A good family friend, Jahangir Hosseini, has been on hunger strike for 42 days now. His story is below.
At the age of 16, I got involved in politics. I was heavily involved in the Iranian revolution and started an anti-Shah group comprised of young people. In my capacity as the leader of this group, I organised secret meetings and lead political discussions.
I started working for the National Iranian Oil Company (NIOC) in Kharg Island when I was aged 18. I was elected as the leader of the NIOC, Kharg Island when I was 18. I resolved workplace disputes, introduced initiatives such as family counselling, advocated for women’s and worker’s rights and drew attention to the demands of workers I represented.
I fought for the wives of workers to be flown to major cities to access health care and to give birth. I campaigned tirelessly for workers on 12 month contracts to be granted permanent contracts, for their years of service to be recognised and for them to receive superannuation. I also fought for contract workers to have the same leave entitlements as permanent staff and for workers to work 40 hour weeks rather than 48 hour weeks, in line with international standards.
I organised regular strikes which heavily impacted upon garbage collection, power and other things. After seeing how active and strong our union was and after hearing about the victories we achieved, workers from other corporations and businesses at Kharg Island signed their names on petitions electing me as the leader of their respective union and forwarded such petitions to the board that managed Kharg Island. I effectively became the leader of a large coalition of unions.
At the time, Kharg Island was the largest offshore crude oil terminal. The Iranian regime was worried that the export of oil would cease if the workers continued to go on strike and could not afford any disruptions.
When I was 21, I was arrested by the Revolutionary Guards. The Iranian regime arranged for my dismissal from the workplace and made attempts to silence me. They perceived the strikes as anti-revolutionary and regarded my actions as provoking workers. I was told it is not an appropriate time to focus on workplace issues and worker’s rights and that our main focus as a nation should be on winning the war against Iraq.
I was barred from working in the private and public sector, could not pursue higher education, could not access my long service leave or superannuation, was prevented from utilising health services and could no longer play for the state soccer team. I have all relevant documentation including a summons to appear at the revolutionary court.
I was imprisoned for 2 years and was convicted on 11 counts which included my involvement with internal and external political organisations (PMOI), provoking workers, organising demonstrations and strikes and anarchy. I developed high blood pressure and was hospitalised. I fled to Turkey, from hospital, and maintained a secret involvement with the PMOI.
I was soon accompanied in Turkey by my wife and 8 month old daughter. Due to security concerns, we fled to Greece and lived in a refugee camp. The UN recognised us as political refugees.
While in Greece, I was heavily involved in refugee issues and politics. I continued my political activism and organised hunger strikes and demonstrations. I went on hunger strike for 55 days for the purposes of condemning human rights violations in Iran and to draw attention to refugee rights. In 1988, I was on hunger strike for 12 days and called for an end to executions in Iran.
I fought for refugee rights and advocated on behalf of many Iranian refugees, all of whom were relocated to Canada and America. After 18 months, we were the only family relocated to Australia. We arrived in Melbourne on 14 January 1989. We had no contacts in Australia and were taken to a hostel where we had to fight for our rights.
We came into contact with PMOI supporters in Australia and have maintained our political activism until today.
I have organised protests condemning the Iranian government for its human rights violations, advocated for refugee rights and regularly spoke to members of parliament and community organisations about refugee rights, the status of women and human rights violations in Iran. The Age and Channel 10 interviewed me following protests against the deportation of Iranian refugees.
Following the signing of a memorandum of understanding by the Australian Coalition government and the Iranian regime, on 3 June 2003, our home was raided by Australian Federal Police. Six houses were raided in Sydney and five in Brisbane. Ours was the only house raided in Victoria. No one was charged. We were all victims of a dirty deal.
Following the raids in Australia, on 17 June 2003, the French police raided the offices of the National Council of Resistance of Iran (NCRI) and arrested its members including its president elect, Mrs Maryam Rajavi. In June 2003, I staged a hunger strike outside the French Embassy for 15 days. I supported international calls for Mrs Rajavi and NCRI members to be released. This hunger strike took place precisely 14 days after the raid on our family home. The raid did not serve as deterrence and instead motivated me to increase my political activism.
