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We examined the effects of using bootstrap weights to account for the complex sample design in analyses of Survey of Consumer Finances (SCF) datasets. No article published in this journal that has used the SCF has mentioned the issue of complex sample designs. We compared results obtained without weights and with application of population and bootstrap weights in a logistic regression, and found no substantial differences between the unweighted and the weighted analyses. We also compared results for an ordinary least squares regression, and found few differences between unweighted and weighted models. Unweighted regressions produced more conservative significance tests than the counterpart, and some econometricians have uggested that unweighted analyses are better for hypothesis testing. If estimation of the magnitudes of effects is important, weighted regression may be better because it produces consistent estimators. Researchers should be cautious in drawing conclusions when weighted and unweighted effects are substantially different. Lindamood, Hanna, and Bi (2007) reviewed articles that used the Survey of Consumer Finances (SCF) datasets and appeared in the Journal of Consumer Affairs. They examined the papers for lack of transparency with respect to a number of methodological issues, including weighting of multivariate analyses and the use of the Repeated Implicate Inference (RII) method. However, no articles in this journal that have used SCF datasets have included a discussion on the issue of complex sample designs (Nielsen and Seay 2014; Nielsen et al. 2009). What is the effect on standard error estimates and hypotheses testing when complex sample designs are ignored? Would a consideration of complex sample designs have changed the major conclusions of analyses of SCF datasets? For our comparisons, we used the logit model of Yuh and Hanna (2010), who analyzed a combination of the 1995-2004 SCF datasets, and employed a normative economic framework to create their hypotheses. We also used an ordinary least squares (OLS) model similar to the Yuh and Hanna logit analyses. We used the 2010 SCF dataset, and slightly modified some of their specifications. We focused on providing comparisons between an unweighted multivariate analysis of a SCF dataset, one that applies population weights only, and one that uses both population and bootstrap replicate weights. We suggest guidelines for how to choose between unweighted and weighted models. BRIEF LITERATURE REVIEW The issue of weighting of multivariate analyses has been controversial. Winship and Radbill (1994) stated that the decision to use sampling weights in regression analysis is complicated, and unweighted regression is preferred if the sampling weights are a function of the independent variables. Deaton (1997, 66) mentioned "the old and still controversial issue of whether survey weights should be used in regression." He stated that the answer should be based on the purpose of the regression, and that "the strongest argument for weighted regression comes from those who regard regression as descriptive, not structural" (Deaton 1997, 71). He suggested that researchers adopt the approach taken by DuMouchel and Duncan (1983) to compare weighted and unweighted estimates (Deaton 1997, 72). Lindamood et al. (2007) compared unweighted versus population-weighted analyses of an SCF dataset for three different logistic regressions. They reported that of 99 coefficient estimates for the three logits, nine had a difference between unweighted and weighted in terms of whether the statistical significance level was less than .05 (nine were significant in the weighted model but not significant in the unweighted model). Only one of the 99 coefficients had a statistically significant effect in the unweighted estimate and a nonsignificant effect in the weighted estimate. They cited Deaton's (1997, 66-73) discussion that weighted regression analysis is suspect for hypothesis testing on datasets with endogenous weights, and recommended that if hypothesis testing is the main research focus, unweighted regression analysis should be used for SCF datasets. …

