'Blow them all away in the name of the lord!'

That, according to Wikipedia, is what the unlamented Jerry Falwell said of US presdient George W Bush’s ‘war on terror’, which he enthusiastically supported.

Falwell, founder of the Moral Majority movement that aimed to bring the most backward fundamentalist christian values to US politics, consistently took the most execrable imaginable position on every issue. He opposed the civil rights movement, indeed, he opposed any rights for minorities.

After the September 11, 2001, attacks Falwell said on the 700 Club, "I really believe that the pagans, and the abortionists, and the feminists, and the gays and the lesbians who are actively trying to make that an alternative lifestyle, the ACLU, People For the American Way, all of them who have tried to secularize America. I point the finger in their face and say 'you helped this happen.'"

"AIDS is the wrath of a just God against homosexuals."

"Labor unions should study and read the Bible instead of asking for more money. When people get right with God, they are better workers."

"I hope to see the day when, as in the early days of our country, we don't have public schools. The churches will have taken them over again and Christians will be running them."

Althought Falwell claimed that The Antichrist ‘must be, of necessity, a Jewish male’, It goes without saying that he was among the foremost ‘christian Zionists’ and a darling of the Anti-Defamation League.

USA Today reports that the bastard has gone to meet his maker before he could do any more damage, and good riddance. It’s almost enough to make you wish there really were an afterlife where assholes like him could suffer eternal torment. Oh well.

Anti-life

In a poll of 1003 American adults conducted 10-13 May, Gallup found that a large majority of Americans think that ‘partial birth abortions’ ought to be illegal. In answer to the question,

Now I would like to ask your opinion about a specific abortion procedure known as "late term" abortion or "partial birth" abortion, which is sometimes performed on women during the last few months of pregnancy. Do you think that this procedure should be legal or illegal?

72% answered that they thought the procedure should be illegal. This is an increase since Gallup asked an almost identical question in October 2003, when a still very substantial majority of 68% opposed the procedure. The proportion advocating making the procedure legal declined from 25% to 22%, while there was also a decline of two percentage points among the undecided.

In a related trend, since October 1989, Gallup have been asking variations on this question,

Would you like to see the Supreme Court overturn its 1973 Roe versus Wade decision concerning abortion, or not?

In the latest iteration, the same population nearly three quarters of whom wanted ‘partial birth abortion’ banned, only 35% wanted to see Roe v Wade overturned, a slight increase since last May, when it was 32%, but rather a more dramatic increase since January 2006, when it was just 25%. In fact, the proportion wanting Roe v Wade overturned has only once exceeded this month’s, in March 2002, when it was 36%. Indeed, with a ‘margin of error’ of ±3 percentage points, we can’t be sure that it isn’t actually higher now.

The 53% preferring the 1973 decision to stand in this month’s survey is actually the lowest ever, declining by a full 15 percentage points since March 2005 and 13 points just in the last sixteen months.

Still, the interesting discrepancy is between the huger majority opposing ‘partial birth abortion’ while only a little over a third wanted women’s right to choose overturned. Could it be something to do with the wording of the question? Isn’t ‘partial birth abortion’ a term coined by the anti-life movement specifically to make the procedure sound frightening? One can’t help wondering what kind of results we would have seen if the question were worded with one of the more conventional and less emotive terms for the procedure, like (intact) dilation and extraction or evacuation.

One of the crucial arguments I always like to make about the anti-lifers is that their concern for foetuses not only doesn’t extend to the parent(s), but to the child the foetus is to become. It is not important to them what kind of life that child will have or how long it will get to live, only that it is born, whatever the cost to the parent(s), to society as a whole, or to the child itself. That’s why I refuse to allow them to coopt the label ‘pro-life’. They are decidedly anti-life in every sense of the term and we might as well make them cop it.

Now hear this!

Due to bandwidth limitations and security concerns, mail privileges have been permanently revoked.

According to an article by Alan Sipress and Sam Diaz in today’s Washington Post,

In a memorandum to troops dated Friday, Gen. B.B. Bell, commander of U.S. forces in South Korea, said the task force had noted "a significant increase in the use of DoD network resources tied up by individuals visiting certain recreational Internet sites," he said. Bell added that the traffic poses "a significant operational security challenge."

The Defense Department began blocking access on its computers to YouTube, MySpace and 11 other Web sites yesterday, severing some of the most popular ties linking U.S. troops in combat areas to their far-flung relatives and friends, and depriving soldiers of a favorite diversion from the boredom of overseas duty…Soldiers and their families frequent the sites to exchange notes, swap pictures and share recorded messages -- a form of digital communication that, along with e-mail, has largely replaced the much-anticipated mail call of previous wars.

In computer rooms on bases in Iraq, Afghanistan and elsewhere, soldiers crowd around rows of monitors, lining up for a chance to glimpse the latest news from home or leave their distinctive boot print in cyberspace. Some postings on YouTube are grainy battle videos shot with small cameras recording the brilliant flare of roadside explosions and crackle of gunfire set to rock music. Others are more melancholy depictions of loss, showing struggling medics and fallen comrades…

The Defense Department Web site policy comes one month after the Army issued a regulation barring soldiers from posting entries on blogs, participating in online discussion groups or sending personal e-mail unless the content is cleared by an superior officer…

Just one more way that the Pentagon supports the troops.

We won't be fooled again

As everybody knows, there’s just one thing that’s more boring than grammar, and that’s statistics. Or maybe it was the other way around? I forget. Anyway, it was in a previous incarnation that I was a grammarian.

Over the last couple of months or so a few things I’ve heard and read here and there have reminded me that a lot of people still aren’t sure what labour force statistics mean. I’ve left some comments around, but I thought I’d try to draw together some basic information that might help people who read this blog to understand where these numbers come from, what they mean, and how they’re used. It’s a dirty job, but somebody has to do it. The whole point of this is to make things clear to people who don’t ordinarily think about statistical issues. I’m grateful to Ablokeimet, who has some considerable expertise in stats, and Meezan, who doesn’t, for their comments and corrections. I’ve actually been sitting on this draft for a few weeks waiting for comments from others on my mailing list, but since none have come through, it must be absolutely intelligible. If you don’t think so, or have any specific queries, please post a comment or email me and I’ll try to respond either directly or by making appropriate revisions, or both.

