By Patricia Mukhim
Child labour in any form deserves the severest condemnation. And people who traffic children to the coal mines of Meghalaya from across the Bangladesh border (as alleged) should be given exemplary punishment. That the Meghalaya Government does not take cognisance of such offences and allows the coal miners of Jaintia Hills to commit every crime under the law is reprehensible. But what can we say of governments that are made and unmade by the coal barons? If there is one thing we need to vote for in the coming elections it is to be liberated from the clutches of the coal mafia. Dare we do it? Can we resist the money they throw at us? Let’s wait and watch!
But let me come to the more troubling aspect of this ‘child labour’ phenomenon in Jaintia Hills which has created a furore not just in Delhi but in much of the developed world. In 2010 Impulse NGO Network published a report of a research they conducted in the coal mines of Jaintia Hills and claimed that 70,000 children are working as bonded labourers in the mines and that they were mostly from Bangladesh and Nepal. BBC was the first to air this news. Where did BBC get the news from? We are told Impulse NGO gave it to them. Now considering that the research was carried out in Meghalaya, was it not proper to share the report first with the media here? Was that not a better way to take the State Government to task?
The other problem with the report is that it was never sent to the National Human Rights Commission (NHRC). Why? NHRC, Chairman Justice KG Balakrishnan said the Commission took suo-moto action on a report in a news magazine which quoted the Impulse report. The magazine says, “The Impulse research shows that according to the 2001 Census of India, there were 1, 22, 992 children below 18 years of age in the Jaintia Hills, out of which 90,368 children were in the age group 5-14. Also, 77.5% of the children in the Jaintia Hills have been categorized as Main Workers (i.e. working full-time) in the Census of India, 2001. The organisation conducted a research study in which they claimed to have personally interviewed, in two phases, a total of 979 children who were found working in the hazardous coal mines in the Jaintia Hills.”
If only 979 children were interviewed then how was the extrapolation made to arrive at a figure of 70,000 children workers? Clearly, the figure was arrived at by taking the number of children working in a particular mine and multiplying them by the total number of mines in Jaintia Hills. Is this authentic research? What was the methodology used? Statistical method involves biases. What percentage of bias was incorporated in the report? Another important question is – who or which organisation (Indian or foreign) has funded the research? Why was the research funded? Was it meant to be an open-ended research or was it just meant to endorse an assumption?
David A. Freedman Department of Statistics University of California says the basic idea in sampling is extrapolation from the part to the whole—from “the sample” to “the population.” There is an immediate corollary: the sample must be chosen to fairly represent the population. Methods for choosing samples are called “designs.” Good designs involve the use of probability methods, minimizing subjective judgment in the choice of units to survey. Samples drawn using probability methods are called “probability samples.” Bias is a serious problem in applied work; probability samples minimize bias. As it turns out, however, methods used to extrapolate from a probability sample to the population should take into account the method used to draw the sample; otherwise, bias may come in through the back door. Probability samples should be distinguished from “samples of convenience” (also called “grab samples”).
Freedman says a typical sample of convenience comprises the investigator’s students in an introductory course. A “mall sample” consists of the people willing to be interviewed on certain days at certain shopping centers. This too is a convenience sample. The reason for the nomenclature is apparent, and so is the downside: the sample may not represent any definable population larger than itself. To draw a probability sample, the investigator begins by identifying the population of interest. The next step is to create the “sampling frame,” a list of units to be sampled. One easy design is “simple random sampling.” For instance, to draw a simple random sample of 100 units, choose one unit at random from the frame; put this unit into the sample; choose another unit at random from the remaining ones in the frame; and so forth. Keep going until 100 units have been chosen. At each step along the way, all units in the pool have the same chance of being chosen.
Taking his case further Freedman argues, “Simple random sampling is often practical for a large population. But when it comes to people, especially when face-to-face interviews are to be conducted, simple random sampling is seldom feasible: where would we get the frame? More complex designs are therefore needed. If, for instance, we wanted to sample people in a city, we could list all the blocks in the city to create the frame, draw a simple random sample of blocks, and interview all people in housing units in the selected blocks. This is a “cluster sample,” the cluster being the block. Notice that the population has to be defined rather carefully: it consists of the people living in housing units in the city, at the time the sample is taken. There are many variations. For example, one person in each household can be interviewed to get information on the whole household. Or, a person can be chosen at random within the household. The age of the respondent can be restricted; and so forth.
Since the sample is only part of the whole, extrapolation inevitably leads to errors. These are of two kinds: sampling error (“random error”) and non-sampling error (“systematic error”). The latter is often called “bias,” without connoting any prejudice. Sampling error results from the luck of the draw when choosing a sample: we get a few too many units of one kind, and not enough of another. The likely impact of sampling error is usually quantified using the “SE,” or standard error. With probability samples, the SE can be estimated using (i) the sample design and (ii) the sample data. As the “sample size” (the number of units in the sample) increases, the SE gradually goes down. If the population is relatively homogeneous, the SE will be small: the degree of heterogeneity can usually be estimated from sample data, using the standard deviation or some analogous statistic. Cluster samples—especially with large clusters—tend to have large SEs, although such designs are often cost-effective.
To understand the authenticity of the Impulse report, a local university like NEHU should have been asked to counter verify the research study. What is problematic in India today is the NGOisation of the democratic space which, Dr Rajesh Dev of Delhi University observes, “tends to corrode the ability of the state to make effective intervention in social welfare issues. The current NGO jargon is “rights-based approach to development.” Where did this jargon come from? Certainly not from India! It’s a jargon of the Bretton Woods institutions such as the World Bank and International Monetary Fund (IMF). It’s a pity that some of us get quickly co-opted into these systems and become reporting agencies on their behalf. While on the one hand the NGOisation of the polity helps to create a critical mass to make the state more responsive. But increased NGOisation as is happening in India today lends credence to conspiracy theories of attempts to discredit the state completely and for the NGO to then assume the role of the state. Earlier the multinational companies used this space in a prolific manner. Now it’s the NGOs that do it.
Why would a US funding agency for instance fund a rights-based agenda in India? I often feel that we are too naïve and accept too many things unquestioningly. Perhaps we in the media too are to be blamed for not spending enough resources to dig deeper into the functioning of NGOs, to prise open their books of accounts and to do a physical test check of their tall claims because funding agencies clearly are not interested in real work. They get carried away by glossy reports which in this day and age are not difficult to commission.
I hope Impulse NGO challenges this article and calls a press conference where their report can be critiqued by the local media.