What Does the Recent Indian Consumption Behaviour Tell?

What Does the Recent Indian Consumption Behaviour Tell?

G C Manna, S K Samanta, Dipankar Coondoo

The paper presents alternative estimates of poverty lines and head count ratios for India and its states and union territories, not only with respect to the fixed all-India calorie norm, but also by considering the state-specific benchmarks. These alternative estimates are found to be much higher than the official estimates. Given the extent of divergence of such a large magnitude, it is high time to revisit the methodological issues of estimation of poverty lines and head count ratios.

The authors are thankful to the Union Ministry of Statistics and Programme Implementation for making available the household level data on consumption expenditure based on NSS 61st round required for preparing this article. The support provided by Siladitya Chaudhuri in extracting the relevant information from the full data set is acknowledged. Views expressed in this article are of the authors and not of the organisations to which they belong.

G C Manna (gc.manna1@gmail.com) is with the National Sample Survey Organisation; S K Samanta is with the Department of Statistics, Kalyani University, West Bengal and Dipankar Coondoo is with the Indian Statistical Institute, Kolkata.

1 Introduction

n Manna (2007), a set of revised estimates of average per capita daily calorie requirements for the Indian population was derived. The suggested calorie norms were rounded off to 2,290 and 2,250 calorie per capita per day for people r esiding in rural and urban areas, respectively. These correspond to calorie norms of 2,400 and 2,100 calories per day per capita for rural and urban underlying the ofﬁcial poverty lines based on which ofﬁcial estimates of incidence of poverty for India in terms of head count ratio (HCR) are made. In that paper, state-speciﬁc calorie norms based on state-speciﬁc age-sex-occupation distribution of persons were also suggested. These suggested norms were based on (i) a more realistic c lassiﬁcation of different types of work as argued by the author, (ii) the latest (1993-94 and 1999-2000) age-sex-occupation d istribution of persons, and (iii) the latest (1998) recommendations on calorie norms by the expert group of the Indian C ouncil of Medical Research (ICMR). It may be mentioned here that since a given amount of calories can result from a lternative bundles of food items consumed, corresponding to a given calorie norm there is a conditional distribution of monthly per capita consumption expenditure (MPCE) and a lower prediction limit of MPCE corresponding to that given amount of calories may be deﬁned as the lower limit of the MPCE interval such that a household selected at random from the universe of all households having the given calorie i ntake will have an MPCE in that interval with a given probability. The present paper inter alia focuses on setting state-level lower p rediction limits of household level MPCE and the corresponding lower conﬁdence limits of average MPCE required to achieve the suggested state-speciﬁc calorie norms.1 These threshold limits have been worked out by analysing the household level consumption expenditure data collected in the latest quinquennial round of the National Sample Survey (viz, NSS, 61st round, 2004-05).2 The percentages of persons below the said threshold MPCE limits have also been worked out for i ndividual states/UTs and compared with the corresponding o fﬁcial estimates of HCR.

The paper is organised as follows. Section 2 brieﬂy describes the data and the related methodology of estimation of household MPCE and per capita daily calorie intake used in the present analysis. Section 3 discusses the methodology adopted for setting the lower prediction limits of household level MPCE and corresponding lower conﬁdence limits of average MPCE associated with the state-speciﬁc calorie norms.

august 8, 2009 vol xliv no 32

Whether the given household level data on MPCE follow a log-	These various MPCE levels have then been compared with the of
normal distribution – an assumption made in Section 3 – has	ﬁcial estimates of poverty lines. Section 6 then presents state
been examined in Section 4. Section 5 presents state-speciﬁc es	wise alternative estimates of the proportion of persons below dif
timates of threshold limits of household MPCE and average MPCE	ferent MPCE levels calibrated in Section 5 and compare their di
as well as estimated average MPCE corresponding to state-speciﬁc	vergences with the corresponding ofﬁcial estimates of HCRs. The
calorie norms. In addition, Section 5 also gives alternative esti	paper is concluded in Section 7.
mates of state-speciﬁc poverty lines associated with the calorie
norm underlying the ofﬁcial poverty lines (viz, 2,400 and 2,100	2	Data Set and Its Limitations
calories for the rural and urban populations, respectively) as well	As already mentioned, for the present exercise, we have used NSS
as the suggested calorie norm (viz, 2,290 and 2,250 calories for	61st round household level consumption expenditure data. The
the rural and urban populations, respectively) based on the cur	survey covered the whole of the Indian Union except a few inacces
rent data set (i e, the NSS 61st round data for the year 2004-05).3	sible areas of the country.4 This data set relates to a representative
Table 1R: Results of Log Normality Test of the Household MPCE Distribution	Table 1U: Results of Log Normality Test of the Household MPCE Distribution
for the Rural Areas	for the Urban Areas
State/UT	PCCR*	Number of Sample	K	Value of χ2 for the	State/UT	PCCR*	Number of Sample	K	Value of χ2 for the
		Households		Truncated Data Set			Households		Truncated Data Set
		Full	Truncated		Observed	Critical Value			Full	Truncated		Observed	Critical Value
		Data Set	Data Set		Value	(α = 0.01)			Data Set	Data Set		Value	(α = 0.01)
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Andhra Pradesh	2,425	5,555	142	8	10.637	15.087	Andhra Pradesh	2,256	2,876	76	8	3.101	15.087
Arunachal Pradesh	2,161	1,503	47	5	3.004	9.210	Arunachal Pradesh	2,158	540	61	5	11.709**	9.210
Assam	2,252	3,350	144	6	3.221	11.345	Assam	2,197	900	64	8	8.091	15.087
Bihar	2,238	4,354	184	8	13.606	15.087	Bihar	2,185	1,398	87	8	7.156	15.087
Chhattisgarh	2,312#	1,997	48	5	0.729	9.210	Chhattisgarh	2,233#	799	56	6	5.019	11.345
Goa	2,332	160	29	4	0.477	6.635	Goa	2,295	238	43	5	2.659	9.210
Gujarat	2,335	2,320	56	6	6.083	11.345	Gujarat	2,256	1,955	60	6	0.717	11.345
Haryana	2,207	1,680	53	4	14.225**	6.635	Haryana	2,232	1,040	36	5	5.055	9.210
Himachal Pradesh	2,244	2,143	97	7	9.676	13.277	Himachal Pradesh	2,227	400	32	5	0.852	9.210
Jammu and Kashmir	2,217	1,882	33	5	10.473**	9.210	Jammu and Kashmir	2,163	884	48	6	2.864	11.345
Jharkhand	2,238#	2,379	95	7	4.430	13.277	Jharkhand	2,185#	1,040	30	4	4.515	6.635
Karnataka	2,372	2,880	62	6	25.397**	11.345	Karnataka	2,263	2,227	54	6	6.860	11.345
Kerala	2,318	3,300	64	4	0.579	6.635	Kerala	2,293	1,950	36	5	1.499	9.210
Madhya Pradesh	2,312	3,838	105	9	4.367	16.812	Madhya Pradesh	2,233	2,075	57	7	3.957	13.277
Maharashtra	2,363	5,014	104	7	1.714	13.277	Maharashtra	2,277	4,993	135	9	7.463	16.812
Manipur	2,202	2,177	186	7	11.659	13.277	Manipur	2,193	1,000	72	7	2.850	13.277
Meghalaya	2,218	1,159	44	5	0.895	9.210	Meghalaya	2,167	437	28	4	2.320	6.635
Mizoram	2,187	800	52	4	4.392	6.635	Mizoram	2,201	1,112	37	4	0.314	6.635
Nagaland	2,210	960	59	5	10.566**	9.210	Nagaland	2,150	320	27	4	1.947	6.635
Orissa	2,312	3,836	108	9	9.376	16.812	Orissa	2,257	1,187	45	5	14.530**	9.210
Punjab	2,248	2,433	100	6	2.876	11.345	Punjab	2,266	1,855	55	6	5.134	11.345
Rajasthan	2,194	3,541	132	7	5.332	13.277	Rajasthan	2,209	1,630	63	7	4.013	13.277
Sikkim	2,240	920	48	5	6.555	9.210	Sikkim	2,254	200	41	5	5.976	9.210
Tamil Nadu	2,467	4,159	71	6	5.003	11.345	Tamil Nadu	2,309	4,137	82	8	7.753	15.087
Tripura	2,285	1,760	41	4	5.547	6.635	Tripura	2,195	560	35	4	1.804	6.635
Uttarakhand	2,172#	1,465	58	6	5.802	11.345	Uttarakhand	2,200#	750	28	4	2.018	6.635
Uttar Pradesh	2,172	7,868	126	9	10.828	16.812	Uttar Pradesh	2,200	3,345	103	9	6.135	16.812
West Bengal	2,277	4,988	141	7	12.791	13.277	West Bengal	2,281	2,889	80	8	12.718	15.087
Andaman and							Andaman and
Nicobar Islands	2,268	268	35	5	5.461	9.210	Nicobar Islands	2,282	359	52	5	1.295	9.210
Chandigarh	2,475	80	31	4	0.270	6.635	Chandigarh	2,226	300	41	5	4.201	9.210
Dadra and Nagar Haveli	2,281	160	22	4	0.632	6.635	Dadra and Nagar Haveli	2,274	80	27	4	1.491	6.635
Daman and Diu	2,467	80	20	4	4.409	6.635	Daman and Diu	2,270	80	33	4	1.044	6.635
Delhi	2,263	59	23	4	0.203	6.635	Delhi	2,230	1,101	57	5	2.196	9.210
Lakshadweep	2,176	70	21	4	0.642	6.635	Lakshadweep	2,212	129	25	4	0.645	6.635
Pondicherry	2,428	160	26	4	0.618	6.635	Pondicherry	2,313	560	32	4	0.060	6.635
All-India	2,286	79,298					All-India	2,249	45,346
* PCCR: State-specific average daily per capita calorie requirement, Manna (2007).	* PCCR: State-specific average daily per capita calorie requirement, Manna (2007).
# PCCR for Chhattisgarh, Jharkhand and Uttaranchal have been respectively taken as the same	# PCCR for Chhattisgarh, Jharkhand and Uttaranchal have been respectively taken as the same
for those of earlier undivided Madhya Pradesh, Bihar and Uttar Pradesh.	for those of earlier undivided Madhya Pradesh, Bihar and Uttar Pradesh.
k: Number of mutually exclusive classes of MPCE formed to perform chi-square test to the	k: Number of mutually exclusive classes of MPCE formed to perform chi-square test to the
truncated data set.	truncated data set.
** Hypothesis that MPCE follows lognormal distribution gets rejected.	** Hypothesis that MPCE follows lognormal distribution gets rejected.
Economic & Political Weekly		august 8, 2009	vol xliv no 32	39

