Saturday, 23 February 2013

“ Family Enterprise versus Individual Enterprise"

 Management Case1- “ Family Enterprise versus Individual Enterprise" Munish Alagh-PDF IIM-A and Formerly Assistant Professor AMSOM, Ahmedabad University. 20-2-2013.


Shikha Uberoi is a 65 year old lady who lost her husband a year ago, since the last ten years she runs a canteen in a school in Nagpur along with her married daughter. Her son, who also is married, but lives in the same house runs a Paratha shop and a small banquet hall in Nagpur.


The Canteen is not subsidized by the school, but the prices are fixed low by the school, even the power, water and other infrastructural expenses are borne by Shikha herself. It is a clear case of the school taking advantage of Shikha’s limited power in a market which has many other potential caterers available to replace her.


Shikha has as many as ten employees required to run the canteen, plus the school expects highest quality ingredients to be used in the canteen, Shikha also has a reputation to protect so even with very low margins cannot afford to compromise on quality. She has to reach school every morning at 6:30 to get things ready.


Yet Shikha is happy, it’s a risk free venture, keeps her occupied and does not at her age involve undue changes and unknown variables. However her son is offering her an alternative.: Invest much less time, money, effort with only three employees a much smaller space, a far more limited menu and get an assured market with a much larger margin. Just help run an extention of the Paratha restaurant in the adjoining shop which is presently lying vacant and where he has built a small cubicle for himself, however the rest of the space is unused and just stores some inventory and restaurant raw material like chairs which can easily be organized, shifted elsewhere and some disposed off.


This alternative has much lesser costs and more assured returns. There is no fixed sunk cost in the Canteen as her son a graduate in Economics argues. The only factors stopping Shikha from making the change are two fold-she maybe exploited by the scool authorities, but she loves the students and alumni including parents and has a long standing relation of respect with the Director of the School since ten long years. But the biggest problem is Inertia for Change-at her age, any effort at something even slightly different is anathema.


What would you suggest for her?



Friday, 15 February 2013

Summary Notes on Agricultural Credit Issues

Summary Notes on Agricultural Credit Issues(Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

Agricultural credit has played a pivotal role in increasing agricultural production in India. The Green Revolution characterised by a higher use of modern inputs like fertilizers, high yielding variety seeds, irrigation and other inputs, increased credit requirements which were provided by the agricultural financial institutions.

The flow of credit to agriculture has increased significantly in the recent period as the total institutional credit to agriculture increased from Rs. 86,981 crore in 2003-04 to Rs. 446779 crore in 2010-11, at an annual compound growth rate of about 25 percent. The actual achievement in flow of credit has exceeded the targets during the period . In terms
of total agency wise share, the commercial banks recorded a considerable growth (from around 36 per cent in TE 1993-94 to about 75 percent in TE 2010-11), while cooperative banks despite their wide network lost their dominant position and their share declined from 58.3 percent inTE 1993-94 to 15.8 percent in TE 2010-11. The share of Regional Rural banks (RRBs) has increased from about 5 percent to 9.4 percent during the above period (Figure 12). Since cooperatives have strong presence in rural areas, the co-operative credit institutions need revamping to improve the efficiency of the credit delivery system in rural areas.

Though the amount of agricultural credit has increased during the last few years, several
weaknesses have crept in which have affected small and marginal farmers’ access to formal sources of credit. The Task Force on Credit Related Issues of Farmers observed that small and marginal farmers especially tenant farmers, oral lessees, share-coppers, who constitute the bulk of farming community, do not have adequate access to formal sources of credit (GoI,2010c). Between TE 1993-94 and TE 2008-09, the share of small and marginal farmers in total operational holdings increased but their share in number of credit accounts decreased from75.3 percent to 69.2 percent and in amount of credit disbursed decreased from 53.6 percent to 48.6 percent (RBI, 2011). On the other hand, for medium and large farmers the share of credit increased from 46.4 percent in TE 1993-94 to 51.4 percent in TE 2008-09 and number of accounts increased from 24.7 percent to 30.8 percent during the period. Similarly, per account credit disbursed across farm sizes had increasing skewed and the gap has widened between small and marginal and large farmers. There are wide variations in the availability of institutional credit per hectare of gross cropped area in different States.

The region-wise per account credit disbursed by commercial banks for different size-class farmers shows that amounts are relatively higher in northern and western region while in north-east and eastern regions credit disbursal is poor, which is a matter of concern. Another issue is decline in rural branches of commercial banks in the post-reforms period. Total number of commercial bank offices has increased significantly since nationalization of banks in 1969, the number of rural branches, which reached its peak in early 1990s (pre-reforms era), hasdeclined significantly in the post-reforms period (Figure 13). In contrast metropolitan, urban and semi-urban branches have increased during this period. Furthermore, share of indirect credit in total credit has increased significantly from less than 20 percent in early-1990s toabout 67 percent in early-2000s and then marginally declined to about 50 percent in 2008-09 (RBI, 2011).

It is a matter of great satisfaction that there has been significant improvement in flow of
agricultural credit in recent years but there is a need to address distributional aspects of
agricultural credit including not much improvement in the share of small and marginal farmers,decline in rural branches, increase in the share of indirect credit in total agricultural credit and significant regional and inter-class inequalities in credit.

Thursday, 14 February 2013

Summary Notes on Instrument of Price Support for Agriculture

Summary Notes on Instrument of Price Support for Agriculture (Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

Agricultural price policy, which is considered integral to the strategy for agricultural
development, played an important role in achieving self-sufficiency in food grains, consumer welfare, improvement in the economic access to food and, through affecting the domestic terms of trade, important influence on growth, employment and income distribution in the economy. The section provides an overview of trends in minimum support price (MSP)/Procurement Price (PP) and recent policy changes in price policy.

The trends in MSP/PP show that increase in rice and wheat prices were higher during the
decade of 1990s as compared to the 2000s. In the 1990s, rate of increase in
MSP/PP of wheat was higher (156.4%) than that of paddy (150.7%).In case of pulses, the rate of increase in MSP/PP was higher during the 2000s compared with 1990s. The rate of increase was the highest in case of moong and lowest in gram. The prices of tur, moong and urad have more than doubled between 1990s and 2000s.

