**7. Relationship between hypertension and COVID 19**

Hypertension is characterized by increased blood pressure (BP) to the extent of 140/90 mmHg. The essential modifiable factors are associated with the development of cardiovascular illnesses with high BP. Hypertension is one of the primary diseases causing worldwide non-communicable infections [28].

Coronavirus infection mainly influences respiratory tract disease. The spreading of infection is extreme in patients who are at risk of cardiovascular diseases. Hypertension causes massive pathophysiological changes in the cardiovascular system includes left ventricular hypertrophy and fibrosis, which makes parson more susceptible to SARS-CoV-2 [28].


**4. Natural language processing**

Natural language refers to the language that humans use to communicate with each other, such as Bahasa Indonesia, English, Spanish, French, or any other language spoken by people. It encompasses the words, grammar, and syntax used in everyday conversations and written texts. Natural language is the form of communication that humans use to express their thoughts, ideas, and emotions [18]. It is characterized by its complexity, variability, and the ability to convey meaning through words, sentences, and discourse. Natural language encompasses various aspects, including

vocabulary, grammar, syntax, semantics, and pragmatics [19]. Natural language processing (NLP) is a field of study that focuses on enabling computers to understand, interpret, and generate human language in a way that is similar to how humans communicate with each other. The aim of natural language processing typically involves constructing a structured representation of unstructured natural language, drawing on insights from linguistics to add organization and coherence to the text [20]. In the field of natural language processing (NLP), researchers and engineers develop algorithms and systems that enable computers to understand, analyze, and generate natural language. This involves applying linguistic and computational techniques to process and interpret text or speech data.

The increased use of natural language or natural language processing that is more similar to human language in video content and AI interactions has transformed the way humans interact with technology. AI technology has made significant advancements in understanding and generating human-like language, enabling more natural and seamless interactions [21]. This technology has improved content discovery and recommendation systems. AI algorithms analyze video metadata, transcripts, subtitles, and user behavior to understand the content and user preferences. This allows for personalized recommendations based on users' interests and viewing history. It can also analyze video transcripts, extract key information, and generate concise summaries or highlight reels. This capability is useful for video platforms, sport broadcasts, news programs, and other content where users may want a quick overview or highlights without watching the entire video. Furthermore, here's how it generates the interaction between humans and technology, especially for video and television industry:

### **4.1 Voice-based assistants**

Voice-based assistants in the video and television industry refer to the integration of voice-controlled features and functionalities within video streaming platforms, smart TVs, and other media devices [22]. These assistants support the user experience by permitting viewers to control and interact with their video content using voice commands. They provide a hands-free and convenient way for users to navigate through menus, search for specific shows or movies, and control playback options. Viewers can simply speak their commands, such as "Play the latest episode of my favorite series" or "Find action movies starring Vin Diesel," and the voice-based assistant will execute the requested action. It also assists personalized recommendations based on user preferences and viewing history. By analyzing the user's voice commands and interactions, these assistants can suggest relevant content that aligns with the viewer's interests, leading to a more tailored and engaging viewing experience. Furthermore, voice-based assistants can provide additional information and context while watching video content. For example, viewers can ask for real-time information about the actors on screen, background details about a particular scene, or even trivia about the show or movie they are watching. It simplifies the interaction between viewers and the content, allowing for a more immersive and interactive experience using natural language commands. Popular examples of AI-powered voice assistants include Amazon's Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana.

#### **4.2 Chatbots and virtual agents**

Chatbots and virtual agents are computer programs designed to simulate human conversation and provide automated responses to user queries and requests. In the

## *Language Context in the Future of Television and Video Industry: Exploring Trends… DOI: http://dx.doi.org/10.5772/intechopen.113309*

context of the video and television industry, chatbots and virtual agents are utilized to interact with viewers, assist them with inquiries, and amplify their overall experience. Chatbots are typically implemented through messaging platforms or embedded within websites or applications. They can engage in text-based conversations with users, answer frequently asked questions, provide recommendations, and offer customer support. Chatbots are programmed with predefined responses based on common queries, and they can use natural language processing techniques to understand and interpret user input [23]. Virtual agents, on the other hand, are more sophisticated and advanced versions of chatbots. They often incorporate artificial intelligence and machine-learning algorithms to understand and respond to user queries in a more context-aware and personalized manner. Virtual agents can handle more complex tasks, such as providing detailed information about shows, helping users navigate through content catalogs, and even engaging in more conversational interactions.

The use of chatbots and virtual agents in the video and television industry offers several benefits. They can provide immediate assistance to users, addressing their queries and concerns in a timely manner. Chatbots and virtual agents can also handle a large volume of interactions simultaneously, ensuring efficient customer service and support. Additionally, chatbots and virtual agents can collect valuable data and insights about user preferences, behavior, and feedback. This data can be used to improve content recommendations, personalize the user experience, and optimize marketing strategies. Overall, chatbots and virtual agents play a vital role in the video and television industry by automating customer interactions, improving user engagement, and providing efficient support services. They contribute to a more seamless and interactive viewing experience for viewers.

## **4.3 Natural language understanding**

Natural Language Understanding (NLU) refers to the capability of a computer system to understand and interpret human language in a way that is similar to how humans understand it [24]. It is a subfield of artificial intelligence and computational linguistics that focuses on empowering machines to comprehend and derive meaning from natural language inputs. NLU involves the use of algorithms and techniques to process and analyze text or speech data and extract relevant information from it. The goal is to enable computers to understand the context, semantics, and intent behind human language, allowing them to respond appropriately and take relevant actions. The model can extract meaning, context, and intent from user inputs, allowing systems to provide more accurate and relevant responses. It augments the overall user experience by enabling more natural and meaningful interactions. NLU techniques encompass various tasks, such as categorizing text into predefined categories or topics based on its content, identifying and extracting specific entities, determining the sentiment or opinion expressed in text, understanding the purpose or intention behind a user's input, and building statistical models that capture the patterns and structure of natural language [25]. This system has essential contribution to the development of the video and television industry.

#### **4.4 Voice-over technologies**

Voice-over technologies relate to the tools and techniques used to record and synchronize spoken narration or dialog with visual content, such as videos, films, animations, or presentations. In voice-over, a voice actor or narrator provides an audio track that complements or boots the visual elements of the content. It facilitates the recording, editing, and mixing of voice recordings to create a seamless integration between the spoken words and the visual media. These technologies often involve professional recording studios, high-quality microphones, audio editing software, and audio mixing equipment to achieve optimal sound quality and clarity. Furthermore, they provide flexibility and versatility in adapting content for different audiences, languages, and cultural contexts. They play an important involvement in delivering effective communication, enhancing storytelling, and creating immersive audiovisual experiences.

#### **4.5 Language generation and content creation**

Language generation and content creation point to the process of using artificial intelligence and natural language processing techniques to generate human-like text and create various forms of content [26]. This includes generating written articles, blog posts, product descriptions, social media posts, and other forms of textual content using algorithms and machine-learning models. These technologies are designed to understand human language, mimic human writing styles, and produce coherent and relevant content that can be used in various applications such as marketing, advertising, and content production. This technology streamlines content production processes, allowing for the creation of engaging and informative video content in a more efficient manner.