sample of 79,298 rural households and 45,346 urban house-(institutional) and durable goods, and “last 30 days” for remainholds, spread over a stratiﬁed random sample of 7,999 villages ing items of consumption. Here we have used data for last 30 and 4,602 urban blocks, covered in the central sample.5 For days for all item groups, including the ﬁve infrequently purd etails of sample design, etc, see NSS Report Number 508 chased item groups. For each household, MPCE is obtained by (December 2006): Level and Pattern of Consumer Expenditure, dividing the aggregate value of monthly consumption expendi2004-2005. ture of the household by the corresponding household size (i e,

In the survey, expenditure incurred by a household on the number of persons normally living together and taking food domestic consumption during the reference period was con-from a common kitchen). Henceforth, the term MPCE will be sidered as the household’s consumption expenditure. The refer-used to refer to MPCE based on the uniform reference period of ence periods used for collection of consumption data were both last 30 days. “last 30 days” and “last 365 days” for relatively infrequently pur-Apart from the value of total consumption, the total quantity chased items, viz, clothing, footwear, education, medical care of consumption during the last 30 days for each of the detailed

items of food, pan, tobacco and intoxi-

Table 2R: Threshold MPCE Limits and Alternative Poverty Lines for the Rural Areas (2004-05)		cants was also collected at the house
State/UT	Threshold MPCE Limits (Rs) Associated with	Alternative Poverty Line (Rs) Based on Current (NSS: 2004-05) Data	Official Poverty Line* (Corresponding to	hold level in the survey. Converting
	SPCCR# as Per LSM	and Different Methods and Calorie Norms	Fixed All-India	these quantities into calorie levels and
	Lower Prediction	Lower Confidence	As Per LSM and Associated	As Per IIM and Associated	As Per IIM and Suggested	As Per IIM and Official	Calorie Norm of	aggregating over items, total calorie
	Limit of Household MPCE	Limit of Average MPCE	with SPCCR (PL1)	with SPCCR (PL2)	Fixed All-India Calorie Norm	Fixed All-India Calorie Norm	2,400 Cal)	intake during last 30 days for each
	(1-α = 0.99)	(1-α = 0.99)			of 2,290 Cal	of 2,400 Cal		sample household is obtained. In our
(1)	(2)	(3)	(4)	(5)	(PL3)(6)	(PL4)(7)	(8)	analysis, we have used these house-
Andhra Pradesh	258.13	656.67	772.18	966.56	784.20	932.78	292.95	hold calorie intake data. The conver-
Arunachal Pradesh	255.07	599.27	742.85	655.24	715.57	767.02	–	sion factors used for converting quan

tities consumed to calorie intake are

Assam 323.96 609.08 667.79 603.62 621.69 748.00 387.64

Bihar 226.65 441.98 484.22 468.22 492.55 538.81 354.36 reproduced from the NSS report in

Chhattisgarh 190.39 447.60 552.44 567.05 554.73 616.30 322.41 Annexure I. Per capita daily calorie

Goa 492.23 1,155.74 1516.36 ** ** ** 362.25 intake (PCCI) has been calculated by

Gujarat 233.82 643.15 825.16 915.39 886.82 956.66 353.93

Haryana 232.99 675.25 895.11 682.66 728.20 788.56 414.76

Himachal Pradesh 276.57 622.23 722.51 645.95 674.24 741.89 394.28

Jammu and Kashmir 292.47 647.34 810.17 648.21 701.19 781.02 391.26

Jharkhand 236.98 457.48 512.37 530.34 562.99 626.81 366.56

Karnataka 160.33 605.92 862.02 945.58 766.85 1,018.73 324.17

Kerala 426.56 956.16 1,139.31 1,259.72 1,202.42 1,427.51 430.12

Madhya Pradesh 213.84 494.34 575.85 622.67 611.08 729.51 327.78

dividing the household calorie intake by the factor “30 multiplied by household size”. Needless to mention, our results and the conclusion in this paper are subject to the limitations, if any, of this household level data set on MPCE and PCCI.

