemen por debajo del 3,5). ¿Oceanía? 7,3, pero sólo computan dos países, y muy desarrollados (Nueva Zelanda y Australia).

# data[].attributes.attachment_link.title
Los países más felices del mundo, en un mapa

# data[].attributes.attachment_link.description
¿Qué hace feliz a un país? Es una pregunta que numerosos estudios y organismos han tratado de responder durante las últimas décadas. Los parámetros empleados para elegir las clasificaciones son variados, y quedan lejos de la perfección.

# data[].attributes.attachment_link.readable_text
¿Qué lugar del planeta es mejor para venir al mundo?, ¿qué países son el 'paraíso terrenal' de los ateos?, ¿dónde viven las mujeres con los senos más grandes? Para conocer estas y otras preguntas de alcance mundial eche un vistazo a estos mapa-mundi.

1. ¿El mejor 'pesebre' para venir al mundo?

Un estudio llevado a cabo por la organización 'Economist Intelligence Unit' ha analizado unos 80 países y, basándose en distintos criterios, como las oportunidades económicas, la salud y las libertades políticas, destaca 15 países como los mejores para nacer. La lista de 'pesebres ideales' la encabezan Irlanda, Australia, Nueva Zelanda y Canadá.

2. ¿El mejor país para ser madre?

Un nuevo informe de la ONG con sede en Londres 'Save the Children' analiza las condiciones que rodean a la maternidad en casi todos los países del mundo y llega a la conclusión de que los países nórdicos son los mejores lugares para ser madre. El África subsahariana y el Asia meridional son los peores.

3. El puzzle de la desigualdad económica

Este mapa nos presenta el llamado coeficiente de Gini de cada país, propuesto por el ideólogo y estadístico italiano Corrado Gini en 1912, que nos sirve para medir la desigualdad por ingresos de los diferentes países, y en el que un coeficiente de 0 sería la igualdad perfecta, mientras que 1 sería la desigualdad total. Los países con más desigualdad (con el coeficiente superior a 0.50) son Camerún, Madagascar, Ruanda y Uganda.

4. La percepción popular de la economía

Pese a que la crisis financiera sigue azotando al mundo, la gente empieza a sentirse más optimista sobre el futuro estado de sus economías, según la encuesta de Gallup 'Índice de Confianza Económica'. Los más optimistas en este sentido son los chinos; EE.UU. lo es menos, pero todavía ve su economía con buenos ojos. Mientras Japón tiene una visión más realista de la vida, la de Rusia es más filosófica.

5. ¿Dónde fuman más marihuana?

Según un informe elaborado por Naciones Unidas, los países que consumen más marihuana son EE.UU., considerado el mercado más importante para los traficantes, y Australia.

En el mapa de las religiones se aprecia que el cristianismo es la religión predominante, ya que abarca aproximadamente al 33% de la religión de la población mundial, incluyendo a católicos, protestantes, ortodoxos, testigos de Jehová, movimiento pentecostal, anglicanos, evangélicos y quákeros.

7. ¿Dónde viven los ateos?

Para los ateos que quieran saber donde residen sus correligionarios, este mapa revela que la mayor cantidad de excépticos en cuestiones espirituales están en China, Japón y algunos países europeos. Sin embargo, también hay muchos más ateos de lo que podría pensarse en lugares como Arabia Saudita, a pesar de que allí se considera delito grave.

8. Un mundo con 26 'coronas'

Actualmente en el mundo existen 26 monarquías, que abarcan una amplia red de reyes, reinas, emperadores, sultanes y emires que gobiernan o reinan en un total de 43 países.

9. "Mens sana in corpore sano": el futbol es mundial

En este mapa se ve claramente cómo un enorme campo de fútbol de color verde se extiende a lo largo de dos tercios del planeta. Este deporte es popular en Europa, África, la mitad occidental de Asia y América (con algunas notables excepciones, como Venezuela y Cuba, donde reina el beisbol). El hockey es popular en Canadá, el fútbol americano en EE.UU., el cricket en la India y el tenis de mesa en China.

