An Introduction, Kamala Das

I don’t know politics but I know the names
Of those in power, and can repeat them like
Days of week, or names of months, beginning with Nehru.
I am Indian, very brown, born in Malabar,
I speak three languages, write in
Two, dream in one.
Don’t write in English, they said, English is
Not your mother-tongue. Why not leave
Me alone, critics, friends, visiting cousins,
Every one of you? Why not let me speak in
Any language I like? The language I speak,
Becomes mine, its distortions, its queernesses
All mine, mine alone.
It is half English, half Indian, funny perhaps, but it is honest,
It is as human as I am human, don’t
You see? It voices my joys, my longings, my
Hopes, and it is useful to me as cawing
Is to crows or roaring to the lions, it
Is human speech, the speech of the mind that is
Here and not there, a mind that sees and hears and
Is aware. Not the deaf, blind speech
Of trees in storm or of monsoon clouds or of rain or the
Incoherent mutterings of the blazing
Funeral pyre. I was child, and later they
Told me I grew, for I became tall, my limbs
Swelled and one or two places sprouted hair.
When I asked for love, not knowing what else to ask
For, he drew a youth of sixteen into the
Bedroom and closed the door, He did not beat me
But my sad woman-body felt so beaten.
The weight of my breasts and womb crushed me.
I shrank Pitifully.
Then … I wore a shirt and my
Brother’s trousers, cut my hair short and ignored
My womanliness. Dress in sarees, be girl
Be wife, they said. Be embroiderer, be cook,
Be a quarreller with servants. Fit in. Oh,
Belong, cried the categorizers. Don’t sit
On walls or peep in through our lace-draped windows.
Be Amy, or be Kamala. Or, better
Still, be Madhavikutty. It is time to
Choose a name, a role. Don’t play pretending games.
Don’t play at schizophrenia or be a
Nympho. Don’t cry embarrassingly loud when
Jilted in love … I met a man, loved him. Call
Him not by any name, he is every man
Who wants. a woman, just as I am every
Woman who seeks love. In him . . . the hungry haste
Of rivers, in me . . . the oceans’ tireless
Waiting. Who are you, I ask each and everyone,
The answer is, it is I. Anywhere and,
Everywhere, I see the one who calls himself I
In this world, he is tightly packed like the
Sword in its sheath. It is I who drink lonely
Drinks at twelve, midnight, in hotels of strange towns,
It is I who laugh, it is I who make love
And then, feel shame, it is I who lie dying
With a rattle in my throat. I am sinner,
I am saint. I am the beloved and the
Betrayed. I have no joys that are not yours, no
Aches which are not yours. I too call myself I.

Creativity

“Creativity and ego cannot go together. If you free yourself from the comparing and jealous mind, your creativity opens endlessly. Just as water springs from a fountain, creativity springs from every moment. You must not be your own obstacle. You must not be owned by the environment you are in. You must own the environment, the phenomenal world around you. You must be able to freely move in and out of your mind. This is being free.”
– Jeong Kwan, Chef’s Table

“True, cognitive biases and limited attention exacerbate these problems and nudges can yield improvement on the margin, but figuring out what matters is hard”

Low Quality Equilibria: There’s an important “new” (e.g. it’s been circulating in working paper form for a while, but is now published) paper in QJE about why hobby woodworkers waste so much money…just kidding, it’s about why people keep buying cheap Chinese knock-off tech products and IKEA furniture…actually it’s about the persistent use of predatory financial products and poor financial decision making…OK, it’s really about the bind that the evidence-based policy movement finds itself it. Well, truthfully it’s actually about agricultural markets in Uganda and why adoption rates of fertilizer and improved seed are low, but not zero. Really, that’s what the paper is about.

