(C) Our World in Data
This story was originally published by Our World in Data and is unaltered.
. . . . . . . . . .



The Our World in Data Dataset of Famines [1]

['Joe Hasell', 'Max Roser']

Date: 2017-12-07

The Our World in Data Dataset of Famines A global dataset of famine deaths from 1860 to 2016.

This article is outdated This text was originally published on December 7, 2017, as part of our topic page on famines. Since then, we have started using more recent and improved data from the World Peace Foundation as our main data source on famines. While we currently do not plan to update our dataset, we are sharing the previous work here for reference.

For our topic page on famines, we assembled a global dataset on famine deaths from the 1860s until 2016. This 'Our World in Data-Dataset of Famines' can be found at the very end of this article and is preceded by a discussion of how famines were defined, how this dataset was constructed, and on which sources it was based.

Intensity vs. magnitude of famines

The Integrated Food Security Phase Classification (IPC) provides a definition of 5 levels of food insecurity of increasing severity, with level 5 constituting famine. According to the IPC, in order for a food insecurity situation to be declared a famine it must meet three specific criteria:

At least one in five households faces an extreme lack of food More than 30 percent of the population is suffering from acute malnutrition (so-called ‘wasting’) At least two people out of every 10,000 are dying each day

Whilst providing a more objective, and hence de-politicized, benchmark for declaring a famine – vital for eliciting a timely humanitarian response – a key aspect of the IPC classification is to provide a graduated system that fits the reality of food crises better than a binary ‘famine or no-famine’ approach. Notice that it focuses on the intensity of the crisis. As discussed by Howe and Devereux (2004), this is distinct from the magnitude of the event, typically understood in terms of the total (excess) mortality that occurred.1 In compiling our table of famine deaths over time, we have naturally used estimates of the latter. It is important to note that there is no institutionally-agreed classification of famines in terms of magnitude. Indeed, for some people, a crisis that resulted in no excess mortality might still be properly considered a famine under some circumstances – there are many terrible outcomes that a severe food crisis can produce other than mortality, such as losses of livelihoods or long-term health impacts. However, in producing our dataset, we decided to implement a lower threshold of mortality for a crisis to be included.

It is worth seeing that these two dimensions – intensity and magnitude – whilst clearly related, are nevertheless independent of each other. A very high-intensity famine, resulting in high mortality rates, may only affect a very small group of people and thus represent a relatively low-magnitude event. Or a low-intensity crisis may extend across a wide area and over a long period, resulting in a high-magnitude famine. A threshold in terms of intensity (i.e. IPC level 5) does not, therefore, straightforwardly map onto any given magnitude threshold.

Since this dataset was published in 2017, our data only includes famines up to and including 2016.

Incomplete or inaccurate historical records

In constructing our table of famine mortality over time, we have relied on a variety of secondary sources (listed below), themselves generated from historical accounts that did not make use of such precise definitions, nor would they have been able to do so given the frequent absence of detailed demographic records. Even if we may imagine a relative degree of conformity through time to the notion that famine consists of ‘a widespread lack of food leading directly to excess mortality from starvation or hunger-induced illnesses’, it is important to note that most of the mortality estimates listed in our table are typically very rough and are often the subject of a good deal of controversy (some examples are discussed in more detail below).2

It should be borne in mind that there may be many motives for different observers, record keepers, or historians to (intentionally or otherwise) over- or underestimate mortality levels according to their sympathies with the affected population or suspected perpetrators.3 As noted by the World Peace Foundation, “generally speaking, better demographic calculations lead to lower estimations of excess deaths than those provided by journalists and other contemporary observers. We might therefore reasonably expect an upward bias in the figures for earlier famines on the record [i.e. from 1870s].”

The earlier back one goes, though, the more one might suspect that the written historical record is incomplete. We start our table from the 1860s. Whilst records for this period are no doubt more complete than earlier times, it is likely that some smaller events that would count as famines by today's standards may not have been recognized or recorded as such and are therefore missing from our table.

How is excess mortality estimated?

