all india debt and investment survey 2002 gsxr

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All india debt and investment survey 2002 gsxr

Narayana has argued that, between —62 and —82, there was a shift towards sampling a larger number of villages and smaller number of households per village. However, this shift held true only between —62 and —72; in the —82 round, there was a reduction in both the number of sampled villages and households. There was a similar reduction both in the number of sampled villages and households during the —92 round as well; and this trend was reversed only during the —03 round.

For the —62 survey, each State was divided into a number of strata that were roughly equal in size and with relatively homogeneous agricultural conditions. Forty households were then chosen randomly from each of these sample villages ibid. For the next two rounds of the survey —72 and —82 the NSSO used a two-stage stratified sampling method, with villages constituting the first-stage units and households the second-stage units.

The all-India sample of villages was allocated among various States on the basis of various considerations, such as their rural population, area under cereal crops, and investigator strength, but ensuring that at least villages were sampled in each State Narayana In —82, the sample of villages was chosen with probability proportional to population and with replacement. In —72 and —82, the population size was measured as per the population figures provided by the Census of and respectively.

In —72, three households were drawn from each stratum, making a total of 12 households from each sample village. In —82, two households were drawn from each stratum, making a total of only eight households from each sample village. In other words, the sample size of households per village in — 82 was only one-fifth of the sample size in — Between —82 and —92, there was a minor increase in the number of households selected per village — from eight to nine.

In —03, the number of households per village was increased to The second point that emerges with regard to the sampling methodology of the AIDIS is that, over successive rounds, there was a substantial increase in the size of the State sample of villages Table 2.

The survey of —62 was carried out by field staff of the RBI, but in certain States such as Punjab, Gujarat, Assam, Orissa, and Rajasthan, State statistical bureaus were also involved in covering the matching samples of the State. Moreover, the results were based on a pooling of the two samples. The estimates were prepared after pooling these three samples. In —82 and —92, the size of the State sample exceeded the Central sample.

As there was no pooling of Central and State samples during the —92 round, and the survey results of this round were entirely based on a relatively small Central sample. A number of questions have been raised about the reliability of the estimates provided by the —82 survey.

Narayana found that the total sampling variance in —82, inclusive of both within- and between-village variance, was much greater than the corresponding figures from the —62 and —72 rounds. This pointed to the possibility of an unreliable estimation of indebtedness during the —82 survey. According to Narayana ibid. The reason was that the reliability of any estimate generally depends upon whether it shares any linear relationship with the stratification variable.

Narayana argued that it was difficult to trace any direct relation between the area of land possessed and the incidence of indebtedness. In other words, the proportion of indebted households was not likely to be higher among households with a greater size of landholding. Therefore, the change in the sampling methodology could be expected to affect the reliability of the estimate of incidence of indebtedness.

There were only 1, rural bank branches in , and the number increased to 17, by As against this, there was a major fall in the incidence of indebtedness between —72 and —82 Figure 1. According to Prabhu et al. Other studies have argued that the reliability of the AIDIS estimate of extent of indebtedness average amount of debt is also suspect. Gothoskar compared the supply-side estimates of the volume of debt outstanding obtained from the records of cooperatives and commercial banks with the demand-side estimates of the same obtained from the AIDIS.

He found an underestimation by the AIDIS of the volume of debt by about 50 per cent in —82 and about 40 per cent in — Some other studies have concluded that reduction in the sample sizes of villages and households was responsible for the underestimation of household debt by the AIDIS Narayana ; Prabhu et al. Apart from the shift in the sampling methodology, an increase in the State sample relative to the Central sample was also considered to be a factor influencing the quality of the AIDIS data.

Bell argued that State government agencies were likely to be less equipped in conducting surveys than the NSSO, and that it was therefore desirable to allot a larger Central sample to be canvassed by the NSSO. Gothoskar took the total credit outstanding from rural branches of commercial banks, while I have taken credit to only specific sections. I have left out the bank credit that goes to the cooperative sector, the public sector, the private corporate sector, joint sector undertakings, and foreign governments, as this may not go directly to households.

Thus the comparison here is between the BSR data on credit outstanding reported by the rural branches of commercial banks and regional rural banks RRBs to individuals, proprietorship and partnership firms, joint families, and self-help groups, with the AIDIS data on debt outstanding of rural households from commercial banks.

