I have been watching the hullabaloo around the TG Bill with a familiar feeling: this is what happens when a public policy question is immediately converted into a morality play.

That does not mean the moral questions are fake. They are not. Rights, dignity, autonomy, humiliation — these are serious questions. But anyone who has spent even a little time watching public policy evolve knows something else too: laws are rarely static, categories are rarely clean, and once courts, legislatures, administrators, and welfare structures all begin interacting with one another, the original principle is almost always forced into harder institutional form.

That is why, before reacting, I wanted to see the paper trail.

So I read the 2019 Act, the 2026 Bill, and then NALSA vs. Union of India judgement (Source) of the Supreme Court of India [to be referred to as NALSA henceforth] word by word. And after reading all three, my first conclusion is this: a lot of people are reacting to an ideological shorthand, not to the actual architecture of the law.

My first reaction to NALSA was mischievous and blunt: this whole thing seemed heavily about men wanting to become women, and I remember thinking — and saying — that I felt bad for women.

Now, to be legally fair, NALSA is broader than that first reaction. Justice Radhakrishnan expressly says that the concerns of female-to-male trans-sexual persons are often “not properly addressed.” Justice Sikri is even clearer: he frames the issue as male-to-female “or vice-versa,” and later says the opposite case is where a person born female behaves like a male person. So no, formally speaking, NALSA is not a one-direction judgment.

But if one reads judgments not only for ratio but also for how they construct social imagination, another fact appears. NALSA runs to 130 pages. Justice Radhakrishnan’s opinion runs till page 86; Justice Sikri’s separate opinion runs till page 127. In the lead opinion’s named personal narratives, the Court gives us Laxmi Narayan Tripathy, Siddarth Narrain, and Sachin — all male-born / transfeminine / hijra-spectrum accounts. Then when Justice Sikri reaches for a named concrete example, he gives us Sunaina — again, a male-born person transitioning to female. So yes, the legal scope is broader. But the testimonial and emotional architecture of the case is heavily tilted in one direction. That is not polemic. That is simply what the text looks like on the page.

And this asymmetry is not mine alone. Public writing on India’s trans discourse has repeatedly noted the relative invisibility of trans men, especially compared to the social visibility of hijras and trans women. A Scroll piece drawn from Invisible Men makes exactly this point: transmasculine lives remain under-seen and often enter public imagination only weakly or through distortion. So when I say the discourse is tilted, I am not inventing an enemy. I am naming a pattern already visible in the literature. Source

Now come to the law, because that is where public policy stops being poetry and starts becoming design.

The 2019 Act took a broad route. It defined a transgender person as someone whose gender does not match the gender assigned at birth, and expressly included trans-man, trans-woman, persons with intersex variations, genderqueer, and socio-cultural identities such as kinner, hijra, aravani and jogta. It also expressly said that a recognised transgender person has a right to self-perceived gender identity.

The 2026 Bill moves in a different direction. It narrows the definition. It removes the express right to self-perceived gender identity. It drops explicit mention of trans-man, trans-woman and genderqueer from the definition. And it inserts a more medical-and-administrative route involving a medical board and the District Magistrate. So this is not a cosmetic change. It is a policy move from self-perception toward institutional verification. That is why the backlash exists. And on that narrow point, the backlash is not baseless. Source

But here is where my public-policy instinct parts company with both the chest-thumping defenders of the Bill and the slogan-only critics of it.

The words self-identification are often used as though the modern State normally functions on self-verification. It does not. My father saw me. My mother saw me. Yet when I needed a passport, I needed a birth certificate. And that birth certificate did not arise merely because my family (or me, myself) knew I had been born. In my case in UP, the elected representative had to sign off that yes, I was born in that part of Banaras, and on that basis the block official signed the certificate.

No, I am not saying birth and gender identity are identical questions. They are not. I am saying something more basic: the Indian state routinely converts lived facts into legally operative facts through attestation, record, seal, signature and official recognition. That is how public administration works in domain after domain. Even taxation does not run on pure inward sincerity. You file an ITR, yes — but it sits within a statutory architecture, a documentary trail, and the possibility of scrutiny.

Which is why I find the absolutist version of the self-identification argument unpersuasive. The serious question is not whether dignity matters. Of course it does. The serious question is whether every inward claim must become self-executing in law, or whether the State may insist on some form, threshold, or institutional process before attaching legal consequences. That is not a BJP question. It is a public policy question.

The same goes for the fraud argument. I have already seen the line that medical-board gatekeeping is pointless because people forge disability certificates too. But that proves only that administrative systems are vulnerable to fraud. It does not prove that all institutional verification is conceptually invalid. If fraud alone defeated certification, large parts of the welfare state, reservation system, taxation system, and licensing system would become impossible to defend.

On the definition point too, precision matters. Critics are right that the 2019 Act was broader and the 2026 Bill narrows the statutory category. But it is also true that NALSA itself was not written in the air as a theory of totally abstract identity. The judgment repeatedly names actual communities — Hijras, eunuchs, Kothis, Aravanis, Jogappas, Shiv-Shakthis — and the final declaration specifically says Hijras and Eunuchs, apart from binary gender, are to be treated as “third gender.” So the Court’s language was always doing two things at once: speaking in broad constitutional terms, and also anchoring itself in socially legible communities.

On exclusion, another clarification is needed. NALSA clearly distinguishes gender identity from sexual orientation. It says they are different concepts. So if people argue as though any legal distinction between the two is itself outrageous, that is simply not what the judgment says. The stronger criticism of the Bill is different: by removing self-perceived gender identity and narrowing the definition, it risks excluding people who were clearly within the 2019 Act’s frame — including trans men, trans women and genderqueer persons outside named socio-cultural or congenital categories. That is a much better objection because it is textual and policy-based, not just emotional. Source Source

The surveillance objection also needs adult treatment. In a modern administrative state, almost every legally consequential act leaves a trail. Birth leaves a trail. Marriage leaves a trail. Property leaves a trail. Tax leaves a trail. Death leaves a trail. The issue is not whether there is a trail. The issue is whether the trail is proportionate, secure, necessary, and non-degrading. So if one wants to criticise the Bill, the smarter criticism is not “how dare the State record anything”; it is that mandatory reporting of surgeries and medical-board scrutiny may be privacy-invasive, excessive, and humiliating in their present design. That is a serious critique. “Trail = surveillance = evil” is too easy. Source Source

As for whether the Bill conflicts with NALSA, the honest answer is: in part, yes.

Yes, the Bill plainly departs from the autonomy-first structure of NALSA and the 2019 Act when it removes the express right to self-perceived gender identity and replaces a broader recognition model with more medical-administrative gatekeeping. On that point, critics have a real case. Source Source Source

But no, it is not true that the comparative jurisprudence discussed in NALSA imagined a world without institutions, registrars, boards, or formal processes. NALSA itself cites cases like A.B. v Western Australia, involving a Gender Reassignment Board, and Norrie, involving the Registrar of Births, Deaths and Marriages. Comparative law moved away from crude biological essentialism, yes — but it did not move into a universe where the State simply never asks a question.

So where does all this leave me?

Not in defence of the State. And certainly not in defence of any party. I am speaking from a simpler place: the place from which one watches public policies emerge, harden, get litigated, generate new administrative anxieties, and then get rewritten in response. Anyone who has seen that process up close knows that principles travel badly once they enter forms, certificates, welfare gates, definitions, and bureaucratic routines.

That is exactly what I think is happening here.

The backlash is not fake. It is rooted in real objections: removal of self-identification, medical-board gatekeeping, narrowing of the definition, exclusion risks, and privacy concerns. But neither am I willing to pretend that every claim of identity must be legally self-executing and that any institutional threshold is, by definition, barbaric. Modern policy design simply does not work like that.

And yes, I still come back to women.

Because one thing that struck me while reading all this is how often the discourse is emotionally and rhetorically organised in a way that expects women to enter late, object politely, and then accept category redesign as the price of being thought decent. I do not accept that bargain. If law redraws categories, then women do not become collateral to elite moral performance.

So let me end plainly.

I am not saying transgender persons are unreal.
I am not saying NALSA only covers one side.
I am not saying the backlash is mere emotion.
And I am not writing in defence of the State.

I am saying something narrower, and therefore stronger:

NALSA is formally broader than the popular retelling of it. But if you count the named testimonies and follow the judgment’s emotional architecture, the narrative weight is heavily tilted toward male-born / transfeminine / hijra-spectrum experience, while female-to-male persons are acknowledged much more briefly. The 2026 Bill then reacts not only to identity claims in the abstract, but to a decade of law and discourse built on that terrain. Whether its answer is constitutionally sound is a separate question. But first, at least be honest about the terrain.

Read the 2019 Act.
Read the 2026 Bill.
Read NALSA.

Then argue.

But at least argue with the documents open.

The Two Indias

The Labour Department publishes a wage notification every six months. Boring, dry, bureaucratic, almost nobody reads it. But buried inside is a quiet act of dignity: a legally mandated minimum wage for every level of manual work.

An ITI-trained electrician — semi-skilled, two-year diploma — is protected at ₹22,568 per month under central government rates (Ministry of Labour and Employment, 2025). His employer cannot pay him less. A labour inspector can show up and check.

Now consider a B.Tech graduate from a mid-tier college in Pune or Noida. She applies to thirty companies. Gets a call. Sits across a hiring manager and is told: “We offer a package commensurate with your experience.” The number in the offer letter is ₹15,000 a month. She takes it, because the alternative is nothing.