The aim of the table below is to show estimates of ‘excess mortality’ – that is to say, the extra number of deaths that occurred during the famine as compared to the number there would have been had the famine not occurred. As noted above, it should be borne in mind that those dying of infectious diseases during famines are normally also included in this. As such, mortality estimates typically try to subtract the ‘normal’ death rate – that is expected in the famine’s absence – from the actual total death rate during the famine. Estimating the latter is far from straightforward given the paucity of reliable demographic statistics typical of even recent famines. This also requires making assumptions about what the ‘normal’ death rate is, leaving even more room for disagreement (see discussion of the Democratic Republic of Congo famine below for an example).

Given this focus on excess mortality, some severe food insecurity situations involving high levels of mortality can nonetheless result in next to no excess mortality where the ‘normal’ death rate for the area in question is already very high. Niger in 2005, which many observers at the time considered to be a famine, is an example of this.4

It is worth noting as well that what is ‘normal’ changes through time. Modern definitions of famine include criteria for nutrition and mortality that would correspond to conditions typical or near-typical of non-crisis conditions in earlier periods for much of the world.5

How should war famines be taken into account?

It should be noted that, based on the work of Alex de Waal at the World Peace Foundation, included in our famine list is a number of events that are described as ‘episodes of mass intentional starvation’. Some of these events are not included in other lists of major famine events of the 20th century (notably some of them are missing from Stephen Devereux’s much-cited 2000 paper, Famine in the Twentieth Century). This suggests that some authors might consider these deaths to be attributable more to conflict and not reliably attributable to famine. Nonetheless, we decided to include these events in our table. Our reasoning here is that the excess mortality associated with many of the famines listed in Devereux (2000) would not have occurred in the absence of conflict, and many of them are not without similar controversy (see below for some more discussion). Thus any distinction between 'famine' and ‘episodes of mass intentional starvation’ seems to be a matter of degree, and as such there appeared no clear reason not to include the latter in our table.

Some controversial mortality estimates

Many of the famines included in our dataset are associated with a wide range of plausible mortality estimates. Two apt examples are the famines in the Democratic Republic of Congo, which took place amidst the Second Congo War beginning in 1998, and that of North Korea from 1995 to 1999. These famines stand out in recent decades for their particularly high mortality. But in both cases, the range of mortality estimates available in the literature is large, with high and low estimates varying by several millions of deaths.6

Where such differences are present, our midpoint estimates are clearly very sensitive to our choice of upper and lower bounds. We have not simply taken the highest and lowest figures published in the public domain, given that more accurate estimates often emerge with time. As noted by the World Peace Foundation, “generally speaking, better demographic calculations lead to lower estimations of excess deaths than those provided by journalists and other contemporary observers." Rather, we have sought to select upper and lower estimates based on the balance of opinion in commonly cited sources, all of which are given (for each individual event) in our dataset of famines. Inevitably though, this meant us taking a position in some controversial cases, discussed in detail below.

Famines with very low mortality

In our table, we have excluded crises where reported excess mortality was lower than 1,000. Our reasons for doing so were twofold. First, in the context of very large margins of error for many of the famines in our table (with upper and lower estimates of excess mortality sometimes several million apart), we felt that including events in which very few people are recorded as dying might give a misleading impression of the accuracy of the rest of the estimates in the table. Second, for many people, substantial excess mortality (due to starvation or hunger-induced diseases) would normally be seen as an integral part of what it means for a crisis to constitute a famine.7

It should be noted that there might be good reasons to make use of a definition of famine that allows for zero or very low excess mortality.8 Nevertheless we felt some lower threshold would be appropriate, though the exact cut-off was a somewhat arbitrary choice.

One of our main data sources is EM-DAT’s International Disaster Database, which lists mortality estimates for a range of disasters. In particular, it provides data on a number of smaller-scale events often not given in the main lists of major famines we have used. We considered those events listed as 'Droughts' as being famines, though we excluded any such disasters with a mortality estimate lower than 1,000 as per our threshold.

