Teacher training and market attitudes in transitioning economies.

Thomas, M. Kathleen ; Campbell, Randall C.

I. Introduction

The National Council on Economic Education (NCEE) has a long history of promoting economic education in the United States and has recently expanded operations overseas with the Cooperative Education Exchange Program (CEEP). In this program, secondary school teachers from Russia, Central Asia, and Central and Eastern Europe complete a Training of Trainers workshop: four six-day seminars covering basic economics, microeconomics, macroeconomics and international economics. Participants are trained by university faculty members from the United States to become trainers themselves. While the primary goal is to train instructors to teach market-based economics, one of the implicit objectives of economic education programs in transitioning economies is to impart an understanding of how markets work in order to make the conversion to a market economy more acceptable (Walstad and Rebeck, 2002). In an effort to assess this objective, teachers participating in training workshops complete a survey entitled the Attitudes Toward the Market (ATM) (Breeden and Lephardt, 2002). Using the ATM, demographic information and a test of economics knowledge, we investigate whether participating in teacher training programs in the Training of Trainers workshops changes the attitudes of participants regarding market economies, and which characteristics are responsible for any changes in attitudes.

Several studies compare attitudes about markets between groups from the U.S. and transitioning economies (e.g., Lopus, 1996; Watts, Walstad and Skiba, 2002). Walstad (2002) examines how teacher training influences the attitudes of the teachers' students in transitioning economies. However, little research exists on the effects of training on changing attitudes of the teachers themselves. To our knowledge, the only research to date on changing teacher attitudes are program reports on CEEP, formerly known as the International Economic Education Exchange Program (IEEEP) (EDC, 1996; EDC, 1997) and an evaluation of the IEEEP in Kazakhstan for the NCEE (Grimes and Millea, 2001). All of these studies find that teachers who attended IEEEP seminars and improved their economics knowledge showed greater support for market economies. This study builds on earlier research by examining teacher attitudes toward free markets while controlling for individual characteristics and allowing for the interaction between teacher attitudes and knowledge.

To determine the effects of training on market attitudes, we develop a system of simultaneous equations and estimate the system using two-stage methods, which are described in Section II. We first estimate a single-equation probit model to understand determinants of pro-market attitudes before teacher training. Following training, we estimate the simultaneous equations model allowing for the interaction between pro-market attitudes and knowledge. We use a two-stage estimation procedure that is similar to two-stage least squares, except that the attitude variable is binary while the knowledge variable is continuous. A t-test indicates that pro-market attitudes do increase following the training. (1) We use the model to understand which factors are likely to lead to a change in attitudes as a result of training.

II. Data and Methodology

Data on attitudes of secondary school teachers in transition economies are from the CEEP Database collected by ORC Macro (2002) and implemented through the NCEE. During the 2002-2003 academic year, 106 participants completed the Training of Trainers program. Data are not random and are unlikely to be representative of the teacher population in the nations represented--teachers in transitioning economies with more positive attitudes toward the market might select themselves into an economic education training program sponsored by a U.S. agency. However, even given this qualification, analysis of the CEEP data can still provide valuable insight into the efficacy of economics education in transitioning economies.

The evaluation instruments we use in this study include the Participation Information Form (PIF), a modified version of the Test of Understanding in College Economics (TUCE) (Saunders, 1991), and the ATM survey. The testing instrument was designed specifically for CEEP and is adapted from the third version of the TUCE and the Advanced Placement Test of Economics (ORC Macro, 2002). The ATM survey consists of 20 questions. Respondents are asked whether they strongly agree, agree, disagree, strongly disagree or are uncertain about various statements regarding a market economy. As one can see in Table 1, half of the statements are positive towards a market economy and half of the statements are negative.

In simplest terms, attitudes toward a market economy can be characterized as either for or against. Utilizing this framework a qualitative response model can be estimated using probit analysis. The probit equation is [y.sub.j] = ([X.sub.j][beta] + [e.sub.1j] > 0), where X is a set of characteristics for the ith individual, [beta] is the vector of estimated coefficients for the jth choice, and [y.sub.j] takes the value of one if the equation in parentheses is true and zero otherwise. In our model, the dependent variable takes on the value of one if more than 75 percent of the respondent's choices are pro-market. (2) Thresholds below 75 percent create an unbalanced sample with nearly all respondents being classified as "pro-market" so as to make statistical inference difficult. (3) Considering the responses "Strongly Agree" and "Agree" to be in agreement with a statement, we define a pro-market attitude in the following manner: respondents exhibit pro-market attitudes if they agree or strongly agree with the positive statements (i.e. agree with a positive statement) and if they disagree, strongly disagree or are uncertain about the negative statements (i.e. did not agree with a negative statement) on the ATM survey. (4) The purpose of this dichotomous approach is to reduce the potential for measurement error inherent in the Likert scale. There may be no discernable difference between a response of "Strongly Agree" and "Agree." By grouping these responses together, we expect to obtain a more accurate depiction of respondents' attitudes.

