Which is the better measure of welfare - per capita consumption, or adult equivalent?

The following example illustrates the motivation for the question. Suppose you have two households, A and C. Household A comprises four adults, and household C comprises two adults and two small children. Suppose each household consumes $100 of food in one month, total. On a per-capita basis, this is $25 per capita in consumption for each member of both households. However, for household C, this measure of welfare actually underestimates the true welfare of the household. The reason is that the children do not need to consume as much as adults, so that a child that consumes $25 of food is much better of than an adult that consumes $25 worth of food. So even though the per-capita consumption of each household is the same, household C is actually better off – the per-capita consumption measure of welfare has underestimated the welfare of household C.

Consider another example. Lets look at small family S, which comprises 1 adult. He buys firewood to heat his home, which contains only himself, for $10. Family A also buys $10 worth of firewood to heat their home. The per-capita consumption of firewood is $10 for household S and $2.5 for household A. However, each member of each household enjoys the same amount of heat – there are economies of scale in heating the room (assume a one room house, since we are talking about poor households here. Maybe the larger household requires more firewood, but certainly not four times as much, if you believe that there are economies of scale here). In this case, the per-capita measure again underestimates the welfare of the larger household.

One option for adjusting for economies of scale and differing needs of household members is to use the adult equivalent scale, which assigns weights to household members to make the adjustment. More information on the adult equivalent scale can be found here: http://www.oecd.org/LongAbstract/0,3425,en_2649_33933_35411112_1_1_1_1,00.html and here: http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBsQFjAA&url=http%3A%2F%2Fwww7.nationalacademies.org%2Fcnstat%2FPoverty_Equivalence_Scales_Betson_Paper_PDF.pdf&rct=j&q=adult%20equivalent%20scale&ei=uZvrTNCNHYWclgforpm0AQ&usg=AFQjCNHXrcw16y_WDjZIZQbzg0hhJx71qA&sig2=b4PKoKi--xVUvmepS7ICnA&cad=rja

I asked some colleagues about this, and I’ve summarized the discussion below.

The adult-equivalent approach makes sense. It seems that theoretically, adult equivalent consumption should always be used because it is a better estimate of individual welfare, assuming one knows the weights. It adjusts for economies of scale in consumption, as well as the fact that children need to consume less than adults. So adult equivalent does not underestimate welfare as much as per capita equivalent does. However, it has significant disadvantages:

1) There is no universally agreed upon standard or empirical way for how to assign weights to the household members. Many just use a best guess.
2) Using it would result in inconsistency with other measures of poverty, such as the poverty line, cutoff, etc. We should be consistent with how we have been measuring poverty.
3) It is more complex; it is harder to explain and administer

The per-capita method is simpler and more transparent, even though it may underestimate consumption of larger households. As a general rule, it is best to go with the equivalence scale that is currently in use.

Another criticism of using adult equivalent consumption as the dependent variable in PMT regressions is that the variables used to calculate adult equivalent from per-capita consumption are variables that appear on the right hand side of the regression. For example, household size and number of children may appear on the RHS, and these are used to adjust per-capita consumption to form adult equivalent consumption, which would go on the LHS. However, controlling for these variables in the regression does not fix the problem of per-capita consumption underestimating welfare (any thoughts on this, anyone?).

One colleague says that there is no right or wrong answer here, but suggests to use whichever equivalence scale (adult equiv or per-capita) that is currently in use in the context of interest.

One observation that I would like to contribute is that I have found that, at least in my experience, using per-capita rather than adult equivalent consumption has yielded regression estimates with greater explanatory power. This is important if you are trying to predict consumption for PMT.

How can I get traffic to this blog?

What I am going to try is using key words from the most controversial topic of my generation: the Israel-Palestinian issue. However, there is a risk: what I’ve noticed is that anyone who stakes out a position, no matter which side such position favours (if any), will be the subject of vicious attacks. This issue is quite unique in that regard; it seems to annihilate any trace of sanity in some people.

I am going to try to be the first person to speak about this issue in a way that keeps me impervious to attacks. Here I go!

1. Israel is often miss-pronounced “is-real” (It should be pronounced “isss-ra’el”). This doesn’t mean, however, that it isn’t real. People have noticed it.

2. “Palestine” could have been a term used to refer to friends of Albert Einstein.

That’s 2 keywords down, and I’m still untouchable. How many more can I get away with? Lets see:

3. Zionism is what is practiced by someone who likes to keep his eye on something (get it? “he has hiS EYE ON it”). Please get your laughter under control before proceeding.

4. PLO… now this is a true story. In grade school, teachers would write “PLO” on the chalkboard when they didn’t want the contents to be wiped off. PLO in this case stood for “Please leave on.” Either that or there was widespread support for the Palestine Liberation Organization among the schoolteachers in my very hick hometown.

