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Journal articles on the topic 'Econometrics probit analysis'

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Published: 4 June 2021
Last updated: 15 February 2022

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1

DE Bondt, G. J., and C. C. A. Winder. "Countries' creditworthiness: An indicator from a probit analysis." De Economist 144, no. 4 (December 1996): 617–33. http://dx.doi.org/10.1007/bf01371942.

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2

Perry, Gregory M., M. Edward Rister, James W. Richardson, and Warren R. Grant. "Analyzing Tenure Arrangements and Crop Rotations Using Farm Simulation and Probit Analysis." Journal of Agricultural and Applied Economics 18, no. 2 (December 1986): 165–74. http://dx.doi.org/10.1017/s008130520000621x.

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AbstractWhole farm simulation analysis and econometric techniques are employed in an analysis of crop rotations and tenure arrangement strategies. The FLIPSIM model is used to analyze a representative Texas Upper Gulf Coast rice and soybean farm. Probit analysis is then used to determine the impact of net cash farm income, land tenure, and crop rotation on probability of survival. Results suggest that, although the simulation model is useful in providing information on the effect at the farm level of following the different strategies, probit results provide greater understanding into the returns and risk inherent to each strategy.
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3

Parikh, Ashok, and Kunal Sen. "Probit with heteroscedasticity: an application to Indian poverty analysis." Applied Economics Letters 13, no. 11 (September 15, 2006): 699–707. http://dx.doi.org/10.1080/13504850500402096.

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4

Hudson, John, and Richard Lark. "A polychotomous probit measure and analysis of inflationary expectations." Journal of Macroeconomics 11, no. 2 (March 1989): 269–80. http://dx.doi.org/10.1016/0164-0704(89)90042-6.

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5

Harrison, R. Wes, Jeffrey Gillespie, and Deacue Fields. "Analysis of Cardinal and Ordinal Assumptions in Conjoint Analysis." Agricultural and Resource Economics Review 34, no. 2 (October 2005): 238–52. http://dx.doi.org/10.1017/s106828050000839x.

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Of twenty-three agricultural economics conjoint analyses conducted between 1990 and 2001, seventeen used interval-rating scales, with estimation procedures varying widely. This study tests cardinality assumptions in conjoint analysis when interval-rating scales are used, and tests whether the ordered probit or two-limit tobit model is the most valid. Results indicate that cardinality assumptions are invalid, but estimates of the underlying utility scale for the two models do not differ. Thus, while the ordered probit model is theoretically more appealing, the two-limit tobit model may be more useful in practice, especially in cases with limited degrees of freedom, such as with individual-level conjoint models.
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Matos, Pedro Verga, Nicoletta Rosati, and Horácio C. Faustino. "Convergence in corporate governance: a probit analysis." International Journal of Economics and Business Research 12, no. 3 (2016): 181. http://dx.doi.org/10.1504/ijebr.2016.080329.

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Alem, Yonas, and Eyoual Demeke. "The persistence of energy poverty: A dynamic probit analysis." Energy Economics 90 (August 2020): 104789. http://dx.doi.org/10.1016/j.eneco.2020.104789.

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8

Rifaat, S. M., and H. C. Chin. "Accident severity analysis using ordered probit model." Journal of Advanced Transportation 41, no. 1 (September 2007): 91–114. http://dx.doi.org/10.1002/atr.5670410107.

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9

High, Sammy. "A PROBIT ANALYSIS OF THE SENATE VOTE ON WIRTH/WILSON." Humanomics 15, no. 4 (April 1999): 48–54. http://dx.doi.org/10.1108/eb018837.

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10

Edwards, Yancy D., and Greg M. Allenby. "Multivariate Analysis of Multiple Response Data." Journal of Marketing Research 40, no. 3 (August 2003): 321–34. http://dx.doi.org/10.1509/jmkr.40.3.321.19233.