Dev and Rao (2010) reported that actual price realized by farmers was higher than MSP/PP during the last three decades. It was also observed that price realization was much lower in states like Orissa, Bihar, Assam, West Bengal and Uttar Pradesh compared to Punjab, Haryana, and Madhya Pradesh. The price policy has a limited role in increasing agricultural production as it mainly influences acreage allocation but not crop productivity. It is important to note that non-price factors such as technology, public investment agricultural research and development, extension services, irrigation, rural infrastructure, etc. play more important role in influencing productivity and production than pricing policy. Therefore, more emphasis on non-price interventions needs to be given to accelerate growth in agricultural sector.

The decentralized procurement policy (DCP) under which foodgrains are procured and
distributed by the State Governments was introduced in 1997. The main objective of
decentralized system of procurement was to increase coverage of more farmers and crops
under MSP operations, improve efficiency of the PDS, providing more variety of foodgrains suited to local tastes and preferences and reduce transportation costs. in the case of rice, States under DCP operations have witnessed a significant increase in their share in procurement. For example, the share of Orissa has increased from 4.5 percent in 1997-98 to 7.9 percent in TE 2009-10 while in case of West Bengal the share has increased from 1.3 percent to 4.8 percent during the same period. The share of traditional states like Punjab and Haryana has declined significantly in post-DCP period.
During 2009-10, rice procurement in DCP States was about 11.9 million tonnes. However, in the case of wheat, procurement in DCP States has not increased except for Madhya Pradesh where it has increased from 3.8 percent in 1999 to 11.2 percent in TE 2010-11.

Under the decentralized system of procurement, the procurement of wheat has increased from less than 2 million tonnes in early 2000s to about 6.1 million tonnes in 2009-10. In 2010-11, the wheat procurement in DCP states has gone down primarily due to Uttar Pradesh withdrawing from the DCP scheme. there has been an increase in procurement by DCP states except in 2006- 07 and 2007-08 for wheat mainly due to aggressive purchases by private companies on expectation of higher market prices and proximity to consumption markets. Therefore, there is a need to increase the scope and scale of DCP in high potential areas like Bihar, Orissa, Chhattisgarh, Assam, West Bengal, Madhya Pradesh, Rajasthan, eastern Uttar Pradesh, Gujarat, etc. However, most of these states have poor market infrastructure as well as less developed private sector trade. Efforts are required to create marketing infrastructure in these regions and also to expand scope of coverage of crops like coarse cereals.

Another problem with agricultural price policy is mixing up the concepts of minimum support price (MSP) and procurement price (PP) but these policy instruments were introduced to serve different purposes. At present first one is not used though it was considered by the official policy during mid-1960s to mid-1970s. The purpose of MSP was to protect farmers against falling prices below a floor price and was determined based on the variable cost of production.

The system of MSP must be restored as it is required to ensure farmers remain in business as long as their variable costs are covered. It will also incentivize farmers to adopt technical change. The government should announce MSP before the sowing season as it would help in area allocation decisions. The procurement price (PP) which is determined based on both the variable cost and the fixed cost of production, should be used to procure foodgrains needed for public distribution system (PDS), welfare schemes and buffer stocks required for food security purpose.

Wednesday, 13 February 2013

Concise notes on Rising Agricultural Subsidies

Concise notes on Rising Agricultural Subsidies (Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

by Munish Alagh

The mounting burden of subsidies compelled the policy planners to make a serious
attempt to reform fertilizer price policy to rationalize the fertilizer subsidy.As part of economic reforms initiated in early-90s, the government decontrolled the import of complex fertilizers in 1992, and extended a flat-rate concession on these fertilizers. But, urea imports continued to be restricted and

The estimates of fertilizer subsidy as per Central government budgets over the years in the post-reforms era show that fertilizer subsidy has increased significantly. The fertilizer subsidy has increased from Rs. 4389 crore in 1990-91to2010-11 (Rs. 54876.68 crore). As a percentage of GDP from agriculture and allied sectors, this represents an increase from 4.5 percent in 1990-91 to 8.3 percent in 2010-11. The total food subsidy has jumped to about Rs. 60600 crore in 2008-09 from 2450 crores in 1990-91, about 24.7 fold increase in less than two decades in absolute terms. But if one looks at the percentage of GDP, then the burden of food subsidies in India is much less than that of many other developing countries. The food subsidy in India as percentage of the GDP has varied from 1.6 percent in 1990-91 to 5.4 in 2009-10, and on an average remained at about 3 percent over the last 19 years.

The above analysis shows that the volume of subsidies increased substantially during the post reforms period. The rate of increase, however, was higher for food subsidy (compound annual growth rate of 17.1% per year) than for fertilizer (13.8%).

During the 2000s, fertilizer subsidy growth has increased significantly (25.2%) as against 13.6 percent during the 1990s, because international prices of fertilizers and raw materials, feedstocks and intermediates increased substantially and yet fertilizer farm gate prices remained constant in the country between 1991 and 2001 and 2002 and 2009. Growth rate in food subsidies was higher (16.6%) during the 1990s compared with 2000s (13.4%).

The main reasons for ever increasing food subsidies are (i) significant increase in
procurement prices of foodgrains, (ii) increased government procurement and storage costs, and (iii) no increase in issue price of foodgrains provided through public distribution system during the last decade. Therefore, in order to contain rising input subsidies, moderate and gradual increase in prices of inputs is necessary to reduce the burden on fiscal and more importantly, for inducing farmers to use these inputs more efficiently. Full decontrol of fertilizer prices may lead to very high increase in prices and adversely affect farm incomes and agricultural production. Sharma and Thaker (2010) have reported that fertilizer subsidy is more equitably distributed among farm sizes and small and marginal farmers have a larger share in fertilizer subsidy in comparison to their share in cultivated area. The benefits of fertilizer subsidy have spread to unirrigated areas as the share of area treated with fertilizers has increased and the share of unirrigated areas in total fertilizer use has also increased. A reduction in fertilizer subsidy is, therefore, likely to have adverse impact on farm production and income of small and marginal farmers and unirrigated areas as they do not benefit from higher output prices but do benefit from lower input prices. Therefore, there is a need to contain fertilizer subsidies but it should not affect production and productivity of small and marginal farmers, who might cut down use of fertilizers if prices increase significantly.