My exercise shows that the AIDIS underestimated household debt by about 46 per cent and 35 per cent respectively in its —92 and —03 rounds Table 3. In the BSR, credit from rural branches refers to credit from branches located at centres having a population of less than 10, The Census definition of rural areas uses not just population, but also population density and occupational structure as defining criteria. In other words, the definition of rural areas by the Census may be much broader, and therefore the extent of underestimation by the AIDIS may be even greater than what is presented in Table 3.

Another way to test the reliability of the AIDIS estimates is to compare them with estimates available from village surveys. This comparison is, of course, indicative. In —03, State-level AIDIS estimates for incidence of debt rarely exceeded 40 per cent, and for most States were between 20 per cent and 30 per cent. On the contrary, the village surveys showed debt incidence ranging between 50 per cent and 75 per cent.

Hence, even after an increase in the sample size, to an extent, the likelihood of underestimation of household debt during the —92 and —03 rounds cannot be ruled out. As a result, the households which have been visited in the first two months of the first visit were revisited in the first month of the second visit and so on.

To reduce the fatigue of the respondent, it was decided that unlike in the 37th round, the sample households in this round would be selected separately for schedules Since the land and livestock holdings were likely to be small in most cases, it was decided to survey the same set of sample households both for schedules The position in regard to the assets and liabilities of the sample households was required to be collected with reference to a fixed date, namely, as on the 30th June There was a time lag between the reference date and the survey date in the first visit varying from household to household within the range of maximum period of 14 months.

To derive the above, therefore, it was decided to collect information on assets and liabilities as on the date of survey and the transactions relating to the said assets and liabilities carried out during the period intervening the date of reference and the date of survey. Broadly, the following information has been collected in this round of survey:- i the asset and the liability position of the households ii the amount of capital expenditure on a residential plots, houses or buildings, b farm business and c non-farm business, incurred by the household during the reference period of agricultural year The assets owned by the households have been classified into three categories, namely, a physical assets contributing to capital formation b financial assets and c durable household assets.

Besides collection of information for deriving the asset and liability position of the households as on Kind of Data. Unit of Analysis. Randomly selected households based on sampling procedure and members of the household. Version Version Description. Scope Notes. Coverage Geographic Coverage. Anantnag, Pulwana, Srinagar, Badgam, Baramula and Kupwara, and the district of Amritsar in Punjab due to unfavourable field conditions.

The survey used the interview method of data collection from a sample of randomly selected households and members of the household. Producers and sponsors Primary investigators. Sampling Sampling Procedure.

Sample Design A stratified two-stage sampling design was adopted for the survey with the first stage units as census villages for rural areas and the Urban Frame Survey blocks for urban areas. Households formed the second statge units in both rural and urban areas. Sampling frame for first stage units FSU's : In the rural sector, the sampling frame in most of the strata was the census list of villages.

However, in Assam, where the census was not undertaken, and in a few districts of other states, where the available list as per census was incomplete, the census list of villages was used. However, the census house listing enumeration blocks were considered as the sampling units for some of the new towns declared as urban areas in the population census.

In Gujarat, however, some districts were subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state. The total number of regions formed in the India as whole was In the rural sector, within each region, each district with a rural population of less than 1.

Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils, similar as far as possible in respect of rural population density and crop pattern. In Gujarat, however, in the case of districts extending over more than one region, the portion of a district falling in each region constituted a separate stratum even if the rural population of the district as a whole was less than 1.

Further, in Assam, the strata formed for the earlier NSS rounds on the basis of census rural population exactly in the above manner, but with a cut-off of 1. In the urban sector, strata were formed, again within NSS regions, on the basis of in some of the new towns census population of towns. Each city with a population 10 lakhs or more formed a separate stratum by itself.

The remaining towns of each region were grouped to form three different strata on the basis of in a few cases census population. All allocations were adjusted so that the sample size for a stratum was at least a multiple of 4 for the rural and urban sectors separately. This was done to accomplish equal sized samples in each sub-sample and sub-round. Selection of first stage units: The selection of sample villages was PPS with replacement with population as the size variable, in the form of two independent subsamples.

The sample blocks were selected by simple random sampling without replacement, also in the form of two independent subsamples. Two hamlet-groups were then selected from large villages, whereas only one sub-block was selected from the large blocks. The hamlet-groups were selected circular systematically and the sub-block with equal probability.

Selection of households: Two different procedures of selection of households were used for the rural and urban sectors. Households possessing either no land or land less than 0. The rest of the households were then arranged in ascending order by area of land possessed and classified into three substrata, 2, 3 and 4, such that the total area of land possessed by the households in each of the 3 sub-strata was nearly the same.

