Inclusive Development :-The quintile income and the poverty line
The rhetoric of “inclusive development” tends often to be lost in vague generalities, when it is not altogether absent in various processes on the ground or in state policy that claims to be inspired by its demands. This note suggests that in at least one specific and restricted area of application – the intersection of poverty, inequality and growth – it should be possible to capture some elementary aspect of inclusiveness by monitoring trends, set against targets, of the “quintile income” statistic. This statistic, which was proposed in earlier work by Kaushik Basu, is a simple and useful aid to verifying the reach of inclusiveness in a specific dimension of development, a theme that is elaborated on in this note.
Every season has its buzzword, and the vogue today, it would appear, is “inclusive development”. One supposes that the term is intended to cover a multitude of desirable aims and goals. As such, it seems reasonable to believe, for instance, that “inclusive development” would have implications for the notions of “national integration” and “citizenship”, and therefore for recent events on the ground in Jammu and Kashmir, the north-east, and the so-called “Maoist Belt”.
Similarly, one must expect that an engagement with “inclusive development” must imply also an engagement with various manifestations of social exclusion based – for example – on caste, religious,and gender identities. A third area of relevance would presumably relate to the extent – measured by both depth and coverage – of social security provisioning for the deprived. This is just a minute sample of the objects of concern of the term under discussion – but the sample is large enough to highlight certain elementary distinctions and contrasts.In particular, it is impossible not to see that there is the engagement in principle and disengagement in practice, just as there are pretty phrases and ugly facts.
Thus, for many, the State’s protestations of “inclusive development” make for a clanging, angling discord when juxtaposed with talk of sedition and anti-national activity; with the facts of manual scavenging, the socio-economic status of Muslims (as revealed in the Sachar Committee’s report), and the scale of sex-selective foeticide in the country; and with the widespread perception that the unique identification (UID) programme which has been advertised as facilitating the “targeting” of public benefits is, on the contrary, a mechanism for excluding large numbers of deserving citizens from the ambit of social assistance (when it is not associated with more sinister forms of intrusive surveillance of the citizenry). But we live in the age of the specialist, and it may not be for me to dwell at any length on these subjects. Having said this, it is also true that a further area of concern when we speak of “inclusive development” relates to the domains of poverty, inequality, and growth.
The Quintile income:-
It appears that the World Bank is planning to maintain and disseminate systematic information on a version of what Kaushik Basu had some years ago advanced as the ‘quintile income statistic’. The quintile income—which we shall find convenient to refer to simply as Q—is just the average income of the poorest quintile (that is to say, poorest 20 per cent) of a population. The quintile income statistic is a very simple, but also very versatile, welfare indicator—one which can be employed to cast light, admittedly in a somewhat elementary way, on aspects of both income poverty and the ‘inclusiveness’ of growth. The World Bank aims to track, subject to the availability of data, country-specific performance with respect to the average income of the poorest 40 per cent of the population (rather than 20 per cent, as Basu had proposed in his original version of the statistic).
The Poverty Line:-
As is well-known, extant protocols of money-metric poverty measurement follow what one may call the route of ‘identification-cum-aggregation’. The identification exercise is concerned with specifying an income ‘poverty line’ designed to distinguish the poor segment of a population from its non-poor segment. The aggregation exercise is concerned with combining information on the distribution of income and the poverty line in order to come up with a single real number which is supposed to signify the extent of poverty in the society under review. A particularly simple aggregate measure of poverty, and one which is very widely employed, is the so-called headcount ratio, or proportion of the population in poverty (that is to say, the proportion of the population with incomes or consumption expenditure levels below the poverty line).
It is important to recognize that the language of a ‘poverty line’ is ill-suited to treating income as anything but a means to an end—specifically the end of avoiding deprivation in the space of human functionings. After all, what is the common sense meaning of the term ‘poverty line’? Is it not a reference to that level of income which, when it is attained, enables an individual to escape deprivation? And what is deprivation, if not a failure to achieve certain ‘minimally satisfactory’ states of being and doing—such as the state of being reasonably well-nourished, reasonably mobile, reasonably free of disease and ignorance, reasonably sheltered against the forces of nature and climate, reasonably equipped to participate without shame in the affairs of one’s society, and so on? And if this is the case, surely the right way of going about fixing the poverty line would be to first make a list of human functionings in respect of which it is reasonable to insist that one should avoid deprivation in order to be counted non-poor; to identify the reasonable cost of achieving each reasonable level of functioning; and to add up all of these functioning-specific costs in order to arrive at the money-metric poverty line.
Notice now that there can be both inter-personal and ‘environment-’ or ‘context-dependent’ factors which can make for differences in the rate at which incomes (or resources in general) are converted into functionings.
