Lok Sabha Question No. 2941 (UNSTARRED), answered on 2023-12-20
Subject: Grants-in-aid to NGOs
Ministry: STATISTICS AND PROGRAMME IMPLEMENTATION
Asked by: Locket Chatterjee

## GOVERNMENT OF INDIA

## MINISTRY OF STATISTICS AND PROGRAMME IMPLEMENTATION

## LOK SABHA UNSTARRED QUESTION NO. 2941 TO BE ANSWERED ON 20.12.2023

## GRANTS-IN-AID TO NGOS

## 2941. MS. LOCKET CHATTERJEE:

Will the Minister of STATISTICS AND PROGRAMME IMPLEMENTATION be pleased to state:

- (a) whether the Ministry has provided grants-in-aid to NGOs and research institutions for special studies or surveys in the last three years, if so, the details thereof; and
- (b) the details of the amount disbursed, the names of NGOs and research institutions, State-wise including the distribution, and specifics of the studies and its outcome?

## ANSWER

MINISTER  OF  STATE  (INDEPENDENT  CHARGE)  OF  THE  MINISTRY  OF STATISTICS AND PROGRAMME IMPLEMENTATION, MINISTER OF STATE (INDEPENDENT CHARGE) OF THE MINISTRY OF PLANNING AND MINISTER OF STATE IN THE MINISTRY OF CORPORATE AFFAIRS [RAO INDERJIT SINGH]

- (a) & (b) Yes,  Sir.  The  details  of  the  amount  disbursed  to  NGOs  and research institutions in last three years, state-wise is given at Annexure
- -I.  The  details  of  the  study  conducted  and  their  outcomes  is  given  at Annexure -II.

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## ANNEXURE REFERRED TO IN REPLY TO THE LOK SABHA UNSTARRED QUESTION NO. 2941 TO BE ANSWERED ON 20/12/2023

The details of the amount disbursed to NGOs and research institutions in last three years, state wise given below in table:

| Financial Year   | Name of State             | Name of Institution                         | Details of Research Study                                                                                                                   | Grant-in Aid provided (₹ in lakhs)   |
|------------------|---------------------------|---------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|
| 2020-21          | 1. West Bengal            | Indian Statistical Institute (ISI), Kolkata | Developing an Appropriate Structure Preserving Estimation (SPREE) Method for Estimation Domain-Level Aggregates from NSSO Household Surveys | 2.56                                 |
| 2020-21          | 1. West Bengal            | Indian Statistical Institute (ISI), Kolkata | Developing an Appropriate Methodology for Estimating Proportion of Villages with Specific Infrastructure Facility                           | 2.45                                 |
| 2021-22          | 1. West Bengal            | Indian Statistical Institute (ISI), Kolkata | Developing an Appropriate Structure Preserving Estimation (SPREE) Method for Estimation Domain-Level Aggregates from NSSO Household Surveys | 2.90                                 |
| 2021-22          | 1. West Bengal            | Indian Statistical Institute (ISI), Kolkata | Developing an Appropriate Methodology for Estimating Proportion of Villages with Specific Infrastructure Facility                           | 2.77                                 |
| 2021-22          | 2. Odisha                 | Development Initiative, Odisha              | The Impact of Remittances from Migrant Workers on their Families and Local Economy Development in Odisha                                    | 8.33                                 |
| 2022-23          | No Fund has been released | No Fund has been released                   | No Fund has been released                                                                                                                   | No Fund has been released            |

## ANNEXURE REFERRED TO IN REPLY TO THE LOK SABHA UNSTARRED QUESTION NO. 2941 TO BE ANSWERED ON 20/12/2023

## 1. Funds released to Indian Statistical Institute (ISI), Kolkata, West Bengal.

## Details of the Study:

Developing an Appropriate Structure Preserving Estimation (SPREE) Method for Estimation DomainLevel Aggregates from NSSO Household Surveys.

## Conclusion of the Study:

Estimation of aggregates in NSS precedes the estimation of rates and ratios. Nevertheless, NSS has long  shielded  its  estimates  of  aggregates  of  households  and  population  of  various  categories  from public  view,  at  least  in  its  presentation  of  survey  findings,  to  avoid  coming  in  conflict  with  the accepted Census estimates. A basic objective of the exercise undertaken by this project was to arrive at a  set  of  NSS  estimates  consistent  with  the  Census  population  counts.  The  project  team  was  able  to produce estimates consistent with Census counts of both households and population, confining itself to minimal calibration. It did this by adjusting the design-based weights for the sample observations to extents varying with the size of the sample households. This was necessary to arrive at a single set of multipliers for estimating individual-and household-level parameters. Given that the width of the gap between  projections/estimates  from  the  Population  Census  and  from  NSS  surveys  tends  to  differ depending on whether it is population or number of households that is being estimated, it is imperative that this method be embodied in any calibration method adopted for NSS data. The proposed minimal calibration method should serve as a basic building block for constructing more intricate calibration methods, or model-based estimation procedures, that would meet the requirement of consistency with Population Census counts or projections of households and population.