#### **4.6 Multimodal interactions**

Multimodal interactions refer to the communication or interaction that involves multiple modes of input and output, such as speech, gestures, facial expressions, touch, and visuals [27]. It allows users to immerse in technology or systems using various modalities simultaneously or interchangeably. For example, a multimodal interaction system may allow users to give commands through voice while also using gestures or touch to navigate or manipulate content. This approach improves the naturalness and effectiveness of human-computer interactions, as it leverages multiple channels of communication to provide a more comprehensive and intuitive user experience. It is also used to communicate with technology in a holistic and natural manner, optimizing the user experience and making technology more accessible to an expanded audience, especially for the video and television industry.

The use of natural language or language that is more similar to human language in video content and AI interactions improves the human-technology interaction by assisting in more intuitive, conversational, and personalized experiences. As AI continues to advance, we can expect even more sophisticated language models and systems that seamlessly integrate into our daily lives, augmenting our interactions with technology. Moreover, the integration of natural language or language that closely resembles human language in video content and AI interactions advance the interaction between humans and technology. This leads to more intuitive, conversational, and personalized experiences.


**2. Some** *ad hoc* **arithmetic procedures for determining an appropriate number of PCs**

#### **2.1 Procedure based on the average of the eigenvalues**

The mean *λ* of the eigenvalues is the sum over the number

$$
\overline{\lambda} = \sum\_{j=1}^{p} \lambda\_j / p. \tag{28}
$$

The sum of the eigenvalues turns out to be equal to the *trace* of the covariance matrix; therefore, the mean eigenvalue is equal to the trace divided by *p:*

One procedure for deciding on the number of PCs to retain is to retain those for which the eigenvalues are greater than average, that is, greater than *λ:* When working in terms of the correlation matrix, this average value is 1. To see this, recall that the correlation matrix is a special case of the covariance matrix, namely, the correlation matrix is the covariance matrix of the standardized variables. It is often preferable to work in terms of the correlation matrix rather than the covariance matrix, to control the effects of different units of measurement and different variances. If a variable has high variance relative to the other variables, the PC will be pulled in the direction of the variable with large variance.

When **S** is taken to be the sample *correlation* matrix, the trace of the matrix is simply *p*, and therefore, the mean *λ* of the eigenvalues is 1.


**7. References**

[10] Ghanes, M. & Zheng, G. [2009]. On sensorless induction motor drives: Sliding-mode observer and output feedback controller, *Industrial Electronics, IEEE Transactions on* 56(9): 3404–3413.

**The Asymmetrical Dual Three-Phase Induction**

**Chapter 16**

Recent research has focused on exploring the advantages of multiphase1 machines over conventional three-phase systems, including lower torque pulsations, less DC-link current harmonics, higher overall system reliability, and better power distribution per phase [1]. Among these multiphase drives, the asymmetrical dual three-phase machines with two sets of three-phase stator windings spatially shifted by 30 electrical degrees and isolated neutral points is one of the most widely discussed topologies and found industrial application in more-electric aircraft, electrical and hybrid vehicles, ship propulsion, and wind power systems [2]. This asymmetrical dual three-phase machines is a continuous system which can be described by a set of differential equations. A methodology that simplifies the modeling is based on the vector space decomposition (VSD) theory introduced in [3] to transform the original six-dimensional space of the machine into three two-dimensional orthogonal subspaces in stationary reference frame (*α* − *β*), (*x* − *y*) and (*z*1 − *z*2). From the VSD approach, can be emphasized that the electromechanical energy conversion variables are mapped in the (*α* − *β*) subspace, meanwhile the current components in the (*x* − *y*) subspace represent supply harmonics of the order 6*n* ± 1 (*n* = 1, 3, 5, ...) and only produce losses, so consequently should be controlled to be as small as possible. The voltage vectors in the (*z*1 − *z*2) are zero due to the separated neutral configuration of the machine, therefore this

Model-based predictive control (MBPC) and multiphase drives have been explored together in [5, 6], showing that predictive control can provide enhanced performance for multiphase drives. In [7, 8], different variations of the predictive current control techniques are proposed to minimize the error between predicted and reference state variables, at the expense of increased switching frequency of the insulated-gate bipolar transistor (IGBTs). On the other hand are proposed control strategies based on sub-optimal solutions restricted the available voltage vectors for multiphase drive applications aiming at reducing the computing cost and

1 The multiphase term, regards more than three phase windings placed in the same stator of the electric machine.

©2012 Gregor Recalde, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

©2012 Gregor Recalde, licensee InTech. This is a paper distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Machine and the MBPC in the Speed Control**

Raúl Igmar Gregor Recalde

http://dx.doi.org/10.5772/50559

**1. Introduction**

Additional information is available at the end of the chapter

subspace has no influence on the control [4].

cited.


The important exception to this trend, however, is sub-Saharan Africa. Poverty rates remain high in sub-Saharan Africa following limited economic growth in the late 20th century, coupled with persistently high levels of inequality in many countries in the region. As we discussed regarding the World Bank estimates based on household survey data, global extreme poverty is becoming increasingly concentrated in sub-Saharan Africa. What this long-run view makes very clear, however, is that the low and stagnating incomes endured by a large share of the population in many sub-Saharan African countries should not in any way be accepted as inevitable. Persistently high levels of extreme poverty were once the rule but are now the exception.

#### *4.3. The History of Extreme Poverty in the Last Two Centuries: Combining Survey and National Accounts Estimates*

In this chapter, we have presented evidence on the history of poverty based on two different methods. We have referred to these as survey and national accounts estimates, reflecting the different average incomes to which the estimates are anchored: in the first case, to household survey data, and in the second case, to average incomes observed in national accounts or historical reconstructions of this.

On the one hand, we have pointed to many sources of uncertainty in estimating global poverty. For recent decades, this is underlined by the differences in the poverty estimates arrived at by these two methods. There are known sources of mismeasurement in both approaches and questions remain as to how best to combine all the evidence available from national accounts and survey data. Estimates for the distant past, relying on reconstructions from historical sources, are no doubt more uncertain still.

On the other hand, we have argued that this uncertainty must not be overstated. For recent decades, both approaches show substantial falls in poverty across a wide range of poverty lines. The scale of the changes seen in average incomes in many parts of the world over the last two hundred years gives us a clear indication that global poverty also fell substantially over this longer timeframe, even allowing for significant uncertainty regarding the historical data. Whilst estimates for any given point in time are highly uncertain, the available evidence consistently points to a range of trajectories that is plausible and a range of trajectories that is not.

Within this range of plausible trajectories, however, is it possible to construct a single time series for the evolution of global poverty over the long term?