Tuesday, 12 February 2013

Concise Edited Summary Notes on Declining Input Use Efficiency

Concise Edited Summary Notes on Declining Input Use Efficiency-(Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

Modern inputs such as improved seeds (HYVs), irrigation, chemical fertilizers, etc. have played an important role in agricultural development in the country. However, there is widespread belief that declining efficiency of agricultural inputs is one of the major reasons for decelerating growth in Indian agriculture and improvement in input use efficiency is essential for accelerating agricultural growth.

Irrigation water Management

Net irrigated area has increased from around 21 million hectares in 1951-52 to over 63 million hectares by 2008-09. Gross irrigated area has increased at faster rate from about 23 million hectares to 88.4 million hectares due to increased intensity of cropping on irrigated lands. Over 85 percent of addition to irrigated area in the last three decades has come from groundwater (mostly from tubewell) and the balance from surface irrigation (almost entirely from large public sector canal system).

Surface irrigation (canals+tanks) which accounted for about 58 percent of NIA in the TE 1953-54 is now estimated to contribute less than 30 percent. The development of tube-well irrigation, supported by investment in electrification and credit provision, has been the main driving force behind irrigation expansion in the country, particularly in the northwest. The area irrigated by government canal system has more than doubled in absolute terms (from 7.5 million hectares in TE 1953-54 to 16.5 million hectares in TE 2008-09) but their share in total irrigated area has shrunk from 35.2 percent to 26.2 percent. The average rate of growth in irrigation potential created during First Plan to Tenth Plan is about 1.47 million hectares per year.

In spite of large investments and increase in area under irrigation, the performance of many irrigation systems is significantly below potential due to inadequate design, use of
inappropriate technology, inappropriate government policies, and poor management practices. It is, therefore, important to ensure active participation of farmers in irrigation management and that would improve the performance and sustainability of irrigation systems.

Another problem associated with irrigation is uneven distribution of irrigated areas among different states. The percentage share of net irrigated area to net sown area varied from 18.2 percent in Maharashtra to 97.8 percent in Punjab.

Irrigation plays an important role in increasing cropping intensity, changes in cropping patterns and enhancing crop yield due to its complemetarity with improved varieties and fertilizer use. It is quite evident that the scope for expansion of net sown area is more or less exhausted, availability of irrigation is fast approaching the physical, ecological and economic limit, and depletion of groundwater resources due to over-exploitation is serious.
Therefore, it is important to focus on rainfed areas, where there is considerable scope for increasing productivity through soil and water conservation measures.

Integrated Nutrient Management

Chemical fertilizers are key element of modern technology and have played an important role in agricultural productivity growth in India. India is the second largest consumer of fertilizers in the world after China, consuming about 26.5 million tonnes. However, average intensity of fertilizer use in India remains much lower than most countries in the world but there are many disparities in consumption patterns both between and within regions of India. Less than 20 per cent of the districts accounted for about half of total fertilizer consumption in the country, indicating a high degree of concentration of fertilizer use (FAI, 2010).

One of the major constraints to fertilizer use efficiency in India is imbalance of applied
nutrients. Nitrogen (N) applications tend to be too high in relation to the amount of potassium(K) and phosphate (P) used. This is partly the result of a difference in price of different nutrients, and partly due to the lack of knowledge among farmers about the need for balanced fertilizer applications. The NPK ratio shows wide inter-regional and inter-state disparity.

Inefficient management of nutrients has led to multi-nutrient deficiency in Indian soils. In
addition to macro-nutrient deficiency, there is growing deficiency of micro and secondary
nutrients in soils.

With the limited arable land resources, and burden of increasing population, development of new technologies and efficient use of available technologies and inputs such as chemical fertilizers will continue to play an important role in sustaining food security in India. However, there is a need to optimize the use and efficiency of fertilizer use through appropriate interventions. In some areas excessive use of fertilizers is a cause of concern as it might lead to environmental degradation particularly land and water resources while in other areas, still about one-fourth of the districts use less than 50 kg/ha of fertilizers. Therefore, there is a need to have two pronged strategy, (i) to monitor districts with high intensity of consumption and take corrective actions to reduce environmental degradation and (ii) to promote fertilizer consumption in low-use districts to improve crop productivity. Of the two price policy instruments, affordable fertilizer prices and higher agricultural commodity prices, the former is more powerful in influencing fertilizer consumption (Sharma and Thaker, 2011). The high product price support policy benefits the large farmers who have net marketed surplus while low input prices benefit all categories of farmers.

Monday, 11 February 2013

Declining Public Expenditure in Indian Agriculture.

Declining Public Expenditure in Indian Agriculture. (Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

A ‘big push’ for public expenditure in agriculture is required to bring about technical change in agriculture, and higher agricultural growth. It is evident that there has been a significant decline in the allocation of public outlay on agriculture as a percent of total public outlay during the post-reforms period compared to what it was in pre-reforms period (Desai and Namboodiri 1997). The share of gross capital formation in agriculture and allied sector in total gross capital formation (at current prices) has declined from about 11.7 percent in 2001-02 to 6.89 percent in 2006-07 and further to 6.6 percent in 2007-08. However, there has been a marked improvement in its share during the last couple of years and reached a level of 8.5 percent in 2008-09 and marginally declined to 8.2 percent in 2009-10. The GCF in agriculture and allied sectors as proportion to the GDP in agriculture which stagnated around 14 percent during the first half of last decade, increased to over 20 percent in 2009-10. However, the GCF in agriculture and allied sectors as percentage to total GDP has remained stagnant at around 2.5 to 3.0 percent. In order to achieve over 4-4.5 percent growth in agriculture sector, there is a need to step up investment in agriculture.