5. Hamas sounds like it could be made up of two words that would be offensive to Muslims (and Jews and vegetarians too)

I’m on a roll! Just a one more now:

6. Benjamin Netanyahu – did his family found an internet search engine? Or maybe his last name is Hebrew for “Nathan – celebrate!” (netan – yahu!)

Ok, I think I’ve exhausted your attention span. I still think I’m safe from vitriol (especially since almost no one reads this blog). But lets see what comes up in the comments.

Who are the poorest in this town?

Suppose you have come up with a way of identifying the poor people in a poor country (where income is hard to verify) in transparent way (one reason for doing this would be for social assistance targeting). How would you go about doing this? Here are a couple of options, as I see them:

1. Suppose you have household survey data that contains variables for easily observable characteristics, such as the household’s house, its assets, characteristics of the household (such as education of the household head, location), etc. as well as actual consumption (adjusted for regional price differences). You could try Proxy Means Testing: that is you regress actual consumption on a set of variables (e.g. gender of household head; age of household head; age of household head squared; presence of car, air conditioner, etc.) to arrive at a set of betas, or weights, which you would then use to predict consumption for out-of-sample households. Those with predicted consumption below a certain cutoff or threshold can be defined as poor. For this example let us define as poor those in the bottom quintile of predicted consumption.

One problem with this approach is that if the regression does not explain a lot of the variation in the data then many errors of inclusion (noon-poor being deemed poor) and errors of exclusion (poor being deemed non-poor) will result. Ideally, your PMT model would be limited to 10-14 variables, and this would limit the explanatory power of the model; the consequence is that the inclusion and exclusion errors would be significant. One mitigating fact, though, is that most of the errors of inclusion consist of those in the second lowest quintile of consumption. In a very poor country, these people are still quite poor.

2. One idea suggested to me was to use open public meetings to determine who the poorest people are. The idea is that you’d gather some people in the village, and ask them: who are the poorest 20 percent in this village? The extent to which this would work might depend on the power dynamics in the village. The advantage is that it could allow local knowledge to inform the determination of who is in the poorest quintile.

Some modifications to this approach are worth considering. For example, one could approach several people in the village and ask them to pick out those they think are in the poorest quintile. If there is significant overlap in the selection, it would be reasonable to conclude that the such overlap constitutes the poorest people in the village.

I went to a small farming village in a dirt-poor Central Asian country a few days ago. I spoke with a couple of farmers, and asked them about how in general to identify the poorest people in the village. It seems that the answer is that ‘you just know.’ One farmer took me to see a lady who, in his opinion, is the poorest person in his village.

He was right – you just know. This lady’s husband left her to become a non-remitting labor migrant. She has a disabled daughter, and at least one young son. She has a house (everyone has property) with a decent sized yard, but the kitchen and other rooms are tell the story about the well-being of this family. To get by, this lady gathers stalks, makes brooms out of them, and sells them for $0.75 each.

While there I saw that others in her village grow crops in their yards right outside their houses (Unlike the farmers, they are not forced to allocate 60 percent of their land to growing cotton). I was wondering if there is potential for this lady to do so as well. I could provide a small loan to pay for inputs (labor, seeds, fertilizer, etc), the farmer can provide technical advice. I can see how small social enterprises like this get started. My only concern is that the funds might be diverted other needs (such buying medicine and food, which is understandable) rather than being spent solely for the purpose of farming, which would not bring in revenue for some time. I’ll have to make sure I have a plan for this for my next trip out there.

What I've been reading lately

1. McMafia: A Journey Through the Global Criminal Underworld (Vintage) by Misha Glenny. Recommend. Its about organized crime in different areas of the world and how they are formed and what they do.

2. Getting Health Reform Right: A Guide to Improving Performance and Equity
It is super BORING read; they should include more anecdotes and link the lessons to these. Still the content is useful.

3. Liar's Poker by Micheal Lewis. Highly recommend. He is hilarious, and has a great writing style.

4. Blink: The Power of Thinking Without Thinkingand Outliers: The Story of Successby Malcom Gladwell. Highly recommend. I want to re-read blink, in fact.

Programming tips - translating docx and xlsx files using google translate

UPDATE: FORGET THIS POST! I wrote an MS Excel macro do to this, which you can add as an "add-in" to your MS Excel Worksheet. You can find instructions on how to do this and how to obtain the .xlam file here.

(This is basically a note to myself reminding me how to do something that I may need to do again)

For my job I (sometimes, many times) have to do programming. Mostly I use STATA, but also use perl and sometimes VB to get tasks done. I often get stuck, and then spend many hours google searching on how to do something. If I'm still stuck, I'll ask one of programmers in the programming group in another unit at work.