Maharashtra 224.16 650.92 807.83 1,059.18 828.35 1,194.66 362.25

Manipur 300.99 590.67 647.63 ** ** ** –

Meghalaya 431.95 751.47 852.50 1,074.82 1,216.32 1,432.51 –

Mizoram 374.91 820.04 982.70 ** ** 728.57 –

Nagaland 406.95 959.95 1,166.00 1,512.15 1,775.10 2,136.65 –

Orissa 188.32 449.01 526.52 487.96 470.22 539.70 325.79

3 The Methodology of Analysis

For a given population of households, the HCR measures the proportion of persons having MPCE below a given poverty line, where the poverty line is

Punjab 297.17 768.78 924.98 767.54 801.27 890.20 410.38 speciﬁed as the estimated average

Rajasthan 285.81 561.50 623.08 558.79 602.33 657.98 374.57 MPCE corresponding to some given per

Sikkim 288.76 740.42 945.26 1232.97 1,348.76 1,603.48 – capita calorie norm. For convenience

Tamil Nadu 267.76 685.47 843.75 1,553.41 956.58 1,290.31 351.86 of discussion, let us treat PCCI and

Tripura 304.09 576.69 682.56 1,019.98 1,029.08 1,229.30 – MPCE as a pair of jointly distributed Uttarakhand 225.02 480.88 568.59 568.19 608.34 725.35 478.02

random variables and denote them

Uttar Pradesh 170.58 433.61 512.81 468.76 522.46 569.24 365.84

West Bengal 238.99 589.21 686.82 617.06 624.48 711.61 382.82

Andaman and Nicobar islands 249.02 852.37 1,264.99 1,366.48 1,426.89 1,728.94 –

Chandigarh 461.07 919.07 1122.35 ** ** ** –

Dadra and Nagar Haveli 358.95 937.55 1317.32 ** ** ** 362.25

Daman and Diu 704.68 1687.12 2355.35 ** ** ** –

Delhi 424.86 1080.81 1505.18 ** ** ** 410.38

as, X and Y respectively. Formally, in the Indian ofﬁcial procedure the poverty line is deﬁned to be a value z of Y such that E(X/Y = z) = x*, where x* is the given per capita calorie norm. Operationally, based on a bivariate data set of observed MPCE class-wise

Lakshadweep 339.22 762.88 1011.90 ** ** ** – means of PCCI and MPCE, the X on Y

Pondicherry 339.70 801.32 1,060.72 1,170.37 1,051.08 1,146.17 – regression line would be estimated and

# SPCCR: State-specific per capita daily calorie requirements as in Manna (2007) and presented in Table 1R . using that z corresponding to x* would

* Source: Press Note dated March 2007 by the Planning Commission. ** Not presented due to doubtful fluctuations in average PCCI over MPCE classes as per Report Number 513, NSS 61st round. be estimated by inverse interpolation. In

august 8, 2009 vol xliv no 32

actual practice, ofﬁcial poverty lines for India for the base year 1973-74 were ﬁrst calibrated following this procedure using NSS 28th round consumption expenditure data. Poverty lines at current prices for subsequent years are being derived by deﬂating the base year poverty lines by suitable consumer price index numbers.

Note that, given x*, the conditional distribution of Y has a prediction interval (y*1-α/2, y* α/2) of value of Y such that probability (y* ≤ Y ≤ y* /X = x*) = 1 – α. In other words, a household

1–α/2 α/2

with observed PCCI = x* will have MPCE in the prediction interval with probability (1 – α). Empirically, given a small PCCI interval (x* ± δ), δ > 0 and small, suppose (xi, yi;i = 1,2,...,n) is the set of sample observations. Suppose the conditional distribution of ln(Y) (= Y0) given ln(X) (= X0) in the

less than perfect correlation between PCCI and MPCE, one has the following inequality:

1 (x*0 – x0 )2[^y*0 –tα/2, n – 2 σ^√(1 + n+∑ (x0i – x0 )2)]≤ [^y*0 – tα/2, n – 2 σ^√(n +∑ (x0i – x0 )2)] ≤ z0 1 (x*0 – x0 )2

or equivalently, the lower prediction limit of household MPCE (i e, Y) is less than or equal to the lower conﬁdence limit of the conditional mean E(Y/X = x*), which in turn is less than or equal to the deﬁned poverty line z. Note that if the variance of the conditional distribution of Y0 given X0 = x*0 (^σ2 is a measure of which) is not large, the lower prediction limit of Y, the lower conﬁdence limit of E(Y/X = x*) and z will be close. A more important issue is the following: Suppose, for a later year ^ z is the poverty line at current prices arrived at by deﬂating the base

corresponding interval is normal with Table 2U: Threshold MPCE Limits and Alternative Poverty Lines for the Urban Areas (2004-05)

