Integrated Household Surveys: An Assessment of U.S. Methods and an Innovation

Abstract

We present a vision for improving household financial surveys by integrating responses from questionnaires more completely with financial statements and combining them with payments data from diaries. Integrated household financial accounts—balance sheet, income statement, and statement of cash flows—are used to assess the degree of integration in leading U.S. household surveys, focusing on inconsistencies in measures of the change in cash. Diaries of consumer payment choice can improve dynamic integration. Using payments data, we construct a statement of liquidity flows: a detailed analysis of currency, checking accounts, prepaid cards, credit cards, and other payment instruments, consistent with conventional cash-flows measures and the other financial accounts.

1. Introduction

During recent decades, interest in the study of household finance has grown rapidly. Campbell (2006) first advanced the case for treating household finance as a distinct field of study in economics. The global Financial Crisis of 2008–09 strengthened that case due to the subprime housing debacle in many industrial economies and its persistent impact on household balance sheets. In particular, the extent and nature of increased leverage and risk in household mortgages and their effects on the real (housing industry) and financial (shadow banking) sectors of the economy were not well known or understood prior to the crisis. Consequently, there is now a focus on household decisionmaking, how households got into this trouble, what transpired in the crisis, and the difficulties encountered thereafter.¹

A hindrance to research and understanding of household economic behavior (real and financial) has been the lack of sufficient data. Relative to other countries, the United States has a large amount of high-quality data on household economic behavior; these data will be examined closely in this paper. Even the U.S. data, however, were inadequate to inform economic agents and policymakers sufficiently to avoid the Financial Crisis. Many efforts are underway to acquire and develop additional needed data; these efforts include the Eurosystem’s Household Finance and Consumption Survey (HFCS), which was inspired partly by the U.S. Survey of Consumer Finances.² Other efforts, such as the National Academy of Science’s call for a substantially revised Consumer Expenditure Survey, aim to reform existing datasets (Dillman and House 2013).

U.S. household survey data exhibit several characteristics that limit their effectiveness. The U.S. statistical system (public and private) is decentralized, with each data source specializing in a part of household activity. Although there are often good reasons for specialization, the result is a general lack of comprehensive measurement of household activity. Many datasets are cross-sectional, which limits their ability to track the behavior of specific households over time, and are gathered infrequently. When data sources are combined in an effort to provide a more comprehensive view of household behavior, the combination of the specialized data sources can create imperfect, if not misleading, views of household economic conditions, due to differences in sampling, measurement, and linkages between microeconomic and aggregate data.³ These imperfections make it difficult to ascertain from the data the extent and nature of important developments, such as adjustments affecting household balance sheets in the wake of Financial Crisis, increases in income inequality, and intergenerational dynamics of household net worth.

Data on household behavior in other countries also exhibit limitations, but there are signs of improvement in response to major economic developments. Most notably, the Financial Crisis reaffirmed the view that household finance is at the center of development economics because financial access is thought to be one of the key factors that could help poor and vulnerable households become more productive and resilient in the face of economic shocks. In addition, there have been payment innovations such as M-Pesa in Kenya, an electronic money issued by a cell phone company, Safaricom, that in many respects is now on par with currency there as a medium of exchange (Jack, Suri, and Townsend 2010). The often-expressed hope in developing economies is that a deeper, more developed financial system can be built on top of such an improved payments system, with some progress evident in countries such as Pakistan.⁴ These developments bring us back to the need for better data on payments, household behavior, and a micro-founded view of the macro economy in developing countries. Fortunately, more countries are producing data from household surveys that are doing a better job of measuring these developments.

We believe an important step forward in understanding household behavior is the development of more reliable and effective measures of household economic activity, both real and financial. Therefore, an overarching goal of this paper is to describe a comprehensive vision for practical implementation of household surveys that are integrated with financial statements and payments data, leaving no gaps in measurement and strengthening the theoretical and applied linkages among measures. The main contributions of this paper are: 1) to assess how well integrated U.S. household surveys are with elements of financial statements for households; and 2) to demonstrate how a diary of U.S. consumer payment choices can be used to construct a new statement of liquidity flows that advances the current state of the art in measuring stock-flow dynamics and thus takes a step closer to realizing the overarching vision of the paper.

Samphantharak and Townsend (2010, henceforth ST) describes the baseline conceptual framework for the design of an integrated survey that has been implemented in the field for almost 20 years and that allows construction of a complete set of household financial statements that is comprehensive and fully integrated. Essentially, ST creates a set of financial accounts akin to those of corporate firms: this set comprises a balance sheet, income statement, and statement of cash flows. The concept is of a household with projects, that is, a collection of assets that earn income from farm and non-farm production activities. This idea of assets earning income also extends to households engaged in wage or salaried labor, meaning those that essentially generate income from their human capital. A key element of this analysis is that all aspects of household situations and behaviors are measured: income, in order to measure the productivity of physical and human capital; assets and liabilities, to measure wealth; and cash flow, to distinguish liquidity from income and profitability. A key to this measurement is that the accounts are required, by construction, to be consistent with one another, thereby eliminating the possibility of gaps. Few surveys feature this dynamic integration.

To illustrate how this works, and as a first step in the paper, we use the ST framework to assess the degree of integration in leading U.S. household surveys. For each survey considered, we tabulate and juxtapose the data of each in the form of corporate financial statements applied to the representative U.S. household. We first construct for each survey a harmonized balance sheet, income statement, and statement of cash flows for a recent time period that matches the survey dates—around 2012—as closely as possible. To ensure maximum accuracy, we have invited assistance from representatives associated with each survey; and to encourage further refinement of this effort, we make our programs available to interested researchers. Then, we use the estimated U.S. household financial statements to characterize the degree of integration by two distinct measures. Integration by coverage reflects the extent to which a survey contains estimates of each line item in the financial statements. All the surveys cover roughly half the income statement items, although most specialize in income or expenditures. However, the coverage of the balance-sheet items varies widely across surveys. Integration by dynamics reflects the extent to which the statement of cash flows accurately measures the law of motion between stocks (shown in the balance sheet) and flows (shown in the income statement). None of the surveys can provide truly direct statements of cash flows, and all of them make large errors relative to indirect estimates of changes in assets and liabilities.

Our assessment of integration in U.S. household surveys is merely a factual statement of results and is not intended to be a criticism of the surveys or a call for reforming them. We recognize and accept the specialty nature of U.S. surveys, which has the benefit of allowing gains from specialization and achievement of each survey’s original goals. For example, the Panel Study on Income Dynamics (PSID) was originally designed to measure poverty and to contribute to its reduction in conjunction with President Johnson’s Great Society programs; the Consumer Expenditure Survey (CE) was designed to gather data for developing accurate price indices; and the Survey of Consumer Finances (SCF) to measure wealth. Although some of these surveys have evolved over the years, particularly the PSID, others retain their original mandate. Yet the specialization and persistence of the U.S. surveys does leave gaps in measurement that can only be overcome by comprehensive integration of the surveys with financial statements. Ironically, because the PSID and SCF are so highly regarded, they are adopted as the gold standard elsewhere in the world, for example, in China and Europe, thus propagating essentially the same gaps in these other surveys as in their U.S. counterparts.

A second step of this paper is to use the Federal Reserve Bank of Boston’s 2012 Diary of Consumer Payment Choice (DCPC) to demonstrate how consumer payment diary surveys can improve the dynamic integration of surveys.⁵ The DCPC directly measures several, but not all, components of the law of motion governing the stock-flow relationship between assets and liabilities (balance-sheet items) and income and expenditures (income-statement items). Because the 2012 DCPC is focused on consumer payments authorized by payment instruments (cash, check, debit or credit card, online banking, and such), it focuses on liquid assets used as payment instruments, including the currency held and used by U.S. consumers. In this respect, the DCPC is similar to the Townsend Thai Monthly Survey (TTMS), which underlies the ST methodology, where currency is the main household asset and payment instrument in rural Thailand. To provide a bridge to our key next step, we compare and contrast the household financial statements constructed with TTMS with those constructed with the DCPC.

The central innovation of this paper is the construction of a new, more detailed analysis of cash flows at the level of liquid asset accounts, where currency, checking accounts, and other liquid assets are distinguished and treated separately. By tracking consumer expenditures that are authorized by payment instruments tied to specific types of liquid asset accounts, the DCPC matches expenditures to the sources of money and credit that fund them. This matching cannot be done feasibly by surveys that track consumer expenditures at the level of individual products (the Consumer Expenditure Survey) or at the level of aggregated expenditure categories (“food away from home”).

Linking all the liquidity accounts to one another and to the expenditures (or investments) they fund makes it possible to better assess the changing landscape of payments taking place in the United States and industrialized countries as well as in emerging-market and low-income countries.⁶ This then links back to the need for data to better inform public policy and to provide consumers with the information they need to improve household decisionmaking and economic behavior. More informative financial accounts come from considering payments, and vice versa: better payments data come from integrated financial accounts. Development of household economic data from dynamically integrated household surveys that include payment diaries might be particularly beneficial for developing countries, where household economic data are scarce, there are few pre-established surveys with prior missions, and payment systems and financial industries are changing rapidly. Of course, payments systems are also changing in the United States. The 2015 DCPC took a small step toward integrating payments and employing the ST framework, as described below. We provide a framework and guidance for policymakers to implement this longer-run vision.

The remainder of the paper proceeds as follows. Section 2 provides an overview of the main U.S. household surveys. Section 3 reviews the ST methodology and explains how it will be used in our analyses. Section 4 assesses the degrees of integration in U.S. household surveys, by coverage and dynamics. Section 5 compares and contrasts the TTMS and DCPC survey data. Section 6 describes the innovation made possible by the interaction of ST’s methods with the DCPC. Section 7 concludes.

2. Overview of U.S. Household Surveys

This section describes the main surveys included in this study, which are used to collect data on U.S. household economic conditions (henceforth, “household surveys”), plus the TTMS. Summary descriptions of these surveys appear in Table 1 in order of chronology based on continuous fielding. Five sponsors produce these U.S. surveys:

TABLE 1.

Overview of U.S. Surveys and Diaries and TTMS

	PSID	CE-S/D	SCF	SIPP	HRS/CAMS	S/D-CPC	TTMS
Sponsor	University of Michigan	BLS	Federal Reserve Board	Census Bureau	University of Michigan	Boston Fed	MIT
Vendor	University of Michigan	Census Bureau	NORC/University of Chicago	Census Bureau	University of Michigan	RAND/University of Southern California	Thai Family Research Project
Frequency	Biennial	Monthly	Triennial	Quarterly	Biennial	Yearly/irregular	Monthly
Period	1968-present	1980-present	1983:Q1-present	1983:Q4-present	2008-present	2012, 2015	1998-present
Statistical Calculations	2011, 2013	2011, 2012	2009, 2012	2010, 2011	2010, 2012	2011, 2012	2012
Questionnaires
Observation unit	U.S. Family unit	U.S. Consumer units	U.S. Primary economic units	U.S Households	U.S. Households	U.S. Consumers and households	Thai Households
Mode(s)	Interview	Interview, diary	Interview	Interview	Interview, mail	Interview, diary	Interview
Data collection	Recall	Recording, recall	Recall	Recall	Recall	Recording (1 day), recall (1 year)	Recall
Measurement period	Past year	Daily expenditures (diary), or past year (survey)	“Average” week for expenditures, past year for income	Past month, past 4 months, or past year	Past year	Daily payments (DCPC), or “typical” week, month, year (SCPC)	Past month
Sampling
Target Population	Total U.S. Non-institutional	Total U.S. Non-institutional	Total U.S. Non-institutional	Total U.S.	U.S. ages 50+ Non-institutional	Age 18+ Non-institutional	Rural and Semi-Urban Households
Sampling Frame	Survey Research Center National Sampling Frame	U.S. Census Bureau Master Address File	NORC National Sampling Frame and IRS data	U.S. Census Bureau Master Address File	Panel of adults born 1931–1941	RAND ALP, USC UAS, GfK Knowledge Networks	Initial Village Census
Sample size	~10,000	~7,000	~6,000	14,000–52,000	9,000–15,000	~2,000	~800
Longitudinal panel	4 consecutive quarters	14 days	None	2.5–4 years	Fixed	3-day waves tied to SCPC annual panel	1998-present

