Relevant bibliographies by topics / The Survey of Consumer Finances

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The Survey of Consumer Finances'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "The Survey of Consumer Finances"

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Kennickell, Arthur B. "Multiple imputation in the Survey of Consumer Finances." Statistical Journal of the IAOS 33, no. 1 (March 2, 2017): 143–51. http://dx.doi.org/10.3233/sji-160278.

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Gans, Joshua, Andrew Leigh, Martin Schmalz, and Adam Triggs. "Inequality and market concentration, when shareholding is more skewed than consumption." Oxford Review of Economic Policy 35, no. 3 (2019): 550–63. http://dx.doi.org/10.1093/oxrep/grz011.

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AbstractEconomic theory suggests that monopoly prices hurt consumers but benefit shareholders. But in a world where individuals or households can be both consumers and shareholders, the impact of market power on inequality depends in part on the relative distribution of consumption and corporate equity ownership across individuals or households. The paper calculates this distribution for the United States, using data from the Survey of Consumer Finances and the Consumer Expenditure Survey, spanning nearly three decades from 1989 to 2016. In 2016, the top 20 per cent consumed approximately as much as the bottom 60 per cent, but had 15 times as much corporate equity. Because ownership is more skewed than consumption, increased mark-ups increase inequality. Moreover, over time, corporate equity has become even more skewed relative to consumption.
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Kennickell, Arthur B., Martha Starr-McCluer, and Annika E. Sunden. "Family Finance in the U.S.: Recent Evidence from the Survey of Consumer Finances." Federal Reserve Bulletin 83, no. 1 (1997): 0. http://dx.doi.org/10.17016/bulletin.1997.83-1.

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Hanna, Sherman D., Kyoung Tae Kim, and Suzanne Lindamood. "Behind the Numbers: Understanding the Survey of Consumer Finances." Journal of Financial Counseling and Planning 29, no. 2 (November 2018): 410–18. http://dx.doi.org/10.1891/1052-3073.29.2.410.

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The Survey of Consumer Finances (SCF) is the most frequently used dataset for research in this journal, but many researchers and readers do not fully understand some of the dataset’s complex details. This article provides insight into important issues that researchers and readers need to understand to accurately conduct and interpret SCF-based research. The issues addressed include the primary economic unit versus the household, identifying the respondent versus the head, limitations of variables in the survey, imputation and implicates, shadow variables, the public dataset versus the full dataset, weighting of analyses, and the use of replicate weights.
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Straight, Ronald L. "Survey of Consumer Finances: Asset Accumulation Differences by Race." Review of Black Political Economy 29, no. 2 (September 2001): 67–81. http://dx.doi.org/10.1007/s12114-001-1003-7.

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Kennickell, Arthur B., Martha Starr-McCluer, and Brian J. Surette. "Recent Changes in U.S. Family Finances: Results from the 1998 Survey of Consumer Finances." Federal Reserve Bulletin 86, no. 1 (2000): 0. http://dx.doi.org/10.17016/bulletin.2000.86-1.

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Amel, Dean F., Arthur B. Kennickell, and Kevin B. Moore. "Banking Market Definition : Evidence from the Survey of Consumer Finances." Finance and Economics Discussion Series 2008, no. 35 (2008): 1–24. http://dx.doi.org/10.17016/feds.2008.35.

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Huston, Sandra J., Michael S. Finke, and Hyrum Smith. "A financial sophistication proxy for the Survey of Consumer Finances." Applied Economics Letters 19, no. 13 (September 2012): 1275–78. http://dx.doi.org/10.1080/13504851.2011.619485.

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Bhutta, Neil, Jesse Bricker, Andrew C. Chang, Lisa J. Dettling, Sarena Goodman, Joanne W. Hsu, Kevin B. Moore, Sarah Reber, Alice Henriques Volz, and Richard A. Windle. "Changes in U.S. Family Finances from 2016 to 2019: Evidence from the Survey of Consumer Finances." Federal Reserve Bulletin 106, no. 5 (September 2020): 1–42. http://dx.doi.org/10.17016/bulletin.2020.106.