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Multiple response questions, also known as a pick any/J format, are frequently encountered in the analysis of survey data. The relationship among the responses is difficult to explore when the number of response options, J, is large. The authors propose a multivariate binomial probit model for analyzing multiple response data and use standard multivariate analysis techniques to conduct exploratory analysis on the latent multivariate normal distribution. A challenge of estimating the probit model is addressing identifying restrictions that lead to the covariance matrix specified with unit-diagonal elements (i.e., a correlation matrix). The authors propose a general approach to handling identifying restrictions and develop specific algorithms for the multivariate binomial probit model. The estimation algorithm is efficient and can easily accommodate many response options that are frequently encountered in the analysis of marketing data. The authors illustrate multivariate analysis of multiple response data in three applications.
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Chen, Jo-Hui, and Wan-Chieh Tsai. "A comparison of international income inequality: an ordered probit model analysis." Applied Economics 44, no. 13 (May 2012): 1701–16. http://dx.doi.org/10.1080/00036846.2011.554371.

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12

ROSS, RUSSELL. "A Probit Analysis of Aboriginal Employment Prospects in New South Wales." Economic Record 69, no. 3 (September 1993): 253–58. http://dx.doi.org/10.1111/j.1475-4932.1993.tb02105.x.

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13

Xu, Ruomei, Yanrui Wu, and Jingdong Luan. "Analysis of farmers’ willingness to adopt genetically modified insect-resistant rice in China." China Agricultural Economic Review 8, no. 3 (September 5, 2016): 368–82. http://dx.doi.org/10.1108/caer-08-2015-0102.

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Purpose Genetically modified (GM) crops, particularly GM grain crops, have been controversial since their commercialization in 1996. However, only a few studies have investigated farmers’ attitudes toward adopting GM grain crops in China. The purpose of this paper is to explore farmers’ willingness to adopt GM insect-resistant rice prior to its commercial release in China and determines the factors that affect farmers’ prospective adoption decisions. Design/methodology/approach The data are collected using a questionnaire. Descriptive statistics are used to analyze the farmers’ potential willingness to adopt GM rice and level of awareness of GM rice and socioeconomic characteristics. Ordered and binary probit models are applied to identify the key factors that affect the farmers’ decision to adopt GM insect-resistant rice. Findings Descriptive statistics show that most farmers have little knowledge of GM rice, approximate 35.5 percent of farmers could plant GM rice, and over half of the respondents are uncertain whether or not they will adopt the new crops. The results of econometric analyses show that increasing output and income, and simplicity in crop management, have positive effects on prospective adoption, whereas the high-seed price of GM rice has a significantly negative effect. Health implications also have a significantly positive effect on the farmers’ decision to adopt GM grain crops. A comparative analysis of ordered and binary probit models demonstrates that farmers are more deliberate in their decisions when they have fewer choices. Aside from the above-mentioned variables, the following factors are also statistically significant in the probit model: government technicians’ recommendations, neighbors’ attitudes, level of environmental risks, and the farmer’s age. Originality/value Information on the major risks and benefits of GM rice was provided to the farmers in the questionnaire. The farmers were then asked to choose from the three ordered alternative answers, namely, “accept,” “uncertain,” and “reject”. Both ordered and binary probit models were applied to comparatively analyze the collected data. This study is one of a handful of studies that employ these econometric models to identify and explain the underlying factors that affect farmers’ decisions. The relevant findings have important implications for future agricultural policy in China.
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14

Munkin, Murat K., and Pravin K. Trivedi. "Bayesian analysis of the ordered probit model with endogenous selection." Journal of Econometrics 143, no. 2 (April 2008): 334–48. http://dx.doi.org/10.1016/j.jeconom.2007.11.001.

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15

Domberger, Simon, and Eric Ah Lik Li. "An Ordered-Probit Analysis of Price and Performance in Competitively Tendered Contracts." Australian Economic Review 28, no. 2 (April 1995): 14–22. http://dx.doi.org/10.1111/j.1467-8462.1995.tb00885.x.

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16

Lloyd, Robert E., and Joseph P. McGarrity. "A probit analysis of the Senate vote on Gramm-Rudman." Public Choice 85, no. 1-2 (October 1995): 81–90. http://dx.doi.org/10.1007/bf01047903.

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17

Pietrovito, Filomena, Alberto Franco Pozzolo, and Luca Salvatici. "Internationalization choices: an ordered probit analysis at industry level." Empirical Economics 50, no. 2 (April 17, 2015): 561–94. http://dx.doi.org/10.1007/s00181-015-0945-8.