I suppose it should come as no surprise that a lot of people aren’t too clear on how to interpret statistics. And I think this is a shame, because, as I keep saying, people use statistics to lie and deceive, but statistics are not necessarily inherently mendacious. As Ablokeimet pointed out in a response to my comment on leftwrites, ‘Statistics are far too important to let the Government fiddle with them…The real masters of society want figures they can trust.’ I hasten to add that not every country’s statistical agency is as accommodating of their ruling class’s requirement for robust statistics as, for example, the Australian Bureau of Statistics. In Pakistan, for example, everybody thinks the government tampers with statistics before they’re released. The long delays between collection and publication and other factors certainly make this seem plausible. It may even be true. But a lot of the statistics we read about in the media are credible and can tell us things we need to know, if we understand what they mean. Among the things they can tell us is whether the politician using them is trying to hoodwink us.

So what I want to do is to reveal for the first time, the deepest, darkest secrets of the statisticians’ craft – secrets so carefully guarded by generations of statisticians that you must often go as far as the end of the book to find them, or even read the glossary on the website!

To begin with, it may be useful to know that the kind of statistics I’m talking about – ‘social’ or ‘population’ statistics – come from two basic kinds of sources – administrative byproduct data and surveys. Administrative data can be very robust and accurate in a jurisdiction that collects it rigorously. If, however, births and deaths are reported haphazardly, for example, then such ‘vitals’ data is useless, or worse.

Then there are two basic kinds of survey data. A census aims to enumerate every unit in the relevant population. If it’s reasonably successful in doing so, then the figures that come from it are quite reliable. There is no ‘standard error’ associated with them, so you can confidently extract data on small geographical areas and so forth. The disadvantage of a Census, however, is that it is very expensive to survey, say, all 300 million odd people in the US. So, for one thing, they usually only happen once every five or ten years. If you’re lucky. Pakistan’s last decennial census took place in 1987. And for another, they can’t gather data with the fine detail possible when you can ask several carefully worded and sequenced questions. To gather a few basic labour force concepts, the Labour Force Survey in Australia administers a questionnaire of 93 questions, while the 2006 Census asked only thirteen questions to collect labour force data.

Because we know that individuals in society are not completely unique in every respect, it’s possible to surmise general characteristics by observing a proportion of the members of the population of interest. That means that each unit observed represents a particular number of others. The process can be refined by ‘stratifying’ the sample by factors such as geographical location, sex, and age. By multiplying each observation by the ‘weight’, which is derived from the proportion of the total population represented by the sample, we arrive at an ‘estimate’. An ‘estimate’ in this sense is not just a guess. It’s the weighted product of the observations.

The disadvantage of a sample survey, of course, is that it introduces the possibility that the units actually selected for observation may not turn out to be quite as representative as we might have hoped. So there is an element of doubt. Fortunately, the level of doubt is quantifiable. That’s why we have stuff like standard errors and confidence intervals and ‘margins of error’. Essentially, the reported estimate is the midpoint, more or less, of a range of values. The broader the range, the higher the level of confidence that the true value lies within it. So for example, if the reported estimate is 10%, it probably means something like ‘We are 85% sure that it is really between 9% and 11% and 95% sure that it’s between 8.5% and 11.5%’ or ‘We think the proportion is 10%. There’s a 15% chance that the true value is less than 9% or more than 11%, but only a 5% chance that it’s below 8.5% or above 11.5%’ . In other words, the 85% ‘confidence interval’ is 9%-11% and the 95% ‘confidence interval’ is 8.5%-11.5%.

The real problems arise when you want to compare small populations. If you wanted to compare the number of women who are employed with the number of men who are employed, that would not be a problem when using a sample survey. You could even disaggregate the data by sex, age, and state to compare, for example, the number of unemployed men aged 25-29 in Victoria with the corresponding population in Queensland. But if you were interested in numbers of farriers in the Northern Territory, or practically anything at all in a particular postcode area, because the numbers representing those populations in the actual sample are very small, the standard error would be so high that you couldn’t trust the estimate at all. This is not an issue with Census data, although statistical agencies won’t release data on populations so well defined that it becomes possible to identify characteristics of individuals.

So that’s kind of a quick summary of where the numbers come from. There’s a lot more to say about it and any statistics textbook, or a popular introduction, will say some of that. But I want to move on to the kind of stuff that I think can really contribute to making the numbers meaningful.

The first thing you need to notice about a number, and it doesn’t matter whether it comes from admin data, or a census, or a sample survey, is whether it’s an absolute number or a proportion. In national level population statistics, the former are usually expressed in thousands and the latter in percentages. Proportions reveal important relations, but if all you have is proportions, it can conceal significant matters of scale. More importantly, if you have the original numbers, you can always calculate the proportions and there is often potential to work out relations that are not explicitly reported. When I see proportions and have no way of recovering the absolute numbers they’re based on, it makes me uncomfortable and suspicious.

For example, you may recall a few weeks ago I was discussing an analysis of some survey results that correlated an ‘AntiSemitism Index’ (ASI) with an ‘AntiIsrael Index’ (AII). The authors of the paper under consideration, Kaplan and Small, found a very strong correlation. The higher the AII, the higher the proportion with a high ASI. In other words, it appeared that those who had more Anti Israel attitudes were also likely to have a lot of anti-Semitic attitudes. What I found when I looked at the original data was that,

While it is quite true that just over 56% of those with an AII of 4 [the maximum for that index] recorded an ASI greater than 5, Kaplan and Small don’t mention that the total number with an AII of 4 is 57, or just 1.14% of the sample of 5004. When the numbers get that small, questions of accuracy start to arise.

The next thing to notice is what the number is counting. I’ve been talking about ‘units’. It matters what the units are. Among the kinds of units that tend to be relevant in social analysis are the person, the family, the household, the dwelling, the job, the enterprise, etc. There was a discussion on leftwrites reacting to the announcement that the Prime Minister had been crowing, “I do believe that after a year in which 276,000 new jobs were created, it is reasonably [sic] to assume that one of the fundamentals to jobs has been the abolition of the unfair dismissal laws.”