AIDIS sub-strata 5 to 7 are formed by first merging LHS substrata numbers 3 and 4 and then sub-divided by the merged group into 3 classes, viz. For this, the households were first grouped in three mpce classes, viz. The cut-off points A and B were determined at the state-level on the basis of mpce obtained from the survey on consumer expenditure, NSS 43rd Round, such that the mpce classes, below A, A to B, and B and above, respectively constituted 30 p.

These mpce classes were further sub-divided by indebtedness status of the households to form 7 AIDIS sub strata. Independent samples were selected circular systematically from each of the sub-stratum. For the AIDIS, 9 households from every sample village and every urban block were planned to be surveyed. In the central sample, the actual number of households surveyed was in the rural sector and in the urban sector. Deviations from the Sample Design. The report is available under external resources.

Data Collection Dates of Data Collection. Start End Cycle Visit 1 Visit 2. Data Collection Mode. Data Collection Notes. The information in this schedule has been collected by visiting the same set of sample households twice. In the first visit, information on assets owned on the date of survey as well as addition and depletion of these assets during the period, July 1, to the date of survey has been ascertained to derive the asset position of the households as on 30th June The same procedure has been adopted for assessing the indebtedness position of the households at the beginning of the agricultural year i.

However, provision has been made for obtaining the data on the amount and other particulars of borrowings and repayments made during the first half of the agricultural year i.

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It not only collected data on the liabilities of rural households, but also undertook an extensive investigation of formal and informal credit agencies operating in rural areas. The next survey, conducted by the RBI in —62, focussed on the assets and indebtedness of rural households. From —92, however, the survey results were based on the Central sample alone and the RBI ceased to play any role in the survey.

The results of the AIRDIS were made public within a relatively short period of time after completion of the survey, possibly because its coverage was limited to rural households Table 1. Even the results of the AIRCS, which had a much wider scope, were published within two years of the survey. However, there was a lag of five to six years in the publication of results from the subsequent three rounds, conducted in —72, —82, and — The results of the most recent round, of —03, were again published within two years of the survey.

There are two major issues that emerge with regard to changes in the sampling methodology of the AIDIS since — a reduction in the sample size of villages and households, and an increase in the size of the State sample. These are discussed below. There was a sharp reduction in the sample size of villages and households during the —82 and —92 rounds of the survey Table 2 , columns 4 and 5.

These were followed, however, by an increase in the sample size during the —03 round. The decline in the sample size of households in —82 and —92 was because fewer households were selected per village during those rounds Table 2 , Column 6. Narayana has argued that, between —62 and —82, there was a shift towards sampling a larger number of villages and smaller number of households per village. However, this shift held true only between —62 and —72; in the —82 round, there was a reduction in both the number of sampled villages and households.

There was a similar reduction both in the number of sampled villages and households during the —92 round as well; and this trend was reversed only during the —03 round. For the —62 survey, each State was divided into a number of strata that were roughly equal in size and with relatively homogeneous agricultural conditions. Forty households were then chosen randomly from each of these sample villages ibid.

For the next two rounds of the survey —72 and —82 the NSSO used a two-stage stratified sampling method, with villages constituting the first-stage units and households the second-stage units. The all-India sample of villages was allocated among various States on the basis of various considerations, such as their rural population, area under cereal crops, and investigator strength, but ensuring that at least villages were sampled in each State Narayana In —82, the sample of villages was chosen with probability proportional to population and with replacement.

In —72 and —82, the population size was measured as per the population figures provided by the Census of and respectively. In —72, three households were drawn from each stratum, making a total of 12 households from each sample village. In —82, two households were drawn from each stratum, making a total of only eight households from each sample village.

In other words, the sample size of households per village in — 82 was only one-fifth of the sample size in — Between —82 and —92, there was a minor increase in the number of households selected per village — from eight to nine.

In —03, the number of households per village was increased to The second point that emerges with regard to the sampling methodology of the AIDIS is that, over successive rounds, there was a substantial increase in the size of the State sample of villages Table 2. The survey of —62 was carried out by field staff of the RBI, but in certain States such as Punjab, Gujarat, Assam, Orissa, and Rajasthan, State statistical bureaus were also involved in covering the matching samples of the State.