In addition, we also chose to omit two recent drought events listed in the database for China of 1,400 and 2,000 excess deaths in 1988 and 1991 respectively, having failed to find any corroborating cross-references to famine having occurred in these years.

Relatedly, some events often described as famines are not included in the table below where the reported excess mortality is considered to be in some sense 'negligible'. As with shifting understandings of what the 'normal', non-crisis death rate consists of, no doubt this is a threshold that has changed considerably over time as demographic analysis of famines has become more precise and excess mortality a relatively rare event. Examples of potentially controversial omissions we have made along these lines include the Highland Potato Famine in Scotland (1846-56), the Bihar famine in India 1966-7 (discussed in more detail below) and Niger in 2005.

Various secondary sources that we have used to compile our table (listed below) themselves use some excess mortality cut-off, but one typically higher than our threshold of 1,000. This means that there may exist records of famines of a magnitude larger than 1,000 excess deaths that are not included in our table (if they did not appear in the International Disaster Database).9 However, given the large-magnitude events in our table, this can only have had a very small effect on the overall trend outlined in our charts.

Famines in independent India: Bihar 1966-67 and Maharashtra 1972-3

The International Disaster Database lists a drought in India in 1965 as killing 1.5 million people. The only food crisis around this time that we could find cross-references for was that in Bihar, more commonly cited as occurring in 1966-67. Official statistics, however, suggest very low excess mortality. Indeed, the famine was sometimes invoked as evidence that independent India had turned a corner in its development, such that it could now cope with a serious drought without sustaining major loss of life.

Dyson and Maharatna (1992), however, regarded the official mortality data to be highly deficient. They concluded that ‘while the available data show little sign of excess mortality in Bihar, we probably cannot exclude this possibility’.10 Drèze (1990) similarly came to the conclusion that ‘there is precious little evidence to support the self-congratulatory statements that have commonly been made about the Bihar famine, e.g. “no exceptional mortality was recorded” or “no one died of starvation”.’11

Given such uncertainty, we decided to exclude this famine from our dataset. Notably, we chose to exclude the EM-DAT figure for 1965: such a high mortality seems questionable given the absence of other corroborating references.

Similar issues surrounded the determination of an excess mortality figure for the Maharashtra crisis in 1972-3. For Drèze (1990) it is clear that, whilst "the crisis was of extreme severity, famine was uncontroversially averted." This was largely due to an enormous public employment program that at its peak employed as many as 5 million people in Maharashtra state alone.

Whilst Drèze considers the available demographic statistics to imply that "mortality rose only marginally, if at all,", and notes that there were no confirmed instances of 'starvation deaths', Dyson and Maharatna (1992) insist that the mortality rates do imply a significant excess mortality of 130,000 people. They arrive at this conclusion based on adjusting the figures to account for systematic under-registration of deaths, the pre-crisis trend in mortality rates, inter-census population growth, and the possibility of excess mortality also occurring in 1972.

In keeping with many other of our listed famine mortality estimates, we decided to provide that figure cited by Devereux (2000), itself quoting the 130,000 figure from Dyson's work.12

It is worth seeing though that our choice to attribute a mortality figure to the Maharashtra drought, but not that of Bihar, stands in contrast to the conclusion of Drèze (1990) – based on consideration of nutrition surveys, asset disposals, and land sales (signs of acute distress), and the extent of migration – that the Bihar famine struck considerably harder. Nevertheless, in the absence of a specific mortality estimate for the Bihar famine, it has been excluded from our list of famines. In any case, the level of uncertainty surrounding both of these famines should be borne in mind.

Great Leap Forward Famine, China 1959-61

By far the largest single event in our dataset is that of China at the turn of the 1960s associated with the economic and social campaign led by Mao Zedong known as the Great Leap Forward. During and immediately after the Chinese famine, however, it remained 'shrouded in mystery’, with the Chinese authorities and some Western observers insisting that, despite successive poor harvests, famine had been averted. In the post-Mao era of the early 1980s, some official demographic data was newly released allowing for the first systematic investigations of the death toll.