This study uses a combination of six Survey of Consumer Finances data sets to examine whether factors affecting credit delinquency differ by the racial/ethnic identity of households. Hispanic households are less likely than white households and white households are less likely than African American households to be delinquent. Our full model with interaction terms shows that the effects of financially adverse events, financial buffers and debt burden on the debt delinquency differ across racial/ethnic groups. Combining African American and Hispanic households into one racial/ethnic minority group as previous studies have done can be problematic. ********** Attention to racial/ethnic minorities' credit acquisition dates back at least to the 1970s (Canner and Smith 1991). Public policy goals have included increasing racial/ethnic minorities' access to credit by eliminating discrimination (Anderson and VanderHoff 1999), and redlining and racial/ethnic preference in credit lending have been investigated (Canner, Gabriel, and Woolley 1991). Racial/ethnic differences in borrowing opportunities and access to credit have been also discussed in empirical literature (e.g., Anderson and VanderHoff 1999; Munnell et al. 1996; Scharfer and Ladd 1981; Tootell 1996). Financial innovation has relaxed credit constraints of households and has provided affordable and easily accessible credit to most Americans (Greenspan 1997). As a result, it has been much easier for households traditionally constrained by the credit market, including racial/ethnic minorities, to take on debt and consume more than income (Lyons 2003). With increases in credit usage of racial/ethnic minorities, higher rates of debt payment problems by African Americans and Hispanics have continued and constitute the most consistent finding across previous studies (e.g., Anderson and VanderHoff 1999; Berkovec and Gabriel 1995; Canner, Gabriel, and Wolley 1991; Getter 2003; Godwin 1999; Sullivan and Fisher 1988; Volkwein et al. 1998). The default rate for African American households was significantly higher than that for white households, even after controlling for household characteristics (Anderson and VanderHoff 1999). African Americans were also twice as likely to lose their homes due to foreclosures as whites (Warren and Tyagi 2004). More recently, African Americans and Hispanics suffered excessively in the subprime crisis (Rivera et al. 2008). Rising household debt payment problems are a concern since they influence other aspects of household finances negatively. Late debt payment is recorded on credit reports, often resulting in lower credit scores. These credit reports are then used to determine the level of risk associated with most loans or insurance. Household debt payment problems can affect access to credit or homeownership. Racial/ethnic minorities without the economic cushion provided by household wealth face severe economic hardships due to household debt problems. In addition, prospective employers may use credit reports in choosing between job candidates (Manning 2001; Scott 2005). The household credit literature has long suggested that there exist racial/ethnic disparities in debt payment performance. Nevertheless, a rigorous analysis of payment performance with attention to borrowers' race/ethnicity is lacking. Previous studies on this issue are limited in two aspects. First, most previous studies combined racial/ethnic groups, especially African American and Hispanic households and then compared these to white households or perhaps a combination of white and "other" households. Combining African American and Hispanic households has been done presumably because the number of credit problems of each racial/ethnic group for any single survey year used in those studies is relatively low, although as Lindamood, Hanna, and Bi (2007) noted, many authors have not offered explanations for combining racial/ethnic groups. …

We examine household saving in the context of a prescriptive model. Using Survey of Consumer Finances data sets in the 1995-2004 period, 57% of households reported spending less than income. Many effects in the multivariate analysis are consistent with a prescriptive model. We discuss other effects in terms of possible differences in the ability to plan or the accuracy of reporting by the respondent. Young households are more likely to report saving than older households. Black households are less likely to report saving than white households. Single female households are less likely to report saving than single male households. ********** The personal savings rate in the United States has been generally decreasing since the 1980s (U.S. Department of Commerce 2008). Stephen Brobeck, Executive Director of the Consumer Federation of America, wrote in 1999: Until recently, low household savings has received limited attention from government officials, the press, and nonprofit organizations, much less than that of continuing campaigns to reduce smoking, curtail drunk driving, use safety belts, test homes for radon, practice safe sex, "say no" to drugs, and even, despite low oil and gas prices, conserve home energy. There has been an increased focus on encouraging Americans to save more, for instance, with the "America Saves" program, which has local educational programs that enroll people in small group activities and one-on-one interactions with coaches to try to encourage saving (Cude and Cai 2006). Many of the social marketing programs Brobeck referred to, such as reducing smoking, have clear and generally agreed upon goals that are appropriate for all people. However, unlike many of the goals of the other programs, it is not rational for all people to spend less than income in all points in their lives. Saving out of current income is becoming increasingly necessary for retirement security, for helping renters become homeowners without excessive risks, and for coping with emergencies. Given the existence of programs designed to increase saving by all households, an investigation into factors related to saving can provide insights into which households that normatively should be saving or not saving. Campbell (2006) noted that normative (prescriptive) research prescribes what households should do whereas positive research describes what households actually do. Campbell described household behavior that was inconsistent with the prescriptions of finance as mistakes, and suggested that research on household finances should explore factors related to household mistakes. Lyons and Neelakantan (2008) have a brief discussion on the implications of Campbell's framework for financial education. There have been many studies that try to explain patterns of household saving (e.g., Dynan, Skinner, and Zeldes 2004) or examine factors related to savings motives (e.g., DeVaney, Anong, and Whirl 2007). There are studies that discuss the best ways to motivate people to save more (e.g., Wiener and Doescher 2008). Our study follows Campbell's (2006) suggestions and attempts to evaluate which households are spending less than income relative to a normative framework. This study uses a multivariate analysis of reporting of spending less than income, and relates the results to what would be expected based on a simple prescriptive certainty equivalence life cycle framework. Therefore, we do not attempt to explain household saving behavior or motives. To the extent that households are accurate in reporting whether they spend less than income, the results should provide useful insights for social marketing programs attempting to promote increased savings. Some patterns we identify may be related to systematic errors in reporting whether households spent less than income, and we also discuss issues related to that possibility. THE LIFE CYCLE MODEL OF CONSUMPTION AND SAVING The standard model for analyzing saving behaviors is the life cycle model of consumption and saving (Modigliani and Brumberg 1954). …