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The Federal Reserve Board’s Survey of Consumer Finances for 2019 provides insights into the evolution of family income and net worth since the previous time the survey was conducted in 2016. The survey shows that over the 2016–19 period, the median value of real (inflation-adjusted) family income before taxes rose 5 percent, and mean income decreased 3 percent. Real median net worth increased 18 percent, and mean net worth rose 2 percent. This survey marks the first in the aftermath of the Great Recession in which between-survey changes in the median outpaced changes in the mean for either measure, indicating that families in large parts of both distributions enjoyed gains in economic well-being. And, while the data also reveal some disparities in the evolution of income and net worth since 2016 across families differentiated by economic characteristics, such as income or wealth, and demographic characteristics, such as age, education, or race and ethnicity, many groups with historically lower income and net worth saw relatively large gains. This article reviews these and other changes in the financial condition of U.S. families, including developments in assets, liabilities, debt payments, and credit market experiences. The findings in this article do not reflect the effects of the COVID-19 pandemic on family finances, as almost all of the data in the 2019 survey were collected before the onset of the pandemic.
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Coulibaly, Brahima, and Geng Li. "Choice of Mortgage Contracts : Evidence from the Survey of Consumer Finances." Finance and Economics Discussion Series 2007, no. 50 (October 2007): 1–18. http://dx.doi.org/10.17016/feds.2007.50.

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Dissertations / Theses on the topic "The Survey of Consumer Finances"

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Lee, Yun Doo. "Retirement Planning Decisions Using the 2013 Survey of Consumer Finances." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2093.

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Abstract This dissertation consists of two essays. The first essay analyzes financial preparation for retirement of American men and women, using the 2013 Survey of Finances. Specifically, for retirement plan, income is an important factor for men and women aged 35-45 because of their insufficient income, health (excellent) for men and women aged 46-59 because of continuing work, number of weeks worked per year for men and women aged 60-67 because they have already retired or will retire and many of them are participating in a part time job. Also, health has significantly positive effects on the share of the financial wealth invested in the stocks while age has significantly negative effects in the analysis. The second essay analyzes the differences between the hippie cohort and the X and Y cohorts for the adequate preparation for retirement. In the hippie cohort, using the Internet or online for getting information to make decisions about investments and saving has positive effects on satisfaction of the retirement income from pensions and Social Security even if it’s statistically insignificant in the X and Y cohorts. In the responses regarding the question of how rate the retirement income from job pensions and Social Security, the findings show that the hippie cohort is more likely than the X and Y cohorts in satisfaction of the retirement income from pensions and Social Security income. The results show that the hippie cohort is better than the X and Y cohorts in preparation for retirement.
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Lyons, Angela Christine. "Household liquidity and financial innovations : evidence from the Survey of consumer finances /." Digital version, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?3008384.

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Grable, John E. "Investor Risk Tolerance: Testing The Efficacy Of Demographics As Differentiating and Classifying Factors." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30762.