The plumber has a rate card. The engineer has a prayer.

This is not a market inefficiency (or the hiding hand of market for that matter) that will self-correct. It is a design failure — sitting at the intersection of outdated labour law, political economy that finds not much value in a white-collar worker, and a hiring culture that has turned informational asymmetry into increasing the bottomline (read as profit). And it is entirely legal.

The Wage Architecture: What the Law Protects — And Doesn’t

India’s minimum wage system recognises four skill tiers. The rates look reasonable on paper:

These rates specifically apply to Area A (major metropolitan cities like Delhi, Mumbai, and Bengaluru) for sectors such as construction, sweeping, and cleaning.

The critical clause: these rates apply only to scheduled employments — industries listed in the Schedule (agriculture, construction, mining, factories). A software firm, a management consultancy, a policy think-tank, an international NGO — none of these are scheduled employments for minimum wage purposes.

The result:

• Nearly 50% of white-collar office workers in India earn less than ₹15,000/month — below the legally mandated floor for unskilled manual labour (Malik, 2024)
• ~30% of urban salaried employees earn less than ₹20,000/month (NSSO)
• 63% of India’s total workforce earns less than ₹10,000/month (PMC/NIH, 2021)

The law was designed for the factory floor and things now have evolved to co-working spaces. It protects the men in overalls. The women in the business-casual blazer is on her own.

The “intern/trainee/fellowship” loophole makes it worse. Companies classify fresh graduates as “trainees” or “apprentices,” which puts them in a legal grey zone where wage law doesn’t apply. Post-graduates are particularly vulnerable — companies know they’ll accept poor pay for the resume line. The development and NGO sector is the most brazen: workers are often ideologically motivated, and organisations exploit this systematically.

The Four Labour Codes: A Reform That Arrived Halfway

On November 21, 2025, India brought four consolidated Labour Codes into force, replacing 29 existing central laws (Reuters, 2025; DLA Piper, 2025). The most sweeping labour reform since independence.

What it changed: the wage definition is broader, the floor wage concept is codified, gig workers are finally in the social security tent.

What it didn’t change: zero obligation on any employer to disclose a salary range in a job posting. No white-collar salary floor. Not a word about what happens in the hours before an employment relationship begins — when a candidate reads a job description, applies, interviews, and receives an offer.

The Labour Codes reformed the employment relationship. They left intact the conditions that allow exploitative employment to be offered in the first place.

The Constitutional Promise

The Constitution doesn’t use the phrase “salary transparency.” But it is not ambiguous about what the state owes its working citizens.

• Article 39(a): Citizens have the right to adequate means of livelihood
• Article 39(d): Equal pay for equal work, regardless of gender
• Article 43: The state shall endeavour to secure — through legislation — a living wage that enables decent standards of life. Not a survival wage. A dignity wage.
• Article 21 (via Olga Tellis, 1985): The Supreme Court held that the right to livelihood is a fundamental right, not a policy aspiration (Supreme Court of India, 1985)

The Fair Wages Committee of 1948 defined the framework clearly: minimum wage (survival), fair wage (reasonable comfort), living wage (full human flourishing) (LegalMantra, n.d.). These were not academic distinctions. They were the normative architecture of what the Indian state was obligated to build.

Seventy-seven years later, a fresh engineering graduate can be offered ₹15,000 a month — below the minimum for an unskilled manual labourer — with complete legal impunity. The constitutional promise of a living wage has been quietly outsourced to employer discretion.

The Global Double Standard

When the Bill & Melinda Gates Foundation posts a Senior Program Officer role in Washington DC, the salary appears in the headline: $262,000–$406,000 (The Ladders, 2025). Not generosity. Compliance.

When a comparable institution posts a field research role in Delhi: “competitive and commensurate with experience.” No number. No range. No floor.

Why the difference? Because law requires it in one place and not the other.

The same organisations that comply in Washington, Toronto, and Vienna see no reason to do so in Delhi. The ethical standard doesn’t travel with the organisation. Only the legal obligation does. This is legal arbitrage.

The “unpaid fellowship” model is the logical endpoint. One India-based policy organisation — the RAAH Policy Fellowship, with publicised ties to public institutions — posted fellowship applications in early 2026 with no compensation information. A review of their website in March 2026 found the FAQ entry for “Is the fellowship paid or stipended?” still contained the original placeholder text: “[Your answer here. Replace this placeholder with a clear, direct response. Avoid hedging.]” (RAAH Policy, 2026). The instruction to avoid hedging went unheeded. In a sector that talks constantly about transparency and governance, the irony is hard to ignore. RAAH is just one example.

“Commensurate With Experience” — The Trap Built Into the Language

The phrase sounds measured. Even fair. In practice, it functions as perpetual underpayment on a timer.

The employer knows the budget. The candidate doesn’t. Without a disclosed range, her only reference point is her last salary. So “commensurate with experience” silently collapses into “commensurate with last CTC.” The candidate who was underpaid before carries that underpayment forward. The candidate who worked for free carries the signal of “someone who worked for free.”

This is exactly what the EU Pay Transparency Directive was designed to dismantle. Salary history is not neutral data — it is the accumulated residue of every discriminatory hiring decision, every gendered underpayment, every exploitative entry-level offer. The EU recognised this as a systemic harm and banned salary history questions outright (Council of the European Union, 2025). India has not.

What the data says about transparency:

Why Policy Is Fundamental

The appeal to employer ethics is tempting, and it doesn’t work. Every country where salary transparency actually improved has a law behind it, not a LinkedIn post.

Austria passed a law in 2011. Colorado passed one in 2021 and levied penalties. The UK, relying on cultural pressure alone, reached ~70% — and has plateaued there. Goodwill has a ceiling. Law doesn’t.

India’s gender pay gap is approximately 28%, among the highest in the world (ILO, 2023). That gap cannot be challenged in a hiring environment where candidates — disproportionately women returning to the workforce — have no access to the salary information needed to identify underpayment. You cannot challenge what you cannot see.

The November 2025 Labour Codes are a necessary reform. They are not a complete one. They protect workers after hiring. They say nothing about the information asymmetry at the moment of hiring — which is where exploitation begins.

Three changes would constitute a minimum meaningful response:

Policy is the architecture of a society’s values. When a nation decides that a semi-skilled plumber’s time has a floor protected by law, but a trained engineer’s time does not — that is a values statement. It says: the worker in overalls deserves the protection of law. The worker in a blazer must fend for herself.

Article 43 promised a living wage — not a market wage, not an employer-determined wage, but a wage enshrined in law. The Fair Wages Committee called it the wage that enables “decent standards of life.” Not survival. Dignity.

Is it time that Gen Z brings about a mass movement for fair and transparent pay? Only time will tell.

References

Adobe. (2023). Future workforce study. Adobe Inc.

Bird & Bird. (2025, December 23). India’s new labour codes: Wages, workers, and welfare rights.https://www.twobirds.com/en/insights/2025/indias-new-labour-codes-wages,-workers,-and-welfare-rights

Council of the European Union. (2025, February 4). Pay transparency.https://www.consilium.europa.eu/en/policies/pay-transparency/

DLA Piper. (2025). New labour codes usher in a new era of compliance. https://knowledge.dlapiper.com

GovDocs. (2025). Pay transparency laws by state. https://www.govdocs.com/pay-transparency-laws/

Harvard Law Review Blog. (2024, November). Effective pay transparency requires benefit transparency.https://harvardlawreview.org/blog/2024/11/effective-pay-transparency-requires-benefit-transparency-h-4890/

Indeed. (2022). US job seeker survey on salary information. Indeed Hiring Lab.

India Briefing. (2025). India Code on Wages, 2019: An employer’s compliance guide.https://www.india-briefing.com/news/india-code-on-wages-2019-employer-guide-41064.html/

International Labour Organisation. (2023). Gender pay gap India. ILO.

LegalMantra. (n.d.). Fair wages in India: Legal foundation, practical gaps and the road ahead.https://legalmantra.net/blog-detail/Fair-Wages-in-India-Legal-Foundation-Practical-Gaps-and-the-Road-Ahead

Littler Mendelson. (2025, January 1). Canada: New Ontario job posting requirements in force.https://www.littler.com/news-analysis/asap/canada-new-ontario-job-posting-requirements-force-january-1-2026

Malik, A. (2024). Silent struggle of India’s white-collar workforce. LinkedIn Pulse. https://www.linkedin.com/pulse/silent-struggle-indias-white-collar-workforce-un-talked-amit-malik-7bthc

Ministry of External Affairs, Government of India. (n.d.). Part IV: Directive principles of state policy. Constitution of India. https://www.mea.gov.in/images/pdf1/part4.pdf

Ministry of Labour and Employment, Government of India. (2025). Variable dearness allowance order, October 2025. Chief Labour Commissioner. https://clc.gov.in/clc/min-wages

PMC/NIH. (2021). The crisis of extreme inequality in India.https://pmc.ncbi.nlm.nih.gov/articles/PMC8353929/

RAAH Policy. (2026, March). Fellowship. Research, Analysis and Advisory Hub. http://www.raahpolicy.org/fellowship/

Remote Rebellion. (2025). New salary transparency laws in Europe.https://remoterebellion.com/blog/new-salary-transparency-laws-in-europe

Reuters. (2025, November 21). India enforces four new labour codes in major overhaul of worker rules.https://www.reuters.com/business/world-at-work/india-announces-implementation-new-codes-reform-labour-laws-2025-11-21/

Saral.pro. (2025). Minimum wage in India: Updated rates. https://saral.pro/blogs/minimum-wages/

SHRM. (2023). New SHRM research shows pay transparency makes organisations competitive.https://www.shrm.org/about/press-room/new-shrm-research-shows-pay-transparency-makes-organizations-competitive-leads-to-increase-qualified-applicants

Supreme Court of India. (1985). Olga Tellis & Ors v. Bombay Municipal Corporation. AIR 1986 SC 180.