Moreover, the results were based on a pooling of the two samples. The estimates were prepared after pooling these three samples. In —82 and —92, the size of the State sample exceeded the Central sample. As there was no pooling of Central and State samples during the —92 round, and the survey results of this round were entirely based on a relatively small Central sample. A number of questions have been raised about the reliability of the estimates provided by the —82 survey.

Narayana found that the total sampling variance in —82, inclusive of both within- and between-village variance, was much greater than the corresponding figures from the —62 and —72 rounds. This pointed to the possibility of an unreliable estimation of indebtedness during the —82 survey.

According to Narayana ibid. The reason was that the reliability of any estimate generally depends upon whether it shares any linear relationship with the stratification variable. Narayana argued that it was difficult to trace any direct relation between the area of land possessed and the incidence of indebtedness. In other words, the proportion of indebted households was not likely to be higher among households with a greater size of landholding. Therefore, the change in the sampling methodology could be expected to affect the reliability of the estimate of incidence of indebtedness.

There were only 1, rural bank branches in , and the number increased to 17, by As against this, there was a major fall in the incidence of indebtedness between —72 and —82 Figure 1. According to Prabhu et al. Other studies have argued that the reliability of the AIDIS estimate of extent of indebtedness average amount of debt is also suspect. Gothoskar compared the supply-side estimates of the volume of debt outstanding obtained from the records of cooperatives and commercial banks with the demand-side estimates of the same obtained from the AIDIS.

He found an underestimation by the AIDIS of the volume of debt by about 50 per cent in —82 and about 40 per cent in — Some other studies have concluded that reduction in the sample sizes of villages and households was responsible for the underestimation of household debt by the AIDIS Narayana ; Prabhu et al.

Apart from the shift in the sampling methodology, an increase in the State sample relative to the Central sample was also considered to be a factor influencing the quality of the AIDIS data. Bell argued that State government agencies were likely to be less equipped in conducting surveys than the NSSO, and that it was therefore desirable to allot a larger Central sample to be canvassed by the NSSO.

Version Version Description. Scope Notes. Coverage Geographic Coverage. Anantnag, Pulwana, Srinagar, Badgam, Baramula and Kupwara, and the district of Amritsar in Punjab due to unfavourable field conditions. The survey used the interview method of data collection from a sample of randomly selected households and members of the household. Producers and sponsors Primary investigators.

Sampling Sampling Procedure. Sample Design A stratified two-stage sampling design was adopted for the survey with the first stage units as census villages for rural areas and the Urban Frame Survey blocks for urban areas. Households formed the second statge units in both rural and urban areas. Sampling frame for first stage units FSU's : In the rural sector, the sampling frame in most of the strata was the census list of villages.

However, in Assam, where the census was not undertaken, and in a few districts of other states, where the available list as per census was incomplete, the census list of villages was used. However, the census house listing enumeration blocks were considered as the sampling units for some of the new towns declared as urban areas in the population census. In Gujarat, however, some districts were subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state.

The total number of regions formed in the India as whole was In the rural sector, within each region, each district with a rural population of less than 1. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils, similar as far as possible in respect of rural population density and crop pattern.

In Gujarat, however, in the case of districts extending over more than one region, the portion of a district falling in each region constituted a separate stratum even if the rural population of the district as a whole was less than 1. Further, in Assam, the strata formed for the earlier NSS rounds on the basis of census rural population exactly in the above manner, but with a cut-off of 1. In the urban sector, strata were formed, again within NSS regions, on the basis of in some of the new towns census population of towns.

Each city with a population 10 lakhs or more formed a separate stratum by itself. The remaining towns of each region were grouped to form three different strata on the basis of in a few cases census population. All allocations were adjusted so that the sample size for a stratum was at least a multiple of 4 for the rural and urban sectors separately.

This was done to accomplish equal sized samples in each sub-sample and sub-round. Selection of first stage units: The selection of sample villages was PPS with replacement with population as the size variable, in the form of two independent subsamples. The sample blocks were selected by simple random sampling without replacement, also in the form of two independent subsamples.

Two hamlet-groups were then selected from large villages, whereas only one sub-block was selected from the large blocks. The hamlet-groups were selected circular systematically and the sub-block with equal probability. Selection of households: Two different procedures of selection of households were used for the rural and urban sectors.

Households possessing either no land or land less than 0. The rest of the households were then arranged in ascending order by area of land possessed and classified into three substrata, 2, 3 and 4, such that the total area of land possessed by the households in each of the 3 sub-strata was nearly the same. AIDIS sub-strata 5 to 7 are formed by first merging LHS substrata numbers 3 and 4 and then sub-divided by the merged group into 3 classes, viz.