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This study was designed to determine whether the variables gender, age, marital status, occupation, self-employment, income, race, and education could be used individually or in combination to both differentiate among levels of investor risk tolerance and classify individuals into risk-tolerance categories. The Leimberg, Satinsky, LeClair, and Doyle (1993) financial management model was used as the theoretical basis for this study. The model explains the process of how investment managers effectively develop plans to allocate a client's scarce investment resources to meet financial objectives. An empirical model for categorizing investors into risk-tolerance categories using demographic factors was developed and empirically tested using data from the 1992 Survey of Consumer Finances (SCF) (N = 2,626). The average respondent was affluent and best represented the profile of an investment management client. Based on findings from a multiple discriminant analysis test it was determined that respondent demographic characteristics were significant in differentiating among levels of risk tolerance at the p < .0001 level (i.e., gender, married, single but previously married, professional occupational status, self-employment status, income, White, Black, and Hispanic racial background, and educational level), while three demographic characteristics were found to be statistically insignificant (i.e., age, Asian racial background, and never married). Multiple discriminant analysis also revealed that the demographic variables examined in this study explained approximately 20% of the variance among the three levels of investor risk tolerance. Classification equations were generated. The classification procedure offered only a 20% improvement-over-chance, which was determined to be a low proportional reduction in error. The classification procedure also generated unacceptable levels of false positive classifications, which led to over classification of respondents into high and no risk-tolerance categories, while under classifying respondents into the average risk-tolerance category. Two demographic characteristics were determined to be the most effective in differentiating among and classifying respondents into risk-tolerance categories. Classes of risk tolerance differed most widely on respondents' educational level and gender. Educational level of respondents was determined to be the most significant optimizing factor. It also was concluded that demographic characteristics provide only a starting point in assessing investor risk tolerance. Understanding risk tolerance is a complicated process that goes beyond the exclusive use of demographic characteristics. More research is needed to determine which additional factors can be used by investment managers to increase the explained variance in risk-tolerance differences.
Ph. D.
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Pelletier, Lou Allan. "Accounting for the male-female earnings differential : results from the 1986 survey of consumer finances." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28264.

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This study seeks to explain the observed differences in the earnings of individual Canadians by sex. The study uses data from the micro data file of the 1986 Survey of Consumer Finances of individuals age 15 and over, with and without income. To a large extent, the study follows the examples presented in other Canadian studies conducted by Holmes (1974), Robb (1978), Gunderson (1980), Goyder (1981) and Ornstein (1983). Employment earnings account for an overwhelming proportion of the total income received by individuals. Thus, the examination of the earnings differential attempts to address the root causes of many of the problems faced by nontraditional families. Canadian society is no longer largely composed of the traditional family with a working father and the homemaking mother. The growing number of dual-earner couples, single and childless adults, and households headed by women presents a difficult challenge for social policy. The male-female earnings disparity is a key component in exacerbating problems that include the availability of credit for women, the feminization of poverty, access to affordable and adequate housing, and adequate incomes for retirement. To effectively address the problems that have resulted from the interaction of greater female participation in the labour force and the formation of alternate household types, planners and policy makers need to address the root problem of sexual inequality in the labour force, and not solely the symptoms. In the context of changing family structure and the economic position of women, the focus of this study is to identify the size of the male-female earnings gap, and to determine the extent to which the earnings gap can be explained by personal, work and productivity-related characteristics. The impact of these factors are analyzed from two points of view. First, the impact of individual factors on the level of earnings are analyzed through a simple comparison of mean earnings of men and women across a variety of characteristics. Second, the influence of these factors on earnings, and the degree of inequality between the earnings of men and women, is analyzed using multiple linear regression analysis. Regression analysis is used to estimate separate earnings equations for men and women. From the separate earnings equations, the wage gap can be partitioned into three parts, due to differences in (1) constant terms, (2) mean levels of the independent variables, and (3) the returns of the independent variables. Further, to assess the impact of occupational and industrial segregation on the earnings gap, a second set of earnings equations are calculated that do not include measures of occupational and industrial segregation. The calculations of separate earnings equations for men and women, for the selected sample, produced an unadjusted earnings ratio of 0.66. After adjustments were made for the ten productivity and productivity-related factors considered in the analysis, including occupational and industrial distributions, the ratio increased to 0.79. This left an earnings gap of $5,985 (1985 dollars) that could not be assigned to any of the measured variables. While part of the unexplained residual may be explained by variables not included in the analysis, or by more careful measurement of existing variables, it seems likely that at least 20 percentage points of the earnings gap is attributable to "an amalgam of different forms of discrimination which, taken together, disadvantage women relative to men", (Denton and Hunter, 1982). Discrimination is defined as different returns in earnings for equal productivity characteristics, as given by the regression coefficients. Of the total earnings gap of 34 percent, approximately 60% of this is attributable to wage discrimination, and approximately 40% is due to differences in productivity-related characteristics Occupational and industrial segregation account for a large proportion of the earnings gap. The adjusted earnings ratio, when occupational and industrial segregation are not considered endowments, is 0.69. Thus, the difference between the full-regression equation and the partial regression equation indicates that occupational and industrial segregation accounts for approximately 30% of the earnings gap.
Applied Science, Faculty of
Community and Regional Planning (SCARP), School of
Graduate
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Amrelle, Kevin A. "Have Homeownership Rates Transitioned Since the Financial Crisis? Evidence from the Survey of Consumer Finances Data." Thesis, The University of North Carolina at Charlotte, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10607538.

