of Improving Data Imputation and

essing Procedures on ERS Farm

Household Income Forecasts

Wei-Yin Loh

University of Wisconsin, Madison

k with David Williams, Daniel Ayasse, Katherine Lim and

Christine Whitt (Economic Research Service)

d conclusions in this presentation are those of the authors and

onstrued to represent any official USDA or U.S. Government

r policy

FCSM 2023 1
Motivation

USDA’s Economic Research Service (ERS): to anticipate trends

ng issues in agriculture, food, the environment, and rural

d to conduct high-quality, objective economic research to

enhance public and private decision making

s each year, ERS produces estimates and forecasts of incomes

asure and predict well-being of farm households

well-being of farm households presented to public and

rs

f project: improve estimates of farm household’s well-being

dataset providing information on farm household well-being is

icultural Resource Management Survey)

bles have high levels of non-response requiring imputation

FCSM 2023 2
Current ERS imputation method

es a number of methods to assign values to missing data

od is imputation with conditional means in cells defined by pairs

phic variables (e.g., 15 cells defined by Age Class and

EDUC1 EDUC2 EDUC3

AGE1AGE2AGE3AGE4AGE5

FCSM 2023 3
o approaches to mean estimation

S

2 be the subsets of nonmissing and missing

yi, respectively

e estimated propensity score (probability that

yi is nonmissing)

e imputed value of

yi if it is missing

e sampling weight (if any)

obability weighted estimate via propensity scores



X

i∈

S1

wi/

πˆi



−

1

X

i∈

S1

wiyi/

πˆi

lue imputation by regression modeling or hot-deck sampling



X

i∈

S1∪

S

2

wi



−

1



X

i∈

S1

wiyi +

X

j

∈

S

2

w

j

yˆ

j



FCSM 2023 4
Regression imputation

sumptions: same correct model for

missing and nonmissing data)

0 0 10 20

x

ear regression model

−10 0 10 20

0 10 20 30

x

y

Regression tree model

FCSM 2023 5
egression imputation (nonlinear)

0 0 10 20

x

ear regression model

−10 0 10 20

0 100 200 300 400 500

x

y

Regression tree model

FCSM 2023 6
wise-constant regression tree model

0 10 20

x

x

≤13.7

x

≤-8.8

x

≤-11.9

174 117

x

≤6.3

37 115

357

FCSM 2023 7
imputation cells using propensity scores

logistic regression and regression tree

0 20 30 40

x

ic regression model

0 10 20 30 40

0.0 0.2 0.4 0.6 0.8 1.0

x

P(Y nonmissing)

Regression tree model

FCSM 2023 8
e-constant propensity score tree model

10 20 30 40

x

x

≤18.54

0.26

x

≤31.58

0.65 0.94

FCSM 2023 9
E regression tree for predicting r981

ain/loss on sale of capital assets)

r978

≤

∗14

4

889

≤2050

$1–499 $1000–1999

r1925

≤

∗68

r1128

≤

∗

1

$2000–2999 $5000–7499

$10000–14999

arket value of farm crops owned and stored

s from the sale of farm and non-farm capital assets

nts for principal producer’s dwelling

ople living in household between 18–64 with health insurance

on 1

FCSM 2023 10
sity score tree for P(r981 = nonmissing)

r978

=NA

4577

0.00

r978

≤

1

6683

1

r998

≤12

r1108

≤

7

196

0.94

211

0.82

174

0.72

s from the sale of farm and non-farm capital assets

public sources (e.g., social security, veteran’s benefits)

or dental insurance costs

FCSM 2023 11
Differences between methods

Current GUIDE Change

Median dollars per household

come -1,198 306 1,504

income: Total 67,873 54,650 -13,223

income: Earned Income 32,428 30,000 -2,428

income: Unearned Income 31,057 18,900 -12,157

usehold income 80,060 71,618 -8,442

Mean dollars per household

come 25,566 28,288 2,722

income: Total 96,688 87,810 -8,878

income: Earned Income 63,530 58,803 -4,727income: Unearned Income 33,158 29,006 -4,152

usehold income 122,255 116,098 -6,157

ncomes may be overestimated using current method

comes exceeded nonfarm households beginning 1990’s (Key et al., 2017)

FCSM 2023 12
Advantages of GUIDE

ly selects predictor variables to form “cells” for imputation

mpute missing values in predictor variables

erval-coded variables

cells explicitly described by decision tree diagrams

FCSM 2023 13
s

D., and Burns, C. (2017). Farm household income volatility: An

g panel data from a national survey. Technical Report

S. Department of Agriculture, Economic Research Service.

2). Regression trees with unbiased variable selection and

tection. Statistica Sinica, 12:361–386.

4). Fifty years of classification and regression trees (with

International Statistical Review, 34:329–370.

3). GUIDE website.

es.stat.wisc.edu/~loh/guide.html.

ge, J., Cho, M. J., and Li, Y. (2019). Classification and

ees and forests for incomplete data from sample surveys.

ica, 29:431–453.

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