E(Y0 /X0 = x0 ) = α + βx0 (= ^μ, say) and the estimated linear regression equa	State/UT	Threshold MPCE Limits (Rs) Associated with SPCCR# as Per LSM	Alternative Poverty Line (Rs) Based on Current (NSS: 2004-05) Data and Different Methods and Calorie Norms	Official Poverty Line* (Corresponding to Fixed
tion is ^y0 = a + bx0, a and b being the ordinary least squares estimates of α and β. The (1 – α)% interval estimate of		Lower Prediction Limit of Household MPCE (1-α - 0.99)	Lower Confidence Limit of Average MPCE (1-α=0.99)	As Per LSM and Associated with SPCCR (PL1)	As Per IIM and Associated with SPCCR (PL2)	As Per IIM and Suggested Fixed All-India Calorie Norm of 2,250 Cal	As Per IIMand Official Fixed All-India Calorie Norm of 2,100 Cal	All-India Calorie Norm of 2,100 Cal)
E(Y0 /X0 = x0 ), where x0 = ln(x), is [^y 0 ± tα/2,n–2 ^σ 1 (x* 0 – x0)2 + n ∑ (x0i – x0 )2√( )], where ^y0 = a + bx* 0, ^σ is the estimated equa	(1) Andhra Pradesh Arunachal Pradesh	(2) 273.21 306.92	(3) 822.99 736.71	(4) 1,058.04 897.35	(5) 1,057.78 **	(PL3)(6) 1,042.35 **	(PL4)(7) 856.44 **	(8) 542.89 –
tion standard error and is estimated as	Assam	223.47	726.69	974.41	1,056.14	1,104.02	922.32	378.84
^σ = (∑ y0i 2 – a ∑y0i – b ∑x0i y0i )/(n – 2), x0 = 1 n ∑x0i is the sample arithmetic	BiharChhattisgarh	182.99 211.66	520.21 664.60	649.55 895.64				435.00 560.00
mean of x0i ’s, i e, of ln(xi)’s; i = 1,2,...,n;	Goa	420.48	1,294.43	1,784.21	**	**	**	665.90
and tα/2,n – 2 is the α / 2 % critical value of the t-distribution with (n – 2) degrees of freedom. The corresponding (1 –α)% prediction interval of Y0 is given by [^y * 0 ± tα/2,n–2 ^σ 1 (x* 0 – x0 )2(1 + + n ∑ (x0i – x0 )2√ )] (Johnston and Dinardo 1997, Chapter 1,	GujaratHaryanaHimachal Pradesh Jammu and Kashmir JharkhandKarnatakaKerala	327.99 331.90 339.38 313.95 193.56 382.86 321.32	1,060.21 872.14 877.14 757.82 492.46 1,045.69 1,021.35	1,428.02 1,155.11 1,181.83 945.47 668.95 1,341.97 1,456.57	1,566.06 1,342.38 932.06 861.88 725.51 1,473.56 1,670.11	1,554.31 1,377.06 987.58 965.89 853.26 1,452.04 1,610.68	1,260.62 1,101.15 ** 722.32 602.19 1,179.53 1,095.12	541.16 504.49 504.49 553.77 451.24 599.66 559.39
Section 1.5.3 for these results). Once	Madhya Pradesh	202.97	672.82	920.09	**	**	869.18	570.15
the lower prediction limit of Y0 /X0 = x * 0	Maharashtra	312.81	1,178.88	1,556.12	2,698.48	2,520.74	1,746.46	665.90
and the lower conﬁdence limit of	Manipur	303.40	611.44	701.36	**	**	636.37	–
E(Y0 /X0 = x * 0) are obtained, the corre-	Meghalaya	442.11	981.68	1,258.38	1,901.24	2,181.82	1,675.17	–
sponding threshold MPCE limits (i e,	Mizoram	400.60	1,031.17	1,350.00	**	**	**	–
lower prediction limit of household	Nagaland	716.60	1,169.25	1,340.54	**	**	**	–
MPCE (Y) associated with the statespeciﬁc calorie norms x * and the lower conﬁdence limit of average MPCE, i e, E(Y/ X = x* ), can be easily derived by	OrissaPunjabRajasthanSikkimTamil Nadu	143.50 374.52 243.50 326.08 261.57	513.60 968.35 672.52 912.41 1,023.35	747.26 1,217.92 854.06 1,216.34 1,430.28	810.57 1,247.95 851.42 1,654.84 1,723.18	805.13 1,220.73 1,016.19 1647.81 1587.32	566.23 944.52 761.36 1,334.74 1,178.66	528.49 466.16 559.63 – 547.42
taking antilog. Estimated average MPCE	Tripura	261.15	802.63	1,135.60	1,009.67	1,056.45	949.21	–

associated with the state-speciﬁc Uttarakhandcalorie norm x * is obtained as expUttar Pradesh	207.53 195.40	714.59 629.55	1,112.35 808.52	869.72 719.95	942.14 777.11	724.16 **	637.67 483.26
^ + ^σ2 2[ μ ] . West Bengal	294.31	911.80	1,182.78	1,541.83	1,479.05	1,160.95	449.32
It should be straightforward to Andaman and
Nicobar Islands	562.70	1,559.52	2,021.18	2,249.57	2,117.09	1,271.14	–

see that if PCCI and MPCE are perfectly correlated, both the intervals deﬁned above collapse to the point

^

y* = a + bx*0 = z0, where z0 = ln(z). In other words, in such a case, the rela-

Chandigarh

Dadra and Nagar Haveli

Daman & Diu

Delhi

Lakshadweep

311.60	1,025.65	1,454.85	1,361.52	1,397.64	1,206.19	–
534.36	1,355.60	1,827.61	3,400.92	2,638.24	1,398.01	665.90
409.38	1,349.92	2,014.29	2,569.21	2,480.64	1,429.88	–
373.85	1,117.94	1,472.32	1,406.31	1,445.95	1,159.94	612.91
282.83	734.27	1,010.87	749.83	769.18	710.56	–

tionship between the calorie norm Pondicherry 253.53 709.18 978.13 1,472.81 1,363.23 1,065.38 –

and the corresponding poverty line # SPCCR: State-specific per capita daily calorie requirements as in Manna (2007) and presented in Table 1U.

* Source: Press Note dated March 2007 by the Planning Commission. is exact. In a more realistic case of ** Not presented due to doubtful fluctuations in average PCCI over MPCE classes as per Report Number 513, NSS 61st round.

Economic & Political Weekly

year poverty line by a consumer price index number. How would

^

z compare with the corresponding z, the lower prediction limit of Y and the lower conﬁdence limit of E(Y /X = x*) for that later year? If the consumer price index number used for deﬂating the base year poverty line is appropriate, it is easy to see that for later years

^

z would be close to the other three quantities if the variance of the conditional distribution of Y given X = x* does not change much over time. In other words, any observed large discrepancy of these four quantities may be due to use of an inappropriate consumer price index number for deﬂation or temporally rising variance of the conditional distribution of Y given X = x* or both.

4 Test of Log Normality of the MPCE Distribution

The derivation of the lower prediction limit of household MPCE

(Y) and the lower conﬁdence limit of average MPCE for a given calorie norm or PCCI (X), as discussed in the preceding section, involves the assumption that the conditional distribution of Y/X = x* follows lognormal distribution. In order to test the empirical validity of this assumption, for each state/UT x sector (i e, rural/urban), we consider the household level truncated data set of (X, Y) with X falling within the class interval (x* ± δ), δ > 0 and small, where x* is the state- and sector-speciﬁc average daily per capita calorie requirements as suggested in Manna (2007). Application of the Pearsonian Chi-square test of normality to

Table 3R: Proportion of Persons Below the Threshold MPCE Limits and Alternative Poverty Lines for the Rural Areas (2004-05)

State/UT Proportion of Persons (%) Below Head Count Ratios (HCR)/Proportion of Persons (%) Below the Threshold MPCE Limits Associated Alternative Poverty Line

with SPCCR as per LSM Based on Current (NSS: 2004-05) Based on Official Lower Prediction Lower Confidence Data and Alternative Poverty Lines Poverty Line* Limit of Household Limit of Average PL1 PL2 PL3 PL4 (Corresponding to Fixed) MPCE (1 -α = 0.99) MPCE (1 -α = 0.99) All-India Calorie Norm of 2,400 Cal)

(1) (2) (3) (4) (5) (6) (7) (8)

Andhra Pradesh 6.3 73.2 81.8 90.8 82.5 90.0 11.2

Arunachal Pradesh 1.5 42.6 59.6 50.1 57.1 61.9 22.3

the truncated data set of ln (Y)6 reveals that ln (Y) follows normal distribution for most of the states/UTs x sector justifying the assumption of the log normality of the MPCE distribution7 for given value of X = x* . It may be seen from Tables 1R and 1U that in most cases the observed values of χ2 are found to be smaller than the critical values for the chosen level of signiﬁ-

Assam 10.0 69.0 78.4 68.1 71.0 85.7 22.3 cance α = 0.01.