In reality, there were probably more than 276,000 jobs created. The estimate of 276,000 was the increase in the number of employed persons. Clearly, some of those persons may have had more than one job and some of those who were already employed might have taken on, or lost, or left, second jobs. There is probably a correlation between the number of jobs and the number of employed persons, but it’s not necessarily straightforward, and in any case, it is decidedly not the same thing. I don’t regard this as a particularly egregious example, since the bastard probably ended up understating what he thought he was talking about. Of course from the perspective of social analysis, we are more concerned about the number of employed persons than the number of jobs. So he was actually reporting the number of interest, but not describing it correctly.

Like most statistics, out of context, a number like this is just propaganda. The specific context that we need to understand the increase in the number of employed persons is the number of persons who have left the labour force through death, retirement, and the like, and the number who have entered, after leaving school, etc., over that period. The only real gain in employment is the difference between the increase in the number of employed persons reported and the net increase in the labour force. That gain for the year to March 2007, the twelve months since the introduction of the draconian WorkChoices legislation, was 46,400 – not as impressive a figure as 276,000.

It might clarify this if we look at the actual numbers.

Employed persons, March 2007	10,389,300
Employed persons, March 2006	-10,112,800
Increase in number of employed persons, March 2006 – March 2007	276,500
Persons in the labour force, March 2007	10,904,300
Persons in the labour force, March 2006	-10,674,200
Increase in number of Persons in the labour force, March 2006 - March 2007	230,100
Increase in Number of employed persons over and above the increase in the size of the Labour force	46,400
(Source ABS, Labour force, Australia. ABS Cat. No. 6202.0, March 2007)

But beyond that, while the increase in the number of employed persons in Australia in the year to March 2007 was higher than the increase over the twelve months to March 2006, 151,600, it doesn’t compare that favourably with the previous twelve months to March 2005, 334,400. As for the real increase of 46,400, it was a big improvement on the 10,000 for the previous year and the 30,000 the year before that. But in the year to March 2004, it was 63,900.

In a more telling example, lenin reported that there was an item in Metro claiming that ‘Almost half the black children in Britain are being raised by single parents, new Government figures reveal.’ The data supporting this assertion was that, ‘The biggest percentage of lone-parent households is among black ethnic groups. Forty-eight per cent of black Caribbean families have one parent, as do 36 per cent of black African households.’ So, what’s wrong with this picture? Leaving aside the confusion between families and households, which is something you expect in the media, how do we know that 48% is the proportion of children who are members of 48% of families? Some families have more children than others.

As a matter of fact, the Office of National Statistics publication Social Trends 2007, which appears to be the source of the data reported by Metro, shows that couple families tend to have more children than one parent families. It’s not actually inconceivable that the proportion of Black children in one parent families is ‘almost half’, but the data presented definitely don’t support that conclusion because they enumerate not children, i.e. persons, but families. In other words, they took an estimate of the number of different sized boxes of apples, so to speak, and presented it as an estimate of the number of apples. As a matter of fact, I don’t think Metro was trying to deceive its readers. They probably didn’t notice themselves that they had confused two different units of enumeration to arrive at a bogus conclusion. The point is not to get sucked in when they do it, whether it’s deliberate or not.

The next thing to be aware of is that classifications matter. The whole point of statistics is to be able to group populations on the basis of some perceived similarity and observe whether there are correlations with other phenomena. For example, you might be interested in knowing whether people aged 15 to 19 are more or less likely to be employed than people 20-24. If so, you’d probably be in luck, because these are standard five year age ranges. But if you wanted to know about people aged 16-22, the data would be harder to find. If the survey collected age in ranges rather than in single years or by date of birth, it might just not be available at all. I’ll come back to classification in a minute.

Finally, definitions matter. Statisticians have to define concepts very carefully so they know in any given case which category a unit belongs in. And the definitions don’t always correspond with common parlance. For example, I mentioned just before that Metro had confused households and families. A straightforward definition of a household would be ‘All the people who usually reside in the same private dwelling’. Obviously, this begs a lot of questions, like how we define ‘usual residence’ and ‘private dwelling’, but I’m going to take that as read for the time being. Significantly, this definition allows the possibility that a household can comprise a lone person.

A family, a much more complicated concept, is a group of related people who usually reside in the same private dwelling. This is fairly close to the common parlance definition, although you might want to include people who don’t live together. However, for statistical purposes, it’s not good enough. Rather than defining the concept precisely, I’m just going to give an example of how it would work. Imagine a household comprising a couple, their 20 year old daughter and her child. For statistical purposes, the couple is one family, the daughter and her child, another. And these two families exemplify two of the categories of the classification of Family type – a Couple family without children and a One parent family.

To get to the point, a household may comprise one person or a group of people, who may or may not be related. A family is necessarily a group of people and must be related in particular ways. There may be more than one family in a household, even if all the household members are related. And it can get even more counterintuitive than that, as in the example, where there was a couple family without children, even though their own biological daughter was right there in the same household, but allocated to a different family.

Now it’s one thing to look in the glossary on the website or at the back of a statistical publication and that’s definitely where you start. But unfortunately, statisticians aren’t always totally honest. For example, you often find a definition of household that includes some reference to the members eating together. The idea is to be able to distinguish ‘boarders’ who eat with the rest of the household, from ‘lodgers’, who make their own arrangements. I won’t go into why this is a bogus distinction at this stage, but the point is that in reality, there is probably no attempt made to establish whether or not they actually do eat together. Unrelated children can count as family members. And so forth. Ultimately, to understand what the real concept behind a number is you have to know what questions were asked and stuff like that.

But what I really wanted to talk about in some detail was Labour force status (LFS). There’s a lot of confusion about the concepts. People bandy about the Unemployment rate, as I mentioned before, but not many people really know what it means.

‘Labour force status’ (LFS) is a variable that applies to a specific population of persons: civilian adults. Apart from children and the military, there may be other exclusions, like people residing in institutions, like prisons and monasteries, but these tend to be associated with survey methodology, for example if the sample excludes institutions. Who counts as an adult for these purposes varies from country to country. The age tends to be associated with something like the age at which it becomes legal to employ a child, or the age they can legally leave school, or the like. It is usually all persons aged 15 and over. Note that ‘15 and over’ is not the same thing as ‘over 15’. It’s the same thing as ‘over 14’. But in the US and the UK, it’s 16 and over, in Pakistan, it’s 10 and over. In Bangladesh, it’s 5 and over!