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Since 1989, significant mortgage finance innovation and federal policies with the intent of increasing homeownership participation particularly amongst minorities were implemented until the 2007 recession. This paper uses the Survey of Consumer Finances to analyze the lasting effectiveness of the mortgage finance innovations and federal policies on owner-occupancy rates leading up to and after the financial recession in 2007 until 2013. The results indicate that policy and macroeconomic factors offer temporary shifts in homeownership participation while household attribute changes have long lasting impact. Trends in the savings patterns of renters work as an effective measure for transitioning into homeownership. Shift-share analysis reinforces the idea that the model coefficients effectively capture household sentiment and macroeconomic conditions. Homeownership participation, especially amongst minorities, improved in 2013 relative to 1989 but the homeownership gap between minorities and white households has grown.

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Topoleski, John. "Behavioral Aspects of Retirement Savings: How do 401(K) Plans Affect Household Asset Accumulation?" ScholarWorks@UNO, 2005. http://scholarworks.uno.edu/td/313.

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The nature of employee retirement plans has changed dramatically over the past fifteen years as employers have been replacing traditional defined benefit retirement plans with defined contribution plans like the 401(k) plan. This dissertation is focused on the impact that 401(k) plan have on household asset accumulation. The first essay looks at how much asset accumulation can be attributed to 401(k) plans as opposed to other factors such as demographics and saver type characteristics. Overall, the conclusions are consistent with recent research that says these plans induce a reshuffling of assets rather than being funded through a reduction in consumption. Controlling for cohort effects reduces the amount of wealth attributable to 401(k) eligibility to a negligible (and statistically insignificant) amount. The second essay considers the impact that borrowing against the assets in 401(k) plan might have on household asset accumulation. Most personal finance advice warns against borrowing against a retirement plan because of the potential negative impact on retirement wealth. This is especially true for borrowers who are also undisciplined savers and do not or cannot maintain their retirement plan contributions during loan period or who separate from their employers before the loan is repaid. For good savers a retirement plan loan only has a modest impact on retirement wealth. Only modest make-up contributions would need to be made to mitigate the impact of a retirement plan loan. It seems that many borrowers may be using retirement loans because they are in financial difficulty. It also appears that borrowers are trying to maintain their retirement savings, but their asset accumulation within broader measures of wealth is below that of households that do not have outstanding 401(k) loans.
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Wilson, Theresa M. "The Effects of Gender, Age, Education, and Risk Tolerance on Credit Card Balances." Miami University Honors Theses / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1209148205.

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Heckman, Stuart J. "A Comparison of Two Savings Measures: An Application of Institutional Theory Among Low-Income Households." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343753078.

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Lee, Jonghee. "Racial/Ethnic Disparities in Household Debt Repayment." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244055120.

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Kim, Kyoung Tae. "The Impact of the 2007 Recession on the Retirement Decisions of U.S. Households: Evidence from the 2007-2009 Survey of Consumer Finances Panel Dataset." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406072629.

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Books on the topic "The Survey of Consumer Finances"

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Board of Governors of the Federal Reserve System (U.S.). Survey of consumer finances. Washington, D.C: Board of Governors of the Federal Reserve System, 2003.