Martin Ravallion's seminal book on the history and measurement of poverty features a chart that shows one possible approach to provide such a long-term

perspective (Ravallion 2016a, Figure 2.1). He presents the long-run poverty estimates of Bourguignon and Morrisson (2002), which are based on the historical national accounts method discussed above, and brings them together with data based on household surveys for the recent period. Bourguignon and Morrisson's estimates were made in relation to a poverty line set specifically so as to result in the same global extreme poverty rate as that found in survey-based estimates for an overlapping year.28 In this way, the researchers sought to account for the 'gap' in the poverty estimates resulting from the two different sets of methods, as discussed in Section 4.1 above. Ravallion (2016a) extends this historical series for the global extreme poverty forward using the survey-based estimates published in Chen and Ravallion (2010).

That chart is reproduced here in Figure 14, using recent World Bank estimates of the share of the population living below the updated international poverty line of \$1.90 per day from 1981 onwards. Prior to this, we use our own historical poverty estimates that were presented above. These apply very similar methods as those used by Bourguignon and Morrisson (2002) but make use of more recently published historical evidence on both the extent of inequality and the level of average income. As in Bourguignon and Morrisson's original study, to produce the historical estimates of extreme poverty shown in Figure 14, we set a poverty line that results in estimates that align with the survey-based data in the overlapping year.29

Supporting this method of combining the two sets of data is the observation made above in relation to Figure 10. There, we saw that estimates of poverty measured relative to a line anchored to a certain point in the global distribution showed similar reductions over time under both approaches. Indeed, we have shown that this was particularly true at the global median, which the extreme poverty line used in Figure 14 fell close to in 1980/81.

The series in Figure 14—as with Ravallion's original chart and any reconstruction of the history of poverty—suffers from the many uncertainties associated with the available evidence. However, the available historical evidence is clear with respect to the broad features of this trajectory: poverty levels were very high in the past, and the share of the world population living in poverty declined significantly.

28 Namely, the estimates of poverty measured using the '\$1 a day' line found in Chen and Ravallion (2001). This relates to a \$1.08 poverty line measured in 1993 prices with which the authors updated the \$1 a day line given in 1985 prices.

29 World Bank estimates of the extreme poverty rate in 1981 and 1982 were 42.4% and 42.1% respectively. We set the extreme poverty line in the National Accounts data at \$5.20 (in 2011 prices). This yields a global poverty rate of 43.0% in 1980, roughly continuing the trend linearly.

Moreover, as we discuss in the following final section, this broad trend concerning *monetary* poverty is also corroborated by historical evidence concerning a range of *non-monetary* metrics.

**Figure 14.** Share of the world population living in extreme poverty, 1820–2017. Source: 1820–1980: Authors' calculations based on Maddison Project Database 2020 (Bolt and van Zanden 2020), van Zanden et al. (2014), and Global Consumption and Income Project (GCIP); 1981–2017: PovcalNet (World Bank). Note: This series is based on the methods employed in the long-run global poverty estimates that appeared in Bourguignon and Morrisson (2002) and Ravallion (2016a). It uses a more recently published set of historical data and, for the period from 1981 onwards, more recent World Bank estimates for the share falling below the updated international poverty line of \$1.90 a day.

#### **5. Other Evidence on Historical Poverty**

#### *5.1. Other Long-Run Estimates of Monetary Poverty*

Figure 15 compares our estimates of the share of the world population living in extreme poverty shown above against three other existing sets of estimates.

Our estimates are shown in red. In green are the original estimates of Bourguignon and Morrisson (2002) whose methodology we have largely emulated in producing our historical estimates. In blue is a series produced by Moatsos (2021) that also follows a similar methodology to that described in the present chapter.30

**Figure 15.** The share of the world population living in extreme poverty, according to four sets of estimates. Source: Bourguignon and Morrisson (2002), Moatsos (2021), and the authors' own estimates.

In pink is a second series from Moatsos (2021) that is instead based on the 'cost of basic needs' approach suggested by Allen (2017). Within this approach, incomes are measured against a poverty line that varies across countries according to the local minimum cost of meeting dietary and other basic needs.

One advantage of the approach is that it gives us an understanding of the history of global living standards in terms of a more readily interpretable definition of poverty: as recently as 1950 the majority of people in the world 'could not afford

30 The approaches are similar in terms of the use of PPP-adjusted incomes and the use of historical national accounts and inequality data to extend estimates of the global income distribution into the past. See Moatsos (2021) for a full description of the methodology.

a tiny space to live, food that would not induce malnutrition, and some minimum heating capacity' (Moatsos 2021, p. 195).

The cost-of-basic-needs estimates are also significant in providing an alternative methodology with which to benchmark poverty estimates, such as those of the World Bank and our own historical estimates presented here, that use incomes adjusted for price differences across countries and expressed in common units of purchasing power. This is one aspect of the World Bank's approach that has been the subject of debate in particular, and which the cost-of-basic-needs approach avoids (see Section 2.1.1 above). Overall, the long-run trends across all four series are remarkably similar. The fact that different researchers using different methodologies and data sources reach similar conclusions concerning the history of extreme poverty greatly increases our confidence in the overall trends.

#### *5.2. Long-Run Evidence from Non-Monetary Metrics*

The poorest people in the world today have worse living conditions more broadly. They live shorter lives, lack access to basic services, and a higher share suffers from hunger and malnutrition. To assess how plausible, or implausible, the historical reconstructions of poverty are, we should therefore look at the historical evidence for the living conditions in the past. Does the historical evidence on non-monetary metrics such as mortality and malnutrition match the reconstructions of high levels of monetary poverty?

#### 5.2.1. Mortality at a Young Age

Figure 16, based on Volk and Atkinson (2013), shows the share of children who died before they reached the end of puberty.31 These data, covering the last 2400 years, relate to a range of different locations from around the world. What is striking about the historical estimates is how very similar the mortality rates for children were across this wide range of 23 historical cultures. Whether in Ancient Rome, Ancient Greece, the pre-Columbian Americas, Medieval Japan or Medieval England,

31 In modern global health statistics, child mortality is defined as the share of children who die before the age of five. The historical research does not provide data for this age cutoff. A cutoff at the end of puberty has the advantage that it captures mortality over the entire course of childhood. To compare the historical estimates with modern global health data, we relied on data from the United Nations Inter-agency Group for Child Mortality Estimation (IGME) which publishes the mortality rate up to the age of 15 for countries around the world.

the European Renaissance, or Imperial China—no matter when and where a child was born, almost one in two children did not survive.

Volk and Atkinson also bring together mortality data from 20 different hunter-gatherer societies from very different locations to give an indication of the youth mortality rate in the type of society that humans in prehistoric times lived in.32 Again they find very similar mortality rates with an average death rate until the end of puberty of 48.8%, almost exactly the same as the historical sample over the last three millennia.