Initial results from this suggested an excess mortality of around 30 million, and this figure gained some currency. Subsequent estimates have tended to be lower.

One of the key issues is how these official data compare with UN estimates of infant mortality and life expectancy for the period 1950-5, which imply significant under-registration in official data. Exactly what assumptions are made about such under-registration have consequences for the ultimate mortality estimate produced. There is necessarily a degree of arbitrariness to such assumptions, with different hypotheses often standing in contradiction to alternative sources of evidence such as historical documentation, and conflicting with the demographic patterns typically observed in famines.

Whilst there is much uncertainty about the exact number of deaths attributable to the Great Leap Forward famine, it seems certain that it represents the single biggest famine event in history in absolute terms. Relative to the size of the population however, the death rate was “modest” compared to that of Ireland in the 1840s or Finland in 1867-8, and was comparable to that of the 1876-9 famine in China.

For our table we use the midpoint between the lowest and highest estimates given in our main sources, 15 million being the lower bound given by Ó Gráda (2009) and 33 million being the upper bound given by Devereux (2000).

A good summary of these issues is given by Ó Gráda (2008).

Democratic Republic of Congo, 1998-2007

The most commonly cited excess mortality estimate for the conflict in the Democratic Republic of Congo (DRC) is the 5.4 million given in a 2007 report by the International Rescue Committee (IRC). The estimates were based on ‘retrospective mortality surveys’ in which interviewers asked a sample of respondents to report the number of deaths that had occurred within their household over a given period. These were then used to make inferences about the number of deaths across the country and, in conjunction with an assumed baseline mortality rate capturing the number of people that would have died anyway in the absence of the conflict, were used to generate the overall excess mortality figure.

Some controversy was generated in 2009 with the publication of the 2009/10 Human Security Report which presented a number of criticisms of the IRC methodology and argued that it had significantly overestimated the death toll. The key debate concerned the baseline mortality rate used, which the Human Security Report considered to be too low, thereby inflating in its view the number of deaths that could be associated with the conflict. In addition, the Report argued that the samples of respondents used in the earliest IRC surveys were unrepresentative and also too small to provide reliable estimates. In particular, it suggested that the areas visited were atypical in that many of them were selected because of there being existing or planned humanitarian operations already in the vicinity, so they were therefore likely to have higher mortality rates than the average location.

The IRC authors meanwhile point to the fact that access to some of the most insecure zones was impossible during the surveys, suggesting a sample bias in the opposite direction.

The overall argument of the Human Security Report is that the available data is not sufficient to form the basis for a credible excess mortality estimate, and any attempt to make one is very sensitive to the choice between a range of plausible alternatives and subject to a very wide margin of error. It does produce an estimate, but only for the period between 2001-7 for which the surveys conducted were more representative and numerous. The report’s ‘best estimate’ for excess mortality over this period is 863,000, compared to the 2,830,600 of the IRC for the same period. However, it points out that this is very sensitive to assumptions about whether the counterfactual baseline mortality rate should be considered to have a trend. For short-lived events a point estimate for the baseline mortality rate is sufficient. To estimate the excess mortality of a long-lived event, the report argues, one should allow for the possibility that the baseline mortality rate would have changed over this period in the absence of the event being studied. In the case of DRC, it might be reasonable to assume that a negative trend in mortality rates observed prior to the outbreak of war would have continued, in which case the Report’s ‘best estimate’ for the 2001-7 period would increase to 1.5 million.

As such, the 863,000 figure that we include as a lower bound in our table should be treated with extreme caution. It completely excludes the period prior to 2001 and also ignores the downward pre-trend in mortality rates (as does the IRC estimate).

On the other hand, all these estimates of excess mortality include ‘violent deaths’ i.e. those directly attributable to conflict and not to the ensuing famine conditions. The IPC report cited does not provide an exact number of violent deaths, but it does claim that “less than 10 percent of all deaths were due to violence, with most attributed to easily preventable and treatable conditions such as malaria, diarrhea, pneumonia, and malnutrition”. As such we do not attempt to subtract violent deaths from the total.