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Avery, Robert B. 1986 survey of consumer finances: Technical manual and codebook. [Ann Arbor: Michigan University, Survey Research Center, 1988.

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Wasantha, Dewage. Sri Lanka revisited: Poverty, income inequality and the 1981/82 Consumer Finances Survey. [s.l.]: typescript, 1988.

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Aizcorbe, Ana. The replacement demand for motor vehicles: Evidence from the survey of consumer finances. Washington, D.C: Federal Reserve Board, 2003.

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Bergstresser, Daniel. Asset allocation and asset location: Household evidence from the survey of consumer finances. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2002.

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Bergstresser, Daniel. Asset allocation and asset location: Household evidence from the survey of consumer finances. Cambridge, MA: National Bureau of Economic Research, 2002.

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Centre for Poverty Analysis (Sri Lanka) and Open Forum on Poverty (27th : 2006 : Centre for Poverty Analysis), eds. Living conditions in the north and east--how different?: Findings from the consumer finances and the socio-economic survey 03/04. Colombo: Centre for Poverty Analysis, 2006.

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Moore, Danna L. Survey of financial literacy in Washington State: Knowlege, behavior, attitudes, and experiences. Olympia, WA: Washington State Dept. of Financial Institutions, 2003.

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Gunewardena, Dileni. Absolute and relative consumption poverty in Sri Lanka: Evidence from the Consumer Finance Survey, 2003-4. Colombo: Centre for Poverty Analysis, 2007.

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Coleman, Kevin A. Estimates of the income and wealth of the elderly using the panel study of income dynamics and the survey of consumer finances and their implications for long-term care. Fairfax, Va: Lewin Group, 1996.

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Book chapters on the topic "The Survey of Consumer Finances"

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Kennickell, Arthur, and Julia Lane. "Measuring the Impact of Data Protection Techniques on Data Utility: Evidence from the Survey of Consumer Finances." In Privacy in Statistical Databases, 291–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930242_25.

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Paiella, Monica. "The Stock Market, Housing and Consumer Spending: A Survey of the Evidence on Wealth Effects." In Issues in Finance, 157–82. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444391602.ch7.

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Hutton, Peter F. "The Consumer." In Survey Research for Managers, 23–47. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-06844-9_2.

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Hutton, Peter F. "The Consumer." In Survey Research for Managers, 23–47. London: Macmillan Education UK, 1990. http://dx.doi.org/10.1007/978-1-349-20698-8_2.

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Nielsen, Robert B., Cynthia Needles Fletcher, and Suzanne Bartholomae. "Consumer Finances of Low-Income Families." In Handbook of Consumer Finance Research, 167–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28887-1_14.

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Kamen, Joseph M., and Abdul G. Azhari. "Interpreting Longitudinal Consumer Survey Results." In Proceedings of the 1984 Academy of Marketing Science (AMS) Annual Conference, 442–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16973-6_95.

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Leaver, Sylvia, and Richard Valliant. "Statistical Problems in Estimating the U.S. Consumer Price Index." In Business Survey Methods, 543–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118150504.ch28.

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Barron, John M., and Michael E. Staten. "1993 CRC Survey Questionnaire and Sample Design." In Consumer Attitudes Toward Credit Insurance, 17–26. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1327-4_3.

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Schetzina, Cathy. "The PhoCusWright Consumer Technology Survey Second Edition." In Trends and Issues in Global Tourism 2009, 113–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92199-8_8.

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Sheehy, Heather. "Consumers and Biotechnology: A Synopsis of Survey and Focus Group Research." In Biotechnology and the Consumer, 1–28. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5311-3_1.

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Conference papers on the topic "The Survey of Consumer Finances"

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Hu, Yili, Ye Chen, and Xiaoyu Chen. "The Influence of Consumer Psychology on Consumer Finance for College Students–Based on the Survey in Yunnan Province." In 4th International Symposium on Business Corporation and Development in South-East and South Asia under B&R Initiative (ISBCD 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200708.030.