But it is also about all of those other things. Here’s the basic story:
Fertilizer and improved seeds boost agricultural productivity substantially. But it’s hard for farmers to tell whether the fertilizer or seeds they are buying are fake. So there are lots of people willing to sell low quality stuff claiming it’s high quality–in Uganda, the fertilizer is regularly diluted (30% of nutrients are missing) and the “improved seed” is fake 50% of the time. Classical economics tells us that markets will drive out the low quality products as people learn who is a reliable seller; or that the market will collapse and no one will be willing to buy the fertilizer or seeds at all. But farming, like almost every other human endeavor depends on lots of factors, not just these inputs. And so it’s not only hard for farmers to tell whether they were sold a “lemon” even even after using it. Did their crops underperform because the were sold fake inputs or because the weather was bad, or they used it wrong, or their land was too degraded, or their were too many of a certain kind of pest, or because they were sick during the planting season, etc.? After all some people buying the fertilizer and seeds did get good stuff and have high yields, so it’s even harder to tell where the problem lies. So the market doesn’t collapse, and low-quality sellers/products don’t get driven out of the market but farmers also–for good reason!–don’t invest in the inputs as much as would make sense based on the theoretical productivity boost.

Here’s where the rant, and the weird introduction to the item, comes in. This situation is incredibly common: in most of life it’s hard to tell whether some input–be it technology, or practice, or advice, or an employee–is high quality before you use it, but also after you use it because of the complex nature of most of life. This basic fact seems to be ignored frequently as researchers, policymakers, and advocates try to explain behavior. In almost all our endeavors we are in a Dunning-Krueger low quality equilibrium. We don’t know enough to tell high quality from low quality ex ante, or ex post (yes, I’m a Calvinist). Determining causality is hard–even the most highly trained economists and social scientists get it wrong all the time!What hope does the average human have of looking at a complex system and determining which of the hundreds of factors involved was responsible for what portion of the outcomes? Behavioral economics explanations for sub-optimal choices are tempting because they tend to skirt this core issue. True, cognitive biases and limited attention exacerbate these problems and nudges can yield improvement on the margin, but figuring out what matters is hard (an opportunity to link, yet again, to one of my favorite papers, [Not] Learning by Noticing [the wrong things].

This is why Amazon or any crowdsourced product reviews are worthless. And it’s why most people, regardless of their financial literacy, can’t consistently tell which financial products are good for them and their situation. And it’s why evidence-based policy is such a hard sell–when a policy with strong evidence behind it fails to live up to expectations is that because the advice was bad, the implementation was bad or circumstances changed?

Low quality equilibria are everywhere, defeating them is hard, and that’s the sobering challenge we face.

-faiV newsletter, Week of July 23rd

Weaponized data (and comms logistics)

Check out this week’s Financial Access Initiative newsletter. The first item is on the politics of evidence-based policy, and a pretty interesting debate and timeline to follow on the release of 2 papers with different conclusions about the effects of minimum wage increases.

Quoting below:

1. Weaponized Data and American Inequality: Last week I linked to a paper finding minimal effects from minimum wage increases, unaware that a huge explosion of debate on this issue was about to occur. If you follow these things at all, you know that last Friday a paper on Seattle’s minimum wage increase was released finding no job losses or cuts in hours. Monday, a different paper finding large losses for households with minimum wage jobs was released. There’s a whole lot out there now on the two papers so I’m not going to rehash those arguments (if you need to catch up, try this or this or this or just scroll through Twitter). I want to focus on the backstory of why there were two papers released so close to each other because it’s important for the future of research and policy-making. As detailed here, what appears to have happened is researchers at UW shared an early draft of their paper (using tax data that is rarely available in minimum wage studies) with the Seattle mayor’s office. The mayor’s office didn’t like the conclusions so asked a different set of researchers to write their own paper–and release it just before the planned date for release of the UW paper. While I have no special insight into the exact details of what happened, the prospect that the report is accurate disturbs me a great deal. It’s a blatant step toward what the author of the Seattle Weekly piece calls “weaponized data.” Be afraid for evidence-based policy. Very afraid.

[…also, see the end of this newsletter for a visualization of missing data. 😃 – “A reddit group put together a map about the data in maps, illustrating where data is missing. Source: @maxcroser and reddit“]