Bihar 6.0 66.2 74.7 71.7 76.2 83.0 42.1

Chhattisgarh 11.0 73.8 84.2 85.4 84.4 88.3 40.8 5 Threshold Limits of MPCE and

Goa 18.1 74.4 77.1 ** ** ** 5.4 Alternative Poverty Lines

Gujarat 2.2 68.7 82.8 87.6 86.7 88.8 19.1

Haryana	0.6	51.2	71.8	52.3	56.8	62.3	13.6 average MPCE, given a calorie norm, are
Himachal Pradesh	1.1	47.2	60.3	50.7	55.0	62.1	10.7 random variables by themselves having a
Jammu and Kashmir	0.9	45.8	65.0	50.0	54.8	62.3	4.6
Jharkhand	7.9	68.8	78.1	80.1	83.8	88.6	46.3 probability distribution, it would be of in-
Karnataka	1.8	80.8	93.2	94.8	90.0	95.6	20.8 terest to know the corresponding threshold
Kerala	12.9	65.9	75.4	76.2	76.2	76.3	13.2 MPCE limits (i e, lower prediction limit of
Madhya Pradesh	10.5	72.7	82.2	85.3	84.6	90.5	36.9 household MPCE and lower conﬁdence limit
Maharashtra	4.7	75.1	84.6	92.2	85.6	94.3	29.6	of average MPCE associated with the given
Manipur	0.1	55.9	67.9	**	**	**	22.3	per capita calorie norm). These threshold
Meghalaya	7.4	74.9	85.3	95.0	97.5	97.8	22.3	limits should indicate the minimum values
Mizoram	2.7	65.8	81.1	**	**	52.9	22.3

Since both household MPCE and estimated

Nagaland	0	59.9	75.6	77.7	79.3	81.5	22.3 calorie norm. We have derived these thresh-
Orissa	16.6	73.3	81.9	78.1	76.1	82.9	46.8 old MPCE limits associated with the state-
Punjab	1.2	56.7	70.2	56.6	59.8	68.2	9.1 speciﬁc per capita daily calorie require-
Rajasthan	5.2	60.1	69.7	59.5	67.0	74.1	18.7 ments (SPCCR) for each state/UT x sector for
Sikkim	1.6	69.2	81.7	89.8	90.0	90.7	22.3
Tamil Nadu	5.9	78.7	86.9	90.6	94.8	94.6	22.8 the year 2004-05 as per the methodology
Tripura	14.2	78.3	87.9	96.6	96.7	98.0	22.3 discussed in Section 3. These estimates
Uttarakhand	0.4	41.4	56.5	56.1	62.2	75.3	40.8 corresponding to the conﬁdence coefﬁcient
Uttar Pradesh	2.9	49.0	64.9	56.5	66.2	72.4	33.4	1-α = 0.99 are presented in Tables 2R/U
West Bengal	2.7	69.5	81.2	72.8	73.7	82.7	28.6	(see columns 2 and 3) (pp 40, 41). The lower
Andaman and								prediction limits of household MPCE are
Nicobar islands	0	51.4	73.4	73.7	73.9	74.9	22.9

of MPCE required for achieving the given

Chandigarh 9.1 62.7 74.7 ** ** ** 7.1

Dadra and Nagar Haveli 38.6 85.7 91.0 ** ** ** 39.8

Daman and Diu 12.9 75.1 80.1 ** ** ** 5.4

Delhi 8.0 71.3 79.7 ** ** ** 6.9

found to be smaller than the ofﬁcial poverty lines (PL) for most of the states/UTs for both rural and urban areas. But, disturbingly enough, state-speciﬁc lower conﬁ-

Lakshadweep 0.1 27.8 43.5 ** ** ** 13.3 dence limits of average MPCE are found to be

Pondicherry 23.8 68.8 81.6 85.2 81.2 85.0 22.9 much higher than the ofﬁcial poverty lines

All-India@ 5.6 65.4 76.9 --82.7 28.3 violating the inequality mentioned in Sec

* Source: Press Note dated March 2007 by the Planning Commission.

tion 3. One reason for this could be the use

** Not presented due to doubtful fluctuations in average PCCI over MPCE classes as per Report Number 513, NSS 61st round. @ All-India estimates in columns 2-4 and 7 are derived as the weighted average of state-wise estimates for which estimates are of different state-speciﬁc calorie norms (viz, presented, with weights as the proportionate shares of the individual states/UTs in estimated total projected population for January 2005. SPCCR) in the derivation of these threshold

august 8, 2009 vol xliv no 32

MPCE limits instead of using the ﬁxed all-India ofﬁcial calorie norm (2,400, per capita per day for rural and 2,100 for urban) as is the case for estimating the ofﬁcial PL. But this argument may hold well, at least partially, with regard to only those states/UTs for which SPCCR is higher than the ofﬁcial calorie norm and not certainly for the remaining states/UTs.

We have also estimated the alternative PLs at current prices using the latest (NSS 61st round: 2004-05) data that take into account the current consumption pattern of the people. We derive four alternative PLs by jointly considering the methodo logy of

estimation and calorie norms. As regards the former, we consider two methods, viz, (1) the Least Square Method (LSM) as discussed in Section 3 which has been applied to the truncated data set, and (2) the Inverse Interpolation Method (IIM) used for the estimation of ofﬁcial PL of the base year 1973-74 applied to the full data set. Finally, four alternative PLs are derived as follows: PL1 based on LSM and calorie norm SPCCR; PL2 based on IIM and calorie norm SPCCR; PL3 based on IIM and ﬁxed all-India calorie norm (viz, 2,290 calorie for rural and 2,250 calorie for urban) proposed in Manna (2007); and ﬁnally, PL4 based on IIM and ﬁxed all-India

calorie norm of 2,400 calorie for rural and

Table 3U: Proportion of Persons Below the Threshold MPCE Limits and Alternative Poverty Lines 2,100 calorie for urban underlying ofﬁcial for the Urban Areas (2004-05)

poverty lines.