Sometimes I want to translate a docx file for an xlsx file using google translate, but preserving the formatting exactly. Right now google doesn't support the xml based docx and xlsx files for upload for translation. I don't understand why, since it is super-easy to do. I spent less than a week writing code in perl to enable this. Here are the steps:

1. change the docx or xlsx file extension to zip
2. unzip the file. This will create a folder with directories and a file.
3. find the document.xml file in the word or excel directory. (you may wish to do the same with the footnotes & endnotes files for word documents)
4. my code pulls the contents of this file for translation, replacing them with placeholders, and putting the contents file in a separate file
5. upload this contents file to google translate (translate.google.com/toolkit)
6. down load the translation
7. I also have code to replace the placeholders file with the translated content, putting the results in a new file
8. replace the document.xml file with the file from the previous step
9. Zip the files back up. Here is where I was having lots of problems.

When I was zipping the files back up, I would zip the containing folder. This is the wrong way to do it - word/excel can't open this. The _rels, docprops, and word directories, as well as the [Content_Types].xml have to be in the root directory, not some containing folder. So then I tried instead selecting the _rels, docprops, and word directories, as well as the [Content_Types].xml file directly and zipping them, and after changing the extension to docx word was able to open it as a word document (word still had to repair the document, but it was able to. If I use Yemuzip to zip the files, then word doesn't have to repair the document to open it.)

The result is a word or excel file that has been translated, while preserving exactly the original formatting. Email shafique.jamal@gmail.com if you want the code. (its in perl)

Something to do in Almaty, part 2

And here is another class...

Something to do in Almaty, part 1

Zumba Fitness Plus! It took three trips, but I finally found it.

Spare change?

Every weekday morning he stations himself on the sidewalk beside the park that lies right in front of the Main Complex of the World Bank in Washington DC. Even though there are other seemingly homeless people occupying the park, he’s usually the only one trying his luck along that strip of side walk in the morning, as though he’s been assigned a shift according to a custom among the park’s residents.

“Spare change?” yells the short shabby black man who greets me and everyone else who passes him on their way to work. This particular gentleman is well spoken, polite and seems capable. His park mates are much less so.

This request presents a dilemma to those of use working in the field of economic development. To give or not to give? The economist will opt for the latter, citing disincentives to work. And yet, what if there is no work available that they could do? And even there were, who would interview them, much less hire them?

I really don’t like giving money to pan handlers- the economist in me wins out. I think that if they can ‘earn’ enough by begging, they will continue to do so and not search for work. At the same time, some of these beggars have no hope of finding work.

To assuage my guilt for not giving, I propose an NGO with the following mandate: To offer the homeless and panhandlers clean up services (shower, shave, haircut), mental health service, interview counseling (how to speak interact with a potential employer), interview clothes, and job placement (and possibly training) services. The job training and placement could be geared toward taxi/truck driving, warehouse work, bricklaying, etc. There are services like this for the unemployed non-homeless, but I think the homeless need services tailored specifically for them. This NGO would hold the hand of a homeless person, taking him or her through the process to employability. It would be a one-stop-shop.

Now I don’t expect a huge uptake among the homeless for this kind of service. This is more for the benefit of people like me who don’t want to give money to someone when we can’t be sure that this person actually needs it, or will spend it appropriately. We could at least then justify our seeming callousness by donating to this NGO instead; we could then say - to ourselves, the panhandler and the world – that there are better options available to the homeless, which we support. Hopefully, it would have the added benefit of bringing some homeless people into the formal, productive economy.

Is the average DC man a jerk?

A good female friend of mine (lets call her K) recently made the following post on facebook: "Men in DC, Disaster..."

I think that there is a selection bias here that has contaminated the results of this experiment. Let us examine the average treatment effect as follows:

If we let: D equal the treatment indicator, where treatment is defined as romantic exposure of a DC guy to K; Y equal the outcome variable, which is K's happiness, then we can define the average treatment effect ATE as:

ATE = E[Y1-Y0] =
( αE[Y1|D=1] + (1-α)E[Y0|D=0] ) + ( (1-α)E[Y1|D=0]−(α)E[Y0|D=1] )

where the first term is the observed outcome: 
αE[Y1|D=1] + (1-α)E[Y0|D=0]

and the second term is the selection bias: 
(1-α)E[Y1|D=0]−(α)E[Y0|D=1].

So that:

(observed outcome) = ATE - selection bias

The first term in the selection bias is the expected happiness of K had she dated DC men that she did not date, and the second term is the expected happiness of K had she not dated DC men that she did date. α is the proportion of DC men that K did in fact date.

Within the selection bias term, I argue that the former term is likely much higher than the latter term, making the overall selection bias highly positive, thus leading to the very negative observed outcome despite the true average treatment effect (romantic exposure to DC men) being actually highly positive.

Why does the selection bias lead to such poor observed outcomes? Because the DC men that get romantic exposure to K are on average worst of all DC men (perhaps she tends to scare the good ones off?)

To eliminate the selection bias, K needs to do some RANDOMIZATION - i.e. she should date a random selection of men and then measure her happiness after each date. The results may very well redeem DC men in general.

Note that this research project is actually self funding (even the jerks will pay for dinner/coffee), which would help in a pitch to the MIT poverty action lab to run the experiment. Good luck to her!