LFS classifies the relevant population of persons into three categories: ‘Employed’, ‘Unemployed’, and ‘Not in the Labour force’ (NILF, or in Canada, ‘Out of the labor force’ (OLF)). To make any sense of statistics like the Unemployment rate, it’s essential to know how each of these categories is defined. You’ll probably find these dark secrets shocking. All the more so because they are accepted everywhere and have been for quite some time. The basic source is RaIf Hussmanns, Farhad Mehran and Vijay Verma, 1990, Surveys of economically active population, employment, unemployment and underemployment: An ILO manual on concepts and methods.

The standard definitions of employed and unemployed as enunciated by the 13^th International Conference of Labour Statisticians in 1982 (pp. 242-243 of Hussmanns et al.) are quite long and complex. But in a nutshell, a person is employed if they work ONE hour or more in the reference period (the period about which the survey asks, usually a week) ‘for pay or profit’ (in cash or in kind), or indeed without pay in a family business or on a farm. In fact, you still count as employed even if you didn’t work at all in the reference week, if you had a job and you were off on holiday, compo, strike, or for certain other reasons.

A person is unemployed if they were not employed, as defined, during the reference period, and were available to start work, and were actively looking for work. This is the population to which employers can point and say, ‘If you don’t like the pay or conditions here, there are plenty of others who’ll be glad to have your job.’ In this way, they help to discipline the employed labour force and impose downward pressure on the general level of wages. For these purposes, people who are not working but are not both available to start work and actively making themselves known to potential employers are irrelevant. If they are not available, because they’re sick, or looking after children, or any other reason, they are no use to potential employers at the relevant time. And if they are not actively looking for work, employers won’t know about them, even if they’re available.

The employed and the unemployed combine to form the ‘Labour force’. Everybody else in the civilian adult population in scope is NILF – full time students (who don’t have jobs), homemakers, retired persons, etc. Also, people who are looking for work, but not available to start at the relevant time and people who would start work if offered a job, but who have given up actively looking – ‘discouraged jobseekers’ – are classified as NILF.

It’s important to point out that these populations are typically identified through household based Labour Force Surveys and the like. Not everybody who counts as unemployed for these purposes is eligible for unemployment benefits, whatever they’re called. For one thing, the activity test tends to be slightly different. But more importantly, some who are statistically unemployed would not be eligible for benefits because of spouse’s or parents’ income, or other factors. Furthermore, those working few hours or for low pay may retain some level of eligibility for unemployment benefits in some jurisdictions. So you don’t actually expect the count of benefit recipients to match the estimate of unemployed persons, although every once in a while somebody notices that they don’t correspond and raises a fuss in the media. Like they do every few years when somebody reveals the bizarre ‘one hour rule’.

So, now we’ve defined five different populations:

The civilian population aged 15 (or whatever) and over.

1. The labour force, comprising

a. Employed persons and

b. Unemployed persons

2. Persons not in the labour force

This basic division of the civilian adult population into the Labour force and PNILF makes sense for the intended economic purposes. But from the perspective of social analysis, it might be more helpful to forget about the Labour force entirely and just distinguish employed persons from not employed persons, like this.

The civilian population aged 15 (or whatever) and over.

1. Employed persons

2. Persons not employed

a. Unemployed persons

b. Persons not in the labour force

You might even want to exclude those persons working short hours or without pay who are currently defined as employed and count them with the unemployed and NILF.

The civilian population aged 15 (or whatever) and over.

1. Fully employed persons

2. Persons not fully employed

a. Persons working short hours or without pay

b. Unemployed persons

c. Persons not in the labour force

Labour force statistics are widely misunderstood and the media contribute to perpetuating the misperceptions. The statistical agencies that collect and disseminate the data could do more to correct them, but to their credit, none of this stuff is really secret – you can find all the definitions and explanatory material you need on the website or in any publication.

Now there are three main indicators that are typically calculated from these populations:

· The Unemployment Rate is the ratio of unemployed persons in the labour force, in other words, it is the number of unemployed persons divided by the number in the labour force, i.e. Unemployed plus employed.

· The (Labour Force) Participation Rate is the proportion of the adult civilian population that is in the labour force, so the labour force divided by the civilian population.

· The Employment-Population Ratio is the proportion of the adult civilian population that is employed, so the employed population divided by the civilian population.

Most economic statistics come from administrative data and surveys of business establishments. Even though labour force statistics come from household surveys, however, they are essentially economic indicators. The unemployment rate, in particular, which is the most commonly reported indicator, is not a social indicator. It is a measure of what labour statisticians call ‘labour market slack’. It is the proportion of the labour force – ‘the currently economically active population’ – that is, the ‘employed’ plus the ‘unemployed’ as defined, who are in the latter category. That is, working less than one hour per week, available to start work, and actively looking for work. In other words, the reserve army of labour, rather than, say, the proportion of people who don’t have an income. The reason it’s important to employers and governments is that they are interested in knowing how many people are available to fill shortfalls in labour and making themselves known to employers. It’s a measure of the discrepancy between the supply and demand of labour. When the unemployment rate is high, there’s a buyer’s market for labour power.

Specifically, it is not an indicator of the proportion of the population who lack means of livelihood. Because of the ‘one hour rule’ and the inclusion of unpaid family workers in defining Employment and the availability and activity tests in the definition of Unemployment, along with a few other less significant factors, the concepts just don’t align with the concerns of social analysts regarding income and so forth.

Usually, I find a more interesting statistic to be the ‘non-employed/population ratio’ – that is, the difference between the Employment/Population Ratio and 100% - the proportion of the civilian population aged 15 and over who are not employed (not to be confused, of course, with ‘unemployed’). In other words, it’s the proportion of the adult civilian population who are either unemployed or NILF. Of course, this is not really an indicator of livelihood, either. But it’s usually easy to find, or to calculate from readily available estimates, and it’s heaps better than the Unemployment rate for this purpose. The reasons it’s not a livelihood indicator are that, for one thing, there are other sources of livelihood than employment, principally investments and benefits. For another, most jobs do not pay enough in one hour per week to really constitute a livelihood. Usually Hours worked data are readily available, so you can calculate more refined indicators, like the proportion of the adult civilian population working full time, or, say, 15 hours per week or more. Other refinements might be to include discouraged jobseekers or other NILF populations with the strictly unemployed to calculate a different kind of ‘non employment rate’.