Share of public expenditure on agriculture and alliedsectors declined from about 6 percent in 6th Plan to about 4.5 percent in Tenth plan. During 11th Plan a higher allocation
of public sector resources was projected for agriculture and allied activities, Rashtriya Krishi Vikas Yojana, in the form of 100% grant-in-aid, was launched in the 11thFive-Year Plan with a projected allocation of Rs. 25,000 crore over and above the other ongoing
programmes to incentivize the States to make higher investment in agriculture. The RKVY,
which provides sufficient flexibility to the States to take into account local needs, has helped in increasing allocation to agricultural sector. Since public participation is highly essential for
successful implementation of agricultural development programmes, people’s involvement in the development endeavors will help

Irrigation, which is a leading input for agricultural growth, expenditure also witnessed a
declining trend (10% in Sixth plan to about 8% in Tenth plan). However, the share of public
sector expenditure under rural development in total expenditure increased from 6.4 percent inthe Sixth plan to 9.2 percent in the Tenth plan. The expenditure on food and fertilizer subsidies has also increased significantly from 6.7 percent in Seventh plan to about 16 percent in Eleventh plan. Two main reasons for reduced share of public sector expenditure under agriculture and allied activities are: one, increased and larger public expenditure on rural development schemes like the Mahatma Gandhi National Rural Employment Guarantee Act.
(MNREGA), other rural development and poverty alleviation programmes, and two, increased and larger spending on food and fertilizer subsidy. It is interesting to note that public expenditure on agriculture research and education as proportion of total expenditure on agriculture and allied sectors, which declined during 7th and 8th plans, increased significantly during the subsequent plan periods. However, public spending on agriculture research, education, and extension is about 0.6-0.7 percent of agricultural GDP (Chand, et. al. 2011),which is much lower than the international norm of 2 percent.

The rationale for higher public spending on agriculture research, education, and extension lies in that fact that (i) public spending for this purpose has high value of marginal product based internal rate of return ranging from about 21 percent to 46 percent (Desai and Namboodiri 1997 and Chand, et. al. 2011), (ii) the sector has budget constraints for increasing number of extension workers, and (iii) it is further needed to undertake development and transfer of location specific new technologies by re-orienting ICAR’s research and SAUs’ higher education (Pal and Singh, 1997, Challa, et. al. 2011). These would require a big jump in allocation of budget for the agriculture and allied sectors both at the central and State government levels in total public spending. The public expenditure for technology-led agricultural growth must be prioritized in favour of agricultural research and education including extension; irrigation and flood control; soil and water conservation; rural infrastructure, rural financial institutions, and rural development and poverty alleviation programmes for creating community assets that directly contribute to agricultural growth.

Thursday, 7 February 2013

Declining Farm Size and Degradation of Natural Resources-Emerging Problems in Indian Agriculture.

Declining Farm Size and Degradation of Natural Resources-Emerging Problems in Indian Agriculture. (Reference: India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan
Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

Small and fragmented land holding

Indian Agriculture is characterized by small and fragmented land holding. There are about 129 million operational holdings possessing about 158 million hectare land with average farm size of only 1.23 hectares, down from 2.3 hectares in 1970-71. The reduction in farm-size has been larger in the case of medium and large farmers than in the case of small and marginal farmers. Around 83 % of the farmers have land holdings less than 2 ha and they cultivate nearly 41% of the arable land. On the other hand, less than 1% of the farmers have operational land holdings above 10 hectares and account for 11.8% of the cultivated land.

Inverse relationship between farm-size and crop-productivity has been well established but participation of small producers in markets remains low due to a range of constraints such as low volumes, high transaction costs, lack of markets and information access. Improved market access can have large impact on small holder incomes but it requires both policy and institutional reforms. Small farm in India is superior in terms of production performance but weak in terms of generating adequate income and sustaining livelihoods. Therefore, another area for policy intervention is land market reforms.As holdings are becoming small, fragmented and uneconomical, marginal farmers may be better off by leasing out the land to other farmers and seek gainful employment outside the sector.

Degradation of Natural Resources

Land and water are two important resources for sustainable growth of agriculture. Health and strength of these scarce resources is degrading at an accelerated pace and productive resources are being diverted from agricultural to other sectors.

Overexploitation of Ground Water Resources.

With 59% of irrigated agriculture and 85% of drinking water supplies dependent on it, groundwater is a vital resource for rural areas in India. Through the construction of millions of private tube-wells and wells, there has been a phenomenal growth in the exploitation of groundwater in the last five decades. The ground-water irrigation was a prime driver of green revolution technology in Mid 1960’s and increasing cropping intensity in the country. However, this era of seemingly endless reliance on groundwater for both irrigation and drinking water purposes is now approaching its limit as an increasing number of wells reach  unsustainable levels of exploitation.

The over exploitation of ground-water is emerging as an increasingly serious problem in agriculturally important districts of the country. The problem is more pronounced in rice-wheat based cropping systems in the Indo-Gangetic plains, and some sugarcane growing regions in the western and southern parts of the country.

A number of policy and institutional factors have been responsible for over-exploitation of ground-water in India. Easy availability of credit from financial institutions for installing tube-wells and provision of highly subsidized or free electricity for pumping in many states has encouraged increased extraction.

Attempts to regulate ground-water extraction by imposing credit restrictions have not been successful because well-off farmers have accessed private resources. A well-defined system of property rights to water that limits individual and collective withdrawls has been absent. The electricity for agricultural sector is highly subsidized in many states and free of cost in some states but low predictability of power supply.

Depletion and degradation of Land resources.

Shifts in resource availability and resulting land-use changes are adversely affecting growth of agricultural sector and national food security. A high degree of degradation of existing land resources has aggravated the problem. The per-capita availability of cultivable land has declined from .27 hectare in 1982 to .18 ha in 2003. this, in turn, is adversely affecting the livelihoods of the farming community in general and small and marginal farmers in particular.

