Applying Alternative Variance Estimation Methods for Totals Under Raking in SOI s Corporate Sample

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1 Applying Alternative Variance Estimation Metods for Totals Under Raking in SOI s Corporate Sample Kimberly Henry 1, Valerie Testa 1, and Ricard Valliant 2 1 Statistics of Income, P.O. Box 2608, Wasngton DC University of Micgan, 1218 Lefrak Hall, College Park MD Abstract: SOI s Tax Year 2006 Corporate sample is a stratified Bernoulli sample of approximately 110,000 corporate income tax returns. Raking adjustments are performed using te sample s design strata, related to te return s size of assets and income, and te primary industry, based on collapsed categories of te six-digit AICS code. We apply several alternatives to estimate te variance of national- and domain-level totals of several key variables of interest: ignoring raking, post-stratification, a Taylor series approximation, and te delete-a-group jackknife replication estimator (wit 100 and 200 groups). Results demonstrate tat te poststratified total ad te gest variance estimates, wle te linearization and Jackknife wen implemented incorrectly produced variance estimates tat were too small, despite large sample sizes. Key words: administrative data, survey sampling, raking, Taylor series approximation 1. Introduction 1.1. SOI s 2006 Corporate Sample Design and Selection Metod Te stratified Bernoulli sample design is used by most of SOI s cross-sectional studies (IRS Winter 2010). In eac study s frame population, every unit as a unique identifier; te Employer Identification umber (EI) is used for corporations. Eac return s EI is used to produce a permanent random number between 0 and 1, denoted r i, for all units in te population ( i U ). Unit i is ten selected for SOI s sample if r i < π, were π is te pre-assigned sampling rate for stratum tat tax return i belongs to. Te Tax Year 2006 frame population includes all corporations organized for profit tat filed a Form 1120 (Corporation Income Tax Return), Form 1120-A (Sort-form Corporation Income Tax Return), Form 1120-F (Income Tax Return of a Foreign Corporation), Form 1120-L (Life Insurance Company Income Tax Return), Form 1120-PC (Property and Casualty Insurance Company Income Tax Return), Form 1120-REIT (Income Tax Return for Real Estate Investment Trusts), Form 1120-RIC (Regulated Investment Companies) or Form 1120S (Income Tax Return for an S Corporation) and posted to te IRS Business Master File (BMF) over te period of July 2006 troug June Canges in te returns information due to tax auditing are not included in te frame population. Table 1 sows te population and sample sizes for te 2006 sample: Table 1. Tax Year 2006 Population and Sample Counts Form Type Population Size Sample Size Form 1120S 4,162,484 31,492 Form 1120 (witout PTXC) and Form 1120-A 2,213,433 49,720 Form 1120-F 30,932 4,353 Form 1120-RIC 11,060 8,636 Form 1120-PC 6,127 1,589 Form 1120-REIT 1, Form 1120-L Special Studies (incl. PTXC) 14,134 14,102 PTXC=Possessions Tax Credit 3692

2 A Bernoulli sample was selected independently from eac stratum, wit rates ranging from 0.25% to 100%. Stratification for SOI s corporate sample first uses 1120 form type. Witn te form type, te population is furter stratified using eiter size of te return s assets alone, or bot size of assets and a measure of income. Forms 1120 (wit neiter Form 5735 nor Form 8844 attaced) and 1120-A are stratified by size of assets and size of proceeds. Te asset value is te largest of te absolute value of te tree asset fields (asset value from te front page and beginning and ending asset values from te Balance Seet). Te proceeds measure is te larger of te absolute value of net income (total income minus total deductions) or te absolute value of cas flow (net income plus net depreciation plus depletion). Form 1120S is stratified by absolute value of total assets and size of ordinary income. Forms 1120 (wit Form 5735 attaced), 1120-F, 1120-L, 1120-PC, 1120-REIT, and 1120-RIC are stratified only using te absolute value of total assets. Te raking adjustments to improve te sample s industry-level estimates are performed only for te 1120/1120-A and 1120S non-certainty strata, i.e., returns tat were selected for te sample wit rates lower tan 100%. Returns in tese 20 strata constitute a large portion of te total corporate sample. Table 2 sows te sample sizes for te samples. Table 2. Raking Strata Sample Counts Form Type /1120-A 23,037 34,349 35, S 13,133 24,737 24,596 Subtotal 36,170 59,086 59,937 Total sample size 146, , ,355 Te total sample size decreased in Four strata were canged from certainty strata to noncertainty strata wc were ten included in te raking Variables and Estimation Domains of Interest We consider eleven variables of interest, all of wose variance estimates are publised in te form of coefficients of variation (IRS 2006): Gross Receipts, et Depreciation, et Income, Cost of Goods Sold, Depreciable Assets, Total Assets, et Wort, Total Taxes Computed After Credits, Total Receipts, Positive Income, and Deficit. Tese variables are more/less correlated wit te stratification variables. Te estimated correlations between te variables and total assets (wc is gly correlated wit te total assets used in stratification) are sown in Table 3: Table 3. Estimated Correlation of Variables of Interest wit Stratification Variables Variable Total Assets Proceeds Gross Receipts 1 7 et Depreciation et Income Cost of Goods Sold Total Assets Depreciable Assets et Wort Taxes After Credits Total Receipts 3 8 Positive Income Deficit