And while I’m clarifying Labour force concepts, there is often confusion about underemployment. There are two kinds of underemployment – visible (or ‘time-related’) and invisible (or ‘inadequate employment situations’). The international definition of visible underemployment is the persons employed less than ‘normal hours’ (often 35 hours per week) who want to work more hours and are either available or actively looking. (In Australia, those who report that they’re available to work more hours are underemployed whether they’re actively looking or not.) Significantly, if you already have a fulltime job that doesn’t pay enough and you worked your full hours in the reference period, you are not underemployed even if you are available to work more hours and are looking. Although it is obviously the principal concern of many who talk about underemployment, only a few countries attempt to measure invisible underemployment, like not working in the occupation or industry you trained for (and after all, who is?).

Finally, I hasten to add that if anyone is thinking of trying to find the working class in labour force statistics, don’t imagine that it’s straightforward. The standard classification of ‘Status in employment’ has four categories. ‘Own account workers’ have their own unincorporated businesses and don’t employ anybody else. ‘Employers’ also have their own unincorporated businesses and do have employees. ‘Contributing family workers’ are the ones who work for nothing in family businesses and farms. Everybody else is an ‘Employee’. That includes many senior executives and managers. The wording and sequencing of questions in labour force surveys and other survey instruments that collect Status in employment data usually preclude systematic identification of another category widely acknowledged to be of interest – ‘Owner manager of own incorporated enterprise’. In case it’s not obvious, the rationale is that the incorporated enterprise is a separate legal entity that employs its owner. The International Classification of Status in Employment (ICSE), by the way, incorporates a number of subcategories that can capture the owner managers and many other more exotic arrangements like members of cooperatives and work gangs. But even if there were surveys that collected status in employment in sufficient detail to support output to the full ICSE, it still wouldn’t unambiguously identify persons by class.

It’s possible to get data on occupation that can help to refine the concept. Data on ‘non-managerial employees’, however, while probably closer to what we would be interested in, is still not nearly good enough. For one thing, it’s still a count of Employees, as defined, and more and more actual employees are technically classified as ‘subcontractors’ or ‘consultants’ and would therefore fall into the category of ‘Own account workers’ rather than ‘Employees’. For another, a lot of non managerial jobs are being reclassified as ‘managerial’, as recently occurred with nurses in the US. Finally, the working class, as Marxists understand it, includes segments of the Unemployed and NILF populations that are even more difficult to identify from labour force statistics.

So now you know.

Birds of a feather

The American Jewish Committee last week honored Colombian President Alvaro Uribe with its Light unto the Nations Award.

“President Uribe is a staunch ally of the United States, a good friend of Israel and the Jewish people, and is a firm believer in human dignity and human development in Colombia and the Americas,” said AJC President E. Robert Goodkind, who presented the award at AJC’s Annual Dinner, held at the National Building Museum in Washington.

“Despite many odds, President Uribe has remained committed to the pursuit of security, peace and broad-based economic growth for all Colombians,” Goodkind said.

According to the CIA factbook, security and peace are evidenced for example by 1.8-3.8 million internally displaced persons, that is, at least 4% and up to nearly 9% of the total population, not nearly as bad as Iraq. As I read it, the fact that they can’t pin it down to within even the nearest two million suggests something really nasty is going on. The economic growth has delivered 49.2% below the poverty line, and as for ‘all Colombians’, some might consider a Gini coefficient of 0.538 evidence of truly gross disparities in economic power and opportunity. But

“Both Colombia and Israel have been forced for decades to face challenges regarding their survival and their citizens have suffered the threat of terror on a daily basis,” he said. “Nevertheless, Colombians like Israelis continue tirelessly to build democratic and prosperous societies, and remain passionate about achieving peace.”

Little could be more convincing of a commitment to democracy and prosperity than similarity to ‘Israelis’. And it goes without saying that it is perfectly appropriate for the American Jewish Committee to confer awards on those who resemble Israelis, because as everybody knows, Israel represents all the Jewish people and, in the immortal words of the EU Monitoring Commission Working Definition of Anti-Semitism, the Jewish people are ‘collectively responsible for actions of the state of Israel’.

And nothing supports democracy like human rights. According to Maria McFarland Sánchez-Moreno, Principal Specialist on Colombia, Human Rights Watch in her 24 April briefing of the United States House of Representatives Committee on Foreign Affairs, Subcommittee on the Western Hemisphere,

Today, Colombia presents the worst human rights and humanitarian crisis in the Western Hemisphere… Extrajudicial executions of civilians by the Colombian military are on the rise.

Colombia is currently the murder capital of the world for trade unionists. Those who are not killed are often threatened, attacked or kidnapped…72 killed in 2006 up from 70 the year before. These killings are not random casualties of Colombia’s conflict, as the Colombian government claims. Trade unionists are especially targeted when exercising their rights to organize and bargain collectively.

Colombia, Israel, the AJC…birds of a feather.

You are what you eat

Yesterday I posted an item about the increase in the Black infant mortality rate in Mississippi and another about US food aid. Well, it turns out that there’s a closer link than might appear.

As there was some discussion of the IMR issue on the Marxmail list, I posted ‘Delta blues’ there. There were a couple of responses. One of them linked to the Qando site, which made an interesting point. According to the author, the reason the US IMR appears so much higher than other industrialised countries is that the US is the only one that actually implements the approved WHO definition of a live birth. Understanding definitions and collection methods at a detailed level is crucial to interpreting statistics, a point I always make myself. I was not aware of the differences the Qando author mentions, although they sound plausible. However, as it only cites newspaper articles and US government sources, I can’t really be sure either that it’s true that the US does, in fact apply the WHO definition, and if so, uniformly, nor that any other countries, much less every other country, does not. So I reserve judgment. If Qando were serious, I would expect links to sources in the countries mentioned, or at least UN statistical metadata sources, to back them up.