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18

Weir, Charlie. "Monopolies and Mergers Commission, merger reports and the public interest: a probit analysis." Applied Economics 24, no. 1 (January 1992): 27–34. http://dx.doi.org/10.1080/00036849200000100.

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19

Harmáček, Jaromír, Miroslav Syrovátka, and Zdeněk Opršal. "Factors of Czech Aid Selection and Allocation: Panel Probit and Tobit Analysis." Politická ekonomie 65, no. 2 (April 1, 2017): 179–97. http://dx.doi.org/10.18267/j.polek.1135.

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20

McCulloch, Robert E., Nicholas G. Polson, and Peter E. Rossi. "A Bayesian analysis of the multinomial probit model with fully identified parameters." Journal of Econometrics 99, no. 1 (November 2000): 173–93. http://dx.doi.org/10.1016/s0304-4076(00)00034-8.

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21

Lee, Chung-Ki, and Seung-Jun Kwak. "Valuing drinking water quality improvement using a Bayesian analysis of the multinomial probit model." Applied Economics Letters 14, no. 4 (March 2007): 255–59. http://dx.doi.org/10.1080/13504850500447299.

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22

Chen, Ming-Yuan, and Jing-Yun Chang. "The choice of foreign market entry mode: An analysis of the dynamic probit model." Economic Modelling 28, no. 1-2 (January 2011): 439–50. http://dx.doi.org/10.1016/j.econmod.2010.08.004.

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23

Chen, Guifu, and Shigeyuki Hamori. "Bivariate probit analysis of differences between male and female formal employment in urban China." Journal of Asian Economics 21, no. 5 (October 2010): 494–501. http://dx.doi.org/10.1016/j.asieco.2010.03.009.

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24

Shi, Lei. "Bayesian analysis of multivariate ordered probit model with individual heterogeneity." AStA Advances in Statistical Analysis 104, no. 4 (June 23, 2020): 649–65. http://dx.doi.org/10.1007/s10182-020-00369-2.

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25

Hayes, Joseph, Michael Roth, and Lydia Zepeda. "Tenure Security, Investment and Productivity in Gambian Agriculture: A Generalized Probit Analysis." American Journal of Agricultural Economics 79, no. 2 (May 1997): 369–82. http://dx.doi.org/10.2307/1244136.

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26

Thompson, Stanley R., and Doyle A. Eiler. "A MULTIVARIATE PROBIT ANALYSIS OF ADVERTISING AWARENESS ON MILK USE." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 23, no. 1 (November 13, 2008): 65–73. http://dx.doi.org/10.1111/j.1744-7976.1975.tb00944.x.

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27

Yang, Joey Wenling, and Jerry Parwada. "Predicting stock price movements: an ordered probit analysis on the Australian Securities Exchange." Quantitative Finance 12, no. 5 (May 2012): 791–804. http://dx.doi.org/10.1080/14697688.2010.494612.

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28

Bernholz, Peter, and Peter Kugler. "The Success of Currency Reforms to End Great Inflations: An Empirical Analysis of 34 High Inflations." German Economic Review 10, no. 2 (May 1, 2009): 165–75. http://dx.doi.org/10.1111/j.1468-0475.2008.00452.x.

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Abstract The estimation of an ordered probit model for currency reforms attempting to end 31 hyperinflations and three huge inflations of the twentieth century shows that the introduction of an independent central bank and the adoption of a credibly fixed exchange rate are crucial for the success of a currency reform. In addition, currency reforms are demonstrated to be more difficult in centrally planned economies than in market economies.
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29

Boyd, James. "Asset Status Proxies and Consumer Preference for ARMs: An Empirical Investigation Using Probit Analysis." Journal of Real Estate Research 3, no. 1 (January 1, 1988): 37–49. http://dx.doi.org/10.1080/10835547.1988.12090539.

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30

Wang, Weiren. "Married female labour force participation: an analysis of probit model with a natural non-participation rate." Applied Economics Letters 3, no. 4 (April 1996): 205–7. http://dx.doi.org/10.1080/758520864.