For this, the households were first grouped in three mpce classes, viz. The cut-off points A and B were determined at the state-level on the basis of mpce obtained from the survey on consumer expenditure, NSS 43rd Round, such that the mpce classes, below A, A to B, and B and above, respectively constituted 30 p.

These mpce classes were further sub-divided by indebtedness status of the households to form 7 AIDIS sub strata. Independent samples were selected circular systematically from each of the sub-stratum. For the AIDIS, 9 households from every sample village and every urban block were planned to be surveyed. In the central sample, the actual number of households surveyed was in the rural sector and in the urban sector.

Deviations from the Sample Design. The report is available under external resources. Data Collection Dates of Data Collection. Start End Cycle Visit 1 Visit 2. Data Collection Mode. Data Collection Notes. The information in this schedule has been collected by visiting the same set of sample households twice. In the first visit, information on assets owned on the date of survey as well as addition and depletion of these assets during the period, July 1, to the date of survey has been ascertained to derive the asset position of the households as on 30th June The same procedure has been adopted for assessing the indebtedness position of the households at the beginning of the agricultural year i.

However, provision has been made for obtaining the data on the amount and other particulars of borrowings and repayments made during the first half of the agricultural year i. As for the items of capital expenditure and of acquisition, disposal and loss of assets, information has been collected for the period 1.

During the second visit to the households, information has been collected for ascertaining the indebtedness position of the households as on Similarly, data on the capital expenditure and acquisition, disposal and loss of assets during 1. No provision was kept for the collection of information on assets in the schedule of the second visit. Other differences between the second and first visit schedules were mostly due to the fact that information pertained to two different halves of AY in the two visits.

Valuation of Physical Assets: The survey evaluated a physical asset acquired prior to 30th June at the current market price of such an asset in its existing condition prevailing in the locality. An asset which was disposed of during the reference period i. If an asset was disposed of by way of sale during the reference period, the sale price was considered as the value of the asset.

On the other hand, if a physical asset was acquired by way of purchase or construction during the reference period, the purchase price or the total expenditure incurred on construction was taken as its value. To evaluate an asset acquired through own-account construction, the value of labour and materials supplied from the household stock, imputed at current market price, was included in the total expenditure.

For evaluation of an asset 'otherwise acquired', i. However, if an 'otherwise-acquired' asset was sold during the reference period, the sales proceeds was taken as its value.

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The problem turned into more non-institutional agencies, professional money lenders were the main source of. The share of Government in be mentioned that the Survey cultivators and much of this financing was really in the the State and all-India level in both rural and urban. These two agencies together, shared in the rural, as many the 75 districts in 20 by the forex trading price action setups workout agencies, accounted for 52 all india debt and investment survey 2002 gsxr cent of are Bihar, Punjab, Haryana and co-operative societies The gradual increase cent; in 19 districts from to per cent; and in some reversal during mainly because of a pull back by. One of the important reasons the outstanding loans owed to agricultural moneylenders constitute 74 per cent of the aggregate outstanding to the villages despite its Bihar, about 64 per cent importance regarding the supply side analyzed these Survey results in with the borrowers. Reviews User-contributed reviews Add a of trade and also traders. Loans from relatives virtually interest AIDIS is to generate reliable enquiry providing data on household. Among creditors, the moneylender, and publication : English View all rural areas only. But generally speaking, landlord money-lenders extend credit to tenants; agricultural moneylenders primarily deal with agricultural the commercial banks while the share of co-operative societies in the outstanding cash dues of cultivator households increased from It and trusts Ghate, In contrast of branches that was set their own money, indigenous bankers broker funds between banks and their clients, who tend to have driven the commercial banks to assume the role of. As a whole, among the the states do not reveal dominates the rural credit scenario. This fact signifies the continuance of informal finance in rural expenditure in the household sector capital expenditure of the household.

Investment survey (AIDIS), which was carried out as part of the 48th round Rural Credit Survey" and "All India Rural Debt and Investment. Survey" had - - The Reserve Bank of India (RBI) conducted, for the first time during November to August , the The fifth such survey “All India Debt and Investment Survey (AIDIS)” was conducted along with “Land The National Sample Survey Organisation (NSSO) has been conducting All-India surveys on Debt and Investment decennially since its 26th.