Besides attaining consistency with the census  counts of households and population, adoption of the minimal calibration method is proposed for the following reasons:

-It is one among the simplest calibration methods that ensures consistency with the census-like counts of  households  and  population.  The  raking  of  the  survey  estimates  to  control  totals  derived  from Population  Census  figures,  in  fact,  is  a  special  kind  of  calibration  based  on  minimizing  distance  of calibration weights from the design-based weights developed by Deville et. al.(1993).

-Minimal  calibration  leaves  open  the  scope  of  its  extension  to  include  other  auxiliary  variables. However,  only  the  auxiliary  variables  which  represent  characteristics  of  households  (and  not  of individuals), such as social group, religion, and house structure type can be used for this purpose.

-Valid measures of sampling error of the calibrated estimates can be obtained by reweighting the subsamples  independently  with  the  same  control  totals  derived  from  external  sources.  The  choice  of control  totals  from  external  sources  depends,  needless  to  say,  on  their  reliability,  especially  for  the reference time point of the NSS survey.

There are, however, a few trivial and non-trivial limitations of the minimal calibration method. They are discussed below:

(a) Unlike the design-based weights, the calibrated weights cannot be determined in advance. But for adjustments necessitated by unit non-responses, the design-based weights can be worked out once the sample selection at different stages is completed. On the other hand, computation of calibrated weights has to wait till the entire survey dataset is processed and is ready for tabulation.

(b) At all-India level, the employment-related calibrated estimates are found to have marginally higher RSEs than the direct estimates. But they are expected to be freer from non-sampling biases that cause divergence of NSS estimates from Census estimates.

(c)  In  case  of  consumption related variables, however, the RSEs of calibrated estimates are in most cases marginally lower than that of the direct estimates.

(d)  The multiplier adjustment factors for the households in the size class 15+ turn out to be unduly high-ranging  from  2.5  to  4.  This  brings  about  undesirably  high  dispersion  in  the  adjusted  weights (multipliers), which in turn tends to increase the RSEs, especially when the study variable is weakly associated with the variable 'household size class'. As the share of size class 15+ in total number of households is very small (0.66% as per PC2011), the project team suggests adopting an appropriate trimming of the multiplier adjustment factors for the households of this size class. For example, the adjustment factor for the 15+ size class may be trimmed to make it equal to that for the size class '11 14'.Alternatively, the households of 15+ size class, just as the single-member households, may be left out of calibration process altogether. Trimming of the adjustment factors for the 15+ size class is not likely to affect much the estimates of population and the number of households.

(e) In all standard processes of calibration and post-stratification involving variables of interest (study variables),  the  calibrated  weights  depend  on  the  choice  of  study  variables  as  well  as  the  associated variables. Whether or not different sets of weights should be used for a survey dataset is an issue much debated upon. In fact, the issue reduces to 'whether to calibrate or not?'

(f) As for the choice of associated variables, there are a number of options. Associated variables may be chosen on the strength of their correlation with the variables of interest to improve efficiency of the calibrated estimates. The results obtained from use of alternative sets of associated variables are bound to  be  different.  For  instance,  the  choice  of  household  size  as  the  only  associated  variable  (for consistency with the population census like estimates of aggregates) will be in conflict with the choice of  social  group  as  the  only  associated  variable  (on  the  basis  of  its  high  correlation  with  the  study variable and the availability of data or reliable estimates for the reference time point of the survey). The suggestion of the project team in this regard is to use a cross-classified data of the two associated variables  as  the  association  structure,  provided  the  cross-classified  data  or  reliable  estimates  are available. In this case, estimates of the required cross-classification can be derived from the PC2001 and PC2011 published figures.

(g)  Estimating  the  number  and  size  distribution  of  households  is  a  crucial  step  for  application  of minimal  calibration.  But,  there  is  no  official  projection  of  number  of  households  and  their  sizedistribution for the survey years. It is thus necessary to develop a standard generalised procedure of making the required projections for both inter-censal and other years with the available datasets.

(h) In the context of (f) and (g) above, the project team suggests exploring other datasets for deriving more control totals for a specific reference time  point. One such is available from the sub-annually conducted survey under the Sample Registration Scheme (SRS) of the ORGI. This is the dataset from which the official estimates of vital statistics are generated every year. But the unit-level data of the SRS are not available in the public domain.