On 19 September 2013, due to my opposition to the 1 September 2013 massacre at Camp Ashraf which lead to the murder of 52 unarmed Iranians and the abduction of 7, I decided to commence a hunger strike. I was the first to start a hunger strike despite being a father to a 3 year old boy, 22 year old daughter and a 27 year old daughter. I have been on hunger strike for 40 days as of 28 October 2013 and have had to deal with cold weather. I have slept in a van after being told on day 12 that we can no longer camp outside Casselden place.
I am mentally strong but physically weak. I made the decision to remain on hunger strike until the hostages are released and am willing to risk my own life. This is the bare minimum I can do for human rights.
Yesterday, Matt Cowgill put up an interesting post on the West Australian economy, which appears to be slowing. The ABS doesn’t publish state accounts quarterly, so despite our sincerest desire to know, it’s tough to tell whether a recession—two quarters of negative growth—is going on in any given state.
Cowgill’s solution is to back out an estimate of growth using data that is closely related to growth but released more frequently, like labour-force data. This technique is used widely among applied economists working with time series. For a blog-post, the ‘Okun’s rule-of-thumb’ estimate is probably sufficient to get an idea about the ballpark rate of economic growth in WA. But how would someone with more at stake go about forecasting the figure?
Firstly, I have a small concern with how unemployment maps onto output. If the stories are correct, Western Australian mining is transitioning from an investment activity to a volume activity. Building mines, railways, and pipelines is labour intensive, while operating them is not. Old relationships between growth and unemployment may not be the best way of forecasting future changes.
One solution is to incorporate more high-frequency series into our estimates of WA’s economic growth. I did this using WA’s unemployment, domestic demand, investment, Perth’s CPI, global iron-ore prices, and Australian mining exports. I use a canned forecasting routine from the R package ‘forecast’ to push forward the series to the end of the financial year, then annualised them, stuck them into a basic error correction model, and used the estimates to forecast WA’s year-on-year GSP growth.
Relative to a basic model using just unemployment to describe GSP growth, this model does significantly better, with a root mean squared error of 0.74 per cent, significantly less than the 1.1 per cent that using unemployment alone gives. R squared is about 69 per cent versus 29 per cent for the unemployment model. In all, it’s not too bad for within-financial year forecasting.
My growth estimate for this financial year is quite high, at a little under 4 per cent, with the 95 per cent confidence region going down to two per cent. In the plot below, the black line is the actual history, the green line is the model output, and the yellow bands are the 95% confidence region.
So this tells us something about the annual forecast—growth has been slower but not terrible. How about the quarterly picture? Of course, we don’t have quarterly GSP to use as a dependent variable, so we need to take the parameters estimated in the model above, and apply it to annualised quarterly data. This gives my estimate of quarterly year-on-year GSP growth for Western Australia:
So most of the four per cent of economic growth I forecast for this financial year are from the periods of high growth in the first two quarters of this financial year. With these y.o.y. estimates, it’s entirely possible that current growth in WA is zero or negative.
Data and code are here.
This post is a short summary of my talk at the Melbourne Users of R Network last week–specifically on the use of synthetic control groups formed by using the proximity matrix of a random forest. Don’t worry if you don’t know what those things are–a plain-English description is below. The slides are available here.
One of the main problems that applied economists work on is working out a ‘treatment effect’ of some policy variable. So we may be interested in whether those who graduate from university (university education being the ‘treatment’) earn more.
A big problem with many of these sorts of questions is that there is selection into treatment; the people who go to university were probably going to make more anyway, and so simply comparing them to those who haven’t graduated is going to be a poor estimate of the true causal effect of university on earnings. What we want to do is compare the treated person to their untreated self.
