State-Level Weighting Update:

Why the Consumer Expenditure Surveys Program

Is No Longer Providing State Weights for New Jersey

August 2023 

Sharon Krieger and David Swanson

Consumer Expenditure Surveys Program Report Series
2 

Introduction

The Bureau of Labor Statistics (BLS) Consumer Expenditure Surveys (CE) program recently began 

providing state weights to its public use microdata users for five states. It began providing them for 

California, Florida, and New Jersey for data collected in 2016; and for New York and Texas for data 

collected in 2017. The goal was to expand the universe of geographic areas for which statistically valid 

expenditure estimates could be computed. Before then, statistically valid expenditure estimates could be 

computed at the national, regional, and divisional levels, but not at the state level.1

Then in 2016, the CE program developed a methodology for calculating state weights that allowed 

statistically valid expenditure estimates to be made at the state level. The methodology was applied to five 

of the more populous states in the country, including New Jersey, because the CE program collected a large 

amount of data in them.2

Being a new product, the CE program considered the state weights to be “experimental,” and posted them 

on its “Research Products” webpage while it monitored their long-term viability. Unfortunately, over time, 

the number of respondent households in New Jersey decreased to the point where producing weights for it 

was no longer viable. That led the CE program to discontinue producing state weights for New Jersey 

beginning with data collected in 2021. This paper explains the technical difficulties that led to that decision.

Background

The CE is a nationally representative household survey that collects expenditure data from a random sample 

of consumer units (CUs) across the U.S. CUs are basically the same thing as households, and the terms are 

often used interchangeably. However, there is a slight difference between them. Households are groups of 

people who live together, while CUs are groups of people who live together and share their living expenses.

Households and CUs are usually the same thing, but occasionally a household will have more than one CU. 

Thus, CUs are slightly smaller than households on average.3

A random sample of CUs is selected for the CE survey in a two-step process. First, a random sample of 

geographic areas is selected from across the U.S. Those geographic areas are small clusters of counties 

1 They could also be computed for the 23 largest metropolitan statistical areas in the country (New York, Chicago, 

Los Angeles, etc.). “Regions” and “divisions” are broad geographic areas defined by the Census Bureau. The Census 

Bureau partitions the U.S. into four regions (Northeast, Midwest, South, and West), and it partitions each region into 

two divisions (e.g., it partitions the Northeast region into the New England and Middle Atlantic divisions), except the 

South, which it partitions into three divisions because its population is so much larger than that of the other regions. 2 For more information on how the state weights are computed, see https://www.bls.gov/cex/stateweightsdocumentation.pdf. The methodology is also described in Susan King’s 2017 JSM paper, “Calculating State Weights 

for the Consumer Expenditure Survey,” at https://www.bls.gov/cex/research_papers/pdf/calculating-state-weightsfor-the-consumer-expenditure-survey.pdf. 3 To be more precise, a consumer unit comprises either: (1) all members of a particular household who are related by 

blood, marriage, adoption, or other legal arrangements; (2) a person living alone or sharing a household with others 

or living as a roomer in a private home or lodging house or in permanent living quarters in a hotel or motel, but who 

is financially independent; or (3) two or more persons living together who use their income to make joint expenditure 

decisions. Financial independence is determined by the three major expense categories: Housing, food, and other 

living expenses. To be considered financially independent, at least two of the three major expense categories have to 

be provided entirely, or in part, by the respondent.
3

called Primary Sampling Units (PSUs), and they range in size from one county to twenty-nine counties. To 

be precise, they are the “Core-Based Statistical Areas” (CBSAs) defined by the Office of Management and 

Budget. Second, within those geographic areas, a random sample of households is selected. Then after the 

households are selected, every CU in those households is interviewed.

The two PSUs that will be of most concern in this paper are the New York and Philadelphia PSUs, because 

most of New Jersey is in them.

The CE program uses the data collected from the CUs in its sample to produce expenditure estimates. 

However, before producing those expenditure estimates, the CE program assigns weights to the CUs. 

Weights are needed to properly reflect the CUs’ probability of being selected for the sample, and their 

likelihood of participating in the survey.