CE-S:http://www.bls.eov/CE/capi/2015/cecapihome.htm

CE-D:http://www.bls.gov/CE/ced/2013/cedhome.htm

TTMS:http://townsend-thai.mit.edu/about/

SIPP:http://www.census.eov/proerams-surveys/sipp/about.html

PSID:https://psidonline.isr.umich.edu/

SCPC:http://www.bostonfed.org/economic/cprc/scpc/

DCPC:https://www.bostonfed.org/economic/cprc/data-resources.htm

SCF:https://www.federalreserve.gov/econresdata/scf/scfindex.htm

HRS/CAMS:https://hrs.isr.umich.edu/about

• University of Michigan, Institute for Social Research (ISIR) – The Michigan ISIR sponsors two surveys. First, the biennial Panel Study on Income Dynamics (PSID), which is “the longest running longitudinal household survey in the world” and that includes data on wealth and expenditures as well as other socio-economic and health factors.⁷ Second, the biennial (even-numbered years) Health and Retirement Survey (HRS), which “has been a leading source for information on the health and well-being of adults over age 50 in the United States” for more than 20 years; the HRS includes the biennial Consumption and Activities Mail Survey (CAMS) for tracking household expenditures in “off” years (odd-numbered).⁸

• U.S. Bureau of Labor Statistics (BLS) – The BLS sponsors the Consumer Expenditure Survey (CE), comprising “two surveys—the quarterly Interview Survey and the Diary Survey—that provide information on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics.”⁹ “As in the past, the regular revision of the Consumer Price Index (CPI) remains a primary reason for undertaking the Bureau’s extensive Consumer Expenditure Survey. Results of the CE are used to select new ‘market baskets’ of goods and services for the index, to determine the relative importance of components, and to derive cost weights for the market baskets.”

• Federal Reserve Board – The Board sponsors the Survey of Consumer Finances (SCF), “normally a triennial cross-sectional survey of U.S. families. The survey data include information on families’ balance sheets, pensions, income, and demographic characteristics. Information is also included from related surveys of pension providers and the earlier such surveys conducted by the Federal Reserve Board.” The SCF collects some consumer expenditures directly.¹⁰

• U.S. Census Bureau – The Census Bureau sponsors the Survey of Income and Program Participation (SIPP), “the premier source of information for income and program participation. SIPP collects data and measures change for many topics including: economic well-being, family dynamics, education, assets, health insurance, childcare, and food security.”¹¹

• Federal Reserve Bank of Boston – The Boston Fed’s Consumer Payments Research Center (CPRC) sponsors the annual Survey of Consumer Payment Choice (SCPC) and the occasional Diary of Consumer Payment Choice (DCPC), both of which measure consumer adoption of payment instruments and deposit accounts and the use of instruments. Originally, the SCPC and DCPC were not integrated like the CE but were developed independently; they are now being integrated. The SCPC collects only the number of payments, while the DCPC also tracks the dollar values. Both provide data on cash and (in later years) checking accounts plus revolving credit. The SCPC contains very limited information about household balance sheets.

These surveys were selected because of their quality and breadth of coverage of U.S. household financial conditions, including relatively large numbers of detailed questions pertaining to the line items of household financial statements (assets, liabilities, income, or expenditures). None of the surveys contains all relevant financial conditions because none was designed to do so. Thus, no single survey is fully integrated with financial accounting statements and no single survey alone can provide complete estimates of household financial conditions. When combined, however, these U.S. household estimates come closer than any single dataset available today to providing a comprehensive assessment of U.S. household financial conditions. These surveys were also chosen because, except for the HRS, they are representative of U.S. consumers. ¹² However, the surveys are implemented with different samples of households (or consumers) and, in some instances, substantively different survey questions, so their estimates are not necessarily comparable.

We reiterate that each survey has its own particular purposes or goals and that none is intended to provide a comprehensive, integrated set of household financial conditions as described in ST. The CE, for example, is primarily intended to produce data on a wide range of consumption expenditures that aid in the construction of the CPI. In contrast, the SCF primarily tracks details of assets and liabilities plus income from all sources but does not track all consumer expenditures. The PSID aims to estimate most income and expenditures but also focuses on collecting data on social factors and health, a practice that might be beneficial for every survey and data source. In any case, the PSID’s breadth limits the amount of detail it can obtain on income and expenditures, so it does not obtain a comprehensive estimate of balance-sheet items. For all of these reasons, the analysis in the next section does not expect or presume to find an individual integrated financial survey, nor does it recommend that any of these surveys change what it is currently doing.

Table 1 summarizes the key characteristics of the selected U.S. household surveys in terms of their basic features, survey methodologies, and sampling methodologies. Surveys are listed in columns in chronological order (left-to-right) based on their initial years of continuous production. The oldest is the PSID, which dates back to the 1960s, while the newest, the SCPC and DCPC, are less than a decade old. Most of the surveys are conducted relatively infrequently, ranging from quarterly (the CE and SIPP) to triennially (the SCF). Although implemented daily for one or two months, the official DCPC has been implemented only three times in five years. The date of statistical calculations refers to the period used to estimate the elements of the household financial statements, as discussed later in the paper. The rows of the table are grouped into sections related to the survey methodology and the sampling methodology. For further comparison, the table also shows corresponding information about the TTMS.

Survey methodologies vary widely across the surveys along several dimensions. One obvious distinction is the mode: survey (PSID, CE-S, SCF, HRS, SIPP, and SCPC) versus diary (CE-D, DCPC) or “diary survey.” This distinction is complicated by the fact that modes also vary for each type of survey or diary, including paper surveys, paper diaries (or memory aids), online surveys—with or without assistance—and interviews; some surveys use mixed-mode strategies. A key differentiating factor among surveys is whether they collect data based on respondents’ recall, where the recall period can vary in length from a period of one week to one year, or based on respondents’ recording the data, where the recording period is typically one day. Recall-based surveys are more susceptible to memory errors and aggregation errors (over time and variable types). Some sponsors field their own survey (Michigan ISIR), while others outsource to vendors (for example, the SCF uses NORC, formerly called the National Opinion Research Center).

The sampling methodologies are relatively similar across surveys. All surveys aim to provide estimates that are representative of some U.S. population measure, except the HRS, which is limited to older households. The main reporting unit varies across surveys from individual consumers to entire households, with some surveys obtaining information about the household from just one member—an important choice that can significantly affect the results of the survey. The surveys also differ in whether the samples are drawn as independent cross-sections or as longitudinal panels. The precision of survey estimates varies widely because sample sizes range from 2,000 to 52,000 reporting units.

Estimates of economic and financial activity for consumers and households are influenced heavily by at least two major types of factors: 1) heterogeneity in the survey specifications, sampling methodologies, and data collection methodologies; and 2) variation across surveys in the content, scope, and nature of questions about real and financial economic activity. Therefore, the reader should not expect estimates of income, expenditures, or wealth from the surveys to coincide. Instead, there might be large discrepancies in estimates of these economic and financial activities even if the conceptual measures are similar. Differences in target populations can naturally produce large differences in economic and financial measures. But even more subtle survey design differences, such as recall versus recording, can produce large differences in the estimated measures. With regard to survey content and questions, even minor differences in wording can elicit differences in measured concepts between surveys. Similarly, the level of aggregation—collecting data on just the total or on the sum of the parts of the total (and then adding them up)—can have dramatic effects on estimates of the total values across surveys.

3. The Samphantharak-Townsend Framework

This section provides a brief overview of the Samphantharak and Townsend (2010), or ST, framework for defining and measuring the integration of household surveys with corporate financial statements.

3.1. Conceptual Framework

There are three main financial statements in the ST “household as corporate finance” framework.¹³ The first statement is the balance sheet or the statement of financial position, which reports all assets and liabilities at a point in time. The difference between assets and liabilities is net worth. In the terminology of corporate financial accounts, net worth is the household’s equity in the household enterprise. The second financial statement is the income statement, which measures flows of revenues and expenses as well as the disposition of net profit into consumption and savings over a period of time. Finally, the statement of cash flows measures money, cash, or other liquid assets flowing into and out of the household as part of the payments system. In practice, cash flows are simply the outflows of cash for the acquisition of inputs of production, as well as for investment and consumption expenditures, and the inflows from sales of product, liquidation of assets, and financing.

The balance sheet is a stock report, while the income statement and the statement of cash flows are flow reports. There is a close connection between the balance sheet and the income statement through the connection between stocks and flows, as summarized in Figure 1. Specifically, profits from production or from salary and other income can be saved or consumed. Consumption is analogous to paying out a dividend to the owner. Positive savings show up as an increase in (real or financial) assets and wealth, reflected in the balance sheet at the end of the period. Likewise, negative savings show up as a decrease in assets and wealth. Indeed, the change in wealth in the balance sheet between two points in time is essentially net savings.¹⁴

FIGURE 1.

Relation Between Household Income Statement and Balance Sheet

Income in corporate financial statements is typically accrued income, based on the idea that expenses of production are not subtracted until revenues from sales resulting from that production are recognized.¹⁵ The essential idea behind this notion of accrued income is that one wants to measure the ultimate return on a project in order to compare that return to alternatives; that is, one wants to measure the opportunity cost in order to see whether the project is warranted, in order to answer the obvious question: do the economic activities the household has adopted “make sense”? Essentially, accrued income is supposed to measure productivity. However, since the accrual basis of accounting does not necessarily recognize revenues or expenses when cash flows in or out of the enterprise, it cannot give analysts a full understanding of the enterprise’s liquidity. For example, a project may be productive with a reasonably high rate of return, but it may become illiquid due to cash-flows fluctuations and the household may even go bankrupt. This example illustrates one of the reasons why the statement of cash flows is needed to obtain a comprehensive understanding.

To summarize, the reconciled financial statements must exhibit the following accounting identities: (1) in the balance sheet, the household’s total assets must be identical to its total liabilities plus total wealth or net worth, (2) the increase in household wealth in the balance sheet over the period must be identical to the household’s savings (adjusted for unilateral transfers); that is, it must be identical to a household’s net income from the income statement minus consumption, and (3) the increase in the household’s cash holdings in the balance sheet must be identical to the household’s net cash inflow in the statement of cash flows, summing over all sources. Both sides of every accounting identity are measured.

One benefit of imposing accounting identities is that we avoid the common problem that a variable generated from one set of questionnaire responses yields a different value when computed from an alternative set of responses. For example, Kochar (2000) finds that household savings in the Living Standard and Measurement Study (LSMS) surveys computed as ”household income minus consumption” is different from household savings computed from “change in household assets.” This discrepancy could come from various problems in questionnaire design. For example, some of the assets might be omitted from total assets, some assets might be financed by liabilities rather than savings, or income and savings might be defined inconsistently. Indeed, as mentioned above, one can use these two different measures of savings, which may differ as indicated, as a consistency check within a survey or diary fielding, with follow-up questions in the case of discrepancies.

ST applied this vision of integrated surveys to the Townsend-Thai Monthly Survey (TTMS). Transactions in the monthly data are like journal entries for an accountant, allowing the analyst to create complete financial accounts. As details of the transaction partners are also recorded, one can map networks within the village and also geographic patterns. Figure 2 illustrates the procedure for creating a household’s balance sheet, income statement, and statement of cash flows from a panel household survey. More information about the TTMS appears in Section 5.

FIGURE 2.

Constructing Financial Statements from a Panel Household Survey

3.2. Details of the Statement of Cash Flows

Because the dynamic accounting of linkages between stocks and flows is central to this paper, we provide a more detailed discussion of this topic. The statement of cash flows (CF) provides an accounting of cash received and cash paid during a particular period of time, thereby providing an assessment of the operating, financing, and investing activities of the firm (or household).