Randomised control trials (RCTs) achieve this, in the statistical sense. Their beauty is that the (large sample) distribution of personal characteristics (both observed and unobserved) for the treatment and control groups are exactly the same. Those swallowing the sugar pill have the same probability of being 40, being female, or having a pushy mother, as the group receiving the real drug. Unfortunately for science, we’re not allowed to run RCTs for many interesting policies. Randomly allocating some people to higher rates of education and others less may be seen to be unethical.
Due to this constraint, Economists often use natural experiments to achieve the same objective; discontinuities in policy, weather, or geography that randomly assign some people to a treatment group and others to the control, otherwise the two groups should be very similar. However, good natural experiments are rare, normally don’t exist in our data-sets, and apply to a relatively restricted set of interesting policy questions. While a good one will get you tenure at a top US university, they don’t seem to be the secret ingredient in building a better evidence base for good policy.
So what if you still have an interesting policy question, good quality data, but no natural experiment? Thankfully, some methods exist that allow us to construct synthetic control groups that look a lot more like the treatment group than the old control group.
One method that has been very popular over the last decade or so is propensity score matching, popularised by Rosenbaum and Rubin (1983), and Dehejia and Wahba (2002). This method works in two stages:
1. You set up a predictive model of the ‘treatment’ (in this case, university completion), using personal characteristics for the independent variables. Normally you’d use a logit/probit style model for this. And
2. For each ‘treated’ observation, get the untreated observation with the closest probability of having gone to university. You then chuck out the unmatched observations–they don’t look much like graduates anyway–and run your regression on the remaining observations. The ‘treatment effect’ in the regression is, hopefully, now closer to the true value.
This is very easily done in R using the ‘arm’ package. Some code is in the presentation linked at the top of this post.
There are still some big problems with propensity score matching. Smith and Todd (2005) found that the results are not very robust to changes in the propensity model in part 1. While we have constructed a match on the observed variables (that in the data), but we still have no idea whether the treated observation is more likely to have a pushy mother or not. Also, we have no idea about the direction or scale of remaining bias. The method is not magical.
My improvement on this method is to use a more robust measure of similarity to help get over the Smith and Todd critique, borrowing from the Random Forest–a tool widely used in predictive analytics. For a deeper discussion of how these work, see here.
A Random Forest is basically a collection of models, called trees–in this case, they are models to predict whether someone went to university. Each of these trees is estimated on only a subset of the data–ensuring no individual survey respondent or survey question makes much of a different to the outcome. For every respondent, we ask all of the trees (there are sometimes thousands) whether they think the respondent went to university or not, based on their personal characteristics. The winning vote is the ‘prediction’ for the random forest for that survey respondent.
Every one of the trees is constructed in such a way so that the branches divide people according to some characteristic– in this case, gender, or age, or mother’s education level, etc. A branch will grow off the tree only if dividing people according to one of these characteristics results in a ‘purer’ division of graduates and non-graduates. That is, each tree is constructed with the aim of some ‘leaves’ containing only graduates, and others no graduates.
When two people wind up in the same leaf, then we know they are similar in several ways. Importantly, they are similar in the ways that matter to whether they will have gone to university. They are said to be proximate. The proximity score is the proportion of terminal leaves two observations have in common. My proximity score matching routine works like so:
1. Run a Random Forest with the treatment as the dependent variable, and include the pre-treatment independent variables. As random forests are build on randomly subset data, you should set the random-number generation seed if you want your results to be replicable. Also, make sure you save the proximity matrices!
2. Match on the proximity scores, also save the proximities of the untreated observations. I find that using these as weights improves my estimates (in terms of decrease in deviation from experimental estimates).
3. Discard unmatched observations. Make sure you don’t duplicate matches!
4. Run your regressions on the remaining data.
Example R code is included in the presentation linked above.