The CUs’ weights are calculated in a three-step process. First, the CUs in the sample are assigned a base 

weight equal to the number of CUs in the population that they represent. If one out of 15,000 CUs in the 

population is selected for the sample, then a CU in the sample is assigned a base weight of 15,000. Second, 

the base weights are increased to account for nonresponse. If only one-third of the sampled CUs respond to 

the survey, then the base weights of the respondent CUs are multiplied by a factor of 3.0 to account for the 

nonrespondents. That makes the nonresponse-adjusted weights equal to 45,000. And third, the nonresponseadjusted weights are adjusted by a “calibration” process to make the respondents’ weights add up to the 

Current Population Survey’s (CPS’s) estimates of the total number of people and households in the 

population. These calibration-adjusted weights are called final weights. If a respondent’sfinal weight equals

50,000, that means it represents 50,000 CUs in the population.

The calibration adjustment process will be of concern in this paper because the decreasing number of 

respondents in New Jersey has been causing that step of the weighting process to fail in recent years.

Calculating the State-Level Weights – Overview

At the state level, the CUs’ weights are calculated in the same three-step process. First, the CUs in the 

sample are assigned a base weight equal to the number of CUs in the population that they represent. Second, 

the base weights are increased to account for nonresponse. And third, the nonresponse-adjusted weights are 

adjusted by a calibration process to make the respondents’ weights add up to CPS’s estimates of the total 

number of people and households in the population. Some minor changes were made to the first two steps 

to make the CUs’ base weights and nonresponse-adjusted weights reflect the state in which they are located, 

rather than the region and division of the country. However, the third step, calibration, will be the focus of 

this paper, because it is where the difficulties for New Jersey occurred.

Calibration is a mathematical procedure that modifies the nonresponse-adjusted weights of the respondents

to make them add up to CPS’s estimates of the total number of households in the population. There are 

infinitely many sets of weights that add up to CPS’s population counts, and calibration uses nonlinear 

programming to find the set of weights that is as close as possible to the nonresponse-adjusted weights 

while also making them add up to CPS’s population counts. It is an iterative procedure which makes a 

sequence of guesses at the weights, with each guess being closer to the nonresponse-adjusted weights than 
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the previous guess. When the guesses stop improving, the procedure is said to have “converged,” and the 

final guess is the best set of weights.

Calibration is a quarterly process that is performed on quarterly data. At the national level it makes the

respondents’ weights add up to 35 CPS population counts, which includes households in the nation, people 

in the nine Census divisions (plus nine more counts for those in the urban parts of those divisions), plus 

seventeen demographic subgroups for age, race, ethnicity (Hispanic, Non-Hispanic), and tenure 

(homeowner, renter). At the state level it makes the respondents’ weights add up to 11 CPS population 

counts, which includes households in the state, people in the state, plus nine demographic subgroups for

age, ethnicity, and tenure. The number of demographic subgroups was reduced at the state level to make 

sure there were enough respondents for the calibration process to converge. With 11 population counts, it 

was empirically observed that the calibration process needs approximately 100 respondents per quarter to 

converge.

Unfortunately, in recent years, the calibration step in the weighting process began to fail for New Jersey, 

even with its reduced number of population counts. The next section describes what led to its failure.

The Problem with New Jersey Data – Decreasing Participation

In recent years, calculating state weights for CUs in New Jersey became difficult because of its decreasing 

number of respondents. Between 2010 and 2021 the number of respondents in New Jersey decreased 50 

percent in both the Interview and Diary Surveys. This decrease can be attributed to two things: (1) the 

changing definitions of the New York and Philadelphia PSUs; and (2) decreasing response rates. Both of 

these things decreased the number of respondents in New Jersey.

The changing definitions of the New York and Philadelphia PSUs decreased the number of respondents in 

New Jersey by decreasing the initial number of CUs in its sample. The CE Survey’s sample size is fixed at 

the national level due to budget constraints, and the initial number of CUs in the sample at the national level 

is allocated to the individual PSUs in the sample proportional to their populations. That means if a PSU’s 

definition changes in a way that causes it to have fewer people, then the PSU will be assigned fewer CUs 

for its sample. That is what happened in New Jersey.