The first step in constructing a cash-flows statement is to define the term “cash.” Despite the label, it is important to remember from the outset that currency is typically only part of this. For advanced industrial economies such as the United States, standard corporate financial statements tend to focus cash flow on the concept of “cash and cash equivalents” (CCE):

• Cash – Currency (coins, notes, and bills) ¹⁶ and liquid deposits at banks and other financial institutions, including demand deposits, other checkable deposits, and savings accounts. This measure is similar to the broad measure of money known as M2.¹⁷

• Cash Equivalents – Short-term investments with a maturity of three months or less that can be converted into cash quickly, easily, and inexpensively (high liquidity, low risk). None of the surveys identify cash equivalents separately from similar investments of longer maturity. Examples include 3-month Treasury bills versus 1-year Treasury bonds and 3-month versus 6-month certificates of deposit).¹⁸

The assessment of U.S. surveys will focus on CCE for the statement of cash flows. For the TTMS and DCPC, however, the statement of cash flows will focus only on currency because Thai households transact primarily in currency (Thai baht) and the 2012 DCPC is a payment diary that does not track the entire balance sheet and has only one liquid asset (currency in U.S. dollars, which is a payment instrument).¹⁹ Most U.S. surveys do not collect data on currency, which is a relatively small portion of liquidity for most U.S. households, and only the SCPC and DCPC do so comprehensively.

Once cash is defined, cash flows for that defined concept (CCE) can be calculated to account for the operating, investing, and financing activities of the firm (or household).²⁰ In particular, the statement of CF includes three main parts:

• CF from production (or operating activities) – These are net cash flows from operating activities of the firm (or household). The direct method shows cash inflows from operations and cash payments for expenses, by major classes of revenue and expense. Equivalently, the indirect method converts net income from an accrual basis to a cash basis, using changes in balance-sheet items.

• CF from investing activities (consumption and investment) – These are net cash flows from investing activities of the firm (or household). Cash outflows are primarily for investment in capital and for the purchase of securities that are not CCE. Cash inflows are the converse, including sales of capital and non-CCE securities. Individual items are listed in gross amounts (inflows minus outflows), by activity. As applied to the household, these are consumption expenditures (on nondurable goods and services) and capital expenditures (on durable goods).

• CF from financing – These are net cash flows from transactions considered to be the financing activity of the firm (or household). Cash inflows occur when resources are obtained from owners or investors, such as by issuance of equity or debt securities. Cash outflows are the converse, in the form of payment to owners and investors or to creditors. As with CF from investing, individual items are listed in gross amounts.

Another type of transaction sometimes associated with the statement of CF is direct exchange, which occurs when non-cash (not CCE) assets or liabilities are traded without implications for cash. Often these exchanges are difficult to classify as either investing or financing activity because they may have elements of both. For that reason, accountants do not agree on whether to include direct exchanges in the statement of CF or to report them in a separate statement. For this paper, we do not include them in statement of CF.

In theory, the statement of CF provides an exact linkage between flows in the income statement and changes in stocks on the balance sheet. To verify this, the statement of CF compares measured cash flows with the measured changes in assets and liabilities from the balance sheet. Total CF is simply the sum of component flows,

$$C{F}_t=C{F}_t^p+C{F}_t^v+C{F}_t^f,$$

where superscript p denotes production (operating activity), v denotes investing activity, and f denotes financing activity. If all financial-statement items are measured accurately and constructed comprehensively, this estimate from the statement of CF should exactly match the change in the stock of cash from the balance sheet,

$$C{F}_t=\Delta A_t^C=A_t^C-A_{t-1}^C,$$

where $A_t^C$ denotes the asset value (end-of-period t) of cash and cash equivalents (superscript C). If these CF identities were to hold exactly using data from a survey, then that survey would be fully dynamically integrated with financial statements. In practice, however, measurement of financial-statement items is neither exact (due to measurement error) nor comprehensive in actual surveys (due to failure to include all items), so we expect to observe errors in the CF identities above (that is, we expect to see less-than-full dynamic integration). One logical measure of the degree to which survey estimates are integrated across time (dynamically) is

$$\text{CF error }=100\times \left[\frac{C{F}_t-\Delta A_t^C}{A_{t-1}^C}\right],$$

which is expressed as a percentage of lagged cash. Smaller CF errors (in absolute value) are interpreted as indicating better dynamic integration of a survey.²¹

This analytical linkage between cash flows (also on the income statement if the cash basis rather than the accrual basis is used) and the stock of cash (balance-sheet items) can be disaggregated into the linkages between individual liquid assets (stocks) in CCE and the gross flows among them. Henceforth, our language assumes the cash basis is used, but our analysis remains valid for the accrual basis, since the real difference between the cash and accrual bases is only the labeling of the transaction; for example, goods sold create an account receivable that is not necessarily cash and does not appear on the statement of cash flows if the latter does not recognize accounts receivable as CCE. Nevertheless, the sale would be recognized as creating an increase in an asset (an accounts receivable item).

To see the point about disaggregation, let $A_{kt}^C$ denote the end-of-period dollar value of a liquid asset in CCE from the balance sheet, where subscript k denotes the account/type of liquid asset (currency, demand deposits, and such) and subscript t denotes the discrete time period (such as month, quarter, or year). Liabilities, L_kt, are defined analogously and primarily represent various types of loans; in principle, liabilities can be viewed as negative-valued assets.²²

Let D_kdt denote the dollar value of deposits into account k on day d (nearly continuous), and W_kdt the analogous withdrawals.²³ Gross cash flows in period t are the sums across all daily flows into and out of an asset type:

$$D_{kt}={\sum }_{d=1}^{N_t^d}{D}_{kdt}\text{ and }{W}_{kt}={\sum }_{d=1}^{N_t^d}{W}_{kdt}.$$

Asset deposits include primarily income of all types (including any capital gains and losses from holding CCE), transfers of another type of asset (or liability) into the account, or unilateral gifts received. Asset withdrawals include primarily payments for goods and services (consumption expenditures or capital goods investment), transfers to another type of asset, or unilateral gifts given. Again, liability flows are defined analogously.

Individual assets are governed by the following law of motion between periods t −1 and t:

$$A_{kt}^C=A_{k,t-1}^C+D_{kt}-W_{kt}$$

$$\Delta A_{kt}^C=D_{kt}-W_{kt}.$$

Individual liabilities are governed by an analogous law of motion where the liability “return” is primarily interest paid.

Finally, the disaggregated cash flows for each CCE type of asset include some that net to zero when aggregated across all account k accounts. For example, if a consumer withdraws $100 in currency (k =1) from a checking account (k = 2), then D_1dt = W_2dt. For this reason, it is informative to track the flows among types of asset (and liability) accounts when analyzing the cash-flows behavior of households. For some types of asset accounts, such as a checking account, withdrawals can be made with multiple payment instruments, such as checks, debit cards, and various electronic bank account payments. Thus, the gross flows between accounts can be further disaggregated by the type of payment instrument used to authorize the flow.²⁴

4. Assessment of Integration in U.S. Household Surveys

This section evaluates the content and structure of the main U.S. household surveys, excluding the SCPC and DCPC, which are not designed to be general surveys of household finance, in relation to corporate financial statements. As noted earlier, no U.S. survey is fully integrated with financial statements in a manner consistent with the ST framework. However, all of the U.S. surveys contain questions that provide estimates of many of the relevant stocks and flows in financial statements. Therefore, the ST framework can be used to organize the survey data into estimates of a representative (average) U.S. household’s financial statements: a balance sheet, income statement, and statement of cash flows. The remainder of this section presents those estimates for each survey and analyzes the results.

The tables in this section report estimates of U.S. financial statements from the surveys. Each statement contains nominal dollar-value estimates for the line-item elements from each survey, aggregated to the U.S. average per household, with the sampling weights provided by the survey programs.²⁵ Selected aggregate measures are supplemented with medians. The line items (rows) of each financial statement reflect our best effort to combine survey concepts into reasonably homogeneous measures.²⁶ Where necessary and feasible, some survey concepts fall into the “other” categories; tables are footnoted extensively to clarify these details. To the extent possible, all economic concepts from each survey are included in the statements. However, the question wording and concept definitions can vary significantly across surveys, so detailed estimates fall short of perfect harmonization. To ensure proper handling, we have provided our preliminary results and software programs to managers or principal investigators of each survey and offered them the opportunity to evaluate and correct our analysis.²⁷

Juxtaposing estimates of the financial statements for each survey provides two benefits. First, and independently of the ST methodology, the financial statements provide valuable information about the relative magnitudes of real and financial economic conditions estimated by each survey. Differences between survey estimates can be large in absolute and relative terms because of the absence of perfect harmonization, as noted above. The aggregate estimates may also diverge due to significant differences in survey or sampling methodologies, described in Section 2, or due to differences in the coverage of statement line items, described below. In any case, the comparison of estimates reveals the relative strengths and weaknesses of each survey in measuring household economic conditions.

Second, juxtaposing the estimates facilitates an easy and quantitative assessment of how well each survey’s questions integrate with the elements of the household financial statements. The degree of integration can be evaluated by at least two standards: 1) the coverage of items in the statements; and 2) the dynamic interaction between stock and flow concepts. With regard to coverage, we can further quantify two types of coverage: 1) the percentage of detailed line items estimated by the survey; and 2) the aggregate dollar values of the estimates. As an example of the first of these coverage measures, suppose that a balance-sheet concept had 10 detailed items and one survey estimated eight of them while another estimated only two of them. Then, the first survey has broader coverage (80 percent versus 20 percent). However, line-item coverage is not necessarily an accurate indicator of value coverage. If a survey had two estimates of the 10 balance-sheet items, and if each one were an estimate of the aggregate of five of the detailed items (for example, short-term assets and long-term assets), then the survey might produce a very high percentage of the total value of assets even though it didn’t include an estimate of each of the 10 items. Still, estimating the aggregate value of five items without estimating each individual item is prone to producing biased estimates due to the adverse effects of recall and reporting errors. The juxtaposed estimates reveal the extent to which this kind of aggregation effect appears in the survey estimates.

4.1. Balance Sheets and Income Statements

Balance sheets constructed from the U.S. surveys appear in Tables 2-a (assets) and 2-b (liabilities). The asset and liability estimates are reported as current market values to the best of our ability, although it is not always possible to be certain of the type of valuation reported by respondents. Assets are divided into financial and nonfinancial categories, with financial assets further divided into highly liquid current assets (short-term) and assets with other terms and liquidity (long-term). For financial assets, surveys usually obtain market values explicitly or by assumption; where they distinguish between face value and market value (for example, for a U.S. government saving bond) the latter is reported. For nonfinancial assets, the valuation issue is almost the same, except the potential distinction is between market value and book value.²⁸ For housing assets, the surveys generally ask for the current (market) value of homes, but we cannot be sure they do not report the purchase price, which is a book value. For business assets, all surveys ask for a current (market) value, although the form of the question varies and may use analogous terms (for example, “sale price”). Liabilities are the current outstanding balances for debt, not the original loan amounts. Liabilities are divided into categories of revolving debt, characterized by an indefinite option to roll over the liability, and non-revolving debt. Because the maturity of debt is generally not known from the surveys and the term varies by debt contract within a category, the nonhousing debt categories are listed in rough order of liquidity from most to least liquid.

TABLE 2-a.