3 In addition to national-level totals of tese variables of interest, we are also interested in te estimated totals for twenty-one major industrial groupings. Te major industry totals, and te number of SOI 2006 sample units and number of units in eac industry in te entire sample and te raking strata, are sown in Table 4 (industries 8 and 21 were removed for small sample size disclosure): Table 4. Major Industries and Sample Sizes Industry # Sample # Units in Major Industry ame Major # Units Raking Strata 1 Agriculture, forestry, fisng, and unting 1,949 1,506 2 Mining 1, Utilities Construction 9,397 7,483 5 Manufacturing 13,070 6,367 6 Wolesale trade 9,669 6,408 7 Retail trade 8,529 6,429 9 Transportation and wareousing 2,424 1, Information 3,065 1, Finance and insurance 20,211 2, Real Estate and rental and leasing 8,645 6, Professional, scientific, and tecnical services 7,315 5, Management of companies (olding companies) 6,589 1, Administrative and support and waste management & remediation services 2,253 1, Educational services Healt care and social assistance 2,837 2, Arts, entertainment, and recreation 1, Accommodation and food services 2,426 1, Oter Services 2,138 1,837 Te number of sample units in eac major industry varies widely, since te industry is not in te sample design. Most of te major industries ave a large number of sample units, wit te exception of utilities (3) and educational services (16). As te variables are gly skewed toward zero, we will see tat ts leads to ger estimated CVs. In addition, most of te industry majors are furter broken into minor-level industries for te Complete Report publication (particularly manufacturing). Several alternative variance estimators for totals estimated wit raking adjustments ave been proposed in te literature. In ts paper, we apply some alternatives to national- and domain-level totals, ten compare te empirical results. 2. Raking Algoritm SOI s Corporate sample uses a bounded raking procedure (O and Sceuren 1987). Te stratumlevel weigts are adjusted to also matc marginal totals by 72 industrial groupings created by collapsing te 6-digit ort American Industrial Classification System (AICS) codes. Tus we ave te setup for raking by stratum and industry tat is sown in Figure 1 on te following page. 3694

4 Figure 1. Raking Setup stratumlevel marginal totals 1i 20i { ˆ } Te weigts w = i n i1 i72 industry-level marginal totals are adjusted suc tat tey add up to te 72 group totals, taken from te BMF (te frame). SOI uses a bounded raking ratio metod to produce tese industry-level weigts. Te algoritm is summarized in te following steps: (1) Te initial weigt (at iteration 0) is te poststratification weigt in eac matrix cell defined by (stratum ID) x (industry ID): w (0) (0) n n = =. (0) Te weigts w add up across strata to te industry totals, te i i s, but tey do not add to te strata totals, te i s, so we adjust tem. (0) (2) Use poststratification to adjust te counts suc tat tey add up to te strata totals, (0) (1) te i s: = I (0). i= 1 Te corresponding weigts are w (1) (1) n =. Tese weigts add up across te industries to te strata totals, te i i s, but tey do not add up to te marginal industry totals, te adjust te population counts again. (3) Use poststratification to adjust te te i i s: (2) (1) 20 (1) i= 1 = Te corresponding weigts are w i i. (2) (2) n i i s, so we (1) counts suc tat tey add up to te industry totals, =. Tese weigts add up across te strata to te industry totals, te i j s, but tey do not add up to te strata totals, te i s. However, te (2) (0) weigts w are closer to adding up to te i s tan te weigts w from step 2. We repeat steps 2 and 3 until te sum of te raked weigts are close enoug to adding up to bot te strata totals, i, and industry totals, i i (bot are witn 001). Ts usually occurs in iterations. During SOI production, te raking-based weigts are furter smooted due to small, sample sizes and reduce te variance estimates. We exclude ts step from our evaluation. ( ) 3695