In any case, while the estimates from US sources may not be strictly comparable with those from other sources, they are strictly comparable with each other. And the principal points of the article were that IMR for Black babies is much much higher than for white babies, both nationally and in Mississippi, and that they are rising.

Another response pointed out that it wasn’t the fault of the US health system that the Black IMR was so high when these grossly obese mothers refused to attend clinics in far off localities without adequate transportation or to access other prenatal care for no reason at all.

Yesterday’s NY Times Magazine had a really fascinating article that goes some way towards explaining why those poor Mississippi women have become so overweight. I’d like to thank Lou Proyect for the link to the article from marxmail.

Michael Pollan, Knight professor of journalism at the University of California, Berkeley and author of The Omnivore’s Dilemma, writes,

As a rule, processed foods are more “energy dense” than fresh foods: they contain less water and fiber but more added fat and sugar, which makes them both less filling and more fattening. These particular calories also happen to be the least healthful ones in the marketplace, which is why we call the foods that contain them “junk.”…the rules of the food game in America are organized in such a way that if you are eating on a budget, the most rational economic strategy is to eat badly — and get fat.

This perverse state of affairs is not, as you might think, the inevitable result of the free market. Compared with a bunch of carrots, a package of Twinkies, to take one iconic processed foodlike substance as an example, is a highly complicated, high-tech piece of manufacture, involving no fewer than 39 ingredients, many themselves elaborately manufactured, as well as the packaging and a hefty marketing budget. So how can the supermarket possibly sell a pair of these synthetic cream-filled pseudocakes for less than a bunch of roots?

For the answer, you need look no farther than the farm bill. This resolutely unglamorous and head-hurtingly complicated piece of legislation, which comes around roughly every five years and is about to do so again, sets the rules for the American food system — indeed, to a considerable extent, for the world’s food system. Among other things, it determines which crops will be subsidized and which will not, and in the case of the carrot and the Twinkie, the farm bill as currently written offers a lot more support to the cake than to the root. Like most processed foods, the Twinkie is basically a clever arrangement of carbohydrates and fats teased out of corn, soybeans and wheat — three of the five commodity crops that the farm bill supports, to the tune of some $25 billion a year. (Rice and cotton are the others.) For the last several decades — indeed, for about as long as the American waistline has been ballooning — U.S. agricultural policy has been designed in such a way as to promote the overproduction of these five commodities, especially corn and soy.

…A food system awash in added sugars (derived from corn) and added fats (derived mainly from soy), as well as dirt-cheap meat and milk (derived from both). By comparison, the farm bill does almost nothing to support farmers growing fresh produce. A result of these policy choices is on stark display in your supermarket, where the real price of fruits and vegetables between 1985 and 2000 increased by nearly 40 percent while the real price of soft drinks (a k a liquid corn) declined by 23 percent. The reason the least healthful calories in the supermarket are the cheapest is that those are the ones the farm bill encourages farmers to grow.

A public-health researcher from Mars might legitimately wonder why a nation faced with what its surgeon general has called “an epidemic” of obesity would at the same time be in the business of subsidizing the production of high-fructose corn syrup. But such is the perversity of the farm bill: the nation’s agricultural policies operate at cross-purposes with its public-health objectives. And the subsidies are only part of the problem. The farm bill helps determine what sort of food your children will have for lunch in school tomorrow. The school-lunch program began at a time when the public-health problem of America’s children was undernourishment, so feeding surplus agricultural commodities to kids seemed like a win-win strategy. Today the problem is overnutrition, but a school lunch lady trying to prepare healthful fresh food is apt to get dinged by U.S.D.A. inspectors for failing to serve enough calories; if she dishes up a lunch that includes chicken nuggets and Tater Tots, however, the inspector smiles and the reimbursements flow. The farm bill essentially treats our children as a human Disposall for all the unhealthful calories that the farm bill has encouraged American farmers to overproduce.

To speak of the farm bill’s influence on the American food system does not begin to describe its full impact — on the environment, on global poverty, even on immigration. By making it possible for American farmers to sell their crops abroad for considerably less than it costs to grow them, the farm bill helps determine the price of corn in Mexico and the price of cotton in Nigeria and therefore whether farmers in those places will survive or be forced off the land, to migrate to the cities — or to the United States. The flow of immigrants north from Mexico since Nafta is inextricably linked to the flow of American corn in the opposite direction, a flood of subsidized grain that the Mexican government estimates has thrown two million Mexican farmers and other agricultural workers off the land since the mid-90s. (More recently, the ethanol boom has led to a spike in corn prices that has left that country reeling from soaring tortilla prices; linking its corn economy to ours has been an unalloyed disaster for Mexico’s eaters as well as its farmers.) You can’t fully comprehend the pressures driving immigration without comprehending what U.S. agricultural policy is doing to rural agriculture in Mexico. [my emphasis]

So it’s not just the starving of the developing world that Uncle Sam’s policies are out for, ordinary poor Americans, and n ot so poor Americans, and American schoolchldren, and Mexican farmers, and many others are in their sights, as well.

But that’s not all. It’s not just a question of making sure many Americans can’t afford to eat healthful food, a proportion of it is actually poisonous, as Elizabeth Williamson reports in today’s Washington Post.

The Food and Drug Administration has known for years about contamination problems at a Georgia peanut butter plant and on California spinach farms that led to disease outbreaks that killed three people, sickened hundreds…

Overwhelmed by huge growth in the number of food processors and imports, however, the agency took only limited steps to address the problems and relied on producers to police themselves, according to agency documents.

Congressional critics and consumer advocates said both episodes show that the agency is incapable of adequately protecting the safety of the food supply

"This administration does not like regulation, this administration does not like spending money, and it has a hostility toward government. The poisonous result is that a program like the FDA is going to suffer at every turn of the road," said Rep. John D. Dingell (D-Mich.)…

In the peanut butter case, an agency report shows that FDA inspectors checked into complaints about salmonella contamination in a ConAgra Foods factory in Georgia in 2005. But when company managers refused to provide documents the inspectors requested, the inspectors left and did not follow up.