The Ladders. (2025). Bill & Melinda Gates Foundation careers, jobs, and salary information.https://www.theladders.com/company/billmelindagatesfoundation-jobs

Trusaic. (2025). India pay transparency requirements. Global Pay Transparency Center.https://trusaic.com/resources/global-pay-transparency-center/india/

TL;DR (Summary): This article uses a 4×4×4 matrix — spanning sector, role, and organization type — to reveal how modern work reproduces caste-like hierarchies. It shows how meaning, money, mastery, and mobility are unevenly distributed, with grassroots and gig workers most undervalued. Even high-skill roles are accessible mainly to the privileged. Moral language in the development sector masks economic exclusion. The piece calls for awareness among new job entrants and systemic pay parity reforms. A disclaimer affirms the equal dignity of all work and critiques systems, not individuals.

Work as Modern Caste

In ancient India, Varna referred to the division of society by functional role: Brahmins (knowledge), Kshatriyas (governance), Vaishyas (commerce), and Shudras (service). While ostensibly based on aptitude (guna) and action (karma), Varna ossified into caste, a rigid system of hereditary social ordering. Today, caste is constitutionally abolished but not socially extinct. And Varna itself reappears — not in temples, but in office towers, Zoom calls, and compensation spreadsheets.

This article reframes the world of modern work, particularly the development sector, through the Varna lens. I propose a typology that helps reveal how discrimination persists in new forms: through the structural devaluation of labor in equity-driven sectors, the use of moral rhetoric to justify low pay, and the reproduction of privilege through sectoral and class filters. This is particularly stark in how both the development sector’s grassroots staff and the gig economy’s lowest-tier workers are treated — expected to deliver vital services without the recognition or reward that higher-tier roles receive.

Disclaimer:This essay uses the Varna framework as an analytical metaphor, not a value judgment. I do not believe any human being is inherently superior or inferior to another. All forms of work — mental or physical, salaried or grant-funded — carry equal dignity. “Grant-based” or philanthropic work is often dismissed as “dusre ka paisa” but I reject that framing. In the end, the Earth belongs to all of us, and any debt we owe is shared. This critique is aimed at systems, not individuals, and is intended to provoke reflection, not judgment.

The Work Varna Fabric (4x4 Matrix)

Let’s begin by laying out the Work Varna Matrix, woven from two primary threads: the function of the industry and the nature of individual contribution. This yields 16 unique “stations” in the modern economy as shown in table 1:

The Third Axis: Organizational Type

This matrix is incomplete without a third axis: the type of organization in which the work is performed. This axis includes:

• For-Profit
• Government / Public Sector
• Not-for-Profit / Third Sector
• Informal / Gig Economy

This axis doesn’t alter the nature of the work itself, but it profoundly shapes the resource environment, compensation philosophy, and symbolic capital attached to that work.

A program director in an NGO and a program director in a private infra firm may occupy the same cell in the matrix, but exist in vastly unequal realities.

And further down the matrix, a frontline mobilizer in a self-help group or an ASHA worker occupies the same “Operations & Support” cell as a logistics clerk in a private firm, but receives neither job security nor adequate pay. They are paid “honorarium” often combined with “respect” in the society for taking it forward.

Why We Work: The Arc of Motivation and Compensation

Drawing from literature into human motivation and the psychology of work, it is safe to say that individuals are driven by more than just financial compensation. Beyond money, key motivators for engagement and satisfaction in work often include:

• Meaning: The profound sense of purpose derived from contributing to something larger than oneself, often manifested through social impact or alignment with deeply held values. This concept is central to Barry Schwartz’s Why We Work, where he argues for the intrinsic satisfaction found in meaningful labor and critiques systems that diminish it.
• Mastery: The innate human desire to develop and refine skills, to achieve competence, and to receive recognition for one’s growing expertise. This continuous pursuit of improvement is a cornerstone of Angela Duckworth’s work on Grit, where sustained passion and perseverance towards long-term goals are highlighted as crucial for achieving high levels of proficiency.
• Mobility: The aspiration for upward career progression, increased responsibility, and enhanced professional visibility. While often linked to financial advancement, mobility also signifies growth, new challenges, and a broader sphere of influence within one’s chosen field.

However, how these motivations manifest and are rewarded depends heavily on where one sits within the Varna matrix and along the organizational axis. Refer Table 2.

The Development Sector and the Caste of Compensation

The development sector — international NGOs, philanthropy, multilateral agencies, social enterprises — is where the irony cuts deepest. This is the third sector: ostensibly equity-driven, justice-oriented, and committed to systemic change. Yet it often enacts economic discrimination masked by moral language.

Positive Discrimination as Concealed Exploitation: Many Brahmin-Varna roles in the third sector — strategy, research, program design — are underpaid compared to their private-sector equivalents. Compensation ceilings are justified through:

• “Mission alignment”
• “Donor restrictions”
• “Doing good should be its own reward”

Historically, Brahmins were revered but often materially poor — surviving on donations, not salaries. Today, many development professionals face the same contradiction: symbolic praise without financial security. Or,

exaggerated praise for doing meaningful work, combined with structural underpayment.

The only way many survive these roles is through class privilege(Arqaam, 2023)(Center for Research on College-Workforce Transitions, 2022). Gone are the days when caste was by birth — now it is by wealth. A doctor’s child becomes a doctor, a judge’s child a judge. And if someone with ‘papa ka paisa’ chooses to enter a low-paying sector, they often depress pay levels further by accepting below-market wages — making it harder for those without such buffers to enter or survive. The unwritten truth of the sector is that much of it runs on the fuel of papa ka paisa (Father’s money or Generational Wealth)

Structural Parallels with Caste

The parallels with caste aren’t metaphorical — they are structural:

• Many NGO workers, especially in research, strategy, or advocacy, are upper caste, urban-educated, English-speaking, subsidized by family wealth.
• Their field-level colleagues — community mobilizers, data collectors — often come from Dalit, Bahujan, or Adivasi backgrounds, paid a fraction, and locked into contractual, precarious roles. Similarly, in the gig economy, delivery workers, sanitation staff, and farm laborers perform essential tasks, often with no benefits, no upward path, and no dignity — mirroring the role of Shudras in the traditional Varna model.
• Intersectionality compounds this: gender, region, language, and caste all shape who ends up where — and who gets to call their underpayment “a choice.”

The development sector thus becomes caste-modernized:

• Brahmin-like NGO strategists underpaid, but structurally buffered
• Shudra-like field staff both underpaid and undervalued
• And both rationalized through a moral economy of austerity

As sociologist Pierre Bourdieu (1986) reminds us, symbolic capital — like being seen as moral or socially impactful — can displace economic capital, but only if one can afford it. This creates a moral elite — those who “work for purpose” because their needs are already met. Or simply put, moral prestige has now become a luxury of the privileged.

Just as caste separated dignity from labor, modern development structures often separate prestige from pay. Those working in “equity” are applauded in public, but economically marginalized in private. And those who can afford to stay long enough to rise often do so because they never needed the money in the first place.

Conclusion: Discrimination by Design

The development sector doesn’t escape the logic of caste. It reenacts it with better grammar. The 4x4x4 matrix (Individual/Industry/Organization Type) helps make visible the new Varna-like ordering of professions. But it’s the third axis that reveals the real engine of stratification: a caste of compensation justified by virtue, but built on exclusion.

For the new generation entering the job market, there must be greater awareness of these three axes — how function, role, and organization type structure access to meaning, money, and mobility.

And for systemic change, governments and institutions must bring in stronger norms for pay parity across sectors and roles. Because pay parity doesn’t just improve fairness — it drives equity itself.

Because when morality becomes the reason you don’t get paid, that isn’t virtue. That’s discrimination — with a mission statement.

Select References & Citations

• Duckworth, A. (2016). Grit: The Power of Passion and Perseverance. Scribner.
• Schwartz, B. (2015). Why We Work. Simon & Schuster.
• Arqaam. (2023, February 2). How unpaid internships are perpetuating inequality. Arqaam. https://www.arqaam.org/2023/02/02/how-unpaid-internships-are-perpetuating-inequality/
• Center for Research on College-Workforce Transitions. (2022). Unpaid Internships & Inequality (CCWT Policy Brief #2). University of Wisconsin-Madison. https://ccwt.wisc.edu/wp-content/uploads/2022/04/CCWT_Policy-Brief-2_Unpaid-Internships-and-Inequality-1.pdf
• Bourdieu, P. (1986). The Forms of Capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education.

In many postcolonial markets, especially India, work ethic is not just economic — it’s moral. You’re not just expected to work hard. You’re expected to endure quietly. Grit, perseverance, professionalism — these become tools of inner repression when unaccompanied by care, agency, or justice. This piece is about that quiet violence.