Rather than matching on the propensity score, I believe the proximity score produces a control group that is more similar to the treatment group than any other existing method, and consequently allows us to produce less biased estimates of the causal effects of policy. In my experiments with this method so far, I have found that:
- Matching on the proximity score results in a more robust matching with the inclusion of extra independent variables than probit/logit methods; and
- Benchmarking against the famous Lalonde dataset, I find my estimates of the causal effect are closer to the experimental estimate than when using propensity score matching
However I should emphasise that if you have a small number of trees, you will have fairly unstable matches. With current memory constraints, it is not feasible to build proximity score matrices on large datasets for lots of trees. So the method is not well suited to large data-sets without re-writing the Random Forest algorithm to iteratively update the proximity matrix.
If you have any experimental/quasiexperimental data you would like to share with me, I’d love to do some more benchmarking of this routine. As it is, I’m 85% sure it’s an improvement on what we have; I’d like to be more sure!
A problem with highly aggregated unemployment statistics is that they mask big differences in the work fortunes of different groups of people. In the ideal world, people made redundant in shrinking sectors can find work quite easily in growing sectors. Unfortunately, that doesn’t appear to be the case—the skills of a worker in a bike shop or shoe factory are different to the skills required to work in a growing sector, like mining or hospitals.
To visualise this, I pulled the ABS’s unemployment data by industry, employment by industry, and the 2006 Census’s education levels by industry, to make this pretty plot. On the horizontal axis we have median quarterly growth in employment from 2001 through 2012 (changing the metric here doesn’t greatly affect the chart). The vertical axis has the median unemployment rate for each industry over the period—again, this is pretty robust to changes in definition. The area of the bubbles represents the amount of employment in 2001. Finally, the colours are darker for those industries with a greater share of workers with a bachelor education or higher.
The code and data to make these plots are here (if you want to make them, you’ll need to change the working directory in the R script).
As I posted the other day, we know there are big differences in the unemployment rates in different sectors, and so it’s not really a surprise to see that unemployment rates tend to be higher in slowly-growing industries. Indeed, the relationship could be spurious: most unemployment observed at a point in time is short-term, though most unemployment (in terms of man-days not working over a period) is long-term. So it could be that we’re repeatedly measuring people just laid off from declining sectors. I’d not bet on that. People in the lagging sectors are less trained than people in low-unemployment sectors, and can’t easily shift industries.
All of this points to something quite sad: while we’ve all heard stories of Cashed Up Bogans in construction and mining making a motza with little formal education, there are other people with a fairly low level of education who haven’t done so well out of the boom. While their unemployment rates have been quite low over the last decade (especially when we compare them to unemployment rates in Europe or the US), any slump in the future would shift all the circles up—especially the circles with less education. Then, it’s far from clear that displaced aluminium smelter workers will be able to find work in professional services or education.
Around the end of my first year of university–about the same time I realised that dhal was the most cost-effective food–I started shopping fairly frequently at Aurora Spices, an Indian grocery store in North Carlton. It’s a pretty standard spice store, with the standard collection of spicy chips, dry-tasting sweets, spices, rice, mortars-and-pestles, beedis, and two-for-one Bollywood films.
Once while picking up the weekly supply of fresh curry leaves, I bumped into a family friend, a south-Indian tablist. He asked me if I’d seen any of these films, and spoke pretty highly of some of the ones on display. Politely, I accepted his suggestion that I should watch one at his recommendation. He pressed a case into my hands: Devdas.
I went home, and watched it. And again within the week. This was a wonderful film: the music was tight, the women beautiful, the story epic, and even the normally terrible Shahrukh Khan was bearable. By the stage I saw it, it was also a few years old. If this movie could exist without me finding out, what else could be out there? I thought.
I was hooked. Within a few months, I’d watched hundreds of Bollywood movies. All I wanted was to replicate the sensation of seeing Devdas the first time. Most were terrible. There were a few exceptions: the cheesy but delightful factory-jobs put together by the Yash Raj company, or the slightly-edgier films starring Abhishek Bachchan. But on the whole, I was serially disappointed. The phase tempered, and I (almost) moved on.