10. ¿Dónde viven las mujeres con los senos más grandes?

Este mapa analiza el tamaño promedio de los senos de las mujeres en todo el mundo basándose en el tamaño de la copa de sostén, desde la A (la más grande) a la D (la más pequeña). Lideran la lista los países nórdicos, Escandinavia y Rusia con un promedio de pechos de 5 o más. La mujeres de Estados Unidos, Alemania, Colombia y Venezuela presentan pechos de un promedio de 4, mientras que Brasil, Argentina, Canadá, Australia, Italia y Francia están entre los países con una media de 3. Si en México y España tienen mujeres con senos de un promedio de 2, es en los países asiáticos, como China y Japón, donde viven las mujeres con los pechos más pequeños.

Estos mapas han sido publicados por medios y portales como ' The Washington Post', targetmap.com y adme.ru.

# data[].attributes.attachment_link.title
El mundo es 'plano': El mejor país para nacer, dónde se fuma más y otros mapas mundanos

# data[].attributes.attachment_link.description
¿Qué lugar del planeta es mejor para venir al mundo?, ¿qué países son el 'paraíso terrenal' de los ateos?, ¿dónde viven las mujeres con los senos más grandes? Para conocer estas y otras preguntas de alcance mundial eche un vistazo a estos mapa-mundi.

# data[].attributes.body
https://elpais.com/economia/2016/09/29/actualidad/1475137891_774458.html

# data[].attributes.attachment_link.readable_text
Medicamentos falsificados incautados en una operaci&oacute;n de la Guardia Civil

Por Internet se pueden encontrar anuncios que prometen vender Viagra e incluso tratamientos contra el cáncer sin receta. Además de ilegal, es peligroso, porque en muchos casos se trata de fármacos falsificados, que no han pasado los controles de seguridad, que pueden ser simples placebos y que incluso pueden contener sustancias nocivas. En total, los medicamentos falsos le cuestan al sector farmacéutico de la Unión Europea (UE) 10.188 millones de euros cada año, lo que representa el 4,4% de las ventas legítimas anuales del sector en la UE. En el caso de España, son 1.170 millones de euros y un 5,9% de la facturación. Además, se producen pérdidas millonarias en impuestos. Estos datos se desprenden de un nuevo informe de la Oficina de Propiedad Intelectual de la Unión Europea (EUIPO), realizado a través del Observatorio Europeo de las Vulneraciones de los Derechos de Propiedad Intelectual.

Vender menos a través de los canales legales le cuesta dinero a las farmacéuticas, y esto, según el organismo comunitario, se traduce en menos empleo e inversiones. "Tal reducción de las ventas se traduce en la pérdida directa de 37.700 puestos de trabajo en el sector farmacéutico en la UE, puesto que los fabricantes y distribuidores legítimos de medicamentos contratan a un número inferior de trabajadores de los que necesitarían si no hubiera falsificaciones", advierte. Si añadimos los efectos en cadena que la falsificación de estos medicamentos tiene en el mercado, se suman a la pérdida 53.200 puestos de trabajo en otros ámbitos de la economía de la UE.

También las arcas públicas sufren por la picaresca de los fármacos falsos. "Se calcula que, en la UE-28, estos productos falsificados han generado pérdidas de 1.700 millones euros en ingresos fiscales en términos de impuestos sobre la renta de las familias, cotizaciones a la seguridad social, e impuesto de sociedades", recuerda la EUIPO.

Un grave peligro para la salud

"Sabemos, gracias al análisis realizado por la Organización Mundial de la Salud (OMS), que se falsifican medicamentos, tanto genéricos como innovadores, desde productos para el tratamiento del cáncer hasta analgésicos muy baratos. Estas falsificaciones pueden resultar tóxicas y constituir un grave peligro para la salud. En nuestro informe se refiere que también pueden ejercer un grave impacto en la economía y en el empleo. Aspiramos a que nuestros datos y nuestros estudios basados en información contrastada ayuden a los responsables de la formulación de políticas a diseñar respuestas al reto que supone la lucha contra los fármacos falsos", ha declarado a través de un comunicado António Campinos, director ejecutivo de este organismo.