The high mortality of children in all world regions is plausible when we consider the evidence on humanity's population growth. We know that population growth was close to zero, while fertility rates were high. The fertility rate, the average number of children per woman in the reproductive age bracket, was high—an average of 6 or more children per woman was certainly not rare (Roser 2014). A fertility rate of 4 children per woman would imply a doubling of the population size each generation; a rate of 6 children per woman would imply a tripling from one generation to the next. However, instead, the population barely increased: historical reconstructions suggest that between 10,000 BCE and 1700, the world population grew by only about 0.04% annually (Roser et al. 2013). A high number of births without a rapid increase of the population can only be explained by one sad reality: a high share of children died before they could have children themselves. The historical evidence that almost half of all children died certainly does not seem consistent with notions that poverty levels were low in the past.

Equally important for the plausibility of the historical poverty reconstructions is the fact that both population growth and declining mortality levels coincide with the decline in poverty that the national accounts data suggest, at both the global and country level. Globally, the chart shows that the global death rate of children younger than 15 declined from close to 50% to below 5% over the course of the last century. And today, populations in places with high levels of poverty still suffer from

32 To study mortality at a young age in prehistoric societies, the researchers need to mostly rely on evidence from modern hunter-gatherers. Here, one needs to be cautious of how reflective modern hunter-gatherer societies are of the past. This is because recent hunter-gatherers might have been in exchange with surrounding societies and 'often currently live in marginalised territories', as the authors state. Both of these could matter for mortality levels.To account for this, Volk and Atkinson have attempted to only include hunter-gatherers that are best representative for the living conditions in the past; they limit their sample 'only to those populations that had not been significantly influenced by contact with modern resources that could directly influence mortality rates, such as education, food, medicine, birth control, and/or sanitation'. The one study on mortality rates of paleolithic hunter-gatherers finds a higher youth mortality rate: 56% did not survive to puberty.

youth mortality rates of up to 15%, while in places with the lowest poverty rates, up to 99.7% of children now survive their first 15 years of life.

**Figure 16.** Youth mortality over the last 2400 years. Source: The mortality estimates for historical societies are from a large number of independent studies collected in Volk and Atkinson (2013). Data for 2017 are from the United Nations Inter-agency Group for Child Mortality Estimation (IGME). Global estimates for the 20th century are based on under-five mortality from Our World in Data. Note: Youth mortality measures the share who died as infants or children before reaching the end of puberty (approximately around the age of 15).

#### 5.2.2. Human Height

A second set of evidence that allows us to study whether the reconstructions of monetary poverty over the last few generations are plausible can be found in the mortal remains of people around the world.

An individual person's height is largely determined by their genetic background, but the average height of an entire population is almost entirely determined by their living conditions, particularly the nutrition and health at a young age (Baten and Blum 2014). This allows historians to reconstruct people's living conditions by relying on the average human height in a population as a proxy measure. When there are no records of population height over time, the average height can be reconstructed from bones.

Figure 17 shows how the height of adult men has changed over the course of the last century (estimates of women's heights are published by the same source and show a very similar change).33 The differences in the height of men born in 1996 correlate closely with levels of monetary poverty today, the height of men born in the richest parts of the world being the tallest, while those born in the poorest countries being among the shortest.

**Figure 17.** Height of adult men, 1896–1996. Source: NCD Risk Factor Collaboration (NCD-RisC), Our World in Data. Note: These data relate to the height of adult men by year of birth. Poor nutrition and illness in childhood limit human growth. As a consequence, the average height of a population is strongly correlated with living standards in a population.

Furthermore, importantly for the question at hand, it is also the case that the historical changes in men's height match closely with the historical reconstructions of monetary poverty presented before. These data show large changes in men's height over time: the global average increased by 9 cm from 1.62 m to 1.71 m, and in the regions and countries that made the fastest progress against poor living conditions, it can be well over 10 cm. The average man in Europe and Central Asia in the

33 The data are published by NCD Risk Factor Collaboration (NCD-RisC).

late 19th century was smaller than the average man in sub-Saharan Africa today.34 Additionally, as is the case with poverty reduction, the smallest improvements over the last century are documented for men in sub-Saharan Africa and South Asia. Large increases, on the other hand, are documented for people in North America, Europe, Central Asia, and Latin America and the Caribbean.

The data shown here goes back to the late 19th century when differences in economic prosperity had already emerged. In line with these differences, men in the least poor regions of the world were already the tallest. Reconstructions of the economic history of Europe suggest very poor economic living conditions during the centuries preceding the Industrial Revolution. Long-run reconstructions of human height in Europe by Koepke and Baten (2005) corroborate this long-run perspective.

#### 5.2.3. Escaping the Malthusian Economy

Lastly, it is economic theory and its empirical support that suggests that the very high levels of poverty that we reported earlier do indeed reflect the living conditions in the past.

The mechanism that prevented progress against poverty and hunger in the past is referred to by economic historians as the 'Malthusian trap'. When the large majority of a society suffers from poverty and hunger, only an increase in production can raise living standards and reduce poverty. However, in the past, such productivity increases occurred only very rarely, and whenever they did occur, they only led to a brief increase in living standards because it ultimately caused an increasing size of the population which left everyone as poor as they were before. Due to this basic mechanism, higher productivity did not result in lower levels of poverty but in a larger number of people.

Ashraf and Galor (2011) develop a formal model of the Malthusian economy theoretically and study the evidence for it empirically. If the economic living standards of people in the pre-growth economy were in fact determined by the Malthusian trap, then we would expect to see a positive correlation between the level of productivity in a region and the density of the population in this area. Figure 18 is taken from their publication and confirms the theoretical prediction for the pre-growth economies in the year 1500.

34 On the question why men in the world's poorest region, sub-Saharan Africa, are slightly taller than men in South Asia, see Bozzoli et al. (2009).

#### **The partial e ect\* of land productivity on population density in 1500 CE**

(logarthmic axis)

**The partial e ect\* of land productivity on income per capita in 1500 CE**

(logarthmic axis)

The colors represent continents: Africa Asia Europe America Oceania

**Figure 18.** The partial effect of land productivity on population density and income per capita in 1500 CE. Source: Ashraf and Galor (2011). Note: The figure depicts the partial regression line for the effect of land productivity while controlling for the timing of the transition from hunting and gathering to agriculture, and the influence of absolute latitude, access to waterways, and continental fixed effects. The x- and y-axes plot the residuals obtained by regressing population density and income per capita, respectively, on these covariates.

All data in this visualisation are reported in the current borders of the world. On the x-axis of both charts, you find the same metric—the productivity of the agricultural land as measured by the quality of the soil and the climate. In the chart on the left we see that those world regions with the most productive land had the highest population density.

On the chart on the right, we see that the higher productivity of the land did not result in higher living standards. The agricultural sector in Spain, India, or Morocco was much more productive than in Finland, Egypt, and Norway, but the people in these countries were not better off—they were merely more numerous. The more productive regions were the more populous regions, and the people in these areas had to share with so many that everyone remained at dismal levels of prosperity.

In the long history before modern economic growth, higher productivity led to larger, but not richer, populations. This mechanism ensured that poverty levels were high everywhere.