Land degradation due to desertification, soil erosion, excessive and unscientific use of agricultural inputs such as irrigation water, fertilizers, agrochemicals, etc and deforestation is accelerating at an unprecedented rate. Land degradation will remain an important issue because of its adverse impact on crop productivity, the environment, and its effect on food security.

The expansion of cultivable land and intensification of production achieved through the use of irrigation have contributed to substantial production increases world-wide. For developing countries, its contribution to the attainment of development objectives of food security, poverty alleviation, and improvement of quality of life of the rural-population has been significant. Salinity and water-logging, soil erosion and water-pollution are a few of the serious problems that have gone hand in hand with irrigation.

Wednesday, 6 February 2013

Agricultural Growth in India.

Agricultural Growth in India.(Reference:India’s Agricultural Development under the New Economic Regime: Policy Perspective and Strategy for the 12th Five Year Plan
Vijay Paul Sharma, W.P. No. 2011-11-01, IIM(A), November 2011.)

We inherit the current status of agriculture in India from the previous decades, it is interesting to study, what was the status of agricultural growth in the last two decades in Indian agriculture?

Decelerating Agricultural Growth-

To study the decelerating agricultural growth in India we consider in the main four major criteria:

1)      Growth rate of real agriculture and non-agriculture GDP.
2)      Trends in area and production of major crops/crop groups.
3)      Growth Rate and level of physical productivity of agriculture, and
4)      High-level agriculture growth patterns in pre and post-reform period.

1) Growth rate of real agriculture and non-agriculture GDP.

Whereas agricultural GDP had started growing in India since the onset of wider technological dissemination period ie-since 1981-82, agricultural GDP grew at 4.8% during the eighth plan period from 1992-97, at around 2 ½ % in the ninth and tenth plan period, between 1997-2007, (2.5%-1997-2002, 2.4%-2002-2007) and 3.3 percent during the eleventh plan period, clearly agricultural growth has decelerated  during the last fifteen years, though it picked up somewhat during the eleventh plan period.

The growth rate of non-agricultural GDP increased from 5.4% to 9.3% in the period from the eighth five-year plan to the tenth five-year plan.

The gap between agricultural and non-agricultural GDP increased significantly in the post-reforms period. The ratio of growth-rate of real agricultural GDP to that of total real non-agriculture GDP was lowest (.27) in 10th five-year plan period compared to that in the 8th five-year plan period (1.07), indicating deceleration in agricultural growth compared with non-agricultural GDP. However, as we saw earlier agricultural growth has once again picked up a bit in the 11th five-year plan period.

2) Changing shares of acreage and production of major crops/crop groups.

During the last three decades net area sown declined from 142 million hectares in triennium ending (TE) 1983-84 to 140.8 million hectares in TE 2008-9, whereas total cropped area increased from 176.4 million hectares to 194 million hectares during the same period.

The area under foodgrains declined by about 6 million hectares, area under pulses has almost remained stagnant, area under wheat has increased by about 4.6 million hectares and rice by 3.7 million hectares, coarse cereals has declined by about 13.6 million hectares from TE 1983-84 to TE 2007-8.

During the last two decades foodgrain production increased by about 28%, cotton by over 200%, fruits and vegetables by 97%, condiments and spices by 66% and wheat by 39%. Pulses did not increase by much, though during 2010-11 there was a record pulses production.

Cropping Pattern shifted towards oilseeds, sugarcane and fruits and vegetables during the 1980’s, whereas in the 1990’s and 2000s, the shift was more fruits and vegetables, sugarcane and cotton and other non food crops, as we saw earlier this increase in area in non food crops since 1983-84 has been at the expense of food crops.

The compound annual growth rate of area under major crops reveal that, during the 1980’s fruits and vegetables witnessed the highest growth rate (3.4%), followed by oilseeds (3.02%) and sugarcane (1.35%). The main reason for significant growth in area under oilseeds during the 1980’s was technology mission on oilseeds and complete protection to domestic industry from imports. During the 1990’s, area under fruits and vegetables agin witnessed the highest growth rate (2.5%), followed by cotton (2.18%) and sugarcane (1.91%). Area under fruits and vegetables grew at an annual compound growth-rate of 5.28% during the 2000s, followed by cotton (3.12%), oilseeds (2.57%) and wheat and sugarcane (about 1.3%). The National Horticulture Mission has helped the growth in fruits and vegetables.

Performance of Indian Agriculture decelerated significantly in the 1990’s.The compound annual growth rates of all crops were significantly lower in the 90’s compared to the 80’s. Rice production recorded a growth of 4.2% in the 80’s and 1.87% in 90’s. Oilseeds growth fell from 5.8% to less than 1%.Foodgrain from 2.24% to 1.9%. The highest increase in growth rate was witnessed in case of cotton (14.28%), followed by fruits and vegetables (6.76%), oilseeds (5.12%), pulses (3.04%) and coarse cereals (2.94%). The increase in growth rate of fruits and vegeatables was primarily due to area expansion.

3.) Growth Rate and level of physical productivity of agriculture,

Average productivity of all crops improved between 1980’s and 2000’s, but the increase was greatest in the case of cotton (89.9%), followed by coarse cereals (59.1%) and oilseeds (41.6%). However growth rate of productivity declined during the 90’s compared to the 80’s.Average productivity can be increased significantly for all crops in India, specifically for Rice, Maize and Milk it is significantly lower than the world average.

4.) High Value Agriculture Growth Patterns-Some Concerns.

Foodgrains particularly cereals are shifting to livestock, fisheries and fruits and vegetables. 2% growth in foodgrain output is aimed for twelfth plan but 4.5% to 6% for animal husbandry, horticulture and high value agriculture segment.

Tuesday, 5 February 2013

Ignoring Statistical Principles in Inductive Heuristics-Preliminary Version of Planned Journal Article!!