New Jersey is right next to New York and Philadelphia, so most of its counties are in those two PSUs. In 

fact, before 2015, every county in New Jersey was in the New York and Philadelphia PSUs. That meant all 

21 New Jersey counties were in the CE’s sample. However, in 2015, the definitions of the New York and 

Philadelphia PSUs changed. That caused five New Jersey counties to be dropped from the sample. Those 

five counties were moved into other newly created PSUs that were not selected for the sample. When that 

happened, the number of counties in New Jersey in the CE’s sample decreased from 21 to 16, and that loss 

of 5 counties decreased the number of CUs assigned to its sample by 10 percent.

In addition, decreasing response rates resulted in fewer respondents in the New Jersey counties that 

remained in the New York and Philadelphia PSUs. That decreased the number of respondents by 40 percent. 
5

Decreasing response rates is a universal problem in the survey world. Its exact cause is not known, but for 

our purposes we simply note that it is a universal problem.4

So, the changing PSU definitions reduced the number of CUs in the sample by 10 percent, and the 

decreasing response rate reduced the number of respondents in the sample by 40 percent. Together, they 

decreased the number of respondents in New Jersey by 50 percent.

Figure 1 below shows the decreasing annual response rates for New Jersey, from 2010 through 2021.

Figure 1. Decreasing Response Rates by Year for New Jersey

Figure 2 below shows the decreasing number of quarterly respondents in New Jersey, from the first quarter 

of 2010 through the first quarter of 2022. This graph shows quarterly numbers instead of annual numbers 

because the problem in state weights occurred in the calibration adjustment process, and that is a quarterly 

process.

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4 Its universality in the CE Survey can be seen in Table 1 at the end of this report, where the number of respondents 

for the two surveys (Interview, Diary), and the five states displayed (California, Florida, New Jersey, New York, 

Texas), all show significant decreases between 2010 and 2021. New Jersey’s decreases are greater than other states, 

but they all show significant decreases in the number of respondents. 5 For more details, and to compare New Jersey’s numbers to other states, see Table 1 at the end of the report for the 

quarterly counts of respondents in New Jersey since 2010, along with those in the other four states.
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Figure 2. Decreasing Number of Respondents by Quarter for New Jersey

Procedural Changes to Account for the Decreasing Number of Respondents in New Jersey

As Figure 2 above shows, between 2010 and 2021 the number of respondents in New Jersey decreased 

from 200 CUs per quarter to 100 CUs per quarter in the Interview Survey, and from 100 CUs per quarter 

to 50 CUs per quarter in the Diary Survey. And as mentioned above, our empirical experience shows the 

calibration process needs around 100 CUs per quarter to converge. That is the reason the calibration process 

had difficulty converging in the Diary Survey in the last few years, and the reason it was starting to have 

difficulty converging in the Interview Survey too.

As mentioned above, the number of demographic subgroups in the calibration process was reduced from 

35 at the national level to 11 at the state level to make sure they had enough CUs for the calibration process 

to converge. However, with only 50 CUs responding per quarter in the Diary Survey, it was no surprise that

some demographic groups had no CUs, or very few CUs, in them. This caused the calibration process to 

fail to converge. It created a problem where the weights of the CUs did not add up to the CPS population 

counts for one or more demographic subgroups, hence an optimal set of weights was not obtained. And 

with only 100 CUs responding per quarter in the Interview Survey, it was no surprise that the same problem 

was starting to occur in the Interview Survey as well.

The CE program circumvented this problem in recent years by making ad hoc adjustments to the calibration 

process. Those ad hoc adjustments began by further reducing the number of demographic subgroups. That 

meant continuing to collapse demographic subgroups until the collapsed subgroups had enough CUs for 

the process to converge. The subgroups that were collapsed changed from quarter to quarter, depending on

the characteristics of the survey respondents, which is what made the procedure ad hoc.

