The resettlement of refugee farmers in East Punjab after Partition

A different version of this piece was published back in 2009, in the OR/MS Today magazine.

__

It is well known that the partition of the Indian subcontinent in 1947 had terrible consequences. Tens of thousands of people died. Millions were displaced and lost their cherished ancestral homes: Muslims left India for Pakistan, and Hindus and Sikhs left Pakistan for India. It was the greatest mass migration in history. But what is less understood is the manner in which the vast numbers of refugees were accommodated and settled into the newly divided regions. The greatest mass migration in history inevitably became the largest resettlement operation in the world.

How was this monumental task achieved? That question might take up many books, and perhaps many have already been written. But we get a glimpse of how it was done in the Indian side of the Punjab (East Punjab) in Refugees and the Republic a chapter in Ramachandra Guha’s post-independence historical narrative India after Gandhi. 

Guha’s chapter appealed to me for a different reason. My specialization is the field of operations research: the quantitative or optimization methods that are now used widely in the attempt to make service systems more efficient. The resettlement or land allocation problem set up by Guha in the chapter seemed to fall squarely in the realm of optimization. I was curious to know how the reallocation of land had been carried out in practice.  

Punjab was one of the partitioned provinces; the eastern part found itself in India while the western in Pakistan. A large number of Muslims had left East Punjab for Pakistan. But there was an even greater influx of Hindus and Sikhs into the east from Pakistan. Most of these refugees were farmers. Together they had abandoned 2.7 million hectares of land in Western Punjab but across the border in India where they now had to make a living only 1.9 million hectares had been left behind by Muslim farmers who had fled the opposite way. The problem was made more complex by three additional factors:

• Each refugee family had a claim on how much they had owned prior to emigrating.
  
• The fertility of the land differed; there were dry, unirrigated districts as well as lush, irrigated regions.
  
• There were demands that families and neighbors be relocated in the same way as they had been in West Punjab. If possible entire village communities had to be recreated.

From the comfort of hindsight – and given how far computing power has grown in the last 6-7 decades – I can imagine formulating the land allocation problem as a large scale mathematical optimization model. The decisions (the x variables in the problem, which the model would be solved for) would be how much land to allocate to each family and where to allocate. The objective, expressed as  a function of the decisions, would be to minimize the difference between claims and actual allocations. The constraints would be equations that expressed limits such as the total land allocated could not exceed the total  land available. There could be other constraints to ensure that families and neighbors are relocated together. The mathematical model would also have to capture the spatial aspect, the variation in fertility and relate them somehow to the allocation decisions.

Even by today’s standards this is a very difficult optimization problem; it also has human or qualitative dimensions that are difficult to capture mathematically. The unenviable task of reallocating land fell upon the Indian government and its civil service workers. As a first step, they assigned each family of refugee farmers 4 hectares irrespective of its past holding; they also gave loans to buy seed and equipment. Viewed from an optimization lens, this 4-acre allotment is an “initial solution” – a feasible assignment to the decisions (the x’s) to get things going, but very far from optimal.

As families began to sustain themselves, applications were invited for them to claim more land, depending on what they had owned in West Punjab. Within a month, there were 500,000 claims. These claims were then “verified in open assemblies consisting of other migrants from the same village. As each claim was read out by a government official, the assembly approved, amended or rejected it.” Refugees tended to exaggerate of course, but were deterred by the open assembly method; if a claim turned out to be false they were punished by a reduction in land.

Sardar Tarlok Singh of the Indian Civil Service and a graduate of the London School of Economics led the rehabilitation operation. He used two simple but interesting rules (we call them heuristics) for allocating land, and this is where the pragmatism in the whole operation comes most clearly to light. Though claims had been filed, because of the reduced acreage, none of the refugees could be assigned as much land as they'd originally owned. Everybody’s claim had to be reduced by a certain percentage. Plus, there had to be some way of accounting for the differing fertility of land.

Sardar Tarlok Singh came up with two measures, the standard acre and the graded cut, which dealt with these issues: 

“A standard acre was defined as that amount of land which could yield ten to eleven maunds of rice. (A maund is about 40 kilograms.) In the dry, unirrigated districts of the east, four physical acres were equivalent to one standard acre; but in the lush “canal colonies” [where irrigation was strong], one physical acre was about equal to one standard acre. The innovative concept of the standard acre took care of the variations in soil and climate across the province.