State/UT Proportion of Persons (%) Below Head Count Ratios (HCR)/Proportion of Persons (%) Below the Threshold MPCE Limits Associated Alternative Poverty Line Note that PL1 and PL2 are important since

	with SPCCR as per LSM		Based on Current (NSS: 2004-05)		Based on Official
	Lower Prediction	Lower Confidence		Data and Alternative Poverty Lines		Poverty Line*
	Limit of Household Limit of Average MPCE (1 -α = 0.99) MPCE (1 -α = 0.99)	PL1	PL2	PL3	PL4	(Corres ponding to Fixed) All-India Calorie
							Norm of 2,100 Cal)
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)

Andhra Pradesh	2.4	56.2	71.1	71.1	70.3	58.8	28.0
Arunachal Pradesh	1.0	44.4	61.0	**	**	**	3.3 PL4 are also useful for a meaningful com-
Assam	0.9	35.8	57.3	65.3	70.0	52.4	3.3	parison of the average MPCE levels across
Bihar	7.9	47.1	62.4	**	**	**	34.6	states because these poverty lines corre-
Chhattisgarh	7.2	48.0	64.7	**	**	**	41.2	spond to a common all-India basket of con-
Goa	4.4	67.3	83.1	**	**	**	21.3	sumption for each state/UT.
Gujarat	1.1	60.6	78.1	81.7	81.4	71.3	13.0	Estimates of these alternative PLs are
Haryana	3.3	49.3	67.8	75.9	77.4	65.4	15.1	presented in columns 4 to 7 of Table 2 R/U
Himachal Pradesh	1.4	32.9	51.7	37.0	40.1	**	3.4	while the ofﬁcial PL is shown in column 8 of
Jammu and Kashmir	0.2	28.7	49.9	40.5	51.7	25.0	7.9	the same table. We ﬁnd that each of the four
JharkhandKarnatakaKeralaMadhya Pradesh Maharashtra	4.3 10.7 2.8 6.1 4.1	24.5 66.3 56.7 53.3 68.2	40.4 77.5 75.7 69.8 80.6	44.5 80.8 79.9 ** 94.1	53.3 80.3 78.4 ** 94.0	34.7 71.9 60.5 66.6 84.7	20.2 32.6 20.2 42.1 32.2	alternative PLs is much higher than the ofﬁcial PL for each state/UT x sector. It is also of interest to note that for rural areas, even PL3 that corresponds to a lower (2,290) cal-
Manipur MeghalayaMizoram	0.1 0.9 0.3	37.0 45.6 48.0	54.6 62.2 70.4	90.9	93.7	42.2 81.3 **	3.3 3.3 3.3	orie norm is much higher than the ofﬁcial PL based on a higher (2,400) calorie norm. As regards PL4 derived afresh based on the
Nagaland	4.7	40.1	50.8	**	**	**	3.3	latest (2004-05) NSS data following exactly
Orissa	6.6	42.7	62.7	67.5	67.2	48.5	44.3	the ofﬁcial procedure, ideally, one would
Punjab	1.0	52.4	65.8	67.1	65.9	50.9	7.1	expect it to be closer to the ofﬁcial PL. But
Rajasthan	2.7	45.7	62.9	62.7	72.7	55.5	32.9	the ﬁndings are otherwise. In fact, for some
Sikkim	0.4	42.9	68.7	85.9	85.8	76.6	3.3	of the states/UTs, PL4 even exceeds twice the
Tamil Nadu	2.3	62.7	79.1	85.6	82.6	69.9	22.2	ofﬁcial estimates. As discussed earlier in
Tripura	1.6	53.3	72.4	66.6	68.9	63.6	3.3	Section 3, the reasons for the above differ

they take into account the SPCCR which were derived by considering the latest agesex-occupation distribution of the people in the respective states/UTs and their calorie requirements. On the other hand, PL3 and

Uttarakhand	1.4	44.0	73.5	55.8	61.0	44.7	36.5 ences could be many, viz, use of inappro-
Uttar Pradesh	5.3	48.8	64.4	56.9	62.0	**	30.6 priate consumer price index numbers for
West Bengal	3.5	54.2	68.7	79.9	78.5	67.9	14.8 deﬂation of base year PL or change in the
Andaman and
Nicobar Islands	1.1	62.7	75.5	79.7	77.3	49.1	22.2 consumption behaviour of the people,
Chandigarh	0.9	36.0	49.9	46.8	48.0	42.0	7.1 change in the variance of the conditional
Dadra and Nagar Haveli	15.8	58.0	76.8	95.5	91.5	60.4	19.1 distribution of MPCE, given the calorie
Daman and Diu	0.1	83.1	93.1	96.8	96.4	85.4	21.2	norm or some combination of these. It is
Delhi	1.2	55.2	71.2	69.3	70.4	57.3	15.2	high time that these methodological issues
Lakshadweep	3.6	24.3	45.2	25.5	26.9	22.6	20.2	are addressed.
Pondicherry	0.4	42.5	60.9	82.0	79.5	65.4	22.2
All-India@	4.0	55.4	70.9	-	-	66.2	25.7	6	Head Count Ratios

* Source: Press Note dated March 2007 by the Planning Commission.

HCR, i e, proportion of persons below a

** Not presented due to doubtful fluctuations in average PCCI over MPCE classes as per Report Number 513, NSS 61st round. @ All-India estimates in columns 2-4 and 7 are derived as the weighted average of state-wise estimates for which estimates are

given PL, is an important indicator of inci

presented, with weights as the proportionate shares of the individual states/UTs in estimated total projected population for January 2005. dence of poverty to the government for

EPWERF

august 8, 2009 vol xliv no 32

launching welfare programmes for the poor. Needless to per capita daily calorie requirements. The paper also presents emphasise, HCR should reﬂect the true poverty situation pre-state-speciﬁc alternative estimates of poverty lines and correvailing at a parti cular point of time for meaningful policy sponding HCRs not only with respect to the ﬁxed all-India formulations. In Table 3 R/U (see columns 4 to 8), we present calorie norm but also by taking into account the state-speciﬁc the HCRs corresponding to the alternative PLs as well as the of-per capita daily calorie requirements. The analysis is based ﬁcial PL for the year 2004-05. Columns 2 and 3 of the table give on the latest available large sample household consumption the proportion of persons below the threshold MPCE levels as-expenditure data collected through the National Sample Sursociated with SPCCR. vey in 61st round (2004-05). A major ﬁnding is that the alter-

We observe that during 2004-05 nearly 6% of all-India rural native poverty lines and head count ratios derived afresh based population and 4% of all-India urban population had MPCE on the latest data are much higher than the ofﬁcial estimates. smaller than the lower prediction limit of household MPCE associ-The observed difference between the two sets of estimates ated with the SPCCR. As regards the HCR, the alternative HCRs in-is very large and calls for re-visiting the methodological issues cluding the proportion of persons below the lower conﬁdence of estimation of poverty line and head count ratio. During limit of average MPCE associated with the SPCCR are found to be the early 1970s, the share of food items in total household much higher than the ofﬁcial HCR. For the year 2004-05, the consumption expenditure was about 73% for rural India ofﬁcial estimate of HCR is placed at 28.3% for rural India and at and 65% for urban India. By 2004-05 this percentage share de25.7% for urban India. As against this, the alternative estimate of clined to only about 55% for rural and 43% for urban, clearly HCR based on PL4 that adopts the same ofﬁcial calorie norm is indicating a drastic change in consumer preferences that has found to be as high as 82.7% for rural India and 66.2% for urban taken place over time. Given this, it is perhaps necessary to India.8 The divergences are alarming. switch over to a more recent base year and redeﬁne and recali

brate the base line poverty lines at the earliest. Equally impor

7 Concluding Observations

tant is the issue whether the calorie norm should continue to be One important issue of discussion of this paper is the setting up taken as the sole criterion for deﬁning and determining the of a lower prediction limit of household MPCE and lower conﬁ-poverty line ignoring altogether other dimensions of living and dence limit of average MPCE associated with the state-speciﬁc human development.