Unfortunately, even with this additional collapsing, the calibration process sometimes still failed to 

converge. That led the CE program to also change the lower bound or upper bound on the calibration 

adjustment factors. That was done in an ad hoc way too, depending on the diagnostics from the nonlinear 

programming software. Ideally, the same lower and upper bounds would be used for both national and state 

weights, but sometimes state weights required a smaller lower bound or a larger upper bound than national 

weights to converge. These two ad hoc procedures indicate that the calibration process was at the limits of 

its capabilities for New Jersey.
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Summary of Adjustments Used for New Jersey for Data Collected in 2019 and 2020

So, two ad hoc adjustments were used to make the calibration process converge: (1) collapsing the 11 

demographic subgroups down to a smaller number of demographic subgroups; and (2) changing the lower 

or upper bounds on the calibration adjustment factors. These adjustments were done in an iterative way

until the calibration process converged.

Table 2 below summarizes the adjustments that were made in the Diary Survey on a quarter-by-quarter 

basis for data collected in 2019 and 2020. During that two-year period, demographic subgroups were 

collapsed in all eight quarters, and the lower bound was decreased or the upper bound was increased in 

three of the eight quarters.

Table 2. Ad Hoc Adjustments Made in the Diary Survey for New Jersey

Quarter Collapsed 

Subgroups

Changed

Bounds

2019Q1 

2019Q2 

2019Q3 

2019Q4  

2020Q1 

2020Q2 

2020Q3  

2020Q4  

The adjustments typically began by collapsing some of the age subgroups together. Then if more collapsing 

was needed, other subgroups were collapsed together, such as ethnicity or tenure. And if that was still not 

enough, then the lower or upper bounds were changed.

Ideally, the lower bound or upper bound should never be hit during the calibration process, but

unfortunately, it happened for CUS in New Jersey. When it happened, the CE program decided to accept 

the results, provided the lower bound was not hit by more than 10 percent of all CUs, and the upper bound

was not hit by more than 10 percent of all CUs. If a lower or upper bound was hit by more than 10 percent 

of all CUs, then the lower bound was decreased or the upper bound was increased, respectively.

For a while these ad hoc adjustments gave the calibration process the help it needed to converge, and the 

results were accepted. And sometimes there was more than one way of collapsing the demographic 

subgroups together to help the process converge. However, this led to the question – which set of weights 

were the right weights? The CE program observed that the results were sensitive to which demographic 

subgroups were collapsed together, and even small changes in the adjustments resulted in large changes to 

the weights. This added to the problem.

Overall, the number of problems kept increasing as the number of respondents kept decreasing. And with 

the long-term downward trend in response rates, it looked like the number of respondents would continue 

decreasing for the foreseeable future. This is when the CE program realized that producing the weights for 

New Jersey was no longer viable.
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Conclusion

To summarize, calculating state weights for CUs in New Jersey became difficult in recent years because of 

the decreasing number of respondents. The decreasing number of respondents was primarily due to 

decreasing response rates, but the changing definitions of the New York and Philadelphia PSUs also 

contributed to it. That decrease caused some of the demographic subgroups used in the calibration process 

to have no CUs, or very few CUs, in them, which caused the calibration process to fail to converge.

For several years the CE program circumvented this problem by reducing the number of demographic 

subgroups at the state level, and sometimes by also changing the lower and upper bounds on the calibration 

factors. The number of demographic subgroups was initially reduced from 35 at the national level and down 

to 11 at the state level to make sure every demographic subgroup had enough CUs for the calibration process 

to work. However, due to the decreasing number of respondents, eventually the 11 demographic subgroups 

were too many for New Jersey, and the CE program had to reduce the number of them even further.

The reduction had to be done in an ad hoc way because the specific demographic subgroups that had no 

CUs, or very few CUs, changed from quarter to quarter. That lack of consistency in the process was 

undesirable from both a conceptual viewpoint and a computer programming maintenance viewpoint. Also, 

it slowed down the data generation process, which delayed the completion of “downstream” activities like 

data publication.