U.S. Surveys: Balance Sheets - Assets, various dates

	PSID	CES	SCF	HRS	SIPP
Assets	422,616	226,314	632,246	556,295	351,702
Median	151,000		170,600	240,000	67,113
Financial assets	163,376	65,537	262,168	205,461	160,651
(% of assets)	(39)	(29)	(41)	(37)	(46)
CURRENT ASSETS.	95,883	65,115	140,176	125,898	102,642
Cash	29,850	30,849	30,354	34,733	12,434
Currency			12
Government-backed currency			12
Private virtual currency
Bank accounts	29,850	30,849	30,342	34,733	536
Checking accounts		17,239	12,660		536
Savings accounts		13,610	17,682
Other deposit accounts			0		11,898
Other current assets	66,033	34,266	109,822	91,165	90,208
Certificates of deposit			4,994	9,354
Bonds		408	8,227	14,860	3,376
Mutual funds/hedge funds			40,964		18,830
Publicly traded equity	56,335	33,858	48,874	66,951
Life insurance	9,698		6,763		68,002
LONG-TERM INVESTMENTS.	67,493	422	121,992	79,563	58,009
Retirement accounts	67,493		97,007	79,563	54,759
Annuities			5,490
Trusts/managed investment accounts			13,773
Loans to people outside the HH		422	5,722		361
Other important assets					2,889
Tangible (physical) assets	259,240	160,777	362,445	336,951	191,051
(% of assets)	(61)	(71)	(57)	(61)	(54)
Business	51,404		108,760	55,006	25,921
Housing assets	188,992	160,777	234,187	264,500	154,795
Primary residence	149,211	149,760	170,159	190,818	147,855
Other real estate	39,781	11,017	64,028	73,682	6,940
Vehicles	18,844		19,498	17,445	10,335
Unknown assets			7,633	13,883
(% of assets)			(1)	(2)

SOURCES: Panel Study of Income Dynamics (PSID) 2013, Consumer Expenditure Survey (CE) 2012, Survey of Consumer Finances (SCF) 2013, Health and Retirement Survey (HRS) 2012, and Survey of Income and Program Participation (SIPP) 2011. See Section 2 for more details.

NOTES: Table entries are average dollar values for the survey’s unit of observation, approximately a household. Assets and liabilites are stocks dated as of the time of the survey, generally the end of the year. Sampling weights provided by each survey were used in calculating the average values in accordance with the survey’s data documentation. A more detailed data appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

TABLE 2-b.

U.S. Surveys: Balance Sheets - Liabilities, various dates

	PSID	CES	SCF	HRS	SIPP
Liabilities	82,288	73,668	112,306	64,614	61,979
Median	18,800		23,000	5,600	3,750
Revolving Debt	2,671	4,512	2,185		2,661
(% of liabilities)	(3)	(6)	(2)		(4)
Credit cards / charge cards	2,671	4,447	2,096
Revolving store accounts		65	89
Non-revolving Debt	79,617	69,156	110,121	64,614	59,318
(% of liabilities)	(97)	(94)	(98)	(100)	(96)
Housing	67,506	58,143	87,223	58,584
Mortgages for primary residence	54,856	52,559	63,889	48,984
Mortgages for investment real estate or second home	12,650	3,086	19,598	4,440
HELOC/HEL		2,498	3,556
Loans for improvement			180	5,160
Loans on vehicles	4,310	3,926	4,508		3,707
Education loans	6,507		5,788
Business loans			10,317		5,338
Investment loans (e.g., margin loans)			289		102
Unsecured personal loans
Loans against pension plan			288
Payday loans / pawn shops
Other loans	1,294	7,087	1,708	6,030	50,171
Net worth (equity)	340,328	152,646	519,940	491,681	289,723
Cumulative net gifts received
Cumulative savings

SOURCES: Panel Study of Income Dynamics (PSID) 2013, Consumer Expenditure Survey (CE) 2012, Survey of Consumer Finances (SCF) 2013, Health and Retirement Survey (HRS) 2012, and Survey of Income and Program Participation (SIPP) 2011. See Section 2 for more details.

NOTES: Table entries are average dollar values for the survey’s unit of observation, approximately a household. Assets and liabilites are stocks dated as of the time of the survey, generally the end of the year. Sampling weights provided by each survey were used in calculating the average values in accordance with the survey’s data documentation. A more detailed data appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

All the surveys report an estimate of total assets in Table 2-a. U.S. households own average assets worth as much as $632,246, according to the SCF, less half that amount, $226,314, in the CE survey. The HRS estimate of $556,295 is close to the SCF estimate, despite being limited to older consumers. The breakdown of asset types is similar for all the surveys. Financial assets generally account for less than half of asset values, 29 to 41 percent, despite variation in the number and type of detailed asset categories. Tangible (physical) assets represent the majority of asset values. Within financial assets, cash accounts for roughly $30,000 for all but the SIPP, where it accounts for roughly $12,000, and most is held in bank accounts. Only the SCF contains an estimate of currency, but even that is not a direct estimate of actual currency holdings of the household.²⁹ Overall, estimates of balance-sheet assets are relatively comprehensive for all surveys, as shown by their similar aggregate values and by the breadth of coverage across detailed asset categories. The SCF is the most comprehensive, with asset estimates in every category except short-term assets other than bank accounts (checking and saving); the PSID, HRS, and SIPP are almost as comprehensive as the SCF. The CE is much less comprehensive and has considerably lower asset values.

All the surveys also report an estimate of total liabilities. U.S. households have average liabilities ranging across the surveys between $61,979 and $112,306, much lower than the value of total assets and exhibiting less variation than across surveys. Housing debt is by far the largest portion of liabilities, ranging from $58,143 to $87,228 in all surveys where it is reported. The HRS asks specifically only about housing-related debt, with a catch-all question for other loans. The SIPP does not permit an exact estimate for housing-related debt, but the “other loans” category most likely includes some housing-related debt. While estimates of balance-sheet liabilities are somewhat comprehensive for most surveys, they are not as comprehensive as the estimates of assets. The aggregate values vary less and there is less line-item coverage across detailed categories of liabilities. Once again, the SCF is the most comprehensive, with liability estimates in nearly every category. The PSID is almost as comprehensive as the SCF. The other surveys are less comprehensive, although in different ways. Given the estimates of total assets and total liabilities, household net worth ranges from $152,646 in the CE to $519,940 in the SCF.

Income statements constructed from the U.S. surveys appear in Table 3. Income is divided into two main categories: compensation of employees (the most common source of U.S. household income) and other income. The latter includes income from all types of businesses owned and operated by households. Expenditures also are divided into two main categories: production costs and taxes. As explained above, the production costs of households are expenditures associated with businesses operated directly by a U.S. household; these businesses include sole proprietorships, partnerships, and certain Limited Liability Corporations (LLC).³⁰ Unlike in Thailand, where most households operate a business (typically agricultural), only a minority of U.S. households have a business.³¹ For the minority of U.S. households with a business, it would be natural to apply corporate financial accounting to income (revenues) and expenses, as in ST. However, none of the surveys provides sufficient information about household business activity, so we use the simpler approximation of revenues as “income” to accommodate the majority of U.S. households without a business. Furthermore, all income-statement estimates are reported on a cash basis of accounting, so revenues and expenses are reported for the period when the cash is received (income) or paid out (expenditures), because this method is the primary way data are collected in the U.S. surveys.

TABLE 3.

U.S. Surveys: Income Statements, various dates

	PSID	CES	SCF	HRS	SIPP
Income	67,187	65,316	83,863	79,779	61,431
Median	44,500	46,774	45,000	46,300	45,396
Labor Income	53,623	51,543	53,192	42,377	48,767
(% of total income)	(80)	(79)	(63)	(53)	(79)
Wages and salaries	53,473	51,543	53,192
Professional practice or trade	113
Other Labor Earnings	37
Production Income	3,748	3,075	11,347		1,144
(% of total income)	(6)	(5)	(14)		(2)
Business income (self-employment)	2,472	2,926	11,347
Rent	1,276	149			1,144
Other income	9,816	10,698	19,324	37,402	18,176
(% of total income)	(15)	(16)	(23)	(47)	(30)
Interest, dividends, etc	2,206	1,204	6,682	18,093
Government transfer receipts	1,302	5,812	10,670	12,415	7,294
Other transfer receipts, from business	131			423
Other transfer receipts, from persons		380	372
All other income	6,177	3,302	1,600	6,471	10,882
Expenditures	1,837	4,345	2,007	0	22,487
Production Costs
(% of total expenditures)
Depreciation
Capital losses
Business Expenses
Cost of Labor Provision
Cost of Other Production Activities
Taxes	1,837	4,345	2,007		2,798
(% of total expenditures)	(100)	(100)	(100)
Employment taxes		2,508			585
Other taxes	1,837	1,837	2,007		2,213
Net income	65,350	60,971	81,856	79,779	38,944

SOURCES: Panel Study of Income Dynamics (PSID) 2013, Consumer Expenditure Survey (CE) 2012, Survey of Consumer Finances (SCF) 2013, Health and Retirement Survey (HRS) 2012, and Survey of Income and Program Participation (SIPP) 2011. See Section 2 for more details.

NOTES: Table entries are average dollar values for the survey’s unit of observation, approximately a household. Income and expenses are reported for the prior 12 months, or annualized where necessary. Sampling weights provided by each survey were used in calculating the average values in accordance with the survey’s data documentation. A more detailed data appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

All of the surveys report an estimate of total income (revenues). U.S. households received average total income of $61,431 to $83,863 per year. Estimates of labor income are even more similar across surveys, ranging only between $42,377 and $53,623, essentially all of which is wages and salaries. Estimates of other income types vary more, ranging between $9,816 and $37,402, but account for less than one-quarter of total income, except for the HRS estimates, which represent 45 percent of total income. Overall, income estimates are the most comprehensive and consistent portion of the household financial statements across surveys, most likely because employment compensation is widespread among U.S. households and the data are relatively easy to collect. Estimates of income other than employment compensation are less uniform across the surveys due to the unavailability of some detailed line-item categories.

Although three surveys (the PSID, CES, and SCF) have estimates of business income, none of them provides much information about household business expenditures. They ask few, if any, questions about household business activity (aside from the mere existence of a home business). No survey has an estimate of production costs for household businesses. Only three surveys with business income have estimates of taxes (these estimates average less than $5,000 per household), and only the CE reports employment taxes. Tax expenditures are those paid directly by households and do not include taxes deducted by employers or paid by third parties on behalf of households.

Given their estimates of total income and total expenditures, all of the surveys provide estimates of net income (income less expenditures), which range from $60,971 (CE) to $81,856 (SCF), as shown at the bottom of in Table 3. The HRS does not collect expenses, so its net income equals total income. Net income is similar to income in the other surveys because expenditures are relatively small (taxes only). Household net income is treated as retained earnings that are distributed to household members for consumption and investment expenditures, which are recorded in the statement of cash flows (described below).

4.2. Quantifying Integration by Coverage

We wish to characterize the degree to which surveys are integrated with household financial statements in terms of coverage. We propose to develop the criteria for measuring this kind of integration by quantifying the extent to which a particular household financial survey covers (includes) the breadth of the line items in standard balance sheets and income statements. There are at least two dimensions along which integration by item coverage could be measured using the estimates from the preceding subsection. One is the fraction of detailed line items for which a survey provides estimates (“line-item coverage”). Another is the fraction of the total dollar value of all line items estimated by a survey (“value coverage”). The two measures are independent and not necessarily highly correlated. A survey could cover most items in the financial statements but underestimate them significantly; likewise, a survey might cover only a small number of items but obtain very high-value estimates if the items covered include mainly the highest-valued items. The latter situation may occur when a survey only collects data on two aggregate subcategories (such as short-term and long-term assets) but collects none on the detailed line items within each subcategory.

We construct the measure of line-item coverage as follows. We define the range of each financial statement as the number of the most detailed line items (rows) from the tables earlier in this section. Then, we count the number of line items (rows) for which each survey provides a dollar-value estimate. The coverage estimate of integration is the proportion of line items estimated relative to the total number of line items. We call this the “item-coverage ratio,” and we construct two separate ratios, one for the balance sheet and one for the income statement. This measure reflects only the extensive margin of coverage because it does not account for the magnitude of the dollar values in each line item; thus, it may not give a complete reflection of coverage for total assets, liabilities, income, or expenditures.

We construct the measure of value coverage analogously, as follows. We use the nominal dollar values for each individual line item in the statements to construct the aggregate total values (sum of all individual items) for each statement and divide the aggregate value by the best available per-household estimate of the relevant metric for the U.S. population. For the balance sheet, we use total assets and total liabilities from the Flow of Funds accounts as the denominator. For the income statement, we use personal income from the National Income and Product Accounts (NIPA). The “value-coverage ratio” represents survey coverage of the intensive margin of coverage. The difference between the two types of ratios reflects the extent to which a survey’s coverage of financial statements is more integrated in its intensive or extensive coverage of financial statements. To the extent that one wishes to construct accurate estimates of aggregate U.S. household financial conditions, the dollar-value ratio may be more important.