## 2. Funds released to Indian Statistical Institute (ISI), Kolkata, West Bengal.

## Details of the Study:

Developing  an  Appropriate  Methodology  for  Estimating  Proportion  of  Villages  with  Specific Infrastructure Facility.

## The  following  short  forms  are  used  to  denote  the  seven  estimators  in  the  tables  and  figures presenting the results:

|    | estimator of proportion of villages using                  | Short form                            |
|----|------------------------------------------------------------|---------------------------------------|
| A1 | Design-based (PPS) estimators at the domain level          | Ratio_est_state or PPSWR              |
| A2 | Design-based (PPS) estimators at the region level          | SR-wise reweighted or rwt-SR          |
| A3 | Design-based (PPS) estimators at the stratum level         | District-wise reweighted or rwt- dist |
| A4 | SRS estimator for sub-stratum 1 and PPS for the rest       | SRS in SS1 or SRS-SS1                 |
| B1 | Logistic model-based estimates at district (stratum) level | District Logistic or log-dist         |
| B2 | Logistic model-based estimates at region level             | SR Logistic or log-SR                 |
| B3 | Logistic model-based estimates at domain (state) level     | State Logistic or log-state           |

Note:  RSE = Relative Standard Errors; and RB= Relative Bias

## Conclusion of the Study:

The results of applying the estimators on the experimental population are summarized in Table 6.1. It shows for each estimator, the number of states (out of 20) with the least RSE among the 7 estimators separately  for  the  10  selected  village  facilities.  The  sum  of  each  row  is  20  (the  number  of  'large' States), when the minimum RSE is unique for the facility. In some cases, where the minimum is not unique, the sum exceeds 20. The final conclusions emerging from the study are summarized below.

Table 6.1: Number of States (out of 20) with least RSEs of the 7 estimators by 10 village facilities

| Village Facility   | All-India Proportio n (Rd) %   | Number of States by estimator   | Number of States by estimator   | Number of States by estimator   | Number of States by estimator   | Number of States by estimator   | Number of States by estimator   | Number of States by estimator   |
|--------------------|--------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|
| Village Facility   | All-India Proportio n (Rd) %   | Ratio_est _state                | SRS in SS1                      | District- wise reweighte d      | SR-wise reweighted              | District Logisti c              | SR Logist ic                    | State Logist ic                 |
| Bus Stop           | 40.70                          | 0                               | 1                               | 0                               | 11                              | 0                               | 3                               | 7                               |

| Navigable Waterway s     | 6.14   |   0 |   3 |   0 |   16 |   0 |   0 |   1 |
|--------------------------|--------|-----|-----|-----|------|-----|-----|-----|
| Primary School           | 78.50  |   0 |   2 |   0 |    6 |   4 |   7 |   2 |
| Secondary School         | 10.97  |   0 |   0 |   0 |   19 |   1 |   0 |   0 |
| Senior Secondary School  | 3.00   |   0 |   0 |   0 |   20 |   0 |   0 |   0 |
| Primary Health Centre    | 3.67   |   0 |   0 |   0 |   20 |   0 |   0 |   0 |
| Regd. Med. Practitione r | 8.18   |   0 |   0 |   0 |   20 |   0 |   0 |   0 |
| Post Office              | 28.46  |   0 |   0 |   0 |   17 |   0 |   1 |   3 |
| Commerci al Bank         | 11.89  |   0 |   0 |   0 |   20 |   0 |   0 |   0 |
| Power Supply             | 70.45  |   0 |   5 |   0 |    1 |   1 |   1 |  13 |
| Sum                      | --     |   0 |  11 |   0 |  150 |   6 |  12 |  26 |

## A. Evaluation based on estimation of bias and sampling error from a sample drawn from a model population

Bias

- The  estimators  A1  and  A2  are  the  least  biased  ones  among  the  seven.  A1  has  RB  between minus 4% and 3% in the vast majority of cases. A2 has RB under 5% in practically all cases.
- The model-based estimators are all subject to high order of bias, rising as parameter value falls. B1, for instance, has RB rising above 15% and going higher up as the parameter falls below 0.2.
- A4 (which uses SRS estimator for sub-stratum 1) has RB exceeding 10% in about 20% of the cases,  the  inverse  association  between  the  parameter  and  the  RB  being  weaker  for  A4  than  for  the model-based estimators.

## Sampling error

- The model-based estimators B1, B2 and B3 are found to have the least RSE in a good proportion of cases, but not when the parameter value is low. Their RSE tends to stay below 15% when the parameter exceeds 0.3, but is very high for lower values of the parameter.