Notes

1 The lower conﬁdence limit of average MPCE for a given calorie norm is the lower limit of the conﬁdence interval of average MPCE corresponding to that calorie norm within which the estimated average MPCE will lie with a given probability.

2 NSS 61st round also collected data on employment-unemployment.

3 These poverty lines have been derived using the inverse interpolation method as adopted for working out the ofﬁcial poverty lines for the base year 1973-74. This ofﬁcial procedure is also described brieﬂy in Section 3.

4 Areas excluded in the survey were Leh (Ladakh) and Kargil districts of Jammu and Kashmir; interior villages of Nagaland situated beyond ﬁve kilometres of a bus route; and villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

5 Fieldwork for the central sample was carried out by the regular ﬁeld investigators of national sample survey organisation. There was a matching state sample for which the responsibility of data collection vested with the respective state

Annexure 1: Procedure for Estimating Calorie Equivalence with Regard to Items of Consumption*

In the present exercise nutrient contents of different items are taken largely from the book entitled Nutritive Values of Indian Foods by C Gopalan, B V Ramasastry and S C Balasubramanian, subsequently revised and updated by B S Narasinga, Y G Deosthale and K C Pant in 1991.

Generally, intake of calorie from an item is derived from the quantities reported as consumed

Economic & Political Weekly

august 8, 2009

governments and union territory administrations. The analysis in this paper is based on the data of central sample.

6 See Annexure 2 for the explanatory note on computation of χ2 from the observed household level data sets.

7 Finally, the test for log normality of the MPCE distribution failed – sometimes only marginally – in case of the rural areas of Haryana, Jammu and Kashmir, Karnataka and Nagaland and the urban areas of Arunachal Pradesh and Orissa. Thus, one has to be careful while using the results pertaining to these states x sectors.

8 Obtained as the weighted average of state-wise estimates of HCR with weights as the proportionate shares of the individual states/UTs in the total estimated projected population for January 2005. Projection ﬁgures are based on the document “Population Projections for India and States, 2001-2006,” Report of the Technical Group on Population Projection constituted by Natio nal Commission on Population (May 2006) and published by the Ofﬁce of the Registrar General of India. However, since the ﬁgures for January

by the sample households. The calorie contents of each item per unit of quantity are shown in the following table.

For some items having variable food content, average calorie contents per rupee are given in the table instead of per unit of quantity. For such items the unit has been shown as “Re”. These ﬁgures multiplied by the value of consumption give the total quantities of nutrients derived from that item.

The chart of calorie contents remains practically the same as that for the 55th round of NSS

vol xliv no 32

2005 are not available in the document, average of projected ﬁgures for October 2004 and March 2005 has been taken.

References

Government of India (1979): “Report of the Task Force on Projections of Minimum Needs and Effective Consumption Demand”, Perspective Planning Division, Planning Commission, January.

– (1993): “Report of the Expert Group on Estimation of Proportion and Number of Poor”, Perspective Planning Division, Planning Commission, July.

– (2006): “NSS Report No 508”, Level and Pattern of Consumer Expenditure, 2004-05.

– (2007): “NSS Report No 513”, Nutritional Intake in India, 2004-05.

Johnston, J and J Dinardo (1997): Econometric Methods, Fourth Edition, McGraw-Hill Companies, Inc.

Manna, G C (2007): “On Calibrating the Poverty Line for Poverty Estimation in India”, Economic & Political Weekly, Vol XLII, No 30.

(1999-2000). The only differences are those due to change in unit of collection of data, e g, “litre” in place of “glass”. For items for which the unit (as explained above) is “Re”, the fall in value of the rupee since the 55th round (1999-2000) has been adjusted by deﬂating the nutrient contents by the appropriate consumer price index (viz, CPI for Agricultural Labourers for rural and CPI for Urban Non-Manual Employees for urban areas). Thus for these items, there are two sets of nutrient content coefﬁcients – one for rural and the other for urban households.

Code	Item	Unit	Calories per	Code	Item	Unit	Calories per	Code	Item	Unit	Calories per
			Unit (kcal)				Unit (kcal)				Unit (kcal)
(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
101 Rice – PDS	kg	3460	184	Pork	kg	1140	247 Other fresh fruits (urban)	Re	8.2
102 Rice – other sources	kg	3460	185	Chicken	kg	1090	250 Coconut, copra	Kg	6620
103 Chira	kg	3460	186	Others: birds, crab, oyster,			251 Groundnut	Kg	5670
104 Khoi, lawa	kg	3250		tortoise, etc	kg	900	252	Dates	Kg	3170
105 Muri	kg	3250	190	Potato	kg	970	253	Cashewnut	Kg	5690
106 Other rice products	kg	3460	191	Onion	kg	550	254	Walnut	Kg	6870
107 Wheat/atta – PDS	kg	3410	192	Radish	kg	170	255	Other nuts	Kg	4100
108 Wheat/atta – other sources	kg	3410	193	Carrot	kg	480	256	Raisin, kishmish,
110 Maida	kg	3480	194	Turnip	kg	290		monacca, etc	Kg	3080
111	Suji, rawa	kg	3480	195	Beet	kg	430	257	Other dry fruits	Kg	3060
112 Sewai, noodles	kg	3520	196	Sweet potato	kg	1200	260	Sugar – PDS	Kg	3980
113 Bread (bakery)	kg	2450	197	Arum	kg	970	261	Sugar – other sources	Kg	3980
114 Other wheat products	kg	3460	198	Pumpkin	kg	250	262 Gur	Kg	3830
115 Jowar and products	kg	3490	200	Gourd	kg	120	263	Candy, misri	Kg	3980
116 Bajra and products	kg	3032	201	Bitter gourd	kg	400	264	Honey	Kg	3190
117 Maize and products	kg	3420	202	Cucumber	kg	130	280 Turmeric	Gm	3.49
118 Barley and products	kg	3360	203	Parwal, patal	kg	200	281	Black pepper	Gm	3.04
120 Small millets and products	kg	2615	204	Jhinga, torai	kg	170	282	Dry chillies	Gm	2.46
121 Ragi and products	kg	3280	205	Snake gourd	kg	180	283 Tamarind	Gm	2.83
122 Other cereals	kg	2615	206	Papaya: green	kg	270	284	Curry powder	Gm	0.8
139 Cereal substitutes	207	Cauliflower	kg	300	285 Oilseeds	Gm	4.5
(tapioca, jackfruit seed, etc)	kg	2090	208	Cabbage	kg	270	286	Other spices	Gm	3.6
140 Arhar, tur	kg	3350	210	Brinjal	kg	240	290	Tea : cups	no	27
141 Gram (split)	kg	3720	211	Lady’s finger	kg	350	291	Tea : leaf	Gm	0
142 Gram (whole)	kg	3720	212	Palak/other leafy vegetables	kg	260	292	Coffee : cups	no	40
143 Moong	kg	3480	213	French beans, barbati	kg	244	293	Coffee: powder	Gm	0
144 Masur	kg	3430	214	Tomato	kg	200	295	Cold beverages:
145 Urd	kg	3470	215	Peas	kg	930		bottled/canned	litre	320
146 Peas	kg	3150	216	Chillis: green	kg	290	296	Fruit juice and shake	litre	250
147 Soyabean	kg	4320	217	Capsicum	kg	240	297	Coconut: green	no	60
148 Khesari 150 Other pulses 151 Gram products	kg kg kg	3450 3400 3600	218220221	Plantain: green Jackfruit: green Lemon	kg kg no	640 510 10	298 Other beverages: cocoa, etc, (rural) 298 Other beverages: cocoa, etc (urban)	Re Re	19 17.1
152 Besan	kg	3400	222	Garlic	gm	1.45	300 Biscuits (rural)	Re	53.4
153 Other pulse products	kg	3400	223	Ginger	gm	0.67	300 Biscuits (urban)	Re	48.1
160 Milk: liquid	litre	1000	224	Other vegetables (rural)	Re	70.6	301 Salted refreshments (rural)	Re	46.1
161 Baby food	kg	3570	224	Other vegetables (urban)	Re	63.6	301 Salted refreshments (urban)	Re	41.6
162 Milk : condensed/powder	kg	4960	230	Banana	no	116	302 Prepared sweets (rural)	Re	35.3
163 Curd	kg	600	231	Jackfruit	kg	880	302 Prepared sweets (urban)	Re	31.8
164 Ghee	kg	9000	232	Watermelon	kg	160	303 Cooked meals	no	1200
165 Butter	kg	7290	233	Pineapple	no	460	304 Cake, pastry	Kg	5000
166 Ice-cream (rural)	Re	10.9	234	Coconut	no	660	305 Pickles	Gm	4
166 Ice-cream (urban)	Re	9.8	235	Guava	kg	510	306 Sauce	Gm	0.6
167 Other milk products (rural)	Re	55.2	236	Singara	kg	1150	307 Jam, jelly	Gm	2.5
167 Other milk products (urban)	Re	49.7	237	Orange, mausambi	no	50	308 Other processed food (rural)	Re	46.1
170 Vanaspati, margarine	kg	9000	238	Papaya	kg	320	308 Other processed food (urban)	Re	41.6
171 Mustard oil	kg	9000	240	Mango	kg	740	310 Pan : leaf	no	2.2
172 Groundnut oil	kg	9000	241	Kharbooza	kg	170	311 Pan : finished	no	3.7
173 Coconut oil	kg	9000	242	Pears, naspati	kg	520	312 Supari	Gm	6.55
174 Edible oil: others	kg	9000	243	Berries	kg	530	331 Toddy	litre	380
180 Eggs	no	100	244	Leechi	Kg	610	332 Country liquor	litre	380
181 Fish, prawn	kg	1050	245	Apple	Kg	590	333 Beer	litre	380
182 Goat meat/mutton	kg	1180	246	Grapes	Kg	710	334 Foreign liquor or
183 Beef/buffalo meat	kg	1140	247	Other fresh fruits (rural)	Re	9	refined liquor	litre	380
*Source: NSS Report No 513: Nutritional Intake in India: 2004-05.
46			august 8, 2009	vol xliv no 32		Economic & Political Weekly