The idea of the graded cut, meanwhile, helped overcome the large discrepancy between the land left behind by the refugees and the land now available to them – a gap that was close to million acres. For the first ten acres of any claim, a cut of 25% was implemented – thus one got only 7.5 acres instead of ten. For higher claims the cuts were steeper: 30% between ten and 30 acres, and on upward, so that those having more than 500 acres were taxed at the rate of 95%.”

With this rule, there clearly were losers, and the losers, of course, were those who had once owned huge tracts of land: “The biggest single loser was a woman named Vidyawati who had inherited land (and lost) her husband’s estate of 11,500 acres spread across thirty-five villages of the Gujranwala and Sialkot districts. In compensation she was allotted mere 835 acres in a single village of Karnal.”

It’s unclear what analysis motivated Tarlok Singh to come up with the specific ranges and the cuts. It’s possible that the taxing mechanism might have left too much land unassigned, and many claimants dissatisfied. Or, given that the overall reduction in total land was about 38 percent (the farmers had left 2.7 million hectares behind and now were being resettled on 1.7 million hectares), the taxing may not have been strict enough. The exact details are unknown.

What is known, though, is that by November 1949, a year and a half after the resettlement began, “Tarlok Singh had made 250000 allotments distributed equitably across the districts of East Punjab”. Even the soft constraints, such as settling families and neighbors together, were met to a large extent, though “the recreation of entire village communities proved impossible.” The resettlements were so successful that “by 1950, a depopulated countryside was alive once again.” Tarlok’s heuristics might have been simple, but they helped solve a complex, large-scale allotment problem in the aftermath of a traumatic event.

Halfway across the world, in the summer of 1947 — the same year that the partition of the Indian subcontinent took place — George Dantzig conceived the famous Simplex Algorithm. In the next decades the Simplex Algorithm, helped by advances in the computing capability, would provide fast solutions to large linear optimization problems. Each fall I teach the algorithm to graduate students from many disciplines. But in 1947, the Simplex algorithm could only tackle a small version of the classical diet problem: optimally choosing food items for a family to minimize costs while ensuring minimum nutritional needs are met. That particular diet problem had only 9 constraints and 27 variables, but took 120 man-days to solve; worksheets were glued together and spread out like a tablecloth to assist in determining the optimum solution [1].

This puts the enormity of resettlement problem in perspective. With half a million people making claims – which means there would be hundreds of thousands of variables and constraints – even an awareness of Dantzig’s algorithm could not have helped the Indian Civil Service.

Nearly 7,000 officials were needed for the resettlement effort; they constituted a refugee city of their own. The problem occupied them for a period of three years. Imagine the paperwork and the records that had to be kept and retrieved; imagine the disputes among the refugees, the flared tempers and the jealousies. But imagine also the perseverance of everyone involved. It made me think about what it is exactly that makes certain large scale operations successful and others not. It’s hard to make generalizations, but one feature seems to be effective coordination across large groups of people: largely error-free lunch deliveries by the Dabbawalas of Mumbai is an example that comes to mind.

Related notes:

1. Guha ends the chapter in his book poignantly. The resettlement, Guha says, may have been successful, but the general sense of loss could not be undone. The migrating Sikhs had left behind a beloved place of worship, Nankana Sahib, the birthplace of the founder of their faith, Guru Nanak. Muslims migrating from East Punjab too had left behind the town of Qadian, the center of the Ahmadiya sect of Islam; the Ahmadiya mosque was visible for miles around. Very few Muslims now lived in Qadian, which was full of Hindu and Sikh refugees. Guha quotes the editor of the Calcutta newspaper Statesman, who wrote that in both Qadian and Nankana Sahib there was “the conspicuous dearth of daily worshippers, the aching emptiness, the sense of waiting, of hope and…of faith fortified by humbling affliction.”

2. The picture shows a boy at a Delhi refugee camp in 1947. Here is the source. The largest refugee camp, though, was at Kurukshetra, consisting of nearly 300,000 people. For their entertainment, film projectors were brought in and Disney specials featuring Mickey Mouse and Donald Duck were screened at night. It was, as one social worker described it, a “two-hour break from reality”. 

References

1. Bazaraa, M., Jarvis, J., and Sherali, H., 2005, Linear Programming and Network Flows, Wiley 3^rd Edition

2. All quoted parts in the piece are from Ramachandra Guha’s India After Gandhi.