North Korea, 1995-1999

The number of people who died in the North Korean famine remains highly uncertain, largely due to the closed nature of the country which has precluded access to official data and other channels of inquiry, such as surveys. Estimates range from the North Korean Government's 'quasi-official' estimate of 220,000 to the 3.5 million arrived at by South Korean NGO, Good Friends Centre for Peace, Human Rights and Refugees by extrapolation from interviews conducted with refugees fleeing the country. Whilst one might naturally be suspicious of the Government's own estimate, the approximate figure has been lent some credence by a recent study by Spoorenberg and Schwekendiek (2012). Via a reconstruction of demographic trends between 1993 and 2008 census data, the authors deduce an estimated mortality between 240,000 and 420,000. A rough consensus seems to have emerged that the 3.5 million is not reliable: the sample of interviewees – people from areas so badly affected that they sought to emigrate – was almost certainly unrepresentative of the country as a whole.13

Over time, estimates made via a variety of methods have tended to suggest increasingly lower excess mortality. For instance, Goodkind and West (2001) put forward 600,000-1 million, with a subsequent study by Goodkind, West, and Johnson (2011) suggesting a mortality towards the lower end of that range. Ho Il Moon in an article for VoxEU argues for a figure of 336,000, again based on the reconstruction of intercensal demographics. We take as our lower bound the 240,000 from Spoorenberg and Schwekendiek (2012) and Goodkind, West, and Johnson's (2011) higher figure of 600,000 as our upper bound.14

Indonesia 1962-68

Pierre van der Eng collates local and international newspaper reports of a series of localized famines that may have affected specific parts of Indonesia intermittently during this period, against a backdrop of more generalized and persistent malnutrition in much of the country (his paper is partly available here). As news reports, these figures are clearly not necessarily all that reliable and naturally focus on total numbers of deaths rather than excess mortality. Nevertheless, taken together they probably do point towards some excess famine mortality. Moreover, this was a period of significant repression of press freedoms in which the government appears to have sought to actively restrict reporting on food crises, such that the reports collated may only represent a subset of famine events that occurred.

In our table we include a zero lower bound and use van der Eng’s total figure of 135,400 deaths as the upper bound, taking the midpoint of these two for inclusion in the graphs presented in this topic page.

Sources

Major sources

Minor sources

The OWID Dataset of Famines

🗂️ This table is also available as a downloadable spreadsheet.