El informe es el noveno de una serie de estudios realizados por la EUIPO sobre el impacto económico de las falsificaciones en los sectores industriales en la UE. En los informes anteriores se examinaron los siguientes sectores: las bebidas espirituosas y el vino; industria discográfica; joyería y relojería; bolsos de mano y maletas; juegos y juguetes; artículos deportivos; prendas de vestir, calzado y accesorios; y artículos de cuidado personal y cosméticos.

En el estudio farmacéutico se incluyen dos casos concretos como ejemplo. Uno localizado en Alemania, por el que un proveedor servía a las farmacias productos falsificados adquiridos en el extranjero, a través de sistemas paralelos e ilegales. Se trataba de cápsulas usadas en tratamientos para pacientes con cáncer. Un enfermo dio la voz de alarma en 2014 al detectar que sus píldoras no eran como las originales. El segundo ejemplo es una operación a nivel mundial contra distribuidores ilegales en Internet. Se iniciaron 429 investigaciones, se suspendieron 550 anuncios en línea de medicamentos ilegales y se clausuraron 2.414 sitios web. Entre los medicamentos falsificados e ilegales (algunos estaban caducados y se había cambiado la fecha) incautados durante la operación había fármacos para la presión arterial, pastillas para tratar la disfunción eréctil, medicamentos contra el cáncer y suplementos dietéticos.

España, muy golpeado por las imitaciones

El informe calcula por países el impacto de la falsificación de medicinas. En España, señala, se estima que hasta 1.170 millones euros, lo que equivale al 5,9% de las ventas del sector farmacéutico español, se pierden cada año como resultado de las falsificaciones (un porcentaje entre los más altos de los grandes países de la UE), y provoca, además, "una pérdida directa de 3.223 puestos de trabajo".

Italia es de los más destacados en cuanto a valores absolutos. En el informe se estima que hasta 1.590 millones -lo que equivale al 5% de las ventas del sector farmacéutico de este país- se pierden cada año como resultado de las falsificaciones, con una pérdida directa de 3.945 puestos de trabajo. En Alemania son más de 1.000 millones de euros (2,9% de las ventas) y 6.951 puestos de trabajo. Y en Francia se estiman más de 1.000 millones (3% de las ventas) y 3.667 empleos. En el caso de Reino Unido, son 605 millones de euros (3,3% de las ventas) y 2.940 puestos de trabajo afectados.

Según el informe, la industria farmacéutica de la UE está formada por más de 40.000 empresas, de las cuales 3.000 son fabricantes y el resto son distribuidores al por mayor. El tamaño medio de las empresas varía considerablemente entre los dos grupos; el de los fabricantes tiene 150 trabajadores por empresa y el de los distribuidores tiene 15 trabajadores por empresa.

Sobre la firma

# data[].attributes.attachment_link.title
La falsificación de medicamentos cuesta 10.200 millones al año a la UE

# data[].attributes.attachment_link.description
Por Internet se pueden encontrar anuncios que prometen vender Viagra e incluso tratamientos contra el cáncer sin receta. Además de ilegal, es peligroso, porque en muchos casos se trata de fármacos falsificados, que no han pasado los controles de seguridad, que pueden ser simples placebos y que incluso pueden contener sustancias nocivas.

# data[].attributes.attachment_link.description
No testing policy. - Only those who both (a) have symptoms and also (b) meet specific criteria (e.g. key workers, admitted to hospital, came into contact with a known case, returned from overseas). - Testing of anyone showing COVID-19 symptoms. - Open public testing (e.g. "drive through" testing available to asymptomatic people).

# data[].attributes.attachment_link.title
La tasa de mortalidad del coronavirus por edades | TRT Español

# data[].attributes.attachment_link.description
La tasa de mortalidad del coronavirus por edades