#### **6. Conclusions**

Poor material living conditions were such a persistent and pervasive reality that, for much of human history, it was unimaginable that it could ever be different. Poverty did not change, and so, it was easy to believe that poverty was unchangeable. The Reverend Thomas Malthus wrote about the living conditions in his native England 'It has appeared that from the inevitable laws of our nature, some human beings must suffer from want. These are the unhappy persons who, in the great lottery of life, have drawn a blank'.35

When Malthus wrote these words in 1789, he was right about the past, but he turned out to be wrong about the world's reality after his death: In the two centuries since his death, many countries broke out of the stagnation of the past, achieved economic growth, and reduced poverty. The reconstructions of poverty presented here make clear that it is *not* an inevitable law of nature that humans must suffer from want. It is not only possible to reduce poverty, but it is a reality.

During the long past when humanity did not make any substantial progress in reducing poverty, there was no one bold enough to even imagine that it could be different in the future. Not only the reality of poverty reduction over the last 200 years stands in sharp contrast to the centuries and millennia preceding it, but with the reduction of poverty, the thinking about poverty has changed as well. Today, poverty is widely considered a social bad, a problem to be solved rather than a reality that needs to be accepted. Ravallion (2013) documents that before the modern reduction of poverty, poverty was considered a social good—'necessary and even desirable for a country's economic success'. That this idea is so repellent for us today makes clear how dramatic the change in perspective has been.

The reality of poverty reduction changed our view on poverty, and this matters substantially for our aspirations for the future. The same evidence that we presented here shows us how far the world has come, and *how* far we still have to go. That fewer than 10% of the world live in extreme poverty is the outcome of unprecedented progress and one of the most atrocious problems that the world faces today. It is the progress that we have made that makes the reality of extreme poverty so appalling—if Malthus was right that poverty was inevitable, we would need to accept the suffering that poverty causes; it is the decline in poverty documented here that makes it clear to us that the world can change and that economic growth and reductions of inequality can reduce poverty.

35 Thomas Malthus (1798)—An Essay on the Principle of Population. Chapter X, paragraph 29, lines 12–15.

As we emphasised before, the international poverty line is a very low poverty line—the name 'extreme poverty' is apt. As long as there are extremely poor people, it is ethically right that the world considers a poverty line that focuses our attention on the very poorest, but our aspirations should of course not be limited to ending only extreme poverty. According to the PovcalNet data, 85% of the world lives on less than \$30 a day—the world is a very long way away from ending poverty relative to these higher poverty cutoffs. The fact that the global mean income (at \$16 per day according to the 2017 PovcalNet data) is only about half of this poverty line shows just how much the world economy needs to grow to bring an end to poverty into reach.

Even after two centuries of unprecedented progress, an extremely large number of people around the world still suffer from poverty and extreme poverty. What we have learned is that poverty is not inevitable; now it is on us to continue and accelerate the progress the world has made. The history of global poverty reduction has only just begun.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## **Global Absolute Poverty: The Beginning of the End?**

**Sanjay G. Reddy**

#### **1. Introduction**

#### *The Idea of Ending Poverty, Rhetoric and Reality*

The first Sustainable Development Goal of "Ending poverty in all its forms everywhere" reflects an admirable collective aspiration.1 The idea that the poor need not always be with us2 is a revolutionary idea, and arguably a modern one3 .

If global development goals such as SDG 1 are not meant to be taken literally but rather to provide a guide to action and a horizon for aspiration (see, e.g., Reddy and Kvangraven 2015), neither the use of demanding words such as "ending" and "all", nor the adequacy of their specific definition in terms of targets and indicators, would lead to excessive preoccupation. If goals are meant to provide a concrete objective for policy-making, or a reference for enabling those who frame and implement policies to be held accountable, then the details of their definition may matter a great deal.

The understanding of the first Millennium Development Goal (to "Eradicate Extreme Poverty and Hunger") showed a gap between rhetoric and implementation, because "eradication" was ultimately interpreted with exceedingly modestly, as an intention to halve, between 1990 and 2015, the proportion of people in the developing

1 I would like to thank Rahul Lahoti for undertaking the calculations, based on the Global Consumption and Income Project, which gave rise to the alternative forecasts reported later in this paper, and for useful suggestions.

2 Jesus of Nazareth was said to have said (Matthew 26, p. 11): "The poor you will always have with you, *but you will not always have me*". There is no reason to interpret this famous remark as an injunction to fatalism, even if it involved the idea that the eradication of poverty was not a proximate prospect.

3 Classical political economists such as Smith, Malthus, and Ricardo, because of their notion that wages were determined by the cost of a modest subsistence, were skeptical of the prospects for economic growth leading to a necessary improvement in living standards of ordinary workers, including in particular the eradication of poverty. The attitude of Smith to China is illustrative. He views it both as a most prosperous country and one in which beggars and distress abound. Smith states (in *The Wealth of Nations*, Book 1, Chapter VIII) that "China has been long one of the richest, that is, one of the most fertile, best cultivated, most industrious, and most populous countries in the world", but also that "The poverty of the lower ranks of people in China far surpasses that of the most beggarly nations in Europe".

world living on less than \$ 1.25 (2005 PPP) a day (Pogge 2004; United Nations 2015).4 Ultimately, the declaration that the first Millennium Development Goal (MDG) had been achieved also turned crucially on an interpretation of the halving of poverty as applying to the global total headcount rather than to regional or national totals. Presumably, the most favorable case for the SDGs would be that they can do both, providing a framework for motivating and directing action and meaningful and well-defined statistical objectives.

This paper examines the likelihood that income poverty will be "ended" by 2030 as demanded by the first Sustainable Development Goal. It is demonstrated that this is unlikely, with the extent of remaining poverty and the regional distribution of poverty depending greatly on the assumptions made. It is also shown that the global economic downturn brought about by policies against COVID-19 has led to a significant setback to the goal. Conceptual issues in estimation, poverty projections, and implications for the attainment of SDG 1 are discussed.

#### **2. Relationship between Goals, Targets, and Indicators: Internal vs. External Views**

As already noted, a basic question when approaching an exercise of a societal nature such as the SDGs is that of the relationship between their public face—the understandings of them in broad social and political contexts—and their technical face—the understandings of them relevant for operational applicability in administrative contexts. Does the technical understanding of SDG 1 correspond to the societal understanding?

An interesting feature of SDG 1 targets and indicators is that they are plural (see United Nations 2019a). The very idea of eliminating poverty in all its forms involves an implicit recognition that any single measure of poverty—which must fail to capture all the forms of poverty that there are—cannot suffice. This recognition is echoed in the fact that diverse targets and indicators were chosen for SDG 1, with indicators referring, for instance, both to "the international poverty line" and to "the national poverty line", to "poverty in all its dimensions", and to the population covered by "social protection floors/systems", having "access to basic services", having "rights to land", affected by "disasters", and that live in localities or countries that adopt and implement "disaster risk reduction strategies". Moreover, each of

4 The poverty lines referred to are in "international dollars", a unit for assessing purchasing power parity (PPP) that is set notionally equivalent to one US dollar in the United States.

these are required to be disaggregated by various sub-categories, such as age, sex, employment status, geographical location (urban/rural), children, disability status, pregnancy status, whether an individual is a work-injury victim, etc. The idea of poverty adopted for technical purposes appears to involve a somewhat haphazard collection of concepts and is less clear, than the umbrella concept of eliminating poverty in all its forms adopted for public purposes.