Year Country Excess Mortality midpoint Excess Mortality lower Excess Mortality upper Source 1846–52 Ireland 1,000,000 1,000,000 1,000,000 Ó Gráda (2007) 1860-1 India 2,000,000 2,000,000 2,000,000 Kumar and Raychaudhuri (1983) 1863-67 Cape Verde 30,000 30,000 30,000 Ó Gráda (2010): 22 1866-7 India 961,043 961,043 961,043 Kumar and Raychaudhuri (1983) 1868 Finland 100,000 100,000 100,000 Ó Gráda (2010): Table 1.1 1868-70 India 1,500,000 1,500,000 1,500,000 The Imperial Gazetteer of India (1907) 1870–1871 Persia (now Iran) 1,000,000 500,000 1,500,000 Okazaki (1986) 1876–79 Brazil 750,000 500,000 1,000,000 World Peace Foundation (2017); Davis (2001) 1876–79 India 7,176,346 6,135,000 8,217,692 Maharatna (1992): Ó Gráda (2010): Table 1.1 1877–79 China 11,000,000 9,000,000 13,000,000 World Peace Foundation (2017); Ó Gráda (2010): Table 1.1 1878-1880 USA (St. Lawreence Island Alaska) 1,000 1,000 1,000 Crowell and Oozevaseuk (2006) 1885-99 Congo 3,000,000 3,000,000 3,000,000 World Peace Foundation (2017) 1888-92 Sudan 2,000,000 2,000,000 2,000,000 World Peace Foundation (2017) 1888-9 India 150,000 150,000 150,000 Kumar and Raychaudhuri (1983) 1888–1892 Ethiopia 1,000,000 1,000,000 1,000,000 World Peace Foundation (2017) 1891–1892 Russia 275,000 275,000 275,000 World Peace Foundation (2017) 1896-7 India 3,887,287 2,624,574 5,150,000 Kumar and Raychaudhuri (1983); Maharatna (1992) 1896-1900 Brazil 1,000,000 1,000,000 1,000,000 World Peace Foundation (2017) 1897-1901 China 1,000,000 1,000,000 1,000,000 World Peace Foundation (2017) 1899-1901 India 2,699,790 1,000,000 4,399,579 World Peace Foundation (2017); Maharatna (1992) 1899-1902 S Africa 42,000 42,000 42,000 World Peace Foundation (2017) 1900-03 Cape Verde 15,500 11,000 20,000 EM-DAT (2017); Ó Gráda (2010): 22 1903-06 Nigeria (Hausaland) 5,000 5,000 5,000 Devereux (2000) 1904-07 Namibia 55,017 34 110,000 World Peace Foundation (2017) 1906-07 Tanzania (south) 118,750 37,500 200,000 World Peace Foundation (2017); Devereux (2000) 1907-08 India 2,683,782 2,148,788 3,218,776 Maharatna (1992) 1910 Niger 85,000 85,000 85,000 EM-DAT (2017) 1913-14 West Africa (Sahel) 125,000 125,000 125,000 World Peace Foundation (2017); Devereux (2000) 1915-18 Greater Syria (including Lebanon) 350,000 350,000 350,000 World Peace Foundation (2017) 1915-16 Turkey (Armenians) 400,000 400,000 400,000 World Peace Foundation (2017) 1917-18 Germany 763,000 763,000 763,000 World Peace Foundation (2017) 1917-19 Persia (now Iran) 455,200 455,200 455,200 World Peace Foundation (2017) 1917-19 East Africa 300,000 300,000 300,000 World Peace Foundation (2017); Devereux (2000) 1919 Armenia 200,000 200,000 200,000 World Peace Foundation (2017) 1920-22 Cape Verde 24,500 24,000 25,000 EM-DAT; Ó Gráda 2010: 22 1920-21 China (Gansu, Shaanxi) 500,000 500,000 500,000 Devereux (2000) 1921-22 USSR 9,000,000 9,000,000 9,000,000 Devereux (2000); Ó Gráda 2010: Table 1.1 1927 China (northwest) 4,500,000 3,000,000 6,000,000 Devereux (2000); Ó Gráda 2010: Table 1.1 1929 China (Hunan) 2,000,000 2,000,000 2,000,000 Devereux (2000); Ó Gráda (2008) 1930-31 Libya 50,000 50,000 50,000 World Peace Foundation (2017) 1932-34 USSR (Ukraine) 5,650,000 3,300,000 8,000,000 World Peace Foundation (2017); Devereux (2000) 1932-34 USSR (Russia, Kazakhstan) 1,500,000 1,500,000 1,500,000 World Peace Foundation (2017) 1934, 1936-7 China (Sichuan) 5,000,000 5,000,000 5,000,000 World Peace Foundation (2017); Ó Gráda (2008) 1940-43 Cape Verde 20,000 20,000 20,000 EM-DAT; Ó Gráda (2010): 22 1941-50 Europe/USSR (collection of WWII-related events) 6,333,000 6,333,000 6,333,000 World Peace Foundation (2017) 1941-45 East/Southeast Asia (collection of WWII-related events) 5,444,000 5,444,000 5,444,000 World Peace Foundation (2017) 1943 China (Henan) 3,250,000 1,500,000 