Figure 3 provides scatter plots of the item-coverage ratio (blue diamonds) and value-coverage ratio (red squares) for the balance sheet and income statement. The feasible range of both ratios is [0, 1], with the upper end indicating that a survey has estimates of every single item in the corresponding financial statement. Recall that the ratios are independent and may not be highly correlated. Thus, the item-coverage ratio does not necessarily reflect how well a survey produces aggregate estimates of the data, and the value-coverage ratio does not necessarily reflect how well a survey covers the number of line items in the financial statements. Also, we make one important adjustment to the income statement ratios to adjust for the application to households. As shown in the next subsection, household consumption and durable goods investment are listed in the statement of cash flows rather than the income statement. However, for the purpose of quantifying the overall coverage of household income and total household expenditures, both business-related expenditures and household consumption or investment expenditures, we include all types of expenditures in constructing the coverage ratios for the income statements.

FIGURE 3.

Financial Statement Line-Item Coverage Ratios for U.S. Surveys

None of the U.S. surveys is completely integrated (ratio of 1.0) with aggregate financial conditions for either statement, as can be seen from Figure 3. In fact, no survey has either type of coverage ratio that is greater than 0.6 for both financial statements. However, four of the five balance-sheet ratios are greater than 0.5 (except CE) and four of the five income-statement ratios are about 0.5 (except SIPP). The key differences across surveys occur in both types of coverage ratios for the balance sheets. The SCF has nearly complete value coverage of the balance sheet (above 0.9 by value) and the HRS has a value ratio about 0.8 (by value). Most surveys have item-coverage ratios of about half of the balance-sheet line items except the SCF, which covers the vast majority of line items. Variation across surveys is less in the item-coverage ratios for income statements.

4.3. Quantifying Integration by Dynamics

We also wish to characterize the degree to which surveys are integrated with household financial statements in terms of dynamics. Our proposed criterion for measuring this kind of integration is a quantification of the extent to which the estimated stock-flow identity holds in the survey estimates of household financial statements. The statement of cash flows is well suited to quantifying this measure of integration because it provides the linkage between the income statement (flows of income and expenditures) and changes in the balance sheet (stocks of assets and liabilities), assuming all stocks and flows are measured exactly and comprehensively. As explained in Section 3, however, the cash-flows error that arises in practice quantifies how well the balance sheet and income statement are integrated over time. Cash-flows errors represent consequences of incomplete item coverage of financial statements, as well as various forms of mismeasurement of the items in the financial statements.

Table 4 reports estimates of the statements of cash flows for each survey. Starting with net income (from the income statement), the estimated change in cash flows is the sum of three types of cash flows: from production, from consumption and investment, and from financing. To construct these statements, we have to estimate the elements of the cash flows from financing using estimated changes in the relevant assets and liabilities from the prior-period balance sheet. This methodology produces a cash-flows estimate that is a residual difference between net income and net cash flows, rather than a direct measure of the gross cash flows in and out of the balance sheet, because the latter are not available from the U.S. surveys. For comparison, we estimate the change in cash holdings directly from the current and prior-period balance sheets.³²

TABLE 4.

U.S. Surveys: Statements of Cash Flows

(Cash defined as Current Assets)	PSID 2010–2012	CES 2011–2012	SCF 2010–2013	HRS 2010–2012	SIPP 2010–2011
Net income (+)	65,350	60,971	81,856	79,779	38,944
Adjustments:
Depreciation (+)	0	0	0	0	0
Change in Account Receivables (−)	0	0	0	0	0
Change in Account Payables (+)	0	0	0	0	0
Change in Inventory (−)	0	0	0	0	0
Change in Other (not Cash) Current Assets (−)	0	0	0	0	0
Consumption of Household Produced Outputs (−)	0	0	0	0	0
Cash flows from Production	65,350	60,971	81,856	79,779	38,944
Consumption expenditure (−)	−43,766	−44,849	−28,850	−45,073	−22,487
Capital (durable goods) expenditure (−)	0	0	0	0	0
Cash flows from Consumption and Investment	−43,766	−44,849	−28,850	−45,073	−22,487
Transfers to/from Long-Term Investments	−362	0	1,231	0	0
Lending (−)	0	−151	1,359	50	4,452
Borrowing (+)	4,230	8,089	−4,349	−3,757	−8,988
Net Gifts Received (+)	0	0	0	0	0
Cash flows from Financing	3,868	7,938	−1,759	−3,707	−4,536
Change in Cash Holding (from Statement of Cash Flows)	25,452	24,060	51,247	31,000	11,921
Change in Cash Holding (from Statement of Balance Sheet)	3,091	17,770	3,843	1,678	−18,622
Cash flows error	22,362	6,290	47,404	29,322	30,543
Internal Error	25%	13%	37%	24%	25%
External Error	30%	8%	61%	39%	42%

SOURCES: Panel Study of Income Dynamics (PSID) 2010–2013, Consumer Expenditure Survey (CE) 2011–2012, Survey of Consumer Finances (SCF) 2010–2013, Health and Retirement Survey (HRS) 2010–2012, and Survey of Income and Program Participation (SIPP) 2010–2011. See Section 2 for more details.

NOTES: Table entries are average dollar values for the survey’s unit of observation, approximately a household. Cash flows are at a yearly rate and are constructed with the most recent prior data available. Sampling weights provided by each survey were used in calculating the average values. A more detailed data appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

The degree of dynamic integration is defined as the difference (error) between the estimated cash flows variables and the change in cash holdings estimated from the current and prior period balance sheets, expressed in dollar terms and as a percentage of the lagged stock of cash. We call this the “internal” cash-flows error because it is calculated using only the survey’s estimates of stocks and flows. However, cash holdings from any particular survey may differ from the actual aggregate U.S. estimate of cash holdings (from the Flow of Funds), so these errors may not accurately represent the true degree of integration. Therefore, we also include the change in household cash holdings from the Flow of Funds (same for each survey) and construct errors in the survey cash-flows estimates relative to the actual Flow of Funds cash to give a better measure of dynamic integration. We call this the “external” cash-flows error.

As measured by their ability to track stock-flow identities in the statements of cash flows, the U.S. surveys exhibit relatively weak dynamic integration, and the degree of integration varies widely across surveys. The absolute value of the internal cash-flows error ranges from $6,290 (CE) to $47,404 (SCF). Note that these errors are just one estimate in a time-series of errors that could be estimated, and other errors might be smaller in absolute value during other periods. However, the sheer magnitude of these internal errors suggests significant gaps in tracking household financial conditions over time, even within the self-contained estimates of a particular survey.³³ The cash-flows errors are reported in percentage terms relative to the two benchmarks: 1) the lagged cash stock from the survey’s balance sheet (internal error); and 2) the lagged cash stock from the Flow of Funds aggregate benchmark data (external error). The internal errors are relatively large, ranging from about 13 percent to 37 percent of lagged cash (CE and SCF, respectively). The survey estimates of cash flows are generally less than the external benchmark: all but one of the external cash-flows errors are even larger in absolute value, ranging from about 11 percent to 61 percent of lagged cash.

5. The TTMS and DCPC

Moving beyond the U.S. household surveys, we now focus on two other surveys that offer improved integration with financial statements and reflect better measurement of certain aspects of household economic conditions. The TTMS and DCPC are quite different in most regards. The TTMS is a comprehensive survey of household economic conditions, including home businesses; it is administered to Thai households, which are relatively low-income, less-developed, and located in rural geographic regions. In contrast, the DCPC is a relatively narrow consumer survey that is administered to U.S. consumers and is focused on payment choices. Nevertheless, the TTMS and DCPC both embody certain elements of improved integration with financial statements. The TTMS is heavily focused on the most basic and liquid M1 portions of “cash” (or current assets). The DCPC includes currency and is unique in this respect among the U.S. surveys that we analyze here. The DCPC also features other means of payment, for example, payments that use deposit accounts, although it does not track the level of these deposits.

This section compares and contrasts the TTMS and DCPC surveys. First, we present estimated balance sheets and income statements for each survey and discuss their degrees of integration by item coverage. Next, for each survey, we describe the methodology for measuring cash flows. Finally, we assess its degree of integration by dynamics, emphasizing its relatively high integration compared with the U.S. surveys. For this section, we combine survey responses from the DCPC with responses from the SCPC because both surveys are needed to estimate the financial statements as thoroughly as possible. For simplicity, we refer to the combined DCPC and SCPC estimates “CPC.”

5.1. Balance Sheets and Income Statements

Balance sheets and income statements constructed from the TTMS and CPC surveys appear in Table 5 and Table 6, respectively. These statements are designed and organized similarly to the analogous statements from the U.S. surveys, with a few exceptions. In these tables, the TTMS and CPC data represent exactly the same time period (October 2012), and the TTMS estimates have been converted to U.S. dollars using the Thai baht exchange rate for October 2012. Unlike the U.S. survey entries, the entries are not annualized because both the TTMS and the DCPC are designed to be monthly surveys.

TABLE 5.

TTMS and SCPC/DCPC: Balance Sheets, October 2012

	TTMS	DCPC/SCPC
Assets	89,082	301,425
Median		146,053
Financial assets	35,553	836
(% of assets)	(40)	(0)
CURRENT ASSETS.	35,321	836
Cash	35,332	836
Currency	30,874	836
Government-backed currency	30,874	836
Bank accounts	4,458
Other current assets	−11
Certificates of deposit
Net ROSCA position	−11
Accounts receivable	0
Bonds
Mutual funds/hedge funds
Publicly traded equity
Life insurance
LONG-TERM INVESTMENTS.	232
Retirement accounts
Annuities
Trusts/managed investment accounts
Other lending	232
Tangible (physical) assets	53,529	148,421
(% of assets)	(60)	(49)
Business assets	334
Agricultural assets	1,243
Housing/household assets	4,582	148,421
Primary residence		148,421
Inventories	8,394
Livestock	290
Other nonfinancial assets	38,687
Unknown assets		152,168
(% of assets)		(50)
Liabilities	5,317	120,689
Median		42,935
Revolving Debt		5,306
(% of liabilities)		(4)
Credit cards / charge cards		5,306
Revolving store accounts
Non-revolving Debt	5,317	115,383
(% of liabilities)		(96)
Housing		67,278
Mortgages for primary residence		67,278
Mortgages for investment real estate
HELOC/HEL
Loans for improvement
Accounts payable	1,480
Loans on vehicles
Education loans
Business loans
Investment loans (e.g., margin loans)
Unsecured personal loans
Loans against pension plan
Payday loans / pawn shops
Other loans	3,837	48,105
Net worth (equity)	83,765	180,736
Cumulative net gifts received
Cumulative savings	56,779

NOTES: Thai Baht converted to U.S. Dollars at a rate of 30.68 Baht per Dollar. Values are stocks as of the time of the survey, which for the CPC is between the beginning of September and the end of October. TTMS entries are at the household level. CPC entries are either at the household level or converted to a household level by multiplying consumer values by 2.045. A more detailed appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

SOURCES: Townsend Thai Monthly Survey (TTMS), Survey of Consumer Payment Choice (SCPC).

Table 6.