Chipke Rehna | चिपके रहना

Imtiaz Ali tells a struggling singer (Akash) in the recently released movie Metro In Dino… That’s what he says at the end of his first meeting with Akash. No promises. No mentorship arc. Just a gentle shove into the void — Be patient, Struggle more, Keep showing up.

For some reason, that hit.

It’s been over a year since I started freelancing. And, have now slowly scaled to multiple clients to a point where my income now matches my old CTC. And, honestly, it is a stupid thing to say, because the real damage is in the waiting. Late payments. No payments. Silence. You remind them thrice. You sound desperate. You stop. They ghost.

And yet, I keep going. Because chipke rehna, right?

There are days I feel proud of this life. I’ve learnt more about people in 12 months than I did since 2010 (When I left my house in Kashi — Varanasi). I’ve learnt what work I’ll never do again. I’ve learnt how not to say yes to desperation. But there are days when I ask: is this growth, or just exhaustion in new packaging?

In postcolonial freelance markets like India’s, where institutional safeguards are weak and cultural obedience runs deep, motivation is often a proxy for submission.

In a video, someone asked Angela Duckworth (Who is a reputed authority on the subject of Grit): What if we don’t know what to be gritty into? She replied — that’s the real crisis. Most people, especially after school, don’t have a clear inner compass. In college, there’s structure. Grades. Goals. Outside that, it’s open field. The cost of picking the wrong thing is high — not just financially, but existentially. And that paralyses people. Her answer? Don’t start with “passion.” Ask two questions:

1. What are your real interests? (Usually visible by age 12–13.)
2. What’s your purpose — the values you want to serve?

Then layer in reality — the need to make money.

Simple right? Three things (Interest, Purpose & Reality) and you can be sure? But I don’t believe it’s as easy as it sounds. And, the intersection of these three reduces possibilities to a new low. Let’s apply this framework to what I am doing — I like helping others (interest), I ensure my work contributes to impact (purpose), and consulting may bring in a lot of money and meaningful career for others. But, how long should I hustle for?

There’s another angle to this, especially if you grew up middle-class in India, like I have.

We’ve been trained to glorify struggle. Mehnat ka fal milega (Hard work pays off). We say this while watching the hardworking drown in debt and the unethical thrive. We say this to children. We say it to ourselves. It’s not encouragement — it’s sedation. Struggle is framed not as a cost but as a credential. Something that proves you deserve success.

But who decides how long you’re supposed to suffer before it pays off?

Ashis Nandy (in The Intimate Enemy) called this mindset inner colonisation — a kind of psychological inheritance where suffering is seen as noble. We moralise pain. We respect endurance. We often equate being stuck to being strong.

‘Chipke rehna’ isn’t advice. It’s a moral injunction. It keeps workers stuck by confusing loyalty with worthiness — a tool of inner repression passed off as virtue.

It’s not just in jobs. It’s everywhere. Women enduring bad marriages are “sanskari” Students enduring toxic coaching are “focused” Workers enduring silence after a layoff are “professional.” We call it resilience. It’s repression. We respect it because we fear the alternative: failure, disobedience, rejection.

And that’s dangerous.

It means even if the system is broken, we don’t question it — we just adjust better. We romanticise the ghosted freelancer, the overworked intern, the burnt-out founder — and call it character. When it might just be exploitation.

That’s where chipke rehna gets messy.

Sometimes it means resilience and sometimes it’s merely inertia. I’ve told myself I’m “building”

I wasn’t building a career. I was surviving a loophole.

To the uninitiated, let me tell you that building a government clientele is the toughest thing one does in my sector. The three core things required are — patience, understanding of idiosyncrasies of officers, and deep pockets to manage cash flows. So much that even people who I look up to have avoided business from government. Recently, having a conversation with a senior (and my ex-boss), I said “Sir, you have children in school, you cant afford delayed payments. I have nothing much to lose, so I can venture into government territory”. So, in five years, either I’d have learnt a lesson or be that outlier who made it happen.

That said, delays are part of the process. Not that I’m so dumb that I entered head on into this. To offset it, I do assignments for non government agencies — that pay well financially and intellectually. Also, i have kind ex-bosses who know my frustration at times and help me manoeuvre those odd feelings. I’m also blessed with family who doesn’t ask me much about career and friends who sometimes contribute my share of the bill.

A while ago I watched a video. The one about how the first sign of civilisation wasn’t a tool, but a healed bone. A broken femur that had mended — proof that someone was cared for, fed, protected. You don’t survive a broken leg alone in the wild. Which means, someone else stopped, waited, fed, protected, helped. That’s when civilisation began. When someone cared enough to stay back.

That changed something for me.

What’s the equivalent of that in our world? Maybe it’s clients who don’t ghost. Maybe it’s work ecosystems that understand creative rhythm. Maybe it’s consultants who don’t undercut. Maybe it’s someone who sees your work and says, “You’re good. Let me help you grow.”

Care, not resilience, was the first act of civilisation. But in our market logic, care is absent — and what’s left is a fetish for survival. That’s not growth. That’s repression.

Because chipke rehna alone can kill your spirit. Care makes chipke rehna bearable. Without it, it’s just another name for quiet collapse.

But amidst all the jargon, a simple question remains: Where does India actually stand when it comes to measurable gender outcomes?

Globally, progress on the gender agenda is tracked through a range of indices: the Gender Development Index (GDI), Gender Inequality Index (GII), Gender Gap Index (GGI), Women, Business and the Law Index (WBLI), and the Social Institutions and Gender Index (SIGI). Many of these align — at least partially — with the Sustainable Development Goals (SDG 2030).

Introduction to Global Gender Indices

Before we proceed to data, and its analysis, let’s understand what these indices are. While there are many global indices specific to gauging progress on gender agenda, I began with ones that are backed by multi-lateral agencies. Thereby, for the sake of simplicity, and applicability, leaving indices like African Gender and Development Index (AGDI), Gender Equality Index (GEI) — European Institute for Gender Equality (EIGE), and the likes. The ones that I consider are as shown below:

Now, for each indicator, the value for India, the global average, the top performer and the worst performer was first culled out from relevant sources. This would not only give India’s current standing, but would also make one understand the index better.

Global Development Index & India

The Gender Development Index (GDI) is published by the United Nations Development Programme (UNDP) to measure gender gaps in human development achievements across three key dimensions: health, education, and economic resources. It is derived from the Human Development Index (HDI) by comparing female and male HDI values separately (i.e. Ratio of Female HDI to Male HDI). The GDI uses four indicators in total: life expectancy at birth, mean years of schooling, expected years of schooling, and gross national income per capita — each disaggregated by gender. The index reflects inequality by showing the ratio of the female HDI to the male HDI. A GDI score closer to one (1) indicates near parity in human development outcomes between women and men.

According to the Human Development Report (HDR) 2023–2024 [click for source data], which is based on data from 2022: Moldova Republic leads the world with the highest GDI of 1.033 (Overall); India, stands at 117th rank with a GDI score of 0.852 (Overall).

Among the four key indicators that make up the GDI — life expectancy at birth, expected years of schooling, mean years of schooling, and estimated gross national income per capita — India performs relatively better in life expectancy (1.047, rank 53) and expected years of schooling (1.003, rank 143), but lags significantly in mean years of schooling (0.725, rank 164) and estimated GNI per capita (0.276, rank 183). These low scores suggest structural and systemic barriers that continue to limit women’s full participation in education and the economy.

Gender Inequality Index & India

The Gender Inequality Index (GII) is a composite measure developed by the United Nations Development Programme (UNDP) to reflect gender-based disadvantage in three key dimensions: reproductive health, empowerment, and labour market participation. Reproductive health is measured by the maternal mortality ratio and the adolescent birth rate; empowerment is assessed through the proportion of parliamentary seats held by women and the female-to-male ratio of population with at least some secondary education; and the labour market dimension is represented by the female-to-male labour force participation rate. A higher GII value indicates greater inequality, while a lower value reflects higher levels of gender parity. A total of 193 countries have been provided score in the official UNDP made available data set. Other than the 193, two countries (Korea and Monaco) data have been provided but not ranked.

The GII for the world as per HDR 2023–2024[click for source data] is 0.462. Denmark ranks first in GII with 0.009 (Overall). India ranks at 108th place with GII value of 0.437 (Overall). Yemen is the last ranked country with GII value of 0.820 (Overall). The global average stands at 0.3347 (Note: Average is computed for countries with a valid GII score as the Dataset provided on official website doesnt have GII values for multiple countries)

Globally, the average maternal mortality ratio stands at 215, while the best-performing country, Belarus, reports just 1 death per 100,000 live births. In contrast, South Sudan faces the highest burden at 1,223. India reports a maternal mortality ratio of 103, ranking 117th. In terms of adolescent birth rate, India stands at 16.3 births per 1,000 women aged 15–19, better than the global average of 41.9, with Hong Kong SAR leading at 1.6 and Niger at the lowest end with 168. For political empowerment, India has only 14.6% women in parliament, ranking 155th globally, far behind Rwanda’s 54.7%. The gender parity in secondary education remains skewed, with India scoring 0.698 on the female-to-male ratio and ranking 163rd, while Libya leads with a ratio of 1.3. Finally, India performs poorly in labour force participation, ranking 181st with a female-to-male ratio of just 0.3711 — well below the global average of 0.633 and far behind Seychelles, which nears parity at 0.99. These indicators highlight persistent gaps across both social and economic dimensions that continue to hinder gender equality in India.