Annexure 2: Explanatory Note on Computation χ2 of From the Observed Household Level Data Sets

To start with, household level full data set for each state/UT x sector is truncated by considering the allowable variations (δ) in PCCI as 25 calories (per capita per day), plus or minus the state-speciﬁc daily per capita calorie requirements (x*) as in Manna (2007). Suppose the population of households in the truncated data set, with PCCI falling within the class interval (x*±δ), is grouped into k mutually exclusive classes according to their value of MPCE, the proportion of households falling in the i-th class being Pi; i = 1, 2, …., k. For the sake of simplicity and uniformity, to start with, value of k is taken as 12 for each state/UT x sector and the various class-limits of y are taken as same as the corresponding values of MPCE as per the following MPCE classes (in rupees) as used in NSS Report Number 508, NSS 61st round – viz, for rural sector the classes are 0-235, 235-270, 270-320, 320-365, 365-410, 410-455, 455-510, 510-580, 580-690, 690-890, 890-1,155, 1,155 and more and for the urban sector the classes are 0-335, 335-395, 395-485, 485-580, 580-675, 675-790, 790-930, 930-1,100, 1,100-1,380, 1,380-1,880, 1,880-2,540, 2,540 and more. Let Pi denote the estimated population proportion under the null hypothesis that ln (Y) follows normal distribution. In fact, Pi is estimated as:

^^

log yiU – μ log yiL – μ

Pi = Φ – Φ

( ) ()

σ^σ^

where Φ (z)= Probability (Z ≤ z) being the cumulative probability of the normal deviate Z, yiU = upper limit of household MPCE for i-th class,

= lower limit of household MPCE for i-th

yiL

class,

^

μ = sample mean of ln (MPCE)’s for the truncated data set of sample households, and

^

σ = standard deviation of ln (MPCE)’s for the truncated data set of sample households. Let n be the total number of sample observations in the truncated data set for a given state/ UT x sector. Then, under the null hypothesis,

k

∑ (fi -npi )2/npi ~

i=1χ2 distribution with (k-3) degrees of freedom; where n denotes the total number of sample observations (i e, no of sample households) in the truncated data set and fi denotes the number of sample observations for the i-th class.

It may be mentioned here that the truncation of the data set as per the method discussed above sometimes – particularly for smaller states and UTs – resulted in only a fewer number of sample households which is not found adequate to perform the chi-square test. In such cases, the allowable variation in PCCI was increased such that the required minimum number of sample observations for any state/ UT x sector are obtained. Finally, allowable calorie variations other than 25 calories used were as follows: (a) for rural areas, 10 calories for Jammu and Kashmir and Uttar Pradesh; 50 calories for Mizoram and Sikkim; 100 calories for Andaman and Nicobar Islands; 200 calories for Pondicherry; 300 calories for Chandigarh, Dadra and Nagar Haveli, Delhi and Lakshadweep; and 500 calories for Daman and Diu; and

(b) for urban areas, 50 calories for Assam, Bihar, Chhattisgarh, Himachal Pradesh, Meghalaya, Nagaland and Tripura; 100 calories for Arunachal Pradesh, Andaman and Nicobar Islands, Chandigarh and Pondicherry; 200 calories for Goa, Sikkim and Lakshadweep; 300 calories for Dadra and Nagar Haveli; and 500 calories for Daman and Diu.

Further, while performing the chi-square test on the truncated data ﬁle, if the expected frequency npi for any class was found small (less than 5 or so), the adjacent classes were coalesced (which resulted in the reduction of value of k) to ensure that each of the expected frequencies was greater than 5. Further, in a few cases the adoption of speciﬁed class-limits of MPCE resulted in smaller values of expected frequencies (less than 5 or so) in one or two classes with higher values in the other classes. In such situations, the speciﬁed MPCE classlimits were modiﬁed to obviate this difﬁculty. The states/UTs where such modiﬁcations were made included Delhi, Goa, Jammu and Kashmir, Meghalaya, Nagaland, Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli, Daman and Diu and Pondicherry in case of rural areas and Meghalaya, Mizoram, Nagaland, Tripura, Dadra and Nagar Haveli, Lakshadweep and Pondicherry in case of urban areas.

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