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31

Zhang, Kairan, Mohamed Hassan, Mahama Yahaya, and Shupeng Yang. "Analysis of Work-Zone Crashes Using the Ordered Probit Model with Factor Analysis in Egypt." Journal of Advanced Transportation 2018 (October 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/8570207.

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Work-zones, due to their nature, are predisposed to hazardous situations. This is a consequence of conducting construction work within the vicinity of, or near, vehicular traffic. The exposure to danger underscores the need for proper understanding of the occurrence of work-zone crashes, as well as the risk factors associated with them. This paper aims mainly to develop a hybrid approach that combines a factor analysis method and an ordered probit model to carry out a comprehensive analysis of work-zone crashes. The paper presents an analysis of work-zone data crashes from 2010 to 2015 that occurred in Egyptian long-term highway maintenance and rehabilitation projects. Factor analysis is used to identify the main and common factors that influence work-zone crashes and to calculate the weight of every factor. The ordered probit model is developed, based on the results of factor analysis scores, to examine the contribution of common factors in the severity of work-zones. The most influential factors that have contributed to work-zone crashes are weather condition, number of lane closures, type of surface construction, road character, day of week, and so forth. In addition, the results indicated that four common factors are significantly affecting the severity of work-zone crashes in Egypt.
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32

Gwilym, Owain ap, Andrew Clare, and Stephen Thomas. "The bid-ask spread on stock index options: An ordered probit analysis." Journal of Futures Markets 18, no. 4 (June 1998): 467–85. http://dx.doi.org/10.1002/(sici)1096-9934(199806)18:4<467::aid-fut6>3.0.co;2-r.

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33

Maruyama, Masayoshi, and Le Viet Trung. "Traditional bazaar or supermarkets: A probit analysis of affluent consumer perceptions in Hanoi." International Review of Retail, Distribution and Consumer Research 17, no. 3 (July 2007): 233–52. http://dx.doi.org/10.1080/09593960701368804.

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34

Vuksanovic, Nemanja, and Ljubinka Joksimovic. "Preschool education as a determinant of educational attainment: An analysis of Serbia." Ekonomski anali 62, no. 215 (2017): 135–68. http://dx.doi.org/10.2298/eka1715135v.

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The subject of this paper is preschool education as a determinant of students? educational attainment in Serbia and factors that influence whether a Serbian child is included in preschool programmes. This paper aims 1) to assess how attendance of preschool programmes in Serbia affects a student?s educational attainment in terms of mathematical, reading, and scientific literacy; 2) to explore how preschool education differs for students who occupy different positions in the distribution of educational attainment; and 3) to examine the determinants of a child in Serbia receiving preschool education. The analysis is based on data from PISA2012 testing. In order to assess the significance and nature of the effect of preschool education on educational attainment in terms of mathematical, reading, and scientific literacy we use unconditional quantile regression. To analyse factors that affect whether a child is included in the preschool programme we apply probit regression. Unconditional quantile regression results suggest that the sign and intensity of the effect of preschool education for more than a year are positive and increasing. The results of the probit regression show that socio-economic family background and parental status in the labour market are the factors that determine whether the student will attend preschool education for more than a year. The results indicate that the educational system in Serbia reproduces poverty and social exclusion.
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Litchfield, Julie, Barry Reilly, and Mario Veneziani. "An analysis of life satisfaction in Albania: An heteroscedastic ordered probit model approach." Journal of Economic Behavior & Organization 81, no. 3 (March 2012): 731–41. http://dx.doi.org/10.1016/j.jebo.2010.12.020.

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Gaeta, Giuseppe Lucio. "Was it worth it? An empirical analysis of over-education among PhD recipients in Italy." International Journal of Social Economics 42, no. 3 (March 2, 2015): 222–38. http://dx.doi.org/10.1108/ijse-08-2013-0186.