Since then, I’ve perhaps seen five or ten Indian films per year. I have no particular interest in Indian culture, language, or history, but find keeping my knowledge of Bollywood reasonably fresh opens more doors than it closes. It’s also a cheap way to keep (cheaply) entertained.
Imagine my surprise, then, when I finally got around to see Delhi Belly a couple of months ago. I’d seen the advertising beforehand, but knew not to get too hyped up by any poster in a spice store (especially after the horror of being subjected to the hyped-up Ra.One last year). The assistant at the subcontinental DVD store on Sydney Road made me buy it. “Dont’ worry, this one is very funny”, he told me. He has burned me before, having given similar praise to the horrid Jodhaa Akbar, but he seemed more genuine this time.
Delhi Belly is about three mid-late-20s flatmates in central Delhi. One is asked to deliver a parcel for his girlfriend, an air-hostess, on behalf of a (presumably) Russian gangster. A second flatmate has Delhi Belly, and so asks the third flatmate to deliver a stool-sample to the doctor. Predictably, the parcels are switched, and the stool sample is sent to a local crime boss.
To say the film is “not Bollywood” is an understatement. It runs for 90 minutes, has a single tune (think Alice Cooper with blazing sitars and pelvic thrusts), is mainly in English, and is very, very vulgar. It has much more in common with Snatch than Dhoom. Above all, though, it’s funny in the Hollywood block-buster sense. Pant-wettingly funny.
Then there’s all the muck. Most Indian films are shot through a rosy lense; the characters of Devdas live in stained-glass palaces; even the Delhi 6 of Delhi 6 looks habitable (oh! the Culture!). Delhi Belly doesn’t tease in this sense. The inner suburbs of developing mega-cities are full of terrible apartments, plumbing problems, and crappy stores selling crappy things. Young professionals there don’t get to live in luxury condos—they share smelly apartments. Delhi Belly makes sure that you, the viewer, is left with no doubt how grubby it all is. It’s so refreshingly honest.
So why do I think this could be an incredibly influential film? Two main reasons.
First, it shows that there is sufficient talent in India to make a first-rate Western-style blockbuster comedy for $5M. With a better foreign release strategy I have no doubt it would have been a box-office hit abroad. Foreign film producers in search of high potential returns should be looking at how to replicate Delhi Belly, but this time market it better abroad.
Second, let’s be honest: when was the last time you cried laughing at a non-US/UK-made film? Of course there are exceptions, but in general, foreign comedies aren’t very funny. This could be a volume-effect. Most US comedies aren’t funny either, and it’s their number that results in a couple of good ones floating to the top. But now that Delhi Belly has broken a few barriers (and made quite a bit of money doing so; it’s box-office return was around 300%), I expect that at the very least we will see plenty of Indian copycat productions. Indeed, just as Bollywood has inspired filmmakers all over the (rest of the) world, I expect this film and the coming rip-offs to similarly spawn good old-fashioned toilet-humour comedies in foreign cinema. That’s exactly what we need.
A solid four stars.
Lately, I’ve been doing a fair bit of work on some large survey data-sets, and have had a recurrent issue: The survey reports factors (Are you married? Single? Divorced? etc.) as numbers. R then automatically treats these as numbers, which has little meaning.
The basic transformation you can make is simply
df$variable <- as.factor(df$variable)
though then you get ugly regression output. It will say “marital=1; marital=2; etc”, which makes it difficult for people who don’t have access to the survey documentation to interpret. As I’m running hundreds of regressions on many surveys, I simply don’t have time to TeX them all up and change these into words.
My work-around, then, is fairly simple. First I define a list (basically a key-pair dictionary)
marital.factors <- list(single = 1, married = 2, divorced =3, etc.)
then simply match the content.
dataframe$marital = names(marital.factors[match(dataframe$marital, marital.factors)])
My regression then automatically treats the variables as factors, and gives me regression output that has some meaning.