How should an analyst or an advocate for poverty reduction respond to this situation? The internal and the external view of the matter may be distinguished. The internal view holds that the technical definition fully determines the meaning of the SDGs. The external view holds that the adequacy of the technical definition of the SDGs must be assessed in light of their broader societal role and responsibility. In the external view, the meaning of the phrase, "eliminating poverty in all its forms" must be examined in light of a broader field of references; accustomed ordinary language uses of terms such as "eliminate" and "poverty" or ambient social and political understandings (as revealed, for instance, by the spirit of political documents such as the Agenda 2030). From the external point of view, although the officially adopted list of targets and indicators (see United Nations (2019a)) provides a relevant, and perhaps even a privileged, reference point, it cannot be viewed as the last word on the subject of whether the goal of "eliminating poverty in all its forms" is adequately being met.

The SDGs ultimately gain their credibility and their authority from their endorsement by political authorities and their acceptance by a wide range of actors; therefore, it seems that the external view demands due attention. Targets and indicators should not become objects of obsession. They must be subject to ongoing scrutiny to assess their individual and joint adequacy for achieving the objective of ultimate interest, "eliminating poverty in all its forms".

#### **3. Slips between Cup and Lip: Questions of Measurement**

There is a wide and well-developed body of literature on the appeal and adequacy of individual poverty measures, which cannot be treated comprehensively here. Many of the questions raised in this literature are relevant to determining the suitability of the chosen SDG indicators. These can guide the application of the external view, since the officially accepted SDG targets and indicators may be inadequate to monitoring whether poverty "in all of its forms" is on course to being "eliminated".

Amartya Sen has noted (see, e.g., Sen 1981) that descriptions of the extent of poverty can be seen as decomposable into two component exercises, viz. identification (e.g., determining who is poor, in what ways and to what degree) and aggregation (e.g., determining the quantity, severity and distribution of poverty in a population). Both exercises can be approached in multiple ways, and there can be reasonable disagreement over the alternative ways of specifying them:

#### (1) Identification

Unidimensionality vs. Multidimensionality:

Should poverty be conceived primarily in terms of inadequate command over material resources (e.g., in the form of income or consumption) or in terms of the presence of deprivations of diverse sorts, whether of means (e.g., access to schooling) or attainments (e.g., years of schooling completed)? In either case, what is the underlying conceptual framework used to determine whether there is inadequacy or deprivation and to guide the selection of indicators?

Adequacy of Thresholds:

In any given dimension (e.g., income or consumption) what is the appropriate threshold to be used in determining adequacy? Specifically, how should a threshold be specified in order for it to have a *meaningful* interpretation as being adequate for poverty avoidance? How should they be defined so as to have a *common meaning* at different points in space and in time? It is not only the setting of a threshold for any one context, but also its translation across contexts to ensure a consistent interpretation that requires reference to a common meaning (see Pogge and Reddy 2010; Reddy 2004, 2007, 2008, 2013, 2020; Reddy and Lahoti 2016; Reddy and Pogge 2006; and Reddy et al. 2008).

#### (2) Aggregation

How should the overall extent of poverty in a society be summarized? For instance, is the number of poor persons, the proportion of poor persons, the typical severity of poverty or a composite measure most suitable? Moreover, is the performance of society in relation to the goal to be judged on the basis of a global aggregate or performance in each region or country? If the latter, what importance is to be given to each region when assessing overall progress?

(3) SDG Targets and Indicators in Light of These Questions

In practice, SDG targets and indicators raise very serious issues. For example, what United Nations (2019a) refers to as the "international poverty line" (the World Bank's \$ 1.90 2011 PPP poverty line, which it has deemed equivalent to its own previous \$ 1.25 2005 PPP poverty line) has come in for serious criticism ["Indicator

1.1.1 Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)"]. It has been argued, for instance, that this line lacks meaning in terms of the real requirements for achieving human well-being. This results both from the absence of sound conceptual and empirical underpinnings, and from distortions generated by the price indices used to attempt to maintain purchasing power over space and time (see previously cited writings, and Reddy and Lahoti (2016) for detailed criticism of the\$ 1.90 2011 PPP line and the claim of equivalence to the prior line).

National poverty lines are not necessarily better, because they correspond to many distinct methodologies, often poorly conceived or executed, and subject to political influence ["Indicator 1.2.1 Proportion of population living below the national poverty line, by sex and age"]. Although such measures may be validated by governments, they may not capture poverty in a sense that can be rationally justified and widely accepted. The debates about the adequacy of national poverty lines in many countries testify to this difficulty. Even if these lines have discernible purchasing power interpretations (which they often do not) these are not common across countries. Additionally, many countries, including even otherwise advanced countries, simply do not have official poverty lines (see, e.g., Reddy 2007, 2013; Subramanian 2012, etc.).

In the case of international poverty lines, and very often also in the case of national poverty lines, the focus has been on a stringent "absolutist" concept of poverty, whereas poverty "in all its forms" implies a more expansive concern. For instance, whereas according to the international poverty line, poverty is almost non-existent in most advanced countries, it is frequently present according to national poverty lines, and even prevalent when unofficial poverty lines and rights-based assessments of conditions of the poor are employed.5

Similarly, efforts to assess multidimensional poverty using a single composite index, although well-intentioned, may capture but also miss a great deal ["Indicator 1.2.2 Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions"]. Such an effort necessarily involves choices about what information to include, or not (depending, in part, on the availability of data), where to place thresholds of adequacy in each dimension, and how to aggregate across dimensions, including, in particular, how to treat correlations

5 See, for instance, the evaluations of the UN Special Rapporteur on Extreme Poverty and Human Rights, available on https://www.ohchr.org/EN/Issues/Poverty/Pages/CountryVisits.aspx (accessed on 1 August 2021).

between dimensions which reflect intensive concentrations of poverty. As a result, aggregate measures of multidimensional poverty may fail to take adequate note of specific deprivations, or of intense concentrations of multiple deprivations, if these do not greatly influence averages.