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Purpose – The purpose of this paper is to provide an empirical examination of factors associated with over-education among PhD graduates in Italy. Design/methodology/approach – The investigation is based on recently released data collected by the Italian National Institute of Statistics by means of interviews with a large sample of PhD recipients, carried out a few years after they obtained their PhD degree. The author measured the mismatch between the current job and previous PhD studies using two direct subjective evaluations of over-education, which distinguish between the usefulness of the PhD title to get the current job position and to perform the current work activities. Even if the incidence of over-education varies according to the measurement applied, the author found that it is highly widespread among PhD recipients. The econometric analyses are aimed at identifying factors associated with over-education and are based on the standard probit model and the bivariate probit model with sample selection which allows to control for self selection into employment. Findings – The results show that over-education is significantly correlated with: first, a number of PhD-related variables, such as the scientific field of study, having attended courses or visiting periods abroad; second, some job-related characteristics, such as working in the academia or being mainly involved in research-related activities; third, the channel of access to the job; and fourth, residential location. Originality/value – This paper contributes to the literature focusing on job-education mismatch by providing, to the best of the author’s knowledge, the first empirical analysis of over-education among PhD recipients in Italy; moreover, it provides some useful insights to evaluate the professional doctoral graduates in Italy.
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Yildirim, Jülide, Nebile Korucu, and Semsettin Karasu. "FURTHER EDUCATION OR RE‐ENLISTMENT DECISION IN TURKISH ARMED FORCES: A SEEMINGLY UNRELATED PROBIT ANALYSIS." Defence and Peace Economics 21, no. 1 (February 2010): 89–103. http://dx.doi.org/10.1080/10242690903105513.

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38

Pang, Xun. "Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time-Series Cross-Sectional Data." Political Analysis 22, no. 4 (2014): 464–96. http://dx.doi.org/10.1093/pan/mpu008.

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Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This article extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with apth-order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.
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Curtis, John, and Lori Lynch. "Explaining Deer Population Preferences: An Analysis of Farmers, Hunters and the General Public." Agricultural and Resource Economics Review 30, no. 1 (April 2001): 44–55. http://dx.doi.org/10.1017/s1068280500000538.

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Wildlife managers must consider the public's preferences for wildlife population levels when determining management policies. In 1996, Maryland farmers, hunters and the general public were surveyed to determine their preferences for increasing, maintaining, or decreasing deer population numbers. Using a random utility theoretic framework with an ordered response probit model, the factors that explain preferences such as residential location, socioeconomic characteristics, landscape damage, agricultural yield loss and vehicle accidents were analyzed.
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Garza-Rodriguez, Jorge, Gustavo A. Ayala-Diaz, Gerardo G. Coronado-Saucedo, Eugenio G. Garza-Garza, and Oscar Ovando-Martinez. "Determinants of Poverty in Mexico: A Quantile Regression Analysis." Economies 9, no. 2 (April 15, 2021): 60. http://dx.doi.org/10.3390/economies9020060.

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Most studies on the determinants of poverty do not consider that the relative importance of each of these determinants can vary depending on the degree of poverty suffered by each group of poor people. For Mexico’s case, the studies carried out so far do not contemplate this approach, even though there is wide variation in the degree of poverty among the different groups of the poor. Investigating these differences is important to design better policies for fighting poverty, which consider how each variable that explains poverty affects each group of people who suffer from poverty differently. This article examines the determinants of poverty for Mexican households. Using data from the Mexican National Household Income and Expenditure Survey (ENIGH) 2018, the study estimates a probit model and a quantile regression model to examine the extent to which the determinants of poverty vary across the poverty spectrum. The results from the probit model indicate that households with more than one member, having a female head, or speaker of an indigenous language are more likely to be poor. The results obtained in the quantile regressions indicate that there are significant differences with the results of the simple ordinary least squares model, especially for households in extreme poverty but also for the other income categories analyzed for several of the explanatory variables used in the models. Households in the categories extremely poor and deeply poor are most affected if they are in the southern region or if the household head speaks an indigenous language or is an elderly person. It is observed that achieving a higher educational level is an effective way to increase income across the poverty spectrum.
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Kaminski, Abigail, Kathleen P. Bell, Caroline L. Noblet, and Keith S. Evans. "An Economic Analysis of Coastal Beach Safety Information-Seeking Behavior." Agricultural and Resource Economics Review 46, no. 2 (July 13, 2017): 365–87. http://dx.doi.org/10.1017/age.2017.17.