5,000,000 World Peace Foundation (2017); Devereux (2000); Ó Gráda (2008) 1943 India (Bengal) 2,550,000 2,100,000 3,000,000 World Peace Foundation (2017); Devereux (2000) 1943-44 Rwanda 300,000 300,000 300,000 Devereux (2000) 1944 Netherlands 10,000 10,000 10,000 Devereux (2000) 1946-48 Cape Verde 30,000 30,000 30,000 EM-DAT (2017); Ó Gráda (2010): 22 1946-47 USSR (Ukraine and Belorussia) 1,300,000 600,000 2,000,000 World Peace Foundation (2017); Devereux (2000) 1957-58 Ethiopia (Tigray) 248,500 100,000 397,000 World Peace Foundation (2017); Devereux (2000) 1959-61 China 24,000,000 15,000,000 33,000,000 World Peace Foundation (2017); Devereux (2000); Ó Gráda (2010): 1.1 1966 Ethiopia (Wallo) 52,500 45,000 60,000 Devereux (2000) 1962-68 Indonesia 67,700 - 135,400 van der Eng (2012) 1968-70 Nigeria (Biafra) 750,000 500,000 1,000,000 World Peace Foundation (2017); Devereux (2000); Ó Gráda (2010): 98 1969-74 West Africa (Sahel) 50,500 - 101,000 World Peace Foundation (2017); Devereux (2000) 1972-73 India (Maharashtra) 130,000 130,000 130,000 Devereux (2000) 1972-75 Ethiopia (Wallo & Tigray) 350,000 200,000 500,000 World Peace Foundation (2017); Devereux (2000) 1974-75 Somalia 20,000 20,000 20,000 Devereux (2000) 1974 Bangladesh 1,000,000 500,000 1,500,000 Devereux (2000); Ó Gráda (2010): 98 1979 Cambodia 1,605,000 1,210,000 2,000,000 World Peace Foundation (2017); Devereux (2000); Ó Gráda (2010): 98 1980 Chad 3,000 3,000 3,000 EM-DAT (2017); Iliffe (1987): 253 1980-81 Uganda 30,000 30,000 30,000 Devereux (2000) 1982-85 Mozambique 100,000 100,000 100,000 Devereux (2000) 1983-85 Ethiopia 795,000 590,000 1,000,000 World Peace Foundation (2017); Devereux (2000); Ó Gráda (2010): 98 1984-85 Sudan (Darfur, Kordofan) 245,000 240,000 250,000 World Peace Foundation (2017); Devereux (2000) 1988 Sudan (south) 175,000 100,000 250,000 World Peace Foundation (2017); Devereux (2000) 1991-93 Somalia 360,000 220,000 500,000 World Peace Foundation (2017); Devereux (2000) 1995-99 North Korea 420,000 240,000 600,000 World Peace Foundation (2017); Spoorenberg and Schwekendiek (2012); Goodkind et al. (2004) 1998 Sudan (Baht el Ghazal) 85,000 70,000 100,000 World Peace Foundation (2017); Devereux (2000) 1998-2007 Democratic Republic of Congo 3,131,500 863,000 5,400,000 Coghlan et al (2007); Human Security Report Project (2011) 2002 Malawi 1,650 300 3,000 Devereux (2002) 2003-05 Sudan (Darfur) 200,000 200,000 200,000 World Peace Foundation (2017) 2003-06 Uganda 100,000 100,000 100,000 World Peace Foundation (2017) 2011 Somalia 255,000 255,000 255,000 World Peace Foundation (2017)

Cite this work This content is outdated, but if you would still like to use it, here is how to cite it:

Our articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as: Joe Hasell and Max Roser (2017) - “The Our World in Data Dataset of Famines” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/the-our-world-in-data-dataset-of-famines' [Online Resource] BibTeX citation @article{owid-the-our-world-in-data-dataset-of-famines, author = {Joe Hasell and Max Roser}, title = {The Our World in Data Dataset of Famines}, journal = {Our World in Data}, year = {2017}, note = {https://ourworldindata.org/the-our-world-in-data-dataset-of-famines} }

[END]
---
[1] Url: https://ourworldindata.org/the-our-world-in-data-dataset-of-famines

Published and (C) by Our World in Data
Content appears here under this condition or license: Creative Commons BY.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/ourworldindata/

You are viewing proxied material from magical.fish. The copyright of proxied material belongs to its original authors. Any comments or complaints in relation to proxied material should be directed to the original authors of the content concerned. Please see the disclaimer for more details.