In addition, the CE program observed that the resulting weights for New Jersey’s respondents were sensitive 

to these procedural changes when the number of demographic subgroups went below 11. Even small 

changes in the demographic subgroups resulted in large changes to the weights, and there was no way of 

knowing which set of weights was right. So, in summary, although the ad hoc procedures worked for several 

years, the CE program ultimately decided that producing state weights for New Jersey is no longer viable. 

As a result, the CE program is discontinuing the production of state weights for New Jersey beginning with 

data collected in 2021.
Table 1. Number of Respondents in the CE Survey by State, 2010 – 2021

Interview Survey Diary Survey

Quarter CA FL NJ NY TX CA FL NJ NY TX

2010Q1 780 398 237 470 522 385 220 127 283 239

2010Q2 810 421 222 446 531 381 189 117 247 311

2010Q3 781 414 212 442 545 373 206 114 253 251

2010Q4 743 420 213 451 527 338 213 109 237 263

2011Q1 739 429 211 455 519 384 224 111 224 252

2011Q2 715 398 216 451 528 399 203 134 231 256

2011Q3 720 375 213 428 509 335 268 132 267 258

2011Q4 737 379 214 454 534 337 217 123 244 250

2012Q1 733 385 220 449 538 367 202 104 228 248

2012Q2 739 397 214 427 535 394 180 101 221 263

2012Q3 729 396 217 435 517 329 233 120 241 237

2012Q4 729 406 226 405 515 375 208 94 230 261

2013Q1 755 403 209 386 536 328 203 100 219 253

2013Q2 766 434 205 374 533 316 206 92 225 244

2013Q3 734 421 202 339 517 269 217 103 166 200

2013Q4 708 387 190 328 473 303 159 96 176 216

2014Q1 779 428 200 360 517 383 205 118 214 260

2014Q2 777 404 192 379 481 341 231 104 232 264

2014Q3 757 410 192 363 504 360 198 103 232 258

2014Q4 740 410 204 381 517 370 209 109 217 234

2015Q1 464 270 128 278 333 335 163 60 181 207

2015Q2 708 406 168 436 483 337 133 80 212 193

2015Q3 689 387 162 423 463 331 171 69 195 169

2015Q4 711 365 144 402 449 309 200 87 205 181

2016Q1 723 354 144 398 512 323 152 80 227 234

2016Q2 675 381 155 409 477 353 153 94 223 206

2016Q3 660 391 150 410 507 346 147 71 210 236

2016Q4 652 394 144 382 468 282 164 71 211 171

2017Q1 652 396 135 388 469 286 153 70 175 210

2017Q2 643 396 129 371 463 315 170 92 203 232

2017Q3 639 339 128 381 432 283 141 82 186 196

2017Q4 601 329 128 372 449 285 171 82 190 205

2018Q1 600 331 118 386 429 322 147 54 183 191

2018Q2 630 332 124 369 402 318 160 57 165 225

2018Q3 619 343 118 338 403 316 197 68 162 214

2018Q4 624 317 119 330 378 323 132 54 158 188

2019Q1 589 312 109 347 413 307 167 62 144 198

2019Q2 582 331 110 325 394 304 167 46 139 176

2019Q3 577 324 93 304 388 306 167 48 145 200

2019Q4 543 333 100 297 356 289 189 41 111 149

2020Q1 548 308 98 320 321 366 227 55 144 243

2020Q2 551 246 96 307 272 274 89 38 119 101

2020Q3 508 264 117 329 286 299 118 40 144 143

2020Q4 537 326 117 326 307 353 174 33 145 186

2021Q1 546 345 107 254 284 302 193 53 133 190

2021Q2 574 362 96 236 305 418 220 51 145 209

2021Q3 598 327 102 263 318 346 175 65 178 191

2021Q4 538 305 92 280 287 305 190 54 160 195

2022Q1 606 296 92 288 341 407 203 71 212 232

Avg. 2010 (Q1 – Q4) 779 413 221 452 531 369 207 117 255 266

Avg. 2021 (Q1 – Q4) 564 335 99 258 299 343 195 56 154 196

% Change, 2010 - 2021 -28% -19% -55% -43% -44% -7% -6% -52% -40% -26%