Global Gender Gap Index & India

The Global Gender Gap Index (GGI) was developed to benchmark progress towards gender parity across four dimensions(sub-index): economic opportunities, education, health and political leadership. These four dimensions have a total of 14 indicators.

Each subindex score is calculated as a weighted average of its component indicators. Economic Participation and Opportunity includes: Labour-force participation rate (.199); Wage equality for similar work (survey) (.310); Estimated earned income (.221); Legislators, senior officials and managers (.149); Professional and technical workers (.121). Educational Attainment includes: Literacy rate (.191); Enrolment in primary education (.459); Enrolment in secondary education (.230); Enrolment in tertiary education (.121). Health and Survivalincludes: Sex ratio at birth (.693); Healthy life expectancy (.307). Political Empowerment includes: Women in parliament (.310); Women in ministerial positions (.247); Years with a female head of state over the last 50 years (.443).

The overall GGGI score is a simple average of the four subindex scores (i.e., 25% each for Economic, Educational, Health, and Political).

The following table presents a snapshot of the Global GGI 2024 [Download report here]. It shows sub-index scores at the global level and detailed indicator-level scores for India, the top-performing country, and the lowest-performing country across the four domains. For each indicator, the table includes India’s score and rank(Out of 146 ranked countries in the report), the highest score and country, as well as the lowest score and country. It helps compare India’s gender parity performance against global benchmarks and highlights where India stands out or lags behind.

Social Institutions and Gender Index

The Social Institutions and Gender Index (SIGI), developed by the OECD (Organisation for Economic Cooperation and Development) Development Centre, offers a comprehensive measure of gender-based discrimination rooted in social institutions — both formal and informal. Unlike indices that primarily capture gender gaps in outcomes such as health or education, SIGI focuses on the underlying drivers of inequality, assessing discriminatory laws, social norms, and practices that restrict women’s rights and agency. It covers four key dimensions: discrimination in the family, restricted physical integrity, restricted access to productive and financial resources, and restricted civil liberties — encompassing a total of 16 indicators and 25 variables. While a regional SIGI report is available for Southeast Asia for 2024, this analysis draws from the 2023 global report(Click here), which includes data from 179 countries. The SIGI is particularly relevant for informing gender-transformative policy reforms and contributes to tracking progress on SDG Target 5.1, which aims to eliminate all forms of discrimination against women and girls.

The following table highlights the best performers, the worst performers, average, and India’s standing:

Women, Business and the Law Index & India

The Women, Business and the Law (WBL) Index (To download the raw data on which the following tables are based, please click on World Bank. 2024. Women, Business and the Law 2024. Washington, DC: World Bank), developed by the World Bank, measures how laws and regulations affect women’s economic participation across 190 economies. Unlike indices that focus on gender gaps in outcomes (e.g., income, health), WBL captures the legal barriers that influence a woman’s ability to enter, remain in, and progress in the workforce. You may consider accessing

The 2024 edition introduces two frameworks:

• WBL 1.0: Measures laws de jure across 8 indicators, aligned with key stages in a woman’s economic life:
  Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets, and Pension.
• WBL 2.0: Expands the framework by assessing the implementation gap between laws and reality through three dimensions: Legal Frameworks (laws and regulations), Supportive Frameworks (policies, enforcement mechanisms, budgetary support), Expert Opinions (on de facto outcomes).

WBL 2.0 includes all 8 original indicators and introduces 2 additional indicators:

• Safety (laws on gender-based violence, child marriage, femicide),
• Childcare (availability, affordability, quality standards).

Each economy receives:

• A WBL 1.0 legal score (0–100),
• A WBL 2.0 legal score,
• Separate scores for Supportive Frameworks and Expert Opinions.

WBL data is available in panel format from 1971 to 2024, allowing historical tracking of legal reforms over time. The WBL Index directly contributes to monitoring SDG Target 5.1, which aims to eliminate all forms of legal discrimination against women and girls.

India’s standing on both the scores (1.0 and 2.0) requires a focused piece. However, the following table, gives a macro level picture of india’s performace on WBL 1.0

The WBL 2.0 index has data across three parameters —Legal 2.0, Supporting Frameworks, and Expert Opinion.

Legal 2.0 covers data from 190 countries. India’s performance on each of its indicators is as shown below:

Supporting Frameworks 2.0 covers data from 190 countries. India’s performance on each of its indicators is as shown below:

WBL 2024 Expert Opinion Data at Economy Level covers only 164 countries (i.e. n=164). Details on each requires another write-up completely for this interesting index. Analysis of the entire WBL index highlights many development priorities for India.

Way Forward: A composite Index at Sub-National Level?

In India, the SDG framework is tracked through the National Indicator Framework (NIF) by MoSPI, while the more visible SDG India Index (NII) by NITI Aayog provides a curated subset for state-level comparisons. The NII is practical, but limited — constrained by the availability and comparability of data across states. Some datasets have long update cycles (1+ years), which affects relevance. Still, it gives a directional sense of progress.

That brings us to a simple proposition: What if we attempted a composite gender index using global frameworks — but localised for Indian states? Not just national averages, but a deeper, disaggregated picture.

MoSPI itself has acknowledged the gap. To quote (Source):

The HDI and GDI are very important indicators for planning purposes. For appropriate comparison of different States, there is a need to develop a standard methodology. Of course, such work can be attempted by NGOs and Research Institutions but there is a strong need for a set of official estimates of these indicators at regular intervals.

What research institutions once did over months — through long cycles of data cleaning and coding — a small group can now attempt, with reasonable rigour, using AI-powered tools. Not a claim — an invitation.

If you, or someone you know, thinks this is worth exploring — reach out.
I’ve seen what’s possible from my experience with SDG localisation in Uttar Pradesh. A working baseline is not out of reach.
And maybe, just maybe, this is how we get a clearer picture of gender progress in India — one state at a time.

Originally Written in May | 2023

Summary

The Infant Mortality Rate (IMR) is the primary focus of this study’s investigation of the relationship between the Swachh Bharat Mission (SBM) and health outcomes in India. In order to examine the connection between sanitation and health, the study makes use of data on the number of toilets constructed and health outcomes. The regression analysis’s findings show that improved household latrine (IHHL) coverage increases are associated with a reduction in IMR, and that population growth has a less significant effect on IMR than IHHL coverage. The findings highlight the need for improved sanitary infrastructure, especially in low- and middle-income countries. Overall, this research makes a substantial contribution to the causes of social justice and long-term sustainability.

Introduction

Poor sanitation and hygiene practices have been and are serious public health concerns in India, with substantial economic and social ramifications. According to research by the World Health Organization (WHO), approximately 50% of the Indian population did not have access to better sanitation facilities, which contributes to the spread of illnesses such as diarrhoea, cholera, and typhoid fever (UNICEF & WHO, 2018).In addition, the absence of sufficient sanitary facilities can lead to the pollution of groundwater, which can potentially create major health concerns.

The Swachh Bharat Mission (SBM) was established in 2014 to improve sanitation and hygiene throughout India and put an end to the widespread practise of open defecation. The mission’s goals included providing access to clean restrooms, spreading education, and encouraging residents to alter their habits (Chaudhary, 2017). By decreasing the number of illnesses, raising productivity, and conserving resources, SBM was also predicted to have a favourable effect on public health, income, and the national economy (Chaudhary, 2017). In addition to saving families approximately $727 million annually in health care expenditures and producing over $151 billion in economic gains (UNICEF, 2018), SBM has reportedly prevented over 300,000 deaths from diarrheal illnesses and protein-energy malnutrition. In addition, SBM has helped achieve one of the Sustainable Development Goals (SDG) for 2030: SDG 6.2’s goal of “ensuring that all people have access to safe and affordable sanitation” (PIB Delhi, 2021). As a result, SBM is a ground-breaking programme that has altered the face of Indian cities and raised the standards of living for millions.

The construction of toilets and other sanitation infrastructure is a key component of the SBM. The mission aimed to provide access to toilets to all the rural and urban households by 2019, by converting insanitary latrines into pour-flush latrines, building community and public toilets, and promoting behavioural change among the citizens. The mission also focused on solid waste management, which involves collection, segregation, transportation, processing and disposal of municipal solid waste. According to the government, more than 10 crore individual household latrines (IHHLs) were constructed under the mission and all the villages declared themselves open-defecation free (ODF) by 2019 (PIB Delhi, 2022). The mission has now entered its second phase, which aims to sustain the ODF status and ensure safe management of solid and liquid waste in all the villages by 2024–25. Thus, the construction of toilets and other sanitation infrastructure has been a crucial step towards achieving the goal of Swachh Bharat.

There are several positive health outcomes associated with better sanitation, including lower rates of water-borne and infectious infections and lower infant mortality rates (IMR). The exposure to faecal pathogens and the risk of diarrheal illnesses, a leading cause of morbidity and death in children under five, can be prevented via sanitation interventions such the provision of latrines and the promotion of hand washing. Intestinal worms and schistosomiasis are two parasites that can stunt a child’s development and are on the decline because to sanitation initiatives (WHO, 2022). In addition, actions aimed at improving sanitation can have significant effects on the environment by lowering water and soil pollution and diverting funds from health care costs. As a result, better sanitation has the potential to enhance the health and happiness of millions of people, particularly in poor and middle-income nations.