Ignoring statistics (particularly probability) in heuristic inductive thinking-
Dr. Munish Alagh, PDF-IIM(A).
Inductive Thinking involves, generalising from the particular to the general.
Heuristics, involve cognitive short-cuts to make a decision easily.
Heuristics people use in inductive reasoning tasks often do not respect the required statistical principles. People consequently overlook statistical variables such as sample size, correlation, and base rate when they solve inductive reasoning problems[1]
Statistical Problems and Nonstatistical Heuristics
As we have seen, people often solve inductive problems by use of a variety of intuitive heuristics—rapid and more or less automatic judgmental rules of thumb. These include the representativeness heuristic (Kahneman & Tversky, 1972, 1973), the availability heuristic (Tversky & Kahneman, 1973), and the anchoring heuristic (Tversky & Kahneman, 1974). In problems where these heuristics diverge from the correct statistical approach, people commit serious errors of inference. The following heuristics, the biases they lead to and the statistical principles that are ignored therein are discussed[2] :

Ø      Representativeness.
Ø      Adjustment and Anchoring.
Ø      Availability.


According to Kahneman and Tversky (1974) there are three types of probabilistic questions with which people are concerned.

What is the probability that object A belongs to class B?
What is the probability that event A originates from process B?
What is the probability that process B will generate event A?

People answer such questions by relying on the representativeness heuristic according to which probabilities are evaluated by the degree to which A is representative of B, that is, by the degree to which A resembles B. For example, when A is highly representative of  B, the probability that A originates from B is judged to be high. On the other hand, if A is not similar to B, the probability that A originates from B is judged to be low.

There is  a type of research on problems of a particular type which has shown that people order the occupations by probability and by similarity in exactly the same way.[3] They consider that a person, Steve, whose probability that he is a librarian, for example, is assessed by the degree to which he is representative of, or similar to, the stereotype of a librarian. This is known as the representativeness heuristic.

Infact people who are asked to assess probability are not stumped, because they do not try to judge probability as statisticians and philosophers use the word. A question about probability or likelihood activates a mental shotgun, evoking answers to easier questions. Judging probability by representativeness has important virtues: the intuitive impressions that it produces are often-indeed, usually-more accurate than chance guesses would be.[4]

This approach to the judgement of probability however leads to serious errors, because similarity, or representativeness, is not influenced by several factors that should affect judgments of probability:

Insensitivity to prior probability of outcomes: 
One of the factors that have no effect on representativeness but should have a major effect on probability is the prior probability, or base-rate frequency, of the outcomes. In case of Steve, for example, the fact that there are many more farmers than librarians in the population should enter into any reasonable estimate of the probability that Steve is a librarian rather than a farmer. Considerations of base-rate frequency, however, do not affect the similarity if people evaluate probability by representativeness, therefore, prior probabilities will be neglected. Certain differing prior probabilities were given for two professions to subjects, in two different cases, also the personality description of several individuals, allegedly sampled at random from a group of 100 professionals, including both the occupations were given. The subjects were asked to assess, for each description, the probability that it belonged to one of the occupations. The odds that any particular description belongs to any one of the professions should be higher when the prior probability of that particular occupation is more. However subjects in the two conditions produced essentially the same probability judgements. Apparently, subjects evaluated the likelihood that a particular description belonged to a particular occupation, from the two, by the degree to which the description was representative of the two stereotypes, with little or no regard for the prior probabilities of the categories.

The subjects used prior probabilities correctly when they had no other information. However, prior probabilities were effectively ignored when a description was introduced, even when this description was totally uninformative. Evidently, people respond differently when given no evidence and when given worthless evidence. When no specific evidence is given, prior probabilities are ignored.[5]
Nisbett and Borgida (1975),quoted in The reference stated above[6] showed that consensus information, that is, base rate information about the behaviour of a sample of people in a given situation, often has little effect on subjects attributions about the causes of a particular target individual's behavior. When told that most people behaved in the same way as the target, subjects shift little or not at all in the direction of assuming that it was situational forces, rather than the target's personal dispositions or traits, that explain the target's behavior.
It is noticed that subjects use prior probabilities correctly when they have no other information. However, prior probabilities are effectively ignored when a description is introduced, even when this description is totally uninformative. Evidently, people respond differently when given no evidence and when given worthless evidence. When no specific evidence is given, prior probabilities are ignored.[7]
Insensitivity to sample size:
To evaluate the probability of obtaining a particular result in a sample drawn from a specified population, people typically apply the representativeness heuristic. That is, they assess the likelihood of a sample result by the similarity of this result to the corresponding parameter The similarity of a sample statistic to a population parameter does not depend on the size of the sample. Consequently, if probabilities are assessed by representativeness, then the judged probability of a sample statistic will be essentially independent of sample size. Indeed, when subjects assessed the distributions of the sample results for samples of various sizes, they produced identical distributions . A similar insensitivity to sample size has been reported in judgments of posterior probability, that is, of the probability that a sample has been drawn from one population rather than from another. Here again, intuitive judgments are dominated by the sample proportion and are essentially unaffected by the size of the sample, which plays a crucial role in the determination of the actual posterior odds [8]. In addition, intuitive estimates of posterior odds are far less extreme than the correct values. The underestimation of the impact of evidence has been observed repeatedly in problems of this type.[9] It has been labeled "conservatism."