5 3. Alternative Estimation Metods for Totals and Teir Variances Metod 1: Before raking. We denote ˆ H = 1 n k variable of interest y estimated using te stratum-level weigts T y = as te national-level total of te k n. Tese are te conditional weigts obtained after post-stratifying te base weigts (based on te inverse probability of selection) to te frame population count witn eac stratum (Brewer 1979). Ts removes variability from te stratum sample sizes being random variables, wc occurs using Bernoulli sampling. We can ten use te stratified simple random sampling variance estimator ( ˆ H ) 1 1 n n ( n 1) 2 2 ( k ). (4.1) var T = y y = k We can modify ts formula for domain (major industry)-level estimation by replacing all y k s wit zk = yk if k j and 0 oterwise. SUDAA does ts wit te subpopn statement, so te standard SUDAA code for stratified simple random sampling was used to produce (4.1) estimates (RTI 2008). Metod 2: post-stratification (PS). We use poststratification to adjust te stratum-level weigts in Metod 1 to also matc te frame population counts of te 72 industry groups. Te estimated 72 total resembles te estimated total after one iteration of te raking algoritm, ˆ T ˆ PS = T i= 1 ˆi i, i were T ˆ i, ˆ i are te estimated total of all y k and estimated population size of post-stratum i, respectively. To estimate te variance, we simply use SUDAA s proc descript (wc uses a linearization variance estimator, p. 407 of RTI 2008). Te raking industry ID in te postvar statement and put te associated 72 group population counts in te postwgt statement. Metod 3: Taylor series linearization estimator (). Te final weigt for unit k in cell is te ratio of te cell population count (from raking) to te cell sample count (a random number, final since it is not in te sample design): wk =. Te raking-based estimator for te total is n ˆ H I Trake = w 1(stratum) i 1(industry) k s k y = = k, (4.2) H I final = y = 1 i= 1 1 were y = δ k U k yk and te sample inclusion indictor for unit k is δ n k = 1 if k s and 0 oterwise. and Teberge (1988) propose a linearization form for (4.2): ˆ H I T = w d, (4.3) linear = 1 i= 1 k s k k H I k = α( k) β ( k) = 1 i= 1 ( k) k + k were d ( lrc Z ) x y, ( k) ( k) raking adjustments (i.e., (0) final ( k) ( k) α β = ), lrc k ( i) α β is te product of te row/column ( k) = 1 if, and 0 oterwise, x k = 1, 3696

6 and Z is te unweigted sample total of k terms, its variance under stratified random sampling is ( ˆ I varl Tlinear ) = Var w i 1 k s kd = k y in te cell (, ) 2 H n 2 1 = 1 n n ˆ ˆ ( ) ( Zk Z) 1 k =. Since (4.3) contains all linear. (4.4) Metod 5: Jackknife replication (JKn). We also consider using a jackknife replication variance estimator (e.g., C. 4 in Wolter 1985). Te jackknife is advertised as being simple to implement witout te complicated analytic decompositions required for metod 4. However ere it requires tat te base weigts are recomputed for eac replicate, ten eac replicate group weigts are raked independently to te marginal totals (Section 2) to produce raking-based replicate weigts to fully capture all te variability under raking. Ts is not equivalent to using te raking weigts originally output from te algoritm to create replicate weigts. Jackknife variance estimation for stratified sampling (Rao and Sao 1992; Yung and Rao 1996; 2000) involves te variance estimator ( ˆ H n 1 var ) ˆ ˆ ( ( ) ) 2 Jk Trake = T 1 k rake k T = rake n, (4.5) were ˆ n H I final T rake( k ) = y 1 1 i 1 ( k ) ( k n = = ) is te estimate of (4.2) obtained wen deleting unit k witn stratum. To avoid producing 56,396 sets of replicate weigts using te deleteone-unit jackknife, we randomly assigned units to replicate groups and use te delete-a-group jackknife (variance estimator 4 on p. 179 in Wolter 1985), dropping an entire group of sample units witn a stratum rater tan one at a time. Since a relatively large number of groups is needed for unbiased variance estimation, we use 100 and 200 groups. Since Valliant et al. (2008) demonstrated tat ts variance estimator performed best using equal-sized groups witn strata, we formed 5 groups/stratum for 100 groups and 10 groups/stratum for 200 groups. To do ts, we randomly excluded a few returns witn eac stratum from calculating te (4.5) variance estimates: 46 (or 8% of te number of raking units) for te 100 groups and 96 (0.17%) for te 200 groups. For te Jkn variance estimator to correctly capture all te variability incurred under te raking algoritm, it is important to apply te raking adjustments to all te replicate weigts. However, te raking algoritm, as implemented by SOI, would not converge for te replicates, we terefore employed two versions of te jackknife. First, we formed te replicates using te stratum-level weigts, wc were ten raked using WesVar s less restrictive raking algoritm to rake eac set of replicate weigts (Wesvar 2010), ten used te raked replicate weigts to calculate (4.5). Since ts is te correct metod, we call ts JKn rigt. We also formed te replicates after te SOI raking algoritm was used on te full sample, wc we call JKn wrong.. Teoretically (Valliant 1993), doing te JKn wrong metod sould produce variance estimates tat are too large, owever if tey are relatively close to te JKn rigt variances, ts is acceptable. 5. Results 5.1. CV Estimates of ational-level Totals 3697