The Sustainable Development Goals (SDGs) and other development targets for India may be affected by the correlation between building toilets and improved health. The Swachh Bharat Mission (SBM) is an initiative to clean up India by eradicating public urination and improving sanitation and hygiene (Water for People India, 2023). Water-borne and communicable illnesses such as diarrhoea, cholera, typhoid, and polio (WHO, 2022) can be mitigated with the provision of toilets through SBM. In addition to being a leading cause of death in children under the age of five, these conditions can also stunt their physical and mental growth. Women and girls, who suffer greater problems and hazards owing to a lack of sanitation facilities, stand to benefit the most from the building of toilets because of the good effects they will have on the income, productivity, and education of the community as a whole. Additionally, building toilets can aid in environmental sustainability by decreasing water and soil pollution and diverting funds from healthcare costs. As a result, India may make progress towards SDGs 3, 4, 5, 6, 8, and 13 by building more toilets, including SDG 3 (excellent health and well-being), SDG 4 (quality education), SDG 5 (gender equality), and SDG 6 (clean water and sanitation).

Motivation

The SBM is one of the greatest sanitation projects in the world, and its effect on public health is a major topic of study and debate. Data on the number of toilets built and health outcomes in India presents a rare chance to apply economic research methodologies to the study of the connection between sanitation and health.

In the context of India’s development objectives and worldwide initiatives to achieve sustainable development, the findings of this study will be of special relevance. The study’s primary goals are (1) to add to the current body of information on the link between sanitation and health, and (2) to give insights that can guide efforts to improve sanitation and public health in India and other low- and middle-income nations.

Therefore, the purpose of this study is to investigate the link between the SBM and health outcomes and make recommendations based on evidence to enhance sanitation programmes in India and elsewhere. This research will contribute significantly to the cause of long-term sustainability and social justice.

Methodology

The data used in this analysis is Press Information Bureau of Government of India. The links for each data source is mentioned below:

1. Individual Household Latrines (IHHL) constructed in each state: https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1847092

2. Infant Mortality Rate: https://www.pib.gov.in/PressReleasePage.aspx?PRID=1796436

In each of the above indicators, the government clubbed some union territories, union territories’ have been omitted from the analysis.

Dependent Variable, IMR is the main subject of the study. The infant mortality rate (IMR) is calculated as the number of newborn deaths per 1,000 live births. Toilet construction, a binary variable that indicates whether a home constructed a toilet via the Swachh Bharat Mission (SBM), is the primary independent variable being studied. This variable is especially relevant for examining how increased sanitary facilities affect the IMR. The analysis will not contain any control variables. The study will only examine the connection between IMR and toilet construction. The information included in the analysis if from the first year of the SBM campaign to the most current year for which statistics were available. This data reveals the total number of toilets built in each state as well as its related IMR throughout the same time period.

Analysis

The regression results in the following table show the equation of the line of best fit for the IHHL-IMR correlation and the predicted value of IHHLs for a given IMR. The equation of the line is in the form y = mx + b, where y is the predicted value of IHHLs, x is the IMR, and m and b are constants. The Population-IMR correlation coefficient is also included for completeness.

Interesting conclusions concerning the elements that affect India’s Infant Mortality Rate (IMR) and Improved Household Latrine (IHHL) coverage were uncovered by the investigation. Four years’ worth of data (2016–2019) were looked into and performed tasks including evaluating the correlation coefficient between the IMR and IHHL and population and running regression analysis.

Findings

Correlation values between the IMR and IHHL coverage ranged from 0.38% to 0.44% during the four years of the study, indicating a moderate relationship between the two measures. This suggests that reduced IMR can be achieved with increased IHHL coverage. In addition, the findings of the regression analysis imply that a drop in IMR is associated with an increase in IHHL coverage, and that this decrease might be significant.

The investigation also showed a modest association between IMR and population, with the correlation coefficient ranging from 0.33 to 0.37, in addition to the link between IMR and IHHL. When compared to the link between IMR and IHHL, the regression analysis shows that the drop in IMR per unit increase in population is less.

The results of the study suggest that infant mortality rates in India might be lowered by improving access to family latrines. The findings indicate that funding family latrines may be an efficient way to boost children’s health in the country. The study also demonstrates the significance of population increase in determining public health policies in India.

Concluding Remarks

The results of this study provide light on the connection between poor sanitation in the home, rapid population increase, and rising rates of infant death in India. The findings stress the need for better family latrines as a method of lowering newborn mortality rates and enhancing children’s health. The results also highlight the need of addressing the issues provided by population expansion in India’s public health policy and programme development.

Way Forward

The study and its findings are extremely pertinent, particularly in the setting of emerging nations where sanitation infrastructure is still deficient. According to the study’s findings, there is a strong positive association between IHHL coverage and IMR, suggesting that better access to sanitary facilities may result in lower newborn death rates. This study has important long-term ramifications. The results can help public health professionals and policymakers develop and execute strategies focused at lowering infant death rates. The study can provide light on the value of sanitary facilities and the necessity of expanding their reach in order to enhance health outcomes.

Future research and study should be conducted in order to confirm the causal link between IHHL coverage and IMR. Studies that follow changes in IHHL coverage and IMR over time can assist demonstrate a causal relationship’s direction. Further proof of the significance of sanitary facilities may be found in studies that look at how they affect other health outcomes, such child growth and development.

Additionally, research can concentrate on figuring out the variables that affect IHHL coverage and the obstacles to its adoption. The design of efficient initiatives to expand the availability of sanitary facilities can be influenced by this knowledge. In conclusion, the study’s significance and potential ramifications emphasise how crucial it is to do more research in this field. Future research may build on this study’s findings to better understand the effects of sanitation facilities on health outcomes and pinpoint efficient ways to expand their availability.

Works Cited

Chaudhary, A. (2017). Swachh Bharat Mission- Need, Objective and Impact. International Journal for Research in Management and Pharmacy, 5–9.

PIB Delhi. (2021, Sep 30). Press Information Bureau. Retrieved from PM to launch Swachh Bharat Mission-Urban 2.0 and AMRUT 2.0 on 1st October: https://www.pib.gov.in/PressReleasePage.aspx?PRID=1759593

PIB Delhi. (2022, August 01). Construction of Toilets under SBM. Retrieved from Press Information Bureau: https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1847092

UNICEF. (2018). Financial and Economic Impacts of the Swachh Bharat Mission in India. UNICEF.

UNICEF, & WHO. (2018). Progress on drinking-water, sanitation and hygiene: 2017 update and SDG baselines.https://www.who.int/publications/i/item/9789241512893: World Health Organization.

Water for People India. (2023, May 2). One handpump improves the health of one family, one village. Retrieved from Water for People India: https://waterforpeopleindia.org/one-handpump-improves-the-health-of-one-family-one-village/

WHO. (2022, March 21). Sanitation. Retrieved from World Health Organization: https://www.who.int/news-room/fact-sheets/detail/sanitation

In my last (Unpacking Buzzwords: The Misuse of ‘Framework’ in Tech Talk), I shared my mixed feelings upon discovering a government press release announcing that India now has a National Blockchain Framework (NBF). Initially, I was excited at the prospect of exploring this new framework, but my enthusiasm was tempered by a sense of dismay-had our months of rigorous research been pre-empted?

For the past six months, my collaborators and I have been deeply immersed in developing a comprehensive framework for blockchain and smart contracts in government. We began with an integrative literature review, dissecting papers on blockchain, conceptual frameworks, smart contracts, and their roles in e-governance. We scoured Google Scholar, analyzed the top 50 openly accessible papers on “conceptual framework,” and delved into 100 papers on smart contracts in e-governance. Our goal was to understand the key components: definitions, purposes, advantages, development processes, and methods to test and validate conceptual frameworks.

But let’s step back for a moment. This article is about the meaning of framework -simply put-for professionals irrespective of their field or industry. My purpose is to give every reader a simple, majboot (robust) and tikau (durable) definition of “framework.”

In Hindi, we often perform sandhi vicchhed -breaking a word apart to understand its essence. Applying that here, we get two meaningful words from “framework”: frame and work .

Frame isn’t just a dot or a line; it’s a structure that encompasses and defines boundaries. Similarly, a framework can’t be just one thing-it’s a network of interlinked concepts. When a researcher looks at a problem, both conceptual and theoretical models describe how they think about knowledge-its nature, origin, and limits-and the structure that helps them do their work (Tamene, 2016). A framework, thus, is a structured system that organizes and guides various concepts and ideas, providing a coherent foundation for planning, executing, and understanding a project or initiative ( Tamene, 2016 ). Kivunja, 2018

In finance, for example, a framework is “a logical system of related objectives and basic concepts that define the purpose, function, and limits of financial reporting” ( Gornik-Tomaszewski & Choi, 2018 ).

After diving deep into those fifty papers, here’s the definition we’ve distilled:

A conceptual framework is a logical system consisting of a web or network of interconnected ideas, theories, and beliefs. It establishes a foundation for theory and practice, defends the importance of a study or project, and ensures consistency in processes. Moreover, it simplifies complex ideas into manageable components, facilitating both academic and professional discussions by establishing uniform standards.

So, how does this framework guide professionals? Is it a tool for research, execution, or both?

This is where work -the second part of the term-comes into play. I often explain that when this web of interconnected ideas, theories, and beliefs is applied to practical tasks-through guidelines, best practices, and standard procedures-it forms a complete framework. It’s not just about the concepts; it’s about putting them into action. This practical application transforms abstract ideas into tangible results, driving innovation and efficiency in our projects and initiatives.