Misconceptions of chance:
People expect that a sequence of events generated by a random process will represent the essential characteristics of that process even when the sequence is short. Thus, people expect that the essential characteristics of the process will be represented, not only globally in the entire sequence, but also locally in each of its parts. A locally representative sequence, how-ever, deviates systematically from chance expectation: it contains too many alternations and too few runs. Another consequence of the belief in local representativeness is the well-known gambler's fallacy. Chance is commonly viewed as a self-correcting process in which a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium. In fact, deviations are not "corrected" as a chance process unfolds, they are merely diluted. Misconceptions of chance are not limited to naive subjects. A study of the statistical intuitions of experienced research psychologists[10] revealed a lingering belief in what may be called the "law of small numbers," according to which even small samples are highly representative of the populations from which they are drawn. The responses of these investigators reflected the expectation that a valid hypothesis about a population will be represented by a statistically significant result in a sample with little regard for its size. As a consequence, the researchers put too much faith in the results of small samples and grossly overestimated the replicability of such results. In the actual conduct of research, this bias leads to the selection of samples of inadequate size and to overinterpretation of findings.
Insensitivity to predictability: People are sometimes called upon to make such numerical predictions as the future value of a stock, the demand for a commodity, or the outcome of a football game. Such predictions are often made by representativeness. The degree to which the description is favorable is unaffected by the reliability of that description or by the degree to which it permits accurate prediction. Hence, if people predict solely in terms of the favorableness of the description, their predictions will be insensitive to the reliability of the evidence and to the expected accuracy of the prediction demonstrated that intuitive predictions violate this rule, and that subjects show little or no regard for considerations of predictability [11]
That is, the prediction of a remote criterion was identical to the evaluation of the information on which the prediction was based The students who made these predictions were undoubtedly aware of the limited predictability never-theless, their predictions were as ex-treme as their evaluations.
This mode of judgment violates the normative statistical theory in which the extremeness and the range of predictions are controlled by considerations of predictability. When predictability is nil, the same prediction should be made in all cases If predictability is perfect, of course, the values predicted will match the actual values and the range of predic-tions will equal the range of outcomes. In general, the higher the predictability, the wider the range of predicted values. Several studies of numerical prediction have
The illusion of validity:

As we have seen, people often predict by selecting the outcome (for example, an occupation) that is most representative of the input (for example, the description of a person). The confidence they have in their prediction depends primarily on the degree of representativeness (that is, on the quality of the match between the selected outcome and the input) with little or no regard for the factors that limit predictive accuracy The unwarranted confidence which is produced by a good fit between the predicted outcome and the input information may be called the illusion of validity. This illusion persists even when the judge is aware of the factors that limit the accuracy of his predictions.

The internal consistency of a pattern of inputs is a major determinant of one's confidence in predictions based on these inputs Highly consistent patterns are most often observed when the input variables are highly redundant or correlated. Hence, people tend to have great con-fidence in predictions based on redundant input variables. However, an elementary result in the statistics of correlation asserts that, given input variables of stated validity, a prediction based on several such inputs can achieve higher accuracy when they are independent of each other than when they are redundant or correlated. Thus, redundancy among inputs decreases accuracy even as it increases confidence, and people are often confident in predictions that are quite likely to be off the mark[12]

Regression to the mean:

 Regression to the mean- involves moving closer to the average than the earlier value of the variable observed. Also regression to the mean has an explanation, but does not have a cause.[13]
An important principle of skill training: rewards for improved performance work better than punishment of mistakes. This proposition is supported by much evidence from research.
Regression to the mean, involves that poor performance is typically followed by improvement and good performance by deterioration, without any help from either praise or punishment.

The feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.

Regression does not have a causal explanation. Regression effects are ubiquitous, and so are misguided casual stories to explain them. The point to remember is that the change from the first to the second occurrence does not need a causal explanation. It is a mathematically inevitable consequence of the fact that luck played a role in the outcome of the first occurence.

Regression inevitably occurs when the correlation between two measures is less than perfect.

The correlation coefficient between two measures, which varies between 0 and 1, is a measure of the relative weight of the factors they share.

Correlation and regression are not two concepts-they are different perspectives on the same concept. The general rule is straightforward but has surprising consequences: whenever the correlation between two scores is imperfect, there will be regression to the mean.

Our mind is strongly biased toward causal explanations and does not deal well with “mere statistics.” When our attention is called to an event, associative memory will look for its cause, more precisely, activation will automatically spread to any cause that is already stored in memory. Causal explanations will be evoked when regression is detected, but they will be wrong because the truth is that regression to the mean has an explanation but does not have a cause.

Regression effects are a common source of trouble in research, and experienced scientists develop a healthy fear of the trap of unwarranted causal inference.

Statistics can be used, but is often not used in intuitive thinking:

Even when judgments are based on the representativeness heuristic, there may be an underlying stratum of probabilistic thinking. In many of the problems studied by Kahneman and Tversky, people probably conceive of the underlying process as random, but they lack a means of making use of their intuitions about randomness and they fall back on representativeness.

Adjustment and Anchoring:

Biases in the evaluation of compound events are particularly significant in the context of planning. The successful completion of an undertaking, such as the development of a new product, typically has a conjunctive character: for the undertaking to succeed, each of a series of events must occur. Even when each of these events is very likely, the overall probability of success can be quite low if the number of events is large. The general tendency to overestimate the probability of conjunctive events leads to unwarranted optimism in the evaluation of the likelihood that a plan will succeed or that a project will be completed on time. Conversely, disjunctive structures are typically encountered in the evaluation of risks. A complex system, such as a nuclear reactor or a human body, will malfunction if any of its essential components fails. Even when the likelihood of failure in each component is slight, the probability of an overall failure can be high if many components are involved. Because of anchoring, people will tend to underestimate the probabilities of failure in complex systems.
The subjects state overly narrow confidence intervals which reflect more certainty than is justified by their knowledge about the assessed quantities.

Anchoring in the assessment of subjective probability distributions.: the subjects state overly narrow confidence intervals which reflect more certainty than is justified by their knowledge about the assessed quantities

it is natural to begin by thinking about one's best estimate of the parameter and to adjust this value upward. If this adjustment like most others is insufficient, then the upper value of the distribution will not be sufficiently extreme. A similar anchoring effect will occur in the selection of the lower value of the distribution, which is presumably obtained by adjusting one's best estimate downward. Consequently, the confidence interval between the lower and upper values of the distribution will be too narrow, and the assessed probability distribution will be too tight.


Availability which is discussed above, is affected by various factors which are not related to actual frequency. If the availability heuristic is applied, then such factors will affect the perceived frequency of classes and the subjective probability of events. Consequently, not only does the use of the availability heuristic leads to systematic biases, there are also effects on the statistical picture which is pictured by us as a result.