7 Here we compare te coefficients of variation (CVs) te estimated standard error of te total to CV T ˆ = T ˆ SE T ˆ of te estimated totals before and after te raking adjustments te total itself: ( ) ( ) are applied (plus te associated amounts from te non-raking strata). Te totals produced using te alternative metods involving are sown in Table 5, wle te associated CV s are in Table 6. Table 5. Alternative Estimates of ational-level Totals (in 000 s) Variable Before Raking PS Raking JKn wrong* JKn rigt* Gross Receipts 23,316,050,615 24,071,677,303 23,237,955,489 23,305,363,739 23,631,535,973 et Depreciation 564,066, ,796, ,052, ,588, ,458,799 et Income 1,933,386,215 1,956,283,519 1,931,313,601 1,932,031,246 1,939,098,498 Cost of Goods Sold 14,803,061,967 15,272,232,347 14,786,820,104 14,808,444,052 14,988,307,543 Depreciable Assets 8,818,499,087 8,999,063,220 8,820,105,138 8,813,906,676 8,89,028,304 Total Assets 73,084,041,882 73,436,454,730 73,037,539,862 73,081,357,938 73,220,148,635 et Wort 25,997,111,327 26,097,315,237 25,986,087,530 25,995,394,790 26,030,600,002 Taxes After Credits 353,141, ,336, ,573, ,146, ,205,839 Total Receipts 27,408,021,944 28,180,368,344 27,324,846,225 27,396,481,502 27,400,408,336 Positive Income 2,239,855,737 2,276,834,896 2,234,567,447 2,238,079,821 2,238,282,373 Deficit 306,469, ,551, ,253, ,048, ,178,587 * estimates from 200 groups sown. Te Table 5 totals are all close to te before raking, wc means tat te industry-level weigting adjustments do not ave a large impact on te national-level totals of our variables of interest. Te estimated Raking, JKn wrong, and JKn rigt totals in Table 5 are difference because we randomly deleted units to create te equal sized JKn groups. But, as Table 5 demonstrates, te resulting difference is negligible. Table 6. CVs (as % s) of ational-level Totals, Under Alternative Variance Estimation Metods Variance metods accounting for raking Before Raking Raking PS Total Total Total JKn wrong Total JKn rigt Total Variable Direct Variance SUDAA JK100 JK200 JK100 JK200 Gross Receipts et Depreciation et Income Cost of Goods Sold Depreciable Assets Total Assets et Wort Taxes After Credits Total Receipts Positive Income Deficit Like te Table 5 totals, te coefficients of variation in Table 6 are also very close. Generally te post-stratification totals ave te largest coefficients of variation across te alternatives. In addition, te and JKn wrong CVs are generally too small, wen compared to bot of te te JKn rigt CVs for te et Depreciation, Cost of Goods Sold, Depreciable Assets, Taxes After Credits, and Deficit variables. Te CVs being smaller wen te replicate weigts are formed incorrectly is te opposite of tose in Valliant et al. (2008), were te incorrect jackknife replicate groups lead to more conservative variance estimates. 3698