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Bai, Attila, Péter Balogh, Károly Pető, and Zoltán Szakály. "Consumer habits and preferences in the renewable energy market." In CARPE Conference 2019: Horizon Europe and beyond. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/carpe2019.2019.10195.

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The world energy consumption was about 567-578 EJ in 2017 which is still increasing – in 2017 by 2.2%, and yearly by 1.7% on average since 2006 (BP, 2018, IEA, 2018). Within this, the consumption of residential segment plays a significant role both in the EU (42%) and in Hungary (35%) as well (Eurostat, 2019). The Hungarian population spend 12.8% (35 EUR/capita/month) of their income on energy sources, and this rate is even higher in case of the pensioners, singles and those who have lower income (KSH, 2019). At the same time, the rate of renewables in the energy mix stagnates for years worldwide (14%), it is a bit higher in the EU and in Hungary than the world average (18-18%, IEA, 2018). Thus, the renewable energy sources can also have serious perspectives in the residential cost reduction, in the sustainable energy consumption and in the local income production. Our aim is to analyse the knowledge of Hungarian consumers on renewable energy sources, their willingness to apply them and the socio-demographic factors on these. To execute the research objectives, a national representative survey with 1000 people was started in April 2019 in Hungary. Based on the results of the questionnaire, it was concluded that the information of the inhabitants is below the average in case of every examined renewable energy source, which is especially true for the biomass-based energy sources. The ease is almost as important as the environment friendliness. An excessively high rate of respondents (34 and 27%) is interested in solar panels and solar collectors, 32% of them seclude themselves from the use of these energy sources. It would be justified to extend these consumer researches in the future for more countries. The authors wish to clarify how the differences in location, income status and residential segment and values influence the spread of these energy sources in the EU and by which tools could support the usage of these in the future. Acknowledgment This research was supported by EFOP-3.6.1-16-2016-00022 „Debrecen Venture Catapult Program”. The research was financed by the Higher Education Institutional Excellence Programme (20428-3/2018/FEKUTSTRAT) of the Ministry of Human Capacities in Hungary, within the framework of the 4.thematic programme of the University of Debrecen. References BP (British Petrol, 2018): Statistical Review of World Energy. 67th edition, pp. 1-52Eurostat (2019): https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statisticsInternational Energy Agency (IEA, 2018): Key World Energy Statistics. www.iea. org/statistics, pp. 1-51KSH (Hungarian Central Statistic Agency, 2019). http://www.ksh.hu/thm/3/indi3_1_2.html
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Farias, Fernando Lucas de Oliveira, Elvis Melo, Juliana Oliveira, Charles Madeira, and André Campos. "Finance Math Game: Uma proposta lúdica interdisciplinar para Ensino de Educação Financeira com Scratch." In Workshop de Informática na Escola. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/cbie.wie.2019.1359.

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A educação financeira - EF como tema transversal obrigatório previsto na BNCC, tem como objetivo proporcionar ao estudante, desde cedo, o desenvolvimento de habilidades relacionadas a bons hábitos de consumo e planejamento financeiro, sendo um desafio a prática docente sua integração com as demais disciplinas do currículo. Neste contexto, o “Finance Math Game” surge como proposta lúdica interdisciplinar para ensino de EF com Scratch. A metodologia utilizada na condução do experimento é norteada pelo Design Thinking. Os resultados obtidos na fase de imersão inicial e avaliação de reação indicam que a proposta é capaz de estabelecer relações entre as dinâmicas do curso técnico de Lazer com os conhecimentos sobre EF, utilizando o pensamento computacional e Scratch como tecnologia potencializadora.
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Delcev, Sanja, and Drazen Draskovic. "Modern JavaScript frameworks: A Survey Study." In 2018 Zooming Innovation in Consumer Technologies Conference (ZINC). IEEE, 2018. http://dx.doi.org/10.1109/zinc.2018.8448444.