While our literature review didn’t explicitly adopt this “break and tell” approach, it has proven effective for the Hindi-speaking professionals I work with. It bridges the gap between abstract ideas and practical application, making the concept more accessible.

My research collaborators and I plan to publish the academic synthesis of our review. We hope that our work will contribute to a more robust and practical understanding of what a framework should entail, particularly in complex professional environments where clarity and structure are essential. Thereby, helping us apply it in the context of smart contracts.

In the end, this experience reinforces a simple truth: clear communication and a deep understanding of foundational concepts are vital. As we continue to tackle challenges across various industries, let’s strive to build frameworks that are both theoretically sound and practically applicable- majboot and tikau in every sense.

#framework #smartcontracts #research #consulting #advisory #conceptualthinking #management #strategicplanning

Originally published at https://www.linkedin.com.

Gates Foundation in Kenya

A Host Country Agreement (HCA) granting diplomatic privileges to the Gates Foundation was signed in October 2024, it has resurfaced in the news due to recent events. On April 8, 2025, the Foundation officially withdrew from the agreement, following legal challenges and public scrutiny. This decision was influenced by a High Court order suspending the privileges and a 21-day ultimatum for the government to provide evidence of the agreement’s revocation. Public discourse has been vibrant, with many questioning the rationale behind granting diplomatic immunity to a private philanthropic organization. Critics argue that such privileges are traditionally reserved for sovereign entities and international organizations, not private foundations(Source: Here). The Law Society of Kenya (LSK) filed a petition challenging the agreement, emphasizing concerns over transparency and legal accountability.

The Gates Foundation has worked in Kenya since 1997, focusing on sectors like agriculture, health, digital finance, and women’s empowerment(Official website of Gates Foundation in Kenya). But while these sectors sound neutral, the real concern lies in how these programmes are implemented — through MoUs, co-programming, and deep alignment with state institutions. This isn’t just support; it’s influence. When a private foundation helps shape national priorities, funds public systems, and gains access to sensitive data, the boundary between partnership and policymaking blurs.

It raises important, but often inconvenient, questions around sovereignty, data privacy, and long-term dependency — issues frequently sidelined in the glow of philanthropic intent.

A Familiar Pattern: The Desi Version

While the scale of the Kenya episode has exploded, I see something familiar unfolding closer to where my feet are grounded — just on a smaller scale. What we’re witnessing is the rise of a strange new hybrid: donor-funded, IIM-style firms that push market supremacy, yet operate outside the market — without being accountable to either market logic or public outcomes.

Let’s unpack this.

According to the India Philanthropy Report 2025 (Bain & Co.), private giving in India is projected to grow at 10–12% over the next five years, primarily led by ultra- and high-net-worth individuals. And this is where the story shifts from theory to lived experience.

An Interview That Said Too Much

I was recently was interviewed for a six-month freelance assignment. The first red flag? A deviation from the process that was initially communicated. But in good faith, I showed up. Then I met the founding director — brilliant, razor-sharp, well-read, and deeply passionate about fixing India’s economy through GDP-led growth. His organization’s core belief is that economic growth is the most effective way to improve human lives at scale. We spoke for over an hour. It was stimulating, thought-provoking… and mildly confusing.

Toward the end, I asked what I thought was a simple question:
“If growth is the best tool, why didn’t you register as a Pvt. Ltd. company instead of a Section 8 NGO?” Sure, not every market-first thinker needs to be a market player. But when belief and structure run on parallel tracks, it’s fair to ask: which one’s real?

Turns out, the invisible hand sometimes hides behind 80G certificates.

GDP ≠ Development

The belief that GDP(Gross Domestic Product) growth is synonymous with development — commonly referred to as the growth imperative — has long shaped economic policymaking. However, numerous economic theories have emerged both in support of and in opposition to this belief. The table below categorizes major theories that support growth imperative:

The following table categorizes major theories that go beyond the GDP narrative.

Now, leaving behind the academic debates and literature reviews, let’s ask a simple question: where would you actually want to live?

1. Would you prefer a society that boasts global brands and luxury malls, but where air quality is so toxic that an evening walk requires a mask — and lung cancer rates are rising? In cities like Beijing or New Delhi, PM2.5 levels often exceed 10 times the WHO safe limit, yet GDP figures continue to climb.
2. Would you choose a society where two billionaires skew the average income, while the other 98 out of 100 people struggle with precarious jobs and rising costs? The GDP per capita may look great, but wealth inequality is staggering — as in South Africa, where the richest 10% own more than 85% of household wealth.
3. Would you move to a country where people are highly productive on paper — working 12–14 hours a day — but suffer burnout, chronic illness, and even premature death? In South Korea, a country with high GDP per capita, the average worker puts in over 1,900 hours a year, and “karoshi” (death by overwork) is still a recognized term in neighboring Japan.
4. Would you feel free in a country where you earn well, live in a polished high-rise, but cannot criticize the government, cannot protest, or even cannot love freely? In Saudi Arabia and China, GDP may be respectable, but freedom of speech, assembly, and press rank among the lowest globally — with scores of 7/100 and 9/100 on Freedom House’s Global Freedom Index, respectively.

So the question is no longer “What’s the GDP?” The real question is: What’s life like — really — for the average person? Or, If GDP were enough, we wouldn’t need a Ministry of Social Justice, or a climate fund, or maternal health schemes. We’d just build highways and wait.

The Roles of the Three Sectors

And maybe this is the larger question we need to be asking: Who should be doing what? Because when boundaries blur, so does accountability.

• The State is supposed to ensure justice, equity, and public goods. It must protect long-term interests that markets ignore or exploit.
• The Market creates innovation and economic value, but is neither built for inclusion nor designed to correct itself.
• The Third Sector exists not to cheerlead markets or mimic consultants — but to hold space for the unheard, to represent the unrepresented, and to question what the other two overlook.

But now, we have third-sector institutions advocating for deregulation, promoting trade-first logics, and designing EoDB reforms — while funded by philanthropy, not markets, and without any mandate from citizens. It’s not just ideological drift. It’s role confusion.

The Rise of GDP Evangelists (And Why Funders Should Care)

We now have a new species in the ecosystem: polished, confident, IIM-educated advisors who promote GDP-first blueprints using donor money — without building anything truly inclusive, participatory, or sustainable.

They have:
– No political accountability like governments
– No financial accountability like private firms
– No moral accountability like grassroots NGOs

Yet they sit in closed-door meetings with ministries, publish “roadmaps,” and tell the rest of us what development means.

To be clear, this isn’t about individuals. Many of these advisors are deeply committed and incredibly capable. But they are trained — by design — to maximise efficiency, enable trade, and improve markets. That’s what their institutions were built to do. So when those same minds are parachuted into spaces that require redistribution, rights, or representation, it’s fair to ask: are we solving the right problems with the right tools?

So what happens?

A powerful businessperson starts shaping policy that benefits their sector or network — all in the name of improving Economic Development Advisory. When a Big Four consulting firm does it, at least their directors sign multiple safeguards to maintain independence while advising the government. But when a Section 8 company steps into the same space, who checks them? Who sets the guardrails?

There are none.

And what we’re left with isn’t impact — it’s ideology on retainer. It’s private interest, dressed up as public good. It’s consulting for influence, minus the compliance. And over time, it becomes what we saw with the Gates Foundation in Kenya: a quest for immunity, not just efficiency.

Why Funders Must Rethink — If the Heart’s in the Right Place

Because money sets precedent, And precedent shapes ecosystems.

If the Gates Foundation is being called out globally for overreach and unaccountable influence, then a desi Gates-like model — embedded in state systems through EoDB, investment promotion, or economic development advisory — deserves the same scrutiny. If the real reason Gates wanted diplomatic status was to operate without oversight, the same instinct will guide its clones: influence without friction, authority without mandate.

Society has come a long way.
It wasn’t just the tribes that hunted better who survived.
It was those that cared for each other, that respected ecosystems, that built relationships beyond barter. And in today’s world, it’s not just about how sharp your strategy is. It’s about whose future your strategy protects.

Closing Note

Markets matter. So do morals. But when we mix them carelessly, the outcome isn’t strategy — it’s satire.

To funders:

Don’t just fund belief systems. Fund structures that align with them. Ask yourself:
– Are we supporting institutions that act like markets without facing market risks?
– Are we enabling short-term conviction — or building long-term capacity?

To the rest of us:

When belief and structure don’t align, Maybe it’s not a vision. Maybe it’s a loophole.

Turns out, the invisible hand sometimes hides behind 80G certificates. And sometimes, even the smartest rooms forget the simplest lesson:

Who should be doing what?

Mini Glossary

• 80G Certificate: Indian tax exemption benefit given to donors of certain NGOs.
• Section 8 Company: A non-profit company under Indian Companies Act, 2013.
• GDP (Gross Domestic Product): Total economic output of a country, often used as a proxy for development.
• EoDB (Ease of Doing Business): A set of reforms intended to simplify business regulation and promote investment.
• MoU (Memorandum of Understanding): A non-binding agreement outlining terms between two or more parties.
• UHNIs/HNIs: Ultra High Net Worth Individuals / High Net Worth Individuals.
• Karoshi: A Japanese term meaning “death by overwork.”