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We estimate a bivariate probit model using data from a survey of Maine and New Hampshire beachgoers to (i) assess the impact of exposure to and contact with beach waters on safety information-seeking behaviors, and (ii) compare information-seeking behaviors for surf conditions and water quality information. We find that individuals who engage in certain high-contact recreation activities (i.e., swimming, fishing, surfing) are more likely to seek out safety information and that some potential drivers of this behavior affect searches differently for surf conditions versus water quality information.
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HARRIS, ANTHONY. "Diabetes, Cardiovascular Disease and Labour Force Participation in Australia: An Endogenous Multivariate Probit Analysis of Clinical Prevalence Data." Economic Record 85, no. 271 (December 2009): 472–84. http://dx.doi.org/10.1111/j.1475-4932.2009.00572.x.

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Magnusson, Erik, and J. A. L. Cranfield. "Consumer Demand for Pesticide Free Food Products in Canada: A Probit Analysis." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 53, no. 1 (February 17, 2005): 67–81. http://dx.doi.org/10.1111/j.1744-7976.2005.00354.x.

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FEICK, LAWRENCE F., ROBERT O. HERRMANN, and REX H. WARLAND. "Search for Nutrition Information: A Probit Analysis of the Use of Different Information Sources." Journal of Consumer Affairs 20, no. 2 (December 1986): 173–92. http://dx.doi.org/10.1111/j.1745-6606.1986.tb00377.x.

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45

Dennis, Donald F. "A probit analysis of the harvest decision using pooled time-series and cross-sectional data." Journal of Environmental Economics and Management 18, no. 2 (March 1990): 176–87. http://dx.doi.org/10.1016/0095-0696(90)90047-3.

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46

Rohit, Bansal, and Khanna Ashu. "New firms measures at the initial performance of Indian IPOs: A Binary Probit analysis." African Journal of Business Management 7, no. 35 (September 21, 2013): 3504–15. http://dx.doi.org/10.5897/ajbm12.1181.

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47

Che, Yi, Yan Zhang, and Linhui Yu. "The development of farm labor market in rural China." China Agricultural Economic Review 7, no. 2 (May 5, 2015): 280–302. http://dx.doi.org/10.1108/caer-01-2014-0001.

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Purpose – The purpose of this paper is to examine key determinants of farm labor market development in rural China. Design/methodology/approach – Probit, Logit, and IV-Probit model are used to provide pertinent empirical analysis. Findings – Analysis of survey data establishes three facts about the farm labor market in rural China: first, households with high farm endowment are more likely to hire farm labor; second, because of the mismatch between farm ability and land size created by egalitarian land reallocation, households with more land reallocations are more likely to participate in farm labor market to adjust such mismatch; third, land rental market and farm labor market seem to be complementary. These results are robust to alternative model specifications, subsamples, alternative dependent variables, and additional controls. Welfare analysis demonstrates that the farm labor market is conducive to agricultural output. Originality/value – The main contribution of this study is to lay out stylized facts in terms of the development of farm labor market using a unique survey data set.
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Basile, Roberto, Anna Giunta, and Jeffrey B. Nugent. "Foreign Expansion by Italian Manufacturing Firms in the Nineties: an Ordered Probit Analysis." Review of Industrial Organization 23, no. 1 (August 2003): 1–24. http://dx.doi.org/10.1023/b:reio.0000005561.07176.30.

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49

Walstad, William B. "Economic Understanding in US High School Courses." American Economic Review 103, no. 3 (May 1, 2013): 659–63. http://dx.doi.org/10.1257/aer.103.3.659.

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The effects of courses on student achievement are studied using 2006 data from the National Assessment of Educational Progress (NAEP) in economics. A regression analysis showed expected and significant achievement differences by course, with the highest scores in advanced economics, followed by general economics. Courses in business and personal finance were not substitutes for advanced or general economics courses. A probit analysis showed that students taking economics courses relative to personal finance courses are significantly more likely to think their courses helped them understand the US economy, the international economy, and current events--but not how to manage personal finances.
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Lee, Myoung-Jae, and Yoon-Hee Tae. "Analysis of Labour Participation Behaviour of Korean Women with Dynamic Probit and Conditional Logit*." Oxford Bulletin of Economics and Statistics 67, no. 1 (February 2005): 71–91. http://dx.doi.org/10.1111/j.1468-0084.2005.00110.x.

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