We identify and present original analyses of four methodological issues related to using Survey of Consumer Finances data sets and illustrate these issues with recent articles published in this journal. The issues are recognizing that the respondent is not necessarily the household head, reporting race and ethnicity in conformity with Survey of Consumer Finances and federal standards, using the repeated-imputation inference method to combine the five implicates in each survey year’s data set, and discussing the use of weighted or unweighted data in multivariate analysis. We found a considerable variation in how authors dealt with these issues, which could hinder replication or comparison of research results. Authors and reviewers should consider methodological issues related to the Survey of Consumer Finances more carefully.

Since the Multi-Fiber Arrangement in 1974, the textile and apparel industry has received more comprehensive and persistent protection than any other industrial sector (Cline 1990). According to Hufbauer and Elliott (1994), tariffs and quantitative import restrictions in place in 1990 cost American consumers about $70 billion, more than 1 percent of Gross Domestic Product (GDP). More than a third, $24 billion, of these consumer costs were attributable to textiles and apparel alone (Hufbauer and Elliott 1994). Currently, trade liberalization programs are underway. The World Trade Organization (WTO) Agreement on Textiles and Clothing provides for the phased liberalization and elimination over the transition period of quotas on textiles and apparel imported from WTO member countries. This agreement was approved as part of the Uruguay Round Agreements Act by the U.S. Congress in December, 1994, and went into effect on January l, 1995. Also, the North American Free Trade Agreement (NAFTA) has created new opportunities for United States' textile and clothing market. Despite this trend toward free trade, the U.S. Committee for the Implementation of Textiles and Apparel (CITA) notified the Dominican Republic, Colombia, Costa Rica, El Salvador, Honduras, Thailand, and Turkey that their exports of underwear to the U.S. have been protested by U.S. manufacturers (Turck 1995). This move is considered to be a prelude to negotiation for reductions of such imports, which increased nearly 90 percent during 1992-1994. The U.S. also notified Jamaica, Honduras, and El Salvador that it plans to limit their exports of nightwear (Turck 1995). Vosko (1993) examined the extent to which trade between Canada and the U.S. was liberalized in textiles and apparel goods under the NAFTA and demonstrated that while moderate liberalization was achieved in textile trade, protectionism was increased in apparel trade. Restrictions of the supply of imports tend to raise their prices, and as the prices of imports rise, the prices of competing domestic goods tend to rise in response (Cline 1990; Dardis 1988; Scott and Lee 1996). It is well known that such price increases cause losses in consumer welfare. Past studies have assessed aggregate consumer welfare loss due to trade restrictions in the apparel industry. Clearly, trade restrictions reduce the efficiency of the economy. As Smith (1998, 441) and others have pointed out, however, equity may also be an important goal in assessing policies. Smith (1998, 441) lists three specific equity goals: equalizing the distribution of power, altering income distribution, and reducing uncertainty. Various interest groups, such as labor unions, use equity concerns to argue against trade liberalization (AFL-CIO 1996; Burtless 1995; Freeman 1995, 17). In terms of equity, the question of which consumer groups stiffer more welfare loss should also be of interest, especially the relative impact on different income groups. There are at least two reasons to believe that the welfare loss is different for different income groups. First, it is suggested in past literature that the price of low-value apparel products tends to be affected more by apparel trade restrictions than high-value apparel products (Bergsten 1972; Christerson 1994; Christerson and Appelbaum 1995). Because low-income consumers are usually buyers of low-value apparel products, they are likely to be affected more by apparel trade restrictions. Second, because consumers with different levels of income respond to price changes in apparel differently, consumer welfare loss due to the same apparel price increase can also vary across consumers with different income levels. While it is self-evident that low-income households get hurt more by the greater increase in low-value apparel products caused by apparel trade restrictions, it is not obvious how consumer welfare loss differs between low- and high-income consumers due to their different responses to price changes. …

The problem of consumers in evaluating energy saving investments suggests the need for a uniform disclosure method. Disclosure for energy conservation is related to the use of disclosure policies in Truth-in-Lending and other areas. Five methods of evaluating energy saving investments are analyzed: the apparent payback method, the present value method, the actual payback method, the loan payment method, and the rate of return method. The rate of return method is the only method that is both easy to understand and valid. The probable limitations in consumer understanding and use of information related to energy conservation, however, make it likely that a combination policy of disclosure, education, and product standards would be the most effective alternative for reaching energy conservation goals.