TTMS and SCPC/DCPC: Income Statements, October 2012

	TTMS	SCPC/DCPC
Income	1,643	5,921
Median		4,413
Censored income		4,789
Labor Income	252
(% of total income)	(15)
Production Income	1,368
(% of total income)	(83)
Business	326
Agricultural activities	1,042
Cultivation	536
Livestock	392
Produce	390
Capital gains	2
Fish and shrimp	114
Other income	23
(% of total income)	(1)
Expenditures	813	1,840
Production Costs	813
(% of total expenditures)	(100)
Business	251
Agricultural activities	529
Cultivation	133
Livestock	292
Capital losses	1
Depreciation	12
Other expenses	280
Fish and shrimp	104
Labor provision	32
Other production activities	1
Taxes		1,840
(% of total expenditures)		(100)
Net income	830	4,081

NOTES: Thai Baht converted to U.S. Dollars at a rate of 30.68 Baht per Dollar. Values are stocks as of the time of the survey, which for the CPC is between the beginning of September and the end of October. TTMS entries are at the household level. CPC entries are either at the household level or converted to a household level by multiplying consumer values by 2.045. CPC household income is originally reported in buckets; precise estimates are imputed with the help of SCF data. A more detailed appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

SOURCES: Townsend Thai Monthly Survey (TTMS), Diary of Consumer Payment Choice (DCPC), Survey of Consumer Payment Choice (SCPC)

In general, the TTMS and CPC financial statements are not really comparable due to the relative magnitudes of their respective economies. The average asset value (Table 5) for TTMS households includes several types of business assets, and is $89,082, and the average asset value for CPC households is $301,425; this measure does not include any business assets. This difference is magnified by the fact that the CPC estimate is well below the highest estimate in the U.S. surveys (Table 2a) because it does not include any current assets beyond currency and approximates tangible assets only roughly. The average liability value is only $5,317 for TTMS households but, at $120,689, is more than 20 times larger for the CPC because there are relatively few borrowing options for Thai households. The disparity between the Thai and U.S. economies is even more evident from the income statements, shown in Table 6, where the average CPC household income is roughly three and one-half time larger than the average TTMS household income ($5,921 versus $1,643), and nearly five times larger net of expenditures ($4,081 versus $830).

One similarity between the TTMS and CPC financial statements is the predominance of currency among current asset holdings. The average TTMS household is estimated to have $30,874 in currency and less than $5,000 in other current assets (mostly bank accounts). The average CPC household has $836 in currency, which is the only type of current asset data collected. Although currency holdings are much lower in U.S. households than in Thai households, the other U.S. surveys (except the SIPP) estimate bank account holdings of about the same magnitude as Thai cash holdings, which are roughly $30,000, as shown in Table 2a. The improved 2015–2016 CPC also contains bank account balances (see below). The accuracy of the data on currency holding in Thai households could be improved, and we come back to this later.

In addition to differences in their respective economies, the TTMS and CPC survey instruments are sufficiently different to inhibit meaningful comparisons. The TTMS aims to collect data on all aspects of Thai household economic behavior, an aim that produces extensive estimates of the line items in the financial statements despite lower economic development. In contrast, the CPC strives to measure payments activity comprehensively and does not aim to cover financial-statement line items widely. For these reasons, comparisons of line-item coverage ratios between these surveys are not meaningful, nor are comparisons with the U.S. surveys.

5.2. Measuring Cash (Currency) Flows

5.2.1. TTMS Survey Instruments

ST apply this household financial accounting framework to households in the Townsend Thai Monthly Surveys (TTMS) and create the accounts from a baseline 1998 comprehensive survey and then month-by-month interviews, currently up to month 205 and counting: that is, they have 17 years of monthly data. There was an initial enumeration of all structures and all households living in a village (or in an urban neighborhood), a census including who is eating and sleeping in what structure, and a description of family relationships across the individuals in these structures. The initial survey was an extensive baseline, measuring not only initial assets and liabilities, but also contracts and relationships, for example, borrowing and labor arrangements. There are month-by-month follow-up interviews with separate modules for assets and liabilities and for revenues and expenses of various production activities. Every transaction is measured in principle, subject to recall, for example, recall of purchases, sales, gifts, and labor supply. A key to implementing this large survey is the creation of rosters, lists of individuals in the household, debts not yet repaid, plots of land under cultivation, and so on, so that enumerators know which questions to ask.

The TTMS asks households for every transaction, such as a purchase, whether it was done in cash (currency), in kind, or as a gift. Again, the period of recall in the survey is the previous month (more exactly, the time since the last interview, which is roughly 30 days). Interviewers do not observe or ask about initial levels of cash holding, but they do try to measure these flows by assuming that the initial cash holding at the beginning of the survey was high enough so that households never run out of cash; that is, cash levels can go to zero but are never negative. Cash holding does hit the zero bound when households purchase a durable or investment good with cash, which is reassuring.

In contrast with this finding, ST infer that on average households hold relatively large cash positions. This leads to two related concerns. First, consumption expenditures in cash may be underestimated. In this case, double-entry bookkeeping hits with a vengeance in the sense that there could be two errors: an underestimate of cash consumption and an overestimate of cash on the balance sheet. Second, households may choose to underreport deposits into and withdrawals from savings accounts, although they typically do confirm many transactions, large and small. In this case, two items on the balance sheet, although offsetting, may be mismeasured.

In addition, because currency is not only a means of payment but also a store of value, it constitutes a relatively large portion of a household’s wealth, on average. Therefore, households are understandably reluctant to report to enumerators how much currency they are holding. A second problem is the frequency of interviews, hence 30-day periods of recall. One potential remedy would have been to have households keep diaries of daily transactions for the entire month, or to use intensive diaries for shorter time intervals per respondent (as the DCPC does) to obtain a measure of aggregate activity. Initial attempts to implement a diary in real time at the request of the households themselves show great promise in dealing with this second problem. We may not know the initial balance (still hidden), but the changes in balances due to better-measured monthly transactions are more accurate. This is a step toward the degree of accuracy of the CPC surveys described below.

At the time of the conception and initiation of the TTMS in 1997, the use of payment devices other than cash was rare in these rural areas. Over time, there has been an increase in card dissemination and small levels of use. The TTMS was modified to incorporate cards into the survey, but measurement has been difficult due to many complex issues, including question design, accounting methods, tracking card payments, reconciling end-of-month statements, separating interest from principal, rolling over debt, and so on. The remainder of the paper describes the Boston Fed’s DCPC, an approach that might have improved the TTMS, and then shows how the integrated financial accounts can be extended with the DCPC data to include multiple means of payment.

5.2.2. CPC Survey Instruments

The 2012 SCPC and 2012 DCPC are related but independent instruments that were implemented around October 2012 with a common sample of respondents from the RAND Corporation’s American Life Panel (ALP). The SCPC is an approximately 30-minute online questionnaire that collects data on consumer adoption and use of bank accounts and payment instruments. The DCPC is a three-day mixed-mode survey with daily recording of payments in a paper memory aid (or other form) plus three daily online questionnaires to input memory-aid data plus answer additional questions based on recall within the day. In 2012, most respondents took the SCPC before their randomly assigned three-day period during October, but some respondents completed the SCPC after the DCPC. The order did not affect survey responses because the instruments are independent.

Cash holdings (stock) data are collected by the SCPC and DCPC, which are related but distinctly different types of survey instruments, as described in Section 2. The SCPC obtains estimates of cash held by respondents on their person (“pocket, purse, or wallet”) or on their property (home, car, or elsewhere).³⁴ The 2012 DCPC obtained estimates of currency (no coins) held by respondents on their person on each of the four nights of the diary, asking the respondent to report amounts by denomination of the bills ($1, $2, $5, $10, $20, $50, and $100) and in total (summed for them in the online questionnaire).³⁵ In October 2012, U.S. holdings of currency on person were on average $56 per person with a median value of $22.

Cash flows—deposits and withdrawals (payments)—are collected by the SCPC and DCPC as well. With regard to cash withdrawals made for expenditures (payments), the SCPC obtains estimates of the number of cash payments “in a typical period [week, month, year],” whereas the DCPC more precisely obtains estimates of the number and value of each cash payment (expenditure) made during a three-day period. Both the SCPC and the DCPC collect data on the number and value of cash withdrawals from bank accounts and other sources. However, because cash withdrawals are relatively rare for most consumers, the DCPC does not obtain estimates that are as comprehensive for individual consumers as does the SCPC, which asks for “typical” currency withdrawals during a longer time period than three days. Only the DCPC tracks currency deposits to bank accounts and other sources plus other unusual currency activity (conversion of currency to/from other assets, exchanging coins for bills, and such).

Two additional differences between the SCPC and DCPC have important implications for their cash data. First, while both surveys ask respondents to record their cash holdings at the time of the survey, the SCPC allows respondents to estimate their holdings, while the DCPC requires respondents to count their cash on person (bills only, no coins) by reporting the number of bills of each denomination, and the online DCPC questionnaire assists respondents in summing the value of their cash holdings. As a result, the SCPC cash holdings data exhibit more rounding (to the nearest $5, $10, or $20) and approximation than the DCPC data. Second, the SCPC collects data on cash payments based on respondents’ recall of their typical behavior, while the DCPC collects data that respondents record in essentially real time at the point of payment. Recall-based estimates of payments are likely to be inferior to recorded estimates due to potential errors from memory loss and time aggregation. For more information about the DCPC and its advantages in measuring consumer expenditures, see Schuh (2017).

5.2.3. Measurement by Recall versus Recording

By way of summarizing the material in this paper so far, we describe the main advantage of TTMS over the U.S. surveys and the innovation in the DCPC relative to the TTMS. The main advantage of TTMS is that it aims to achieve complete integration with household financial statements by line-item coverage and by stock-flow dynamics. To see this point, consider the following illustrative system of equations that reflects the subset of TTMS financial statement estimates for the cash-flows dynamics of M1 liquid assets:

$$\widetilde{\Delta A_{1t}}=\widehat{D_{1t}}-\widehat{W_{1t}}+{\eta }_{1t}$$

$$\widetilde{\Delta A_{2t}}=\widehat{D_{2t}}-\widehat{W_{2t}}+{\eta }_{2t}$$

$$\widetilde{A_1}=\widetilde{A_{1t}}+\widetilde{A_{1t}},$$

where the two assets, k = {1,2}, are currency (1) and demand deposits (2) and η denotes a composite measurement error. An overhead circumflex (“hat”) denotes a variable that is estimated directly by the survey (TTMS). The exception is that the TTMS does not directly collect cash holdings every period, unlike the DCPC. Instead, the TTMS makes an estimate of the initial stocks, $\left(\widehat{A_{1,0}},\widehat{A_{2,0}}\right)$, and then uses these stock-flow identities to impute the estimates of cash stocks in subsequent periods, denoted by an overhead tilde (~). In the imputation procedure, the TTMS enforces the constraints imposed by the principles of integration, such as $\widetilde{A_{kt}}\ge 0$, and makes judgmental adjustments where necessary.

Conceptually, the TTMS is fully integrated. It achieves complete integration by line-item coverage because it estimates all items of the balance sheet (A_1t A_2t) and cash-flows statement (D_1t, D_2t, W_1t, W_2t). As a result, the TTMS would also achieve complete integration by dynamics, provided it covered 100 percent of the dollar values of the items; in this case, the stock-flow dynamics would hold without error. However, it is essentially impossible for a survey to reach complete value coverage, due to sampling errors, among other challenges. For this reason, the TTMS imputes the periodic stock of currency using a judgmental estimate of the starting value of currency holdings for each household and adjusts it periodically if the stock-flow law of motion produces an invalid level estimate. Of course, the TTMS cannot claim to achieve full integration by dynamics or by item coverage in terms of dollar value, as TTMS estimates likely have measurement errors, as all surveys do. Nevertheless, the TTMS is generally much more integrated than the U.S. surveys analyzed earlier, which have much less than full integration by coverage (item or value) and relatively large errors in cash-flows dynamics. The links between the income statement and the balance sheet were not incorporated into these U.S. surveys.

In particular, one type of measurement error likely occurring in the TTMS cash-flows estimates arises from recall-based low-frequency (monthly) estimates of cash flows. As noted, recall errors may occur from memory loss due to time aggregation over the days of the month or over the number of cash deposits and withdrawals (payments). To see this, note that monthly currency withdrawals,

$$W_{1t}={\sum }_{d=1}^{D_t}{\sum }_{k=1}^{K_t}{W}_{1kdt},$$

are the sum over all opportunities and days, where 28 ≤ D_t ≤ 31 and K_t ≥ 0. Like most U.S. surveys, the TTMS obtains an aggregate recall-based estimate of monthly cash withdrawals, $\widehat{W_{1t}}$, from deposits to currency, without measuring each individual cash withdrawal, W_1kdt. The same measurement issue holds for currency deposits, which are less frequent and thus may be measured with less error.