The chosen SDG targets and indicators can offer only a partial picture of the extent of poverty in 'all of its forms'. The credibility of the measures which have already been chosen or are likely to play roles as SDG 1 indicators is undermined by various weaknesses. As such, those who adopt the 'external view' of the appropriate relationship between the public and the technical faces of the SDGs cannot, therefore, accept as definitive the picture of global poverty that is presented by such measures alone. This having been said, the measures which are most readily available and are most prominently circulated are also likely to continue to provide public reference points for SDG monitoring. For this reason, we employ in what follows conventional measures (in particular, the \$ 1.90 2011 PPP poverty line and some variants of them within the World Bank's favored "money metric" international poverty line approach) despite our conviction that better poverty monitoring methods are possible and deserve significant additional investment. The use of alternative lines within the money metric approach offers one way of recognizing the uncertainties involved, although a limited one. We have argued extensively elsewhere against the existing measures of global income poverty, and also made a case for an international project to develop credible alternative measures based on the cost of achieving income-dependent human capabilities. Therefore, we shall not make this case again here.6

#### **4. Poverty Projections to 2030**

What is the likely evolution of poverty to 2030? We draw on the survey data of the Global Consumption and Income Project (GCIP) (see Lahoti et al. 2016) and consider alternative poverty lines and growth scenarios. Using the data of the GCIP, we were able roughly to replicate the poverty estimates of the World Bank for the SDG initial year (2015), and prior years, although with small discrepancies in estimates

6 In addition to the readings cited elsewhere in this paper, see also the debate of the author with World Bank economist Francisco Ferreira on the credibility of its existing global poverty estimates, conducted in early 2019: https://www.worldbank.org/en/news/video/2019/03/05/smackdown-debatehow-credible-are-the-world-banks-global-poverty-estimates-how-can-they-be-improved (accessed on 10 February 2020).

for individual regions and countries.7 In the baseline scenario we considered, we employed projected real income growth rates for individual countries from an available source (the U.S. Department of Agriculture International Macroeconomic Data Set8 ) which provides publicly available forecasts to 2030, unlike other prominent forecasting sources (notably the IMF World Economic Outlook Database9 ). We considered both USDA estimates from immediately before the pandemic (January 2020) and more recent ones (January 2021) revised as a result of the pandemic in order to gauge the effect of COVID-19 on global poverty projections.10

These are summarized by region and for the world in Table 1. It may be observed that growth projections for the decade fell for all regions as a result of the pandemic, with the world as a whole expected to have annual per capita growth rates that are almost half a percentage point lower than previously expected. In South Asia, the Middle East and North Africa, and Latin America and the Caribbean, more than one percentage point of annual per capita growth is expected to be lost.

**Table 1.** USDA projected compound annual per capita income growth rate between 2020 and 2030.

In the analyses we report on below, we also consider 'low' and high' forecasts, which are based on greater and lesser per capita real income growth rates than those projected by the USDA (one percentage point higher or lower than the baseline growth rate, respectively). Projections can vary greatly depending on the source and

7 This reflects the presence of some differences between the sources and underlying assumptions of the two databases.

8 See https://www.ers.usda.gov/data-products/international-macroeconomic-data-set/ (accessed on 25 January 2020).

9 See https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/index.aspx (accessed on 25 January 2020).

10 These were downloaded in January 2020 and in March 2021, respectively.

its assumptions, and this gives reason to consider different possibilities. For instance, the USDA estimates of growth rates in sub-Saharan Africa are considerably lower than those of the IMF—a difference which is potentially consequential, due to the presence of high poverty rates in the region. The IMF projected (prior to the onset of the pandemic) an average annual per capita income growth rate through 2024 of 3.89% per annum, whereas the USDA estimate through 2030 was 1.33% per annum.

Population projections were drawn from the same source and used to calculate expected per capita real income growth rates. These alternative growth rates were used to project the initial year per capita real consumption levels for percentiles of national populations, and to arrive at estimated future levels for these same percentiles. Regional consumption levels at each percentile were determined by aggregating national information using the methods described by Lahoti et al. (2016). These were then compared to the real (\$ 2011 PPP) poverty line used (also expressed in terms of real per capita consumption levels) to estimate alternative poverty headcounts and headcount ratios for individual countries, major regions, and for the world as a whole.

The poverty lines chosen were (all in\$ 2011 PPP) \$ 1.90, \$ 2.52, \$ 3.10 and \$ 5.04. The first of these is the "absolute" poverty line, which has been claimed by the World Bank to be equivalent to its previous \$ 1.25 (2005 PPP) IPL (accepted as an SDG indicator by the United Nations). The \$ 3.10 line is the higher poverty line applied by the World Bank (for reasons that are unclear, because limited conceptual justification has been offered for it). The \$ 5.04 line is that which was deemed necessary for meeting basic nutritional requirements in the United States in 2011, according to the Thrifty Food Plan of the USDA (see the discussion in Reddy and Lahoti (2016) of why this should, in principle, provide some guidance as to the minimum cost of basic human requirements elsewhere too, if the PPPs used are taken at face value as preserving purchasing power over relevant commodities). The \$ 2.52 line is half of this basic nutritional standard for the United States, providing a more stringent alternative. In neither case is any allowance made for non-nutritional capabilities.

The current poverty headcount ratios in 2020 in each world region for the various poverty lines used are shown in Table 2.

The baseline pre-pandemic forecast generated the same estimate of the \$ 1.90 2011 PPP global poverty headcount ratio for 2030 as United Nations (2019b), namely, six percent (compared to eleven percent in 2020), as can be seen in Table 3(a). The projected 2030 poverty headcount ratio in sub-Saharan Africa is 36%, considerably greater than that for any other world region. The projected 2030 headcount ratio was expected to be between zero and three percent in every other region. Adopting more

favorable assumptions leads to lower poverty headcount ratios. Growth rates that are two percent higher (closer to IMF estimates) lead to the projected headcount ratio for sub-Saharan Africa being lowered to 27%, and the world headcount ratio falling by one percentage point, to 5%.

**Table 2.** Poverty headcount ratios (%) for different poverty lines for 2020. Source: own estimates based on Global Consumption and Income Project data.

**Table 3.** (**a**) Poverty headcount ratio estimates (%) for \$ 1.90 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount ratio estimates (%) for \$ 1.90 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

**Table 3.** *Cont.*

As shown in Table 3(b), for the baseline scenario and the \$ 1.90 poverty line, the lower growth estimates as a result of the pandemic lead to a higher expected 2030 level of the poverty headcount ratio in sub-Saharan Africa (42%; six percentage points higher than under the pre-pandemic scenario) and in all regions other than East Asia. The expected 2030 world poverty headcount ratio is raised by two percentage points, or one-third of the pre-pandemic projection, to 8% of the global population. The expected world poverty headcount ratio is also raised in all other scenarios. In the most unfavorable case corresponding to growth rates two percentage points lower than in the baseline estimate, it rises a full three percentage points (to 11% of the global population), with the majority of the population in sub-Saharan Africa (53%) expected to remain in poverty even in 2030.

As can be seen from Table 4(a), under the baseline pre-pandemic growth estimate, the total number of poor persons expected to remain worldwide in 2030 is 515 million people, with the total varying between 385 million and 696 million depending on the growth scenario. The vast majority of these are projected to be in sub-Saharan Africa under all of the scenarios. As shown in Table 4(b), the revised growth estimates as a result of the pandemic lead to much higher estimates of the number of poor, ranging from 470 million to 954 million, with 659 million projected in the baseline scenario.

**Table 4.** (**a**) Poverty headcount estimates for \$ 1.90 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount estimates for \$ 1.90 IPL in 2030 using post-COVID-19 growth estimates.