“Errors” in probabilistic reasoning are in fact not violations of probability

Most so-called “errors” in probabilistic reasoning are in fact not violations of probability theory. Examples of such “errors” include overconfidence bias, conjunction fallacy, and base-rate neglect.[14]
Over-confidence bias-systematic discrepancy between confidence and relative frequency is termed “overconfidence.”
Has probability theory been violated if one’s degree of belief (confidence) in a single event (i.e., that a particular answer is correct) is different from the relative frequency of correct answers one generates in the long run? The answer is “no.” It is in fact not a violation according to several interpretations of probability. According to the frequentists, probability theory is about frequencies, not about single events. To compare the two means comparing apples with oranges. According to subjectivists a discrepancy between confidence and relative frequency is not a “bias,” albeit for diff erent reasons. For a subjectivist, probability is about single events, but rationality is identified with the internal consistency of subjective probabilities. So, in conclusion, a discrepancy between confidence in single events and relative frequencies in the long run is not an error or a violation of probability theory from many experts’ points of view. It only looks so from a narrow interpretation of probability that blurs the distinction between single events and frequencies fundamental to probability theory. [15]

Conjunction fallacy-

The original demonstration of the “Conjunction fallacy” was with problems involving matching a description of a lady, with a) her profession and b) her profession and an activity she was involved in. Subjects were asked which of two alternatives was more probable. Tversky and Kahneman, however, argued that the “correct” answer is a), because the probability of a conjunction of two events, such as b), can never be greater than that of one of its constituents. They explained this “fallacy” as induced by the representativeness heuristic. They assumed that judgments were based on the match (similarity, representativeness) between the description of the lady and the
two alternatives. That is, since the lady was described based on her activity and b)
contains her activity people believe that b)is more probable.
Is the “conjunction fallacy” a violation of probability theory, as has been claimed in the literature? Has a person who chooses b) as the more probable alternative violated probability theory? Again, the answer is “no.” Choosing b) is not a violation of probability theory, and for the same reason given above. For a frequentist, this problem has nothing to do with probability theory. Subjects are asked for the probability of a single event (that the lady has a particular profession), not for frequencies. Note that problems which are claimed to demonstrate the “conjunction fallacy” are structurally slightly different from “confidence” problems. In the former, subjective probabilities ( a) or b)) are compared with one another; in the latter, they are compared with frequencies. To summarize the normative issue, what is called the “conjunction fallacy” is a violation of some subjective theories of probability. It is not, however, a violation of the major view of probability, the frequentist conception.[16]

The base-rate fallacy

The example is from Casscells, Schoenberger, and Grayboys (1978, p. 999) and presented by Tversky and Kahneman (1982, p. 154) to demonstrate the generality of the phenomenon:

If a test to detect a disease whose prevalence is 1/1000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease, assuming you know nothing about the person’s symptoms or signs?

Sixty students and staff at Harvard Medical School answered this medical diagnosis problem. Almost half of them judged the probability that the person actually had the disease to be 0.95 (modal answer), the average answer was 0.56, and only 18% of participants responded 0.02. The latter is what the authors believed to be the correct answer. Note the enormous variability in judgments.
Little has been achieved in explaining how people make these judgments and why the judgments are so strikingly variable.

But do statistics and probability give one and only one “correct” answer to that problem?

The answer is again “no.” And for the same reason, as the reader will already guess. As in the case of confidence and conjunction judgments, subjects were asked for the probability of a single event, that is, that

“a person found to have a positive result actually has the disease.” If the mind is an intuitive statistician of the frequentist school, such a question has no necessary connection to probability theory.

A more serious difficulty is that the problem does not specify whether or not the person was randomly drawn from the population to which the base rate refers.[17]

Discussion :

Statistical principles are not learned from everyday experience because the relevant in-stances are not coded appropriately.

The lack of an appropriate code also explains why people usually do not detect the biases in their judgments of probability.

The inherently subjective nature of probability has led many students to the belief that coherence, or internal consistency, is the only valid criterion by which judged probabilities should be evaluated. From the standpoint of the formal theory of subjective probability, any set of internally consistent probability judgments is as good as any other. This criterion is not entirely satisfactory, because an internally consistent set of subjective probabilities can be incompatible with other beliefs held by the individual. Consider a person whose subjective probabilities for all possible outcomes of a coin-tossing game reflect the gambler's fallacy. That is, his estimate of the probability of tails on a particular toss increases with the number of consecutive heads that preceded that toss. The judgments of such a person could be internally consistent and therefore acceptable as adequate subjective probabilities according to the criterion of the formal theory. These probabilities, however, are incompatible with the generally held belief that a coin has no memory and is therefore incapable of generating sequential dependencies. For judged probabilities to be considered adequate, or rational, in-ternal consistency is not enough. The judgments must be compatible with the entire web of beliefs held by the individual. Unfortunately, there can be no simple formal procedure for assessing the compatibility of a set of probability judgments with the judge's total system of beliefs.

[1] Nisbett, Richard E., Krantz, David H., Jepson, Christopher. And Kunda, Z.(1983, p. 339)
[2] Nisbett, Richard E., Krantz, David H., Jepson, Christopher. And Kunda, Z.(1983, p. 340)

[3]Amos Tversky and Daniel Kahneman, “On the Psychology of Prediction,” (1973).
[4]  Daniel Kahneman, Thinking, Fast and Slow  (2011).
[5] Tversky and Kahneman (1973)
[6] Nisbett, Richard E., Krantz, David H., Jepson, Christopher. And Kunda, Z.(1983, p. 341)
[7] Tversky and Kahneman, “On the Psychology of Prediction.” (1973)
[8] D Kahneman and A Tversky, “Subjective Probability: A Judgment of Representativeness,” Cognitive Psychology 3(1972);430-54
[9] W Edwards,Conservatism in Human Information Processing, 1968
[10] Kahneman and Tversky,1972.
[11] Kahneman and Tversky,On the Psychology of Prediction, 1973
[12] Ibid.
[13] Kahneman, 2011-chapter 17
[14] Gigerenzer Gerd (1991,p.83)
[15] Gigerenzer Gerd (1991,p.88)
[16] Gigerenzer Gerd (1991,p.91)

[17] Gigerenzer Gerd (1991,p.92)