In a letter sent to California growers in late 2005, [director of the FDA's food-safety arm Robert E.] Brackett wrote, "FDA is aware of…approximately 409 reported cases of illness and two deaths."

"We know that there are still problems out in those fields," Brackett said…

According to Caroline Smith DeWaal, who heads the Center for Science in the Public Interest, a consumer-advocacy group, "When budgets are tight . . . the food program at FDA gets hit the hardest."

Capitalism, gun crime and ideology

Here are some extracts from lenin’s superlative analysis of the criminal justice system and the response to the Virginia Tech massacre: Capitalism, gun crime and ideology.

The Virginia Tech massacre continues to attract commentary…You cannot extricate the lone nut from the total prior circumstances, and those extend well beyond the availability of potentially damaging anti-depressants or even the availability of guns...the system - including not only the courts and police, but also the process of lawmaking - is designed to fail at reducing crime…

They have created a heavily privatised penal system, knowing that this gives an incentive to providers to ensure that they do not have any effect in reducing recidivism, since high crime equals high profits. …that system is integrated into a capitalist society in which the very definition of crime results from an ideology appropriate to claims of 'property rights'…large amounts of harmful activity, which involve intent (inasmuch as the agents of it, usually corporations, cannot reasonably be unaware of the harmful effects their conduct has), is not criminalised…

The reality of incarceration of mostly young, mostly working class, mostly male, and disproportionately black, people in America is a result of a process of decision-making mediated by ideology, that runs the whole gamut of the system. It runs from the decision as to what constitutes a crime, inflected as it is by class power and the lobbies and basic assumptions that append to it…to the enforcement of cops, often racist ones, but less famously often hostile to the working class and poor; to the decisions of magistrates and parole boards, whose decisions reflect the same basic biases…

…every time a new substance like that comes into the market, there is a similar struggle for control before it becomes routinised: this accounts for some of the acute increases in violent crime, while the routinised control by violent gangs and the necessity of paying for the stuff through robbery and so on accounts for a part of the ongoing high rates of crime...

…Aside from being exploited and oppressed, their lack of control over the means of production, never mind the means of state rule, means that they are denied adequate healthcare, and often killed by the companies they pay to cure them; are often killed by their work, if they can find employment; are often killed by the food that they eat, especially if it happens to be low-cost food. They are criminalised, often for harmless behaviour, imprisoned, often for nonviolent offenses and petty crimes. Others are incorporated into illicit economies that operate through coercion….

Read the whole thing!

What's the catch?

As executives, lobbyists, bureaucrats, and NGO workers scarfed ‘heaping platters of jumbo shrimp, lamb chops and crab-stuffed mushrooms… at the food aid conference organized by the Department of Agriculture and the Agency for International Development’ in Kansas City last Tuesday, according to Celia W. Dugger in today’s NY Times, an estimated 850 million people around the world were starving. Actually, the Millennium Development Goals site reckons that it was 823.1 million in 2002, the latest date that they have data for, so we can be reasonably sure that it’s much more than that five years down the track. Over a quarter of them were in that well known powerhouse of capitalist development, the posterchild of Thomas L Friedman and the Eustonites, India. And the MDG data exclude many countries where we know there are undernourished people, including Yemen, Somalia, Afghanistan, and of course the ‘Land of the Free’ itself. In some, but definitely not all, cases, this is likely because the data aren’t available.

Charles Worledge, who works for the Long Island-based Sealift Inc., a major shipper of American food to the hungry, offered an insight essential to understanding the politics of food aid.

“I thought this was a charity,” he explained during the party, for which another shipping company played host. “It’s not. It’s a business.”

This will come as no surprise to anyone who’s paid any attention to aid in general and food aid in particular. It virtually goes without saying that most of the money going into food aid is specifically earmarked for purposes other than feeding hungry people.

A Government Accountability Office report released on the eve of this conference described in stark detail a system rife with inefficiencies: the amount of food shipped over the past five years has fallen by half as shipping and other logistical costs have soared. Only a little more than a third of federal food aid spending actually buys food. [my emphasis]

And in line with Worledge’s insight, we needn’t look far for the motivation.

Early in the 1950s, the government was the farmer’s buyer of last resort when commodity prices fell, and as a result it sat on mountains of grain. Public Law 480 and the Food for Peace program, adopted in 1954, provided a way to dispose of the surplus grain, which was costly to store, and at the same time feed the world’s hungry people. The law mandated that food for the program be grown domestically.

It was the storage costs that motivated this generosity, and, as a distant second, the desire to appear generous.

Over the years,…the requirement that it be grown in the United States never changed. In recent years, the United States has bought more than half the food for its aid programs from just four agribusinesses and their subsidiaries: Archer Daniels Midland, Cargill, Bunge and Cal Western Packaging, according to the Agriculture Department.

Unsurprisingly,

some in Congress, as well as lobbyists for interest groups that benefit from food aid, warn that untying aid from requirements that the food be grown in America and mostly shipped on American-flagged vessels would shatter the political coalition that has sustained the program for decades and made the United States the world’s largest food aid donor.

“Are we committed to eradicating hunger because it’s feasible, not terribly expensive and our moral obligation as the richest society in human history?” asked Christopher B. Barrett, a Cornell University economist and the co-author of “Food Aid After Fifty Years.” “Or are we just trying to placate a few agribusiness, shipping and NGO constituencies with a handout?”

But ‘Some researchers and advocates say it is time to rethink the American approach to fighting world hunger’.

It would be rude for researchers and advocates to point out that the US government, in part through its World Bank and IMF proxies, that has actually caused its fair share of world hunger in the first place. In any case, specifically, it was at the last food aid conference in Kansas City two years ago,

that the Bush administration pushed for a fundamental change in food aid that would have diminished profits to domestic agribusiness and shipping companies. It proposed allowing a quarter of the Food for Peace budget to be used to buy food in poor countries near hunger crises, rather than buying only American-grown food that had to be shipped across oceans… such a policy would speed delivery, improve efficiency and save many lives.

Now what kind of thing is that for George W Bush to be advocating. Surely his job as leader of the free world is to administer the US government in the interests of all US capital, not just his oil and armaments buddies. Why shouldn’t Archer Daniels Midland and Sealift Inc get a piece of the action?