- The RSEs of A1 and A3 fall with rise in the parameter and are brought down below 15% only when the parameter exceeds 0.35. But the RSE of A2 tends to stay under 15% even when the parameter value is low.
- The RSE of A2 was under 5% for 64 out of 200 parameters estimated (followed by 38 for B2), under 7.5% for 134 parameters (followed by 52 for B2), and under 10% for 177 (followed by 66 for A3).
- Out of 200 parameters estimated, A2 has the least RSE among all seven estimators in case of 150 parameters, followed by B3 (26parameters) and then B2 (12 parameters).
- All things considered, A2, the estimator using ratio-method reweighting at region level, appears to be most suitable.
- B. Evaluation based on time pattern followed by the estimators when applied to NSS data of different rounds

The pattern of round-to-round changes displayed by the estimators A1 (estimator used at present) and A2 (the estimator found most suitable on the basis of the criteria of estimated RSE and estimated RB) do not provide evidence of greater stability of the A2 over A1. This cannot, in fact, be expected, given the  level  of  sampling  error  they  are  subject  to.  Our  findings  relating  to  RSE  of  A2  quite  certainly illustrate that it is not feasible to reliably assess the magnitude of very small year-to-year changes in the  spread  of  facilities  across  a  State's  villages  from  the  village-level  data  collected  along  with  the normal household surveys of the NSS. Measuring small changes in a State's progress in this respect inevitably requires a larger sample size.

## C. Developing suitable model-based estimators

Among the estimators examined in the study, the model-based estimators are found to have the least RSE in a good proportion of cases. But, needless to say, the model-based estimators explored in the study are not suitable for estimating village characteristics under the sample design generally used by the NSS. But, the common pattern followed by the RBs and RSEs of all the three estimator indicate a determining role of bias in the mean square error. Most importantly, the strong association between the RSE and population parameter point at the possibility of building appropriate models for estimation of village characteristics that could possibly be better than the design-based estimators.

## Sum Up

Among the estimators examined in the project, A2, the estimator using ratio-method reweighting at region level, appears to be most suitable. But, even this estimator is not suitable for providing reliable estimates for the small states/ U.T.s. Further, with the currently-used sample size, it is not feasible to assess the magnitude of very small year-to-year changes even for the 'large' states.

## 3. Funds released to Development Initiative, Odisha.

## Details of the Study:

The Impact of Remittances from Migrant Workers on their Families and Local Economy Development in Odisha.

## Conclusion of the Study:

From the above findings of the study it can be concluded that internal migration  and remittances in Odisha is a household survival strategy. Major part of the remittances spent on basic subsistence need of the household in all most all the six districts of the state we have studied. In a state where there is high landlessness, dominance of small and marginal peasants, huge unemployment wide spread mass poverty, debt ridden peasants; migration and remittances for them is blessing in disguise. Remittance has lead to positive economic outcome in terms of increasing household expenditure, better economic condition,  increasing  in  saving  pattern  and  reducing  the  household  indebtedness.  This  is  a  positive form of social transformation. Remittances are household strategy to counter backwardness.

The empirical result also provides positive social impact of remittance on the households. Remittance leads  to  better  educational  facilities  and  better  access  to  health  and  medical  facilities.  Households receiving remittances feel better in the village social standing. Remittances are safety net for them.

Migration  is  an  integral  part  of  modern  capitalist  development.  Hence  migration  and  remittances impact to a great extent on changing the production relation. One noteworthy feature of the study is that even after migration and remittance the migrant households have not withdrawn their labour from the  village  labour  market.  However  as  expected  the  wage  rate  in  the  village  labour  market  has  not increased due to the migration and remittances. This is partly due to mechanisation of agriculture and partly due to labour circulation and use of seasonal migratory labour as replacement labour. However, remittances have raised the bargaining strength of the migrating and remittance receiving households. Remittances have set the labouring household free to sale their labour in the village labour market or to migrate.  Now  there  is  no  attachment  with  village  rich  peasants  or  patriarchal  ties  or  patron-client relationship. This is the dynamics of remittances in changing the rural structure.

One shortcoming of this study is that it has collected data on migrant households from the source area. So  that  what  is  happening  to  the  migrants  those  who  are  remitting  money  at  the  destination  is  not finding place in the report. Hence the situation of the remitterat destination has remained untold.

To conclude, the results of this study suggest that future research on internal migration and remittances should  collect  data  from  both  the  source  area  as  well  as  the  destination  of  the  remitter  for  better understanding of its impact on the remittances receiving households.

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