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Xin Li and Chen Qian. "A survey of network function placement." In 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2016. http://dx.doi.org/10.1109/ccnc.2016.7444915.

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Sun, Huiping, Junde Song, and Zhong Chen. "Survey of Authentication in Mobile IPv6 Network." In 2010 7th IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2010. http://dx.doi.org/10.1109/ccnc.2010.5421695.

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Lit, Yanyan, Sara Kim, and Eric Sy. "A Survey on Amazon Alexa Attack Surfaces." In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2021. http://dx.doi.org/10.1109/ccnc49032.2021.9369553.

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Yue, Xin. "A survey of image registration based on features." In International Conference on Consumer Electronics, Communications and Networks, edited by Zhenghong Shang, Hui Liu, Donglan Yu, and Zhenping Qiang. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/cecnet130311.

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Peng, Chun-Cheng, Yuan-Zhi Wang, and Chun-Wel Huang. "Artificial-Neural-Network-Based Consumer Behavior Prediction: A Survey." In 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2020. http://dx.doi.org/10.1109/ecbios50299.2020.9203699.

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Butun, Ismail, and Ravi Sankar. "A brief survey of access control in Wireless Sensor Networks." In 2011 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2011. http://dx.doi.org/10.1109/ccnc.2011.5766345.

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Reports on the topic "The Survey of Consumer Finances"

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Bergstresser, Daniel, and James Poterba. Asset Allocation and Asset Location: Household Evidence from the Survey of Consumer Finances. Cambridge, MA: National Bureau of Economic Research, October 2002. http://dx.doi.org/10.3386/w9268.

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Bernheim, B. Douglas, Katherine Grace Carman, Jagadeesh Gokhale, and Laurence Kotlikoff. The Mismatch Between Life Insurance Holdings and Financial Vulnerabilities: Evidence from the Survey of Consumer Finances. Cambridge, MA: National Bureau of Economic Research, October 2001. http://dx.doi.org/10.3386/w8544.

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Chapagain, Matrika. Boomers and Finances: An AARP Bulletin Survey. AARP Research, March 2014. http://dx.doi.org/10.26419/res.00077.001.

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Gillespie, Rebecca, and Maya King. AMR Consumer Perceptions Survey. Food Standards Agency, August 2021. http://dx.doi.org/10.46756/sci.fsa.elb852.

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As part of the UK national action plan on antimicrobial resistance (AMR), the Food Standards Agency (FSA) is working to improve the scientific evidence base around consumer perceptions and understanding. A consumer survey was carried out in 2016 and 2019, and replicated in 2021, to understand current views and awareness, and to identify any changes over time.
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Abhyankar, Nikit, and Amol Phadke. Impact of Large Scale Energy Efficiency Programs On Consumer Tariffs and Utility Finances in India. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1005175.

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McDonald, Ashley B., Daniel V. McGehee, Bobbie D. Seppelt, Susan T. Chrysler, Natoshia M. Askelson, and Linda S. Angell. National consumer survey of driving safety technologies. Iowa City, Iowa: University of Iowa Public Policy Center, July 2015. http://dx.doi.org/10.17077/qnd8-f572.

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Keenan, Teresa A. Consumer Views on Prescription Drugs Survey Report. Washington, DC: AARP Research, July 2021. http://dx.doi.org/10.26419/res.00476.001.

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Sabelhaus, John, David Johnson, Stephen Ash, David Swanson, Thesia Garner, John Greenlees, and Steve Henderson. Is the Consumer Expenditure Survey Representative by Income? Cambridge, MA: National Bureau of Economic Research, October 2013. http://dx.doi.org/10.3386/w19589.

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Ma, Jennifer. Education Saving Incentives and Household Saving: Evidence from the 2000 TIAA-CREF Survey of Participant Finances. Cambridge, MA: National Bureau of Economic Research, February 2003. http://dx.doi.org/10.3386/w9505.

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Lewis, Tasha L. A Survey of Current Consumer Technology Use for Apparel Shopping. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-512.

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