8 5.2. Variance Estimates of Major Industry-Level Raking Totals To compare te variance estimates for te domain-level raking-based totals, Figure 2 on te following pages sows plots of te ratio of te four raking alternatives for eac variable of interest to te variance of te JKn rigt wit 200 groups. In eac plot, te 19 majors are sorted by descending total sample size (see Table 4). Figure 2. Ratio of Alternative Variance Estimates for Raking Totals to Jkn Rigt Wit 200 Groups, by Major Industry Gross Receipts et Depreciation 3699

9 Figure 2. Alternative Estimated Coefficients of Variation of Major Industry-Level Totals cont d et Income Cost of Goods Sold Depreciable Assets 3700

10 Figure 2. Alternative Estimated Coefficients of Variation of Major Industry-Level Totals cont d Total Assets et Wort Taxes After Credits 3701

11 Figure 2. Alternative Estimated Coefficients of Variation of Major Industry-Level Totals cont d Total Receipts Positive Income Deficit 3702

12 In all plots, ratios equal to one indicate tat a variance estimate is equivalent to te JKn rigt results wit 200 replicate groups. We see tat te alternative industry-level variance estimates are generally smaller tan te JKn 200 rigt variance estimates, indicated by ratios less tan one. Ts indicates tat te linearization metod and implementing te jackknife incorrectly lead to smaller variance estimates. Tere generally is less of a difference for te JKn 100 rigt variance estimates. It is also difficult to discern any patterns related to te sample size, from larger industries on te left of eac plot to te smaller industries on te rigt. 6. Conclusions We applied some alternative estimators of totals and teir variances to data collected in SOI s 2006 corporate sample. For our application, te post-stratification estimated totals (wit poststrata defined by 72 industry groups) ad larger variances tan eiter te stratified estimator (wit no poststratification or raking) or te raking estimator (wit margins defined by design stratum and industry). For alternatives used to estimate te variance of totals under raking adjustments, generally te Linearization and group jackknife wit incorrectly formed replicate groups metods produced variance estimates tat were bot too small, despite aving large sample sizes. References, D. A. (1983). On te variances of asymptotically normal estimators from complex surveys. International Statistical Review, Vol. 51, pp , D.A. and Teberge, A. (1988), "Estimating te Variance of Raking Ratio Estimators", Canadian Journal of Statistics, Vol. 16 Supplement, pp Brewer, K.R.W., Early, L.J., and Joyce, S.F. (1972), Selecting Several Samples from a Single Population, Australian Journal of Statistics, Vol. 14, pp Deville, J.C., Sarndal, C.E. (1992). Calibration Estimators in Survey Sampling, Journal of te American Statistical Association, Vol. 87, pp Deville, J.C., Sarndal, C.E. and, Sautory, O. (1993). Generalized Raking Procedures in Survey Sampling. Journal of te American Statistical Association, Vol. 88, pp Internal Revenue Service (2006), Statistics of Income 2006 Corporate Income Tax Returns, IRS, Publication Internal Revenue Service, Statistics of Income Bulletin, Winter 2008, Appendix A, Description of te Sample and Limitations of te Data, pp Internal Revenue Service (2010), Statistics of Income Bulletin, Winter 2010, Wasngton, D.C., pp Rao, J..K. and Sao, A.J. (1992). Jackknife variance estimation wit survey data under ot deck imputation, Biometrika, Vol. 79, pp Researc Triangle Institute (2008). SUDAA Language Manual, Release 1, Researc Triangle Park, C: Researc Triangle Institute. O, H. L. and Sceuren, F. J. (1987), "Modified Raking Ratio Estimation," Survey Metodology, Statistics Canada, Vol. 13, o. 2, pp

13 Yung, W. And Rao, J..K. (1996). Jackknife linearization variance estimators under stratified multi-stage sampling. Survey Metodology, 22, Yung, W. and Rao, J..K. (2000). Jackknife variance estimation under imputation for estimators using post-stratification information. Journal of te American Statistical Association, Vol. 95, pp Valliant, R., Brick, M. J,, and Dever, J.A. (2008). "Weigt Adjustments for te Grouped Jackknife Variance Estimator." Journal of Official Statistics, Vol. 24, o. 3, pp Valliant, R. (1993), Poststratification and Conditional Variance Estimation, Journal of te American Statistical Association, Vol. 88, o. 421, Teory and Metods, pp