‘Congress in each of the past two years killed the proposal, which was opposed by agribusiness and shipping interests who stood to lose business’. Opponents of the proposal ‘warn that cash sent to poor countries can be misused or stolen, and that a mismanaged program to buy food in poor countries could drive up food prices’. But that can’t be why the Bush administration is ‘pushing’ for this – just to drive up third world food prices? It’s got to be because he wants to keep the corn for ethnol.

Large attachments

Delta blues

As a lot of people know, the UN has instituted a program of Millennium Development Goals (MDGs), which set standards for achieving certain targets across a range of development indicators that they have identified by the year 2015. One of these indicators is the Infant Mortality Rate (IMR), the proportion of ‘infants dying before reaching the age of one year per 1,000 live births in a given year.’

According to Indicators for Monitoring the Millennium Development Goals: Definitions Rationale Concepts and Sources (UN, 2003), the rationale for collecting IMR is that

Infant mortality rates measure child survival. They also reflect the social, economic and environmental conditions in which children (and others in society) live, including their health care. Since data on the incidence and prevalence of diseases (morbidity data) frequently are unavailable, mortality rates are often used to identify vulnerable populations.

According to the MDG website, in 2004 IMR ranged from 165 per 1000 live births per year in Afghanistan, the same as in 2000, doubtless thanks to the US invasion and concomitant full liberation of Afghan women, and Sierra Leone, down to 2 per 1000 per year in Iceland. It may be of interest that the UK, Australia, and New Zealand have pretty good records on this indicator, with 5/1000. Cuba, with 6/1000, scores a little better than the US with 7/1000. (Note that the MDG figures differ rather significantly from the 2007 estimates in the CIA World Factbook and Wikipedia.)

But that 7/1000 ratio is just the tip of the iceberg. Erik Eckholm, writes in this morning’s NY Times, ‘The national average in 2003 was 5.7 for whites and 14.0 for blacks.’ That 14/1000 figure puts Blacks in the US in the same league as those in Mauritius. Curiously, the Eckholm article claims that the most recent national figure from the US are for 2003, while the MDG table claims 2004 data. In any case, more recent data are available at a state and county level. Specifically, ‘In Mississippi, infant deaths among blacks rose to 17 per thousand births in 2005 from 14.2 per thousand in 2004’. An IMR of 17/1000 puts Mississippi Blacks in with Albania and Vietnam. A Venezuelan or Syrian mother has a better chance of seeing her baby survive to see its first birthday. And that was two years ago, with an apparently very steep increase in progress.

And that’s the state average. ‘In the past 10 years, the infant mortality rate for blacks in most of the Delta has averaged about 14 per thousand in some counties and more than 20 per thousand in others.’ Tonga has an IMR of 20/1000, it’s a higher rate than claimed for Colombia or Lybia.

“I don’t think the rise is a fluke, and it’s a disturbing trend, not only in Mississippi but throughout the Southeast,” said Dr. Christina Glick, a neonatologist in Jackson, Miss., and past president of the National Perinatal Association.

That’s how the most powerful, prosperous, democratic, and free country in history treats Southern Black women and children, among the most vulnerable members of American society.

Fun and games

Liberated women having a good time: ‘US Defence personnel Alicia Raper, Aviation Ordnance Petty Officer 3rd Class, and Brenda Cantu, Aviation Ordnance Airwoman, attach a Maverick Missile to a United States Navy P-3 aircraft during Exercise Talisman Sabre (2005).’

Operation Talisman Sabre

The Australian Defence Force [ADF] and the United States military are gearing up to engage in 6 weeks of intensive training and war games, or what is known as Operation Talisman Sabre. The operation, which will involve nearly 14 000 US troops and over 12 000 Australian personnel, will run from the end of May to July 2nd, and will be the largest military exercise ever held in Australia. Talisman Sabre will take place at various Australian military locations, including the Australian-US "training facilities": Shoalwater Bay [QLD], Bradshaw and Delamere Range [NT] and the Coral Sea.

The activities that will be carried out by Australian and US troops are aggressive and offensive military strategies, including bombing, parachute drops and onshore landings, combining land, sea and air forces practising with high-tech military equipment. Furthermore, these will be carried out on areas of high environmental significance, i.e. world heritage areas, such as the Great Barrier Reef Marine Park, and natural heritage listed sites, which include indigenous sites and Ramsar wetlands. These areas are habitat to many migratory birds and threatened species such as dugongs and humpback whales. Environmental impacts identified by the Department of defence include effects on air quality, fire potential, noise pollution, waste disposal and spills and erosion from amphibian craft landings and weapon target zones.

Operation Talisman Sabre will involve US nuclear powered vessels [all US ‘attack class’ submarines are nuclear powered] and may also be carrying nuclear weapons, as well as depleted uranium munitions. The US has a ‘NCND’ policy regarding nuclear weapons – that is to ‘Neither Confirm Nor Deny’, meaning that Australians will not know if these lethal substances will be a part of the upcoming war games.

Not only does the existence of US bases in Australia carry with it various hazardous environmental issues, various social ills are also a part of the parcel. In the Philippines and Japan, US bases have become the centre of major social problems. Specifically, they are linked to significant rises in levels of prostitution, drugs, alcoholism, rape, sexually transmitted diseases and abuse of women and children. The Australian experience so far is similar.

Apart from the already mentioned disastrous consequences of Operation Talisman Sabre it is imperative to note that this training is far more than general military training. Rather, its specific aim is to conduct collective training and exercise inter-operability between Australian and US forces. Indeed, Operation Talisman Sabre represents the gradual fusion of the Australian military into an extended arm of the US government.

The military exercise Talisman Sabre and the establishment of 3 new US ‘training bases’ are further steps to the accelerating militarisation of Australia. The support of the Howard government in the expansion and upgrading of the US’ military capabilities is ludicrous and must be opposed, as does the overall support that Howard continues to show for Bush’s ‘war on terror’.

[Info courtesy of Paddy Gibson]

A coalition of anti war and environment groups are helping organise people in Sydney to get up to Shaolwater Bay to participate in the "Peace Convergence" against upcoming Talisman Sabre war games from June 21-25.

For further information contact or Paddy on 0415 800 586.