Number of Poor (in millions)

**(b)**

We also considered alternative poverty lines, recognizing that the \$ 1.90 2011 PPP IPL may be inadequate for specific countries and regions, or globally. For each of these, we once again considered alternative global growth scenarios (the baseline aggregate GDP growth scenario plus or minus one or two percentage points per annum). The estimated poverty headcount ratios and headcounts for regions and for the world are reported for distinct poverty lines and, in each case, for pre- and post-pandemic growth estimates, in Tables 3–10.

The pattern that the majority of the poor remaining in 2030 are expected to be in sub-Saharan Africa does not change when the distinct growth scenarios are applied uniformly across regions (although the specific proportions do, with South Asia becoming a major contributor to the poverty total at the higher poverty lines and under the more unfavorable global growth scenarios. For the pre-pandemic growth estimates, even if the most favorable growth scenario for sub-Saharan Africa (baseline plus two percentage points) is compared with the most unfavorable growth scenario for South Asia (baseline minus two percentage points) and the highest poverty line is considered (\$ 5.04 2011 PPP; see Table 10a), a greater number of poor are expected to be in sub-Saharan Africa (977 million) as compared to South Asia (913 million). The number of poor people in the world in this scenario, even if the other world regions experience strong growth, is more than two billion persons. Considering the various "pure" scenarios, including the baseline and those which raise or lower growth rates uniformly across all regions, leads to the conclusion that at least 2.7 billion people will remain in poverty in all of these scenarios. Even if we consider the lower \$ 2.52 2011 PPP and \$ 3.10 2011 PPP poverty lines, we find that, in all scenarios, at least half a billion people will remain in poverty in 2030. These are hardly circumstances in which poverty will have been 'eliminated'. Considering the less optimistic post-pandemic growth estimates only accentuates this conclusion.

**Table 5.** (**a**) Poverty headcount ratio estimates for \$ 2.52 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount ratio estimates for \$ 2.52 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

**Table 6.** (**a**) Poverty headcount estimates for \$ 2.52 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount estimates for \$ 2.52 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

**Table 7.** (**a**) Poverty headcount ratio estimates for \$ 3.10 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount ratio estimates for \$ 3.10 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

**Table 8.** (**a**) Poverty headcount estimates for \$ 3.10 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount estimates for \$ 3.10 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

**Table 9.** (**a**) Poverty headcount ratio estimates for \$ 5.04 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount ratio estimates for \$ 5.04 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

North Africa 27 22 32 18 37 South Asia 47 40 55 33 62

Africa 83 80 86 76 88 World 35 31 39 27 43

Sub-Saharan

**Table 10.** (**a**) Poverty headcount estimates for \$ 5.04 IPL in 2030 using pre-COVID-19 growth estimates; (**b**) poverty headcount estimates for \$ 5.04 IPL in 2030 using post-COVID-19 growth estimates. Source: own estimates based on Global Consumption and Income Project data.

The alternative growth scenarios considered in the tables involve the application of the "same" poverty line in different world regions. However, there is a question as to whether or not the poverty lines involved are in fact the same in a meaningful sense, as a result of deficiencies in current PPPs as constant price indices for the cost of poverty avoidance. In the presence of these problems, poverty lines which, for different world regions or countries, capture the cost of poverty avoidance in terms of purchasing power over commodities necessary to avoid poverty locally according to a common criterion may correspond to *di*ff*erent* nominal PPP dollar amounts. It cannot be known what these discrepancies are without full-fledged studies leading to the construction of suitable country-specific poverty lines reflecting a common understanding of what poverty avoidance demands. The \$ 1.90 2011 PPP poverty line is highly conservative for developed countries (such as the 'base country' for PPP price indices, the United States, where it is clearly inadequate to avoid poverty even according to absolutist standards such as those offered by the Thrifty Food Plan). There is therefore reason to think that more realistic poverty lines, when expressed in 2011 PPP dollars, would be higher for at least some countries. It is likely that more realistic poverty lines would be attained through adjustments that vary across countries and regions. Any appearance that different poverty lines (in \$ 2011 PPP units) are being applied in different regions as a result of such modifications would be only an optical illusion, reflecting the need to correct for systematic mismeasurement of the PPPs being used at present, when applied to poverty lines, in order to ensure that they correctly measure the same thing everywhere.

#### **5. Poverty Reducing the Impact of Economic Growth, and Implications**

Where is the poverty-reducing impact of economic growth the greatest? The relationship between a change in the growth rate and the incremental reduction in poverty in each world region at each poverty line defines a "semi-elasticity". We report in Table 11 the impact of the growth rate being one percentage point less (baseline minus one) or one percentage point more (baseline plus one) at different poverty lines, and for different world regions. It can be seen that the impact of a one percentage point increase or decrease in the growth rate (from the baseline level) is greatest in terms of both headcount ratio and headcount in sub-Saharan Africa, at lower poverty lines. This changes, however, as the poverty line is raised, with South Asia becoming the world region where a change in the growth rate has the largest impact on both the headcount ratio and the total headcount of poverty. At the highest poverty line studied, a one percentage point change makes a difference in the headcount of 142 million in South Asia compared to 43 million in sub-Saharan Africa

(for post-pandemic growth estimates). Growth is poverty-reducing everywhere. A one percentage point increase in global growth makes a difference of between 105 and 321 million poor worldwide, depending on the poverty line chosen, for post-pandemic growth estimates. Growth benefitting sub-Saharan Africa and South Asia has a greater impact. The region where the impact is greatest depends on the poverty line.

At higher poverty lines, additional growth has a sizable impact on poverty in all regions, with its impact on poverty in East Asia rising considerably. At the highest poverty line, the impact of additional growth on the number of poor in East Asia surpasses that on the number of poor in sub-Saharan Africa (although the impact is less than in South Asia). These conclusions qualify the widespread presumption that addressing the problem of absolute poverty worldwide requires a singular focus on sub-Saharan Africa. Especially at higher poverty lines, income poverty is a global problem, and sustaining growth throughout the developing world is important for its reduction.

**Table 11.** (**a**) Headcount semi-elasticities of growth based on pre-COVID-19 growth estimates for different poverty lines (2011 PPP); (**b**) headcount semi-elasticities of growth based on post-COVID-19 growth estimates for different poverty lines (2011 PPP). Source: own estimates based on Global Consumption and Income Project data.

**Table 11.** *Cont.*

#### **6. Conclusions**

Sustained economic growth in developing countries—especially the poorest—is required for global income poverty reduction. The likelihood of achieving the first Sustainable Development Goal of "ending poverty" has diminished, as a result of the economic setbacks experienced in the wake of COVID-19. Our picture of the likely extent of worldwide progress by 2030, and of where remaining poverty is likely to be concentrated, are both greatly dependent on specific assumptions, such as the

poverty line used. Even in the most favorable scenarios, the world will reduce but not "end" poverty. Under less favorable ones, mass poverty is likely to remain a significant concern.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

Subramanian, Sreenivasan. 2012. *The Poverty Line*. New Delhi: Oxford University Press.

© 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).