Thus, a pregnant or lactating mother will typically need more nutritional resources than a person who, other things equal, is not in this condition. Similarly, a differently abled person would typically need more resources to achieve the functioning of mobility than one who is not so. Apart from such individual heterogeneities, are also differences wrought by variations in the objective environment. Thus, a person living in unsanitary conditions without access to clean drinking water might be expected to require more food to achieve the same nutritional status as one whose absorptive capacity is not compromised by infected potable water. Similarly, a person living in a cold climate would require more resources to expend on protective clothing than one living in a temperate climate. We owe all of these insights to Amartya Sen who, many years ago, employed this line of argumentation to assert that poverty is best seen as an absolute concept in the space of functionings, but (and precisely because of variations across regimes in the ability to convert resources into functionings), as a relative concept in the space of resources (including income).
The practical issue is this: for poverty comparisons to be meaningful, the poverty standard must be invariant across the contexts of comparison. But invariant in what space? In the space of functionings (which is compatible with variability in the space of resources), not in the space of real incomes or of commodity bundles.
Yet, in practice, the World Bank’s ‘dollar-a-day’ international poverty line preserves invariance in the space of real incomes, while India’s official poverty lines preserve invariance in the space of commodity bundles. Regrettably, the language of a ‘poverty line’—in terms of which incomes or resources are seen as a means to the end of avoiding deprivation in the space of functionings—is wholly incompatible with such postulated invariance of real incomes or commodity bundles. The resulting estimates of ‘poverty’ are, quite straightforwardly put, hard to interpret in any conceptually coherent or meaningful way. And the problem cannot simply be taken care of by impatient assertions regarding the unavoidability of some element of arbitrariness in the specification of an income poverty line
Rectification of standard practice would require that poverty be treated as an absolute conception in the space of human functionings, and as a relative conception—allowing for both interpersonal and contextual heterogeneities—in the space of incomes. This is a practically very difficult exercise to implement, but is the price that must be paid for treating income—in terms of the language of a ‘poverty line’—as a means to an end. Failing this, income could be treated as an end in itself, in which case the quintile income can be employed as a legitimate money-metric indicator of poverty. Over-time comparisons of the actual quintile income with reasonably targeted levels based on a normative growth rate should yield a picture of how money-metric poverty has fared over time. Suitable comparisons of the over-time performance of the average incomes of the richest and the poorest declines over time—should yield a picture of the inclusiveness or otherwise of growth. In conclusion, there is a strong case for replacing dollar-a-day-type approaches to the estimation of money-metric poverty by a more straightforward ‘quintile income approach’, which can also be employed in order to pronounce judgment on whether or not growth in income has been ‘pro-poor’ or inclusive.
Darknet, also known as dark web or darknet market, refers to the part of the internet that is not indexed or accessible through traditional search engines. It is a network of private and encrypted websites that cannot be accessed through regular web browsers and requires special software and configuration to access.
The darknet is often associated with illegal activities such as drug trafficking, weapon sales, and hacking services, although not all sites on the darknet are illegal.
Examples of darknet markets include Silk Road, AlphaBay, and Dream Market, which were all shut down by law enforcement agencies in recent years.
These marketplaces operate similarly to e-commerce websites, with vendors selling various illegal goods and services, such as drugs, counterfeit documents, and hacking tools, and buyers paying with cryptocurrency for their purchases.
Anonymity: Darknet allows users to communicate and transact with each other anonymously. Users can maintain their privacy and avoid being tracked by law enforcement agencies or other entities.
Access to Information: The darknet provides access to information and resources that may be otherwise unavailable or censored on the regular internet. This can include political or sensitive information that is not allowed to be disseminated through other channels.
Freedom of Speech: The darknet can be a platform for free speech, as users are able to express their opinions and ideas without fear of censorship or retribution.
Secure Communication: Darknet sites are encrypted, which means that communication between users is secure and cannot be intercepted by third parties.
Illegal Activities: Many darknet sites are associated with illegal activities, such as drug trafficking, weapon sales, and hacking services. Such activities can attract criminals and expose users to serious legal risks.
Scams: The darknet is a hotbed for scams, with many fake vendors and websites that aim to steal users’ personal information and cryptocurrency. The lack of regulation and oversight on the darknet means that users must be cautious when conducting transactions.
Security Risks: The use of the darknet can expose users to malware and other security risks, as many sites are not properly secured or monitored. Users may also be vulnerable to hacking or phishing attacks.
Stigma: The association of the darknet with illegal activities has created a stigma that may deter some users from using it for legitimate purposes.
AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing speech, making decisions, and understanding natural language.
Virtual assistants: Siri, Alexa, and Google Assistant are examples of virtual assistants that use natural language processing to understand and respond to users’ queries.
Recommendation systems: Companies like Netflix and Amazon use AI to recommend movies and products to their users based on their browsing and purchase history.
Efficiency: AI systems can work continuously without getting tired or making errors, which can save time and resources.
Personalization: AI can help provide personalized recommendations and experiences for users.
Automation: AI can automate repetitive and tedious tasks, freeing up time for humans to focus on more complex tasks.
Job loss: AI has the potential to automate jobs previously performed by humans, leading to job loss and economic disruption.
Bias: AI systems can be biased due to the data they are trained on, leading to unfair or discriminatory outcomes.
Safety and privacy concerns: AI systems can pose safety risks if they malfunction or are used maliciously, and can also raise privacy concerns if they collect and use personal data without consent.