Bibliography

• Inkeles, A., & Smith, D. H. (1974). Becoming modern: Individual change in six developing countries. Harvard University Press.
• Keynes, J. M. (1936). The general theory of employment, interest, and money. Palgrave Macmillan.
• Krugman, P. (2009). The return of depression economics and the crisis of 2008. The New York Times. https://www.nytimes.com/
• Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. https://doi.org/10.1016/0304-3932(88)90168-7
• Mankiw, N. G. (2016). Macroeconomics (9th ed.). Worth Publishers.
• Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037. https://doi.org/10.1086/261420
• Rostow, W. W. (1960). The stages of economic growth: A non-communist manifesto. Cambridge University Press.
• Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press.
• Schumpeter, J. A. (1942). Capitalism, socialism, and democracy. Harper & Brothers.
• Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94. https://doi.org/10.2307/1884513
• Daly, H. E. (1991). Steady-state economics (2nd ed.). Island Press.
• Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In P. A. David & M. W. Reder (Eds.), Nations and households in economic growth (pp. 89–125). Academic Press.
• Georgescu-Roegen, N. (1971). The entropy law and the economic process. Harvard University Press. https://doi.org/10.4159/harvard.9780674281653
• Jackson, T. (2009). Prosperity without growth: Economics for a finite planet. Routledge.
• Kallis, G. (2011). In defense of degrowth. Ecological Economics, 70(5), 873–880. https://doi.org/10.1016/j.ecolecon.2010.12.007
• Latouche, S. (2009). Farewell to growth. Polity Press.
• Layard, R. (2005). Happiness: Lessons from a new science. Penguin Books.
• Raworth, K. (2017). Doughnut economics: Seven ways to think like a 21st-century economist. Chelsea Green Publishing.
• Victor, P. A. (2008). Managing without growth: Slower by design, not disaster. Edward Elgar Publishing.

Recently came across the article “Assessing Equity in Universal Health Coverage in Rural India” by Dinesh Ahirwal and Anjali Dhengle (EPW, Vol. 60, Issue 13). The authors use two nationally representative datasets — NSSO (2018) and NFHS-5 (2019–21) — to examine how equitable the PM-JAY has been in reaching rural populations in India. Their key finding is striking:

Even though PM-JAY was designed to protect vulnerable households, many of the poorest and most marginalised groups are “doubly excluded” — they are not enrolled in public health insurance and yet incur high out-of-pocket health expenses when they seek care.

To better understand who gets covered, the authors analyse enrolment patterns in non-contributory public health insurance (NPHI) using NFHS-5. Excluding employer and private schemes, they construct a residual insurance variable (as per Mohanty et al., 2023) to represent PM-JAY-like schemes. A second regression identifies predictors of enrolment across socio-economic dimensions, framed using the PROGRESS equity lens (Place, Race, Occupation, Gender, Religion, Education, Socioeconomic status, Social capital). This reveals not just financial but structural and social exclusion in rural health coverage.

In my article Digitalisation, Gig Economy and Inclusion in India, I argue that inclusion must go beyond gender.

Social justice, as a concept, outlines the goals of a fair and equitable society where everyone’s rights are recognized and protected. Both concepts are interconnected, with social inclusion serving as a critical pathway to realize the aspirations of social justice.

The Ministry of Social Justice and Empowerment (MoSJE), Government of India, engages with a wide spectrum of marginalized groups — including Scheduled Castes (SC), Denotified and Nomadic Tribes, Economically Backward Classes (EBC), Other Backward Classes (OBC), Safai Mitras, people engaged in begging, those affected by substance use disorders, the elderly, and transgender individuals.

The CHE rates for MoSJE citizens are as highlighted in the following table:

These figures highlight the need for targeted policy convergence. Many of these groups — especially transgender individuals(CHE rate twice that of Male individuals), elderly, and people with disabilities — face CHE rates that are significantly higher than the average, despite being key constituencies of both MoSJE and the PM-JAY framework.

Recommendations

1. MoSJE and the National Health Authority (NHA), under the Ministry of Health and Family Welfare, should establish joint priority-setting mechanisms to align social protection and health financing goals.
2. Leverage existing beneficiary databases (e.g., for transgender persons, PwDs, elderly, SC/OBC) to proactively link eligible individuals with PM-JAY enrolment processes.
3. Revisit the SECC 2011 eligibility filters — especially the education (D6) and occupation (D7) criteria — because they often fail to capture the real-life circumstances of vulnerable people like informal workers, unpaid caregivers, or semi-literate self-employed individuals.
4. Deploy MoSJE’s field ecosystem (like district-level functionaries or civil society partners) for community-based awareness and enrolment facilitation in rural and marginalised clusters.
5. Develop gender and identity-sensitive design provisions in PM-JAY to ensure responsiveness to the needs of groups like transgender persons and elderly single-member households.

Concluding Remarks

Health equity and social justice cannot operate in silos. PM-JAY’s universal health coverage goals will remain incomplete unless its implementation converges with the inclusion mandate of MoSJE. The evidence is clear: those most in need are still paying the most. The way forward is not only more data — but also more deliberate convergence.

Recently, I had to build a case for ICT4D investment in Uttar Pradesh. The brief was straightforward: understand the stress on officers vis-à-vis the population they serve, and highlight how digital tools can bridge that administrative capacity gap. But I knew better than to walk into a room with opinions. I did what I usually did while working with multilaterals — rely on government data, let numbers speak.

Step 1: Making the Table I Thought Was Enough

I began with the basics. First, I downloaded the projected state-wise population in 2024 from the National Commission on Population, Ministry of Health’ and Family Welfare(source). Then, I collected the sanctioned and deputed AIS(IAS + IPS + IFS) cadre strength from the Press Information Bureau (source).

Then came a simple calculation: number of deputed AIS officers per 100,000 citizens.

I now had a clean, comparable indicator across states. I felt content. This would anchor my argument for ICT tools in high-stress states. But then I got curious — how does this vary with economic strength or poverty?

I looked up GSDP from MoSPI. The latest state-wise data wasn’t available for all, but the RBI’s Handbook of Statisticshad the values for 2022–23 and projections for 2023–24 (source). For poverty, I leaned on the Multidimensional Poverty Index (MPI), which I’d used earlier in this write-up on Varanasi. This is how the table looked:

Now the real fun began.

Step 2: Feeding the Sheet to multiple tools and probing hard

I uploaded the dataset to R, some AIs, and even used Excel . I asked not for surface-level stats, but for regressions, significance tests, and model diagnostics. Below is what emerged after multiple iterations — cutting past chatty summaries and pushing toward real econometric robustness. This process

What the Data Says: The Heart of the Findings

The First Model: Deputed Officers vs GSDP

This was a simple bivariate regression. I wanted to see if economically stronger states deploy more officers.

• Coefficient: 3.09e-6
• R-squared: 0.499
• p-value: < 0.001

What it means:
For every ₹100 crore increase in a state’s GSDP, there are ~0.31 more officers deputed. It explains ~50% of variation in officer count — moderate, but meaningful.

Then I Tried MPI

I expected poorer states to have more officers. More development challenge, more need, right?

• Coefficient: 2240.20
• p-value: 0.115
• R² = 0.132

Interpretation:
A state’s MPI increasing by 0.01 implies 22 additional officers, but the relationship wasn’t statistically significant.

This confused me. Either the intuition was wrong, or the model was. So, I changed both.

Step 3: A Better Model — Going Log-Log

Real-world administrative processes are rarely linear. They scale with complexity, not linearly with numbers. So I used a log-linear model:

This One Worked: The Log-Log Model Results

The results’ screenshots are below

Suddenly, poverty emerged significant. Once you control for economic size, states with higher poverty actually do have more officers, proportionately. The first model failed to catch this because GSDP and MPI are correlated — without controlling for one, the other gets shadowed.

But Is the Model Clean?

Here’s what I checked:

• ✅ Multicollinearity? Nope. VIF for both predictors was ~1.
• ✅ Heteroskedasticity? No evidence. Breusch-Pagan p = 0.17.
• ✅ Model Specification? RESET test clean. No omitted variable bias.
• ✅ Outliers? Only one state had Cook’s D > 0.5. So results are stable.

Visualising It: Patterns You Can See

I plotted AIS officers per 100k population vs GSDP and MPI.

Even visually, the density of AIS officers goes down as GSDP rises (richer states, more people, same officers). But the MPI plot showed a counter-intuitive pattern: poorer states aren’t always better staffed. That’s a governance equity issue.What’s the Story Behind the Numbers?

Final Thoughts

What started as a simple case for ICT4D in Uttar Pradesh — just a neat table comparing officers per citizen — quickly unfolded into something deeper. As I kept probing, the data pushed back, revealing its own structures, biases, and buried signals. It made me wonder:

Should officer deployment follow only GDP? Or also development need? Or something else entirely? Do decision-makers commission studies like these before sanctioning new posts? Could structured, data-backed insights improve how AIS cadre strength is allocated across India?

Maybe. Maybe not. For now, I move on — to the next task, another bullet point, and perhaps, another deep dive… just to write one well-evidenced bullet point.

Mini Glossary for Article Context

Use these lines in parentheses or tooltips within the article:

• OLS Regression: A basic statistical method that estimates the relationship between one outcome (like number of officers) and one or more predictors (like GDP or MPI).
• Breusch-Pagan Test: A diagnostic that checks if the error terms in a regression model have consistent variance — important to ensure reliability of estimates.
• VIF (Variance Inflation Factor): A measure to detect multicollinearity, or when predictor variables are too closely related to each other and may distort results.
• Cook’s Distance: A test to identify observations that have an unusually large influence on the model — helpful to catch outliers that might skew interpretation.