By comparison, daily payment diaries like the DCPC represent an innovation in the measurement of stock-flow dynamics by recording high-frequency (daily) cash flows. For example, the DCPC obtains an estimate of each individual cash withdrawal, $\widehat{W_{1kdt}}$, by type, so the DCPC estimate of aggregate monthly cash withdrawals is the sum of individual withdrawals estimates,

$$\overline{W_{1t}}={\sum }_{d=1}^{D_t}{\sum }_{k=1}^{K_t}\widehat{W_{1kdt}},$$

denoted by an overhead line. Therefore, if high-frequency (daily) recorded estimates of cash flows are more accurate than low-frequency (monthly) recall-based estimates, then we expect that

$$\left|\overline{W_{1t}}-W_{1t}^{*}\right|<\left|\widehat{W_{1t}}-W_{1t}^{*}\right|,$$

at least on average, if not period-by-period as well. Consequently, the DCPC estimates of the stock-flow law of motion for currency,

$$\Delta A_{1t}=\overline{D_{1t}}-\overline{W_{1t}}+{\mu }_{1t},$$

are likely to be a better measure than those from the TTMS for the reasons enumerated above: 1) DCPC estimates of monthly currency flows are sums of individual opportunity-day flows. 2) DCPC estimates of currency holdings are obtained each period, not derived from an initial condition (estimate) using the estimated flows. In this sense, the DCPC estimates improve the integration of surveys with financial statements and offer the opportunity for enhanced analysis of household behavior, as demonstrated below.

5.3. Statements of Cash Flows

The statements of cash flows constructed from the TTMS and CPC surveys appear in Table 7. In most respects, these cash-flows statements are designed analogously to the statements of cash flows from the U.S. surveys (Table 4), and the elements are defined similarly to those in the balance sheets and income statements for TTMS and SCPC/DCPC (Tables 5–6). One exception is that the TTMS and DCPC represent cash flows and balance-sheet changes for one exact month (October 2012) rather than annual (or lower-frequency) flows. Also, bear in mind that the TTMS cash flows from financing equal the actual changes in the balance-sheet stocks. Therefore, the estimated change in currency from the cash-flows statement equals the change from the balance sheet by definition; hence, the cash-flows error is exactly zero because the stock-flow principle of motion is an identity, a significant step forward. Thus, the TTMS appears fully integrated by dynamics, but this integration is “artificially” high because it is derived rather than estimated directly.

TABLE 7.

TTMS and DCPC: Statements of Cash Flows, October 2012

(Cash defined as Currency)	TTMS	DCPC
Net income (annual basis) (+)	8,750	69,207
Net income (monthly basis) (+)	729	5,767
Adjustments:
Depreciation (+)	94	0
Change in Account Receivables (−)	−37	0
Change in Account Payables (+)	0	0
Change in Inventory (−)	80	0
Consumption of Household Produced Outputs (−)	−6	0
Net Capital Gains (+)	−1
Cash flows from Production	859	5,767
Consumption expenditure (−)	−245	−6,767
Capital (durable goods) expenditure (−)	−77	0
Cash flows from Consumption and Investment	−327	−6,767
Change in Demand Deposits (−)	−67	-421
Change in NFDA deposits (−)	na	59
Change in Foreign Currency (−)	na	-2
Change in Credit Card Balance (−)	na	1,292
Change in Long-term Assets (−)	76	-669
Change in Other Debts (−)	4	na
Cash flows from Financing	13	259
Change in Currency Balance (from Statement of Cash Flows)	544	-741
Change in Currency Balance (from Statement of Balance Sheet)	544	164
Cash flows error	0	905
Internal Error	na	135%

NOTES: Thai Baht converted to U.S. Dollars at a rate of 30.68 Baht per Dollar. Values are stocks as of the time of the survey, which for the CPC is between the beginning of September and the end of October. TTMS entries are at the household level. CPC entries are either at the household level or converted to a household level by multiplying consumer values by 2.045. CPC household income is originally reported in buckets; precise estimates are imputed with the help of SCF data. A more detailed appendix and the Stata programs used to construct the tables are available at https://www.bostonfed.org/about-the-boston-fed/business-areas/consumer-payments-research-center.aspx.

SOURCES: Townsend Thai Monthly Survey (TTMS), Diary of Consumer Payment Choice (DCPC), Survey of Consumer Payment Choice (SCPC)

Cash flows in Thai and U.S. households differ in both magnitude and type. Net income is naturally much larger, $5,767 versus $729, in U.S. households. Adjustments to net income for accrual-based income in the statements of cash flows are modest for Thai households that have business income (a total increase of $130), and not measured for U.S. households ($0), so the difference in cash flows from production are still large, $5,767 versus $859. However, cash flows for consumption and investment by U.S. households are very large, estimated at $6,767, relative to net income but much smaller relative to income, estimated at $327, for Thai households. Similarly, U.S. cash flows from financing are larger, $259 versus $13, and more diverse, notably with respect to credit cards (which were not included in the 2012 TTMS). The estimated changes in currency from cash flows are roughly similar, $−741 versus $544, despite larger differences in net income and other flows. Finally, the cash-flows error analysis is not relevant or comparable. The TTMS error is zero ($0) by definition because the balance-sheet changes are restricted to equal the cash flows. In contrast, the DCPC error is a legitimate derivation from estimates of all components of the stock-flow relationship. However, the error, $905, is relatively large, 135 percent of lagged currency, because the DCPC was not designed or implemented in a way that would ensure full dynamic integration. Instead, the DCPC calculations illustrate the potential advantage of a payment diary in tracking the gross flows of currency and the stock-flow dynamics in financial statements.

6. An Innovation toward Better Integration

This section introduces an innovation to cash-flow accounting that demonstrates a second advantage of the DCPC for moving another step toward complete ST integration of surveys and financial statements. The previous section explained how payment diaries like the DCPC produce better estimates of cash flows and stocks than monthly surveys do. In addition, payment diaries can produce estimates of cash flows that directly link individual asset and liability accounts to cash flows via the payment instrument, rather than just linking aggregate categories of assets and liabilities to aggregate categories of cash flows. The remainder of this section describes the linkage between the balance sheet and payment instruments and then presents a new analysis of cash flows by account, before concluding with a preview of further innovations in the 2015 DCPC.

6.1. Payment Instruments and Balance-Sheet Accounts

Table 8 depicts the linkage between payment instruments and their associated balance-sheet accounts: assets and liabilities. Payments are funded (settled) by one of two broad types of accounts: money (asset) and credit (liability). Money includes transactions balances, or M1 (currency plus checking accounts), plus certain non-transaction balances, which are part of M2. The latter are savings, but in some cases can support a limited number of payments directly from or to the account (account-to-account, or A2A, transfers). Payments funded by money are usually settled instantly (with cash) or with delays of at most a couple days. Alternatively, credit accounts fund payments that are settled much later; non-revolving credit accounts (charge cards) require consumers to repay their debt during a certain period (typically a month), while revolving credit accounts (credit cards) offer consumers the option of rolling over some of the debt (up to a credit limit) to the future indefinitely in exchange for incurring interest charges. Monetary assets and unused credit limits are the liquidity that fund payments that are tracked by instrument in the DCPC.³⁶

TABLE 8.

Payment Instruments and their Balance Sheet Accounts

Balance Sheet Accounts	Payment Instruments
Assets (money)
Currency	U.S. currency
	Foreign currency
	Private currency (e.g., Bitcoin)
Traveler’s check	Traveler’s check
Checking accounts owned by consumers (demand and other checkable deposits)	Checks (personal or certified)
	Debit card
	OBBP
	BANP
Checking accounts owned or managed by financial institutions or non-financial payment service providers (but may have pass-through deposit insurance for consumers)	Cashier’s check
	Prepaid card
	Money order
Savings accounts owned by consumers (“non-transactions” accounts in the non-M1 part of M2 with direct payment capability)	Checks
	Debit card
	OBBP
	BANP
Liabilities (credit)
Revolving credit	Credit card
Non-revolving credit	Charge card
	Text/SMS

Source: Authors’ analysis and Greene, Schuh, and Stavins (2016).

The linkage between payment instruments and balance-sheet accounts merits additional discussion before moving ahead. Table 8 reveals that in U.S. household balance sheets the linkage is not one-to-one, due to the proliferation of accounts and payment instruments in the U.S. monetary and payment system. This linkage complexity is most evident in the variety of instruments that can access various types of deposit accounts (including saving accounts in M2). In particular, debit cards, various types of checks, and electronic banking methods (OBBP and BANP) all can be used to authorize payment or transfer from different types of accounts. In addition, the linkages depicted in Table 8 reflect aggregation of individual accounts within a type of account that the overall pattern does not reveal. For example, the 2012 SCPC indicates that 38 percent of U.S. consumers have more than one demand deposit (checking) account (DDA), and 57 percent of consumers with multiple DDAs have multiple debit cards, typically one (per account holder) for each DDA. Consequently, the linkages between accounts and instruments can be disaggregated further to match specific accounts and instruments within the categories of Table 8. For example, a consumer (or household) may own two DDAs with a debit card for each; thus, it would be necessary to link DDA #1 to debit card #1, and similarly for the other account and card. The 2012 DCPC accurately measures the linkages between types of accounts and types of instruments (such as DDAs and debit cards), but it does not measure the linkages between specific individual accounts and specific individual instruments.

6.2. Cash Flows by Account

Given the linkage between accounts and instruments, the DCPC can also link balance-sheet accounts (or types of cash stocks) to household expenditures on consumer nondurable goods and services (or types of withdrawal flows).³⁷ Theoretically, a payment diary could link balance-sheet accounts for household capital goods to payments for investment in durable goods, but the 2012 DCPC did not track these concepts. In any case, the payment instrument plays the pivotal role because, for each payment, it directly links the balance sheet—that is, the asset or liability funding the payment—to consumer expenditures broadly defined (more broadly than narrow consumption) for each payment transaction.

Our major innovation of this paper is the “Statement of Account Flows,” which is constructed using the DCPC and appears in Table 9. The rows in this new type of financial statement are generally formatted as in a statement of cash flows, but separately for each payment account. For example, the first column is the statement of currency flows, which records the inflows and outflows of currency for each type of transaction, starting with currency inflow from production activities (monthly basis) in Row A and followed by currency outflow from consumption and investment activities in Row B (separating consumption expenditure in Row B1 from capital expenditure in Row B2). Next, Row C and its subsidiary rows report the net currency flows from financing activities and its components: deposits (inflows; the C1 rows) of currency from each other account (DDA, nonfinancial deposit accounts (NFDA), foreign currency, long-term financial assets (LTFA), revolving debt, and other debt) and withdrawals (outflows; the C2 rows) of currency to each of those accounts. The remaining rows compare the changes in currency balances from the statement of currency flows above (Row D) with those estimated from the balance sheet (Row E), plus an estimate of the error (in value and percentage of prior-period balance, Rows F and G, respectively).

TABLE 9.

DCPC Statement of Account Flows, October 2012

	Flows associated with accounts
	Currency	DDA	NFDA	Foreign currency	LTFA	Revolving debt	Other debt	All
A. Production (inflows)	388	5,379	na	na	na	na	na	5,767
B. Consumption and investment (outflows)	−1,038	−4,422	−58	na	-	−1,249	na	−6,771
B.1 Consumption expenditure	−1,038	−4,422	−58	na	-	−1,249	na	−6,771
B.2 Capital (durable goods) expenditure	na	na	na	na	-	na	na	na
C. Financing	−91	−536	−1	2	na	−43	669	0
C.1 Deposits (inflows)	498	564	20	2	na	na	669	1,753
From currency	−	564	15	2	na	na	8	589
From demand deposits	455	−	2	na	na	na	643	1,100
From non-financial deposit accounts	21	na	−	na	na	na	0	21
From foreign currency	0	na	na	−	na	na	na	0
From long-term financial assets	na	na	na	na	−	na	na	0
From revolving accounts	22	na	3	na	na	−	18	43
From other debt	na	na	na	na	na	na	−	0
Addendum: Total deposits (inflows)	886	5,943	20	2	na	na	669	7,520
C.2 Withdrawals (outflows)	−589	−1,100	−21	0	na	−43	na	−1,753
To currency	−	−455	−21	0	na	−22	na	−498
To demand deposits	−564	−	na	na	na	na	na	−564
To non-financial deposit accounts	−15	−2	−	na	na	−3	na	−20
To foreign currency	−2	na	na	−	na	na	na	−2
To long-term assets	na	na	na	na	−	na	na	0
To revolving accounts	na	na	na	na	na	−	na	0
To other debt	−8	−643	0	na	na	−18	−	−669
Addendum: Total withdrawals (outflows)	−1,627	−5,522	−79	na	na	−1,292	na	−8,524
D. Change in account balance (from Statement of Account Flows)	−741	421	−59	2	na	−1,292	669	−1,004
E. Change in account balance (from Balance Sheets)	164	na	na	na	−4,501	−673	9,489	−8,816
F. Flow error	905	na	na	na	na	−619	−8,820	7,812
G. Error (% lagged account balance)	135%	na	na	na	na	92%	93%	−89%

NOTE: DDA are demand deposit accounts; NFDA are nonfinancial deposit accounts; LTFA are long-term financial assets.

Similarly to the statement of currency flows in the first column, the remaining columns of the table represent information for the flows of DDA, NFDA, foreign currency, LTFA, revolving debt, and other debt, with the final column reporting the row sum. This provides the link from aggregate cash to each of the payments mechanisms. Importantly, note that the total net flows concept in Row C appears in the last column (“All”) as exactly zero by construction, since what goes into one payment account comes from another.

Total average account balances of U.S. consumers declined $1,004 in October 2012, according to the DCPC, as average consumption, at $6,771, exceeded total account flows from production activities, which were $5,767. This change in account balances tabulated from account flows resulted from much larger gross inflows and outflows, as withdrawals, at $8,524, exceeded deposits, which were $7,520. However, the decline in account balances estimated from the statement of account flows was considerably smaller in absolute value than the corresponding change estimated from balance-sheet stocks, which was $8,816. Therefore, the statement of account balances suggests that the DCPC is likely incomplete and may have considerable measurement errors, despite its conceptual promise for better integration by dynamics. One obvious area of incompleteness in the statement of account flows is that deposits of income to DDAs are not measured directly, but rather assumed to equal the difference between net income and currency deposits to income.³⁸

The statement of account flows exhibits at least two interesting results with economic implications that may be useful for future research to link real (consumption) and nominal (financial) household choices. First, 99 percent of consumption, at $6,771, is funded by payments from DDAs (65.3 percent), from credit cards (18.4 percent), and from currency (15.3 percent). This result reflects heterogeneity in consumer payment choices, which may have implications for payment systems and for household budgeting and management of liquidity. Second, the gross-flow magnitudes are not small relative to income and consumption, which raises questions about the efficiency of the monetary system and relates to the classic literature on money demand: Why are U.S. households holding relatively large amounts of their liquid assets in payment accounts (just as Thai households hold so much in currency)? Also, it is still not entirely clear why consumers make such large transfers between currency and DDA, two assets that have the same monetary nature (M1) and are essentially equivalent for the settling of exchange. Evidence from the Survey of Consumer Payment Choice indicates that many U.S. consumers still rate the characteristics of currency (cost, speed, convenience, recordkeeping, and such) high relative to other payment instruments, and merchant acceptance of instruments is still not universal. Nevertheless, these large transfers between currency and DDA likely involve costs that may be reduced by the use of electronic money. All together, the account flows provide new data with advantages that potentially offer greater insight than existing data and research do into household financial decisionmaking and the optimal design of the payments system more generally.

6.3. Improvements to the 2015 DCPC

While the 2012 DCPC introduced an innovation to the measurement of currency flows that has enhanced the degree of integration for one type of asset (currency), its coverage of financial statements has been relatively low, due to its limited mission and purpose. However, expanding the DCPC to measure the stocks of other assets from which consumers make payments not only increases coverage and integration but also provides important information for studying payment choices. For example, the analysis of the demand for currency and payment cards (debit and credit) by Briglevics and Schuh (2014) was limited by the lack of data on checking account balances. Also, the results in Schuh (2017) demonstrating the close correspondence between payments and personal income were produced without the benefit of direct measurement of the receipt of income by DCPC respondents.

Consequently, in 2015 the Boston Fed undertook to make major improvements to the SCPC and DCPC that substantially improved their integration with household integrated financial statements and the ST methodology. Improvements to the coverage of balance sheets included adding:

• Additional short-term liquid assets other than currency, including balances held in checking (DDA) and nonbank deposit accounts, such as prepaid cards, PayPal, etc. [SCPC and DCPC]

• Collection of outstanding debt balances from credit card bill payments. [DCPC only]

Improvements to coverage of income and cash-flows statements included adding:

• More intentional and detailed classification of expenditures based on official National Income and Product Account (NIPA) definitions of consumption, which increases the precision of the distinction between consumption and non-consumption expenditures. [DCPC only]

• Collection of the actual dollar values, types, and frequencies of personal income receipts, which will permit direct comparison of aggregate DCPC income with NIPA income.³⁹ [DCPC only]

• Increased precision and information about the timing and nature of bill payments, which will improve the classification of expenditures and expand the capability to link payments to assets, and especially to liabilities (such as outstanding debt other than credit card debt).

Data from the 2015 and 2016 DCPC are in the process of being analyzed and prepared for publication in the near future.

6.4. Lessons for Survey Design

For all of the household financial surveys covered in this paper, and for any other similar survey, there is a relatively clear and straightforward path to developing complete integration with household financial statements. At least two main steps would need to be taken:

1. Obtain complete item coverage. All of the surveys are missing some line items from the balance sheet, income statement, or statement of cash flows. Adding survey questions to obtain estimates for each of these line items would provide complete item coverage. Of course, the coverage of a line item is not sufficient for full integration because errors may arise from sampling, question design, and other factors. Also, further disaggregation of the line items of the financial statements reported earlier may be required to achieve accurate aggregate estimates. Nevertheless, conditional on accurate estimation, comprehensive coverage of line items is a necessary step toward full integration. The surveys should also take into consideration innovations in financial instrument and payment methods, as they provide alternatives or replacements.

2. Ensure exact stock-flow identities. All surveys could improve the accuracy of their estimation of the dynamic identities inherent in the statement of cash flows. The use of high-frequency payment diaries appears to be one promising method for achieving this improvement. Provided the estimation of stocks (assets and liabilities) is relatively accurate, it is the estimation of aggregate flows (income and expenditures) over relatively long periods of time (minimum one month, but up to one year or more) that is the key survey methodology issue. Survey methods other than high-frequency payment diaries may yield improved estimates of aggregate flows, but it is not apparent which are the most successful. Further research is needed on this matter.

These two items are necessary for improving the integration of household financial surveys with household financial statements; they may also have interaction effects: for example, the omission of an asset from the balance sheet prevents improvements in the statement of cash flows. However, there may be other development issues to address as well, such as further improvements in the survey sampling frames.

7. Extensions and Conclusions

While the development issues necessary for integration are reasonably clear and straightforward, countervailing factors may inhibit comprehensive integration. One factor may be the lack of motivation, mandate, scope, or directive by the survey sponsors. Relatedly, the expansion of one survey may begin to overlap the coverage of another, which might be problematic for sponsors. For example, the SCF and CE each have relative strengths that, when combined, might move the collective dataset much closer to full integration of the accounts, but expansion of one or both of these surveys would create significant and costly duplication and would likely trigger a call for streamlining. Finally, an obvious inhibiting factor is the lack of sufficient budgetary resources to expand the survey and diary program, although budgetary resources are jointly determined with the previously mentioned factors.

The preceding discussion is equally relevant for the CPC survey and diary. Like all surveys, the 2012 SCPC and DCPC have advantages and disadvantages relative to the other surveys. However, one promising feature of the CPC survey and diary is that they have considerable room for quality improvements to the questionnaires that do not require additional budgetary resources, alternative sampling methods, or broader scope of operation and directive. The Boston Fed implemented the following improvements in the SCPC and DCPC during the fall of 2015, and the results will be forthcoming in future research.

• Separately identifying the payer (consumer) and payee rather than defining merchant categories that combine payee and type of expenditure, a separation that enables a far richer understanding of the purposes and reasons for the expenditure (including whether or not the expenditure was expected and the source of funding for unexpected expenses).

• Improvements to the statement of cash flows include additional information on how households finance their expenditures, and also provide additional real-time error-checking of online questionnaire responses, using stock-flow identities among assets, income, and expenditures.

These improvements highlight the fact that payment diaries link individual expenditure entries of the income statement with their associated assets and liabilities in the balance sheet and the detailed statement of cash flows in ways that have not been realized in other studies, including ST. However, the improvements are modest relative to the additional innovations that would be required to achieve complete integration, so much more research and data collection are needed.

The CE also is undergoing a redesign and improvement effort in response to recommendations from a National Academy of Sciences review panel, as described in National Research Council (2013). The report recommends considering three new prototype designs:

• Design A – Detailed expenditures through self-administration. This method would improve respondent reporting of expenditures and reduce respondent burden in data collection.

• Design B – A comprehensive picture of income and expenditures. This method would use technology, financial records, financial software, and budget balancing to improve estimates of the income statement.

• Design C – Dividing tasks among multiple integrated samples. This method would improve estimation of income-statement items through better use of sampling methodology.

While these improvements are valuable and promising, the NAS report does not appear to discuss or advocate the concept of integration beyond improvements to estimation of the income-statement line items.

A detailed discussion of research coming from the TTMS, SCPC, DCPC, and the other U.S. surveys is outside the scope of this paper. Many excellent contributions make use of each of the various surveys, and some use combinations of them. At the same time, analysts are limited in what they do without the integration of the accounts; indeed, a literature review would be useful to enumerate these strengths and limitations and to illustrate what might be done with improved data. Of course, this would take us well beyond the current endeavor.

Relatedly, although we have aggregated up to a common “representative” set of financial accounts, one would often like to disaggregate to some degree and go back to the underlying data organized by the accounts. Given the recent interest in the observed heterogeneous outcomes across U.S. communities in the lead-up and fall-out from the Financial Crisis, it would be natural to disaggregate by geography (ZIP code, SMSA, commuting zone, county, state). Unfortunately many of the surveys were not designed to be representative at this level or lack sufficient observations to provide statistical significance. Indeed, one ends up taking one piece of data from one survey, another from another, and so on. But the available data are not organized systematically under the conceptual framework of integrated financial accounts. This, too, would seem to be a worthwhile endeavor that is beyond the scope of the current paper.

In the broader introduction to this paper and in the measurement efforts in the last few sections, we stressed the importance of payments data that could make it possible to distinguish among the payment instruments, align with more conventional measures of cash flow, and be used to calculate changes in balance-sheet items and income statements. Again, we have not had space in this paper to describe this connection in more detail. Suffice it to note that innovation in financial markets and monetary policy all point to issues related to the still-important use of currency and issues related to the potential of alternative media of exchange based on new asset accounts. Indeed some papers in the literature already note that the impact of monetary policy as previously conducted was a function of the industrial organization of banks at a local level. In particular, the willingness and ability of households to substitute across cash and demand deposits was found to be crucial in gauging the impact of policy. Better data on payments is thus central to understanding the impact of monetary policy moving forward.

Although we have presented standard accounting practices, the measurement provided by the accounts should be consistent with the measurement suggested by theoretical models. For example, if there were complete markets for contingent claims, then future income flows would be conceptualized as discounted future income adding to contemporary wealth. Contingent assets lose value when the expected states of the world on which their value depends do not occur, but they gain in value if the contracted state is realized. Wealth or net worth would move only with aggregate shocks. With incomplete markets and contracts, it is easier to envision wealth as the buffer stock or pension fund used to deal with this uninsured uncertainty. In any event, there needs to be a review of the contracts and implicit understandings a household has entered into and scrutiny, in turn, of how to treat these in the accounts. This, as well, remains the subject of another paper.