Table 1 gives the percentage of teachers who give pro-market responses for each question, both before and after the training. On average, the teachers give pro-market responses to 78 percent of the questions on the ATM survey prior to the workshop. One teacher gives pro-market responses to only 35 percent of the questions, while one teacher gives a pro-market response to every question. On the ATM survey taken before training, 97 percent of the teachers exhibit a pro-market attitude on question number four: "In my opinion, a market system leads to quality and technological development." Question number eighteen receives a pro-market response prior to the workshop from only 42 percent of the teachers: "In my opinion, a market system provides employment opportunities for all who desire." After training, question number eighteen still receives the least amount of support, but now 51 percent of the teachers give pro-market responses. Question 20 receives the most support after training--98 percent: "In my opinion, a market system encourages innovation and entrepreneurship" The responses indicate that the program had the greatest impact on changing attitudes regarding employment (questions 12 and 18), providing consumers with a higher standard of living (questions 8 and 17), and the degree of government intervention (question 14).

The empirical analysis includes the estimation of a probit model to assess the effects of various demographic variables on the development of pro-market attitudes before training. In addition, we estimate a simultaneous probit-OLS model following training to assess the effects of demographic characteristics on post-training attitudes and to examine whether knowledge impacts pro-market attitudes and whether pro-market attitudes impact knowledge.

The independent variables and their definitions are included in Table 2. Data are from the PIF and include educational attainment, experience and basic demographic information. Controlling for country fixed effects would be ideal, however, sample size prohibits this approach. As an alternative specification we group the respondent's country into a categorical variable related to speed of reform. This category is based upon research by Aslund, Boone and Johnson (1996) who characterize countries as rapid reformers, gradual reformers, delayed reformers or non-reformers (see Table 3). Their characterizations are a function of price stabilization and liberalization. Assuming that the pace of reform reflects a nation's willingness to transition to a market economy, controlling for a respondent's country of residence should at least partially capture the influence of the current political and economic climate on individual attitudes. In this case, we would expect teachers from rapidly reforming countries to exhibit more pro-market attitudes than teachers from other countries. However, if rapid transition creates a backlash against reforms, then we would expect teachers from rapidly reforming countries to exhibit less pro-market attitudes than teachers from other countries.

Research in the U.S. indicates that learning economics influences attitudes about economic issues (Allgood and Walstad, 1999; Becker, Walstad and Watts, 1994). Consequently, in addition to overall educational attainment, we control for prior training in economics using two variables: Economics Field and University. Economics Field is a dummy variable equal to one if the respondent specialized in economics at her highest level of education. We expect teachers specializing in economics to have more positive attitudes toward markets relative to other fields, however we anticipate the link to be mild because those who studied economics in the former Soviet Union received vastly different training from those students who studied economics in a market economy (Lopus, 1996). A typical college course in economics would include Marxist thought and the history of the Communist Party at the exclusion of basic microeconomic theory central to most Western economics courses (Alexeev, et al., 1992).

University is a dummy variable equal to one if the respondent took an economics course at a university. We cannot hypothesize how university training might influence market attitudes. Perhaps teachers who taught themselves economics were able to learn more Western ideas not taught in the universities and are therefore more likely to have positive attitudes toward markets. On the other hand, more formalized training might be associated with greater degrees of market orientation.

We also expect teaching experience to influence market orientation. We control for experience at two levels: total years of teaching experience and the number of years teaching economics. Teachers with more experience are likely to be older, and evidence from Russian surveys indicates that younger people tend to be more supportive of market reforms (Clarke, 1993). Unfortunately, CEEP data do not include the age of the workshop participants, thus, we expect the estimated coefficients on teaching experience to pick up some of the negative influence of age on market attitudes. In addition, teachers with greater experience, even in economics, are likely to have received their training earlier. We expect teachers trained before the fall of the Soviet Union to have received economics instruction with less emphasis on free-market principles (Lopus 1996). Thus, we hypothesize that teaching experience is negatively related with pro-market attitudes.

Although prior training in economics implies learning, a more direct measure of learning economics is an educational outcome such as a test score. Participants in the teacher training workshops took a modified version of the TUCE in microeconomics and macroeconomics both before and after training. We control for the microeconomics pre-test in the probit model predicting a market orientation before training. We do not include both the microeconomics and macroeconomics TUCE scores in any of the models because the scores on these exams are highly correlated with one another. (5)

Research using U.S. data indicates that changing someone's knowledge of economics can alter his attitude toward economic issues and policies (Watts, Walstad and Skiba, 2002). Because the TUCE exam is based upon Western economic thought, we expect teachers with higher TUCE scores to be more likely to hold positive opinions regarding market economies. In addition, we expect that changes in market attitudes may affect learning. Teachers who develop stronger pro-market attitudes during the training may be expected to perform better on the TUCE test taken after training. This interaction between attitudes and learning leads us to estimate the following simultaneous equations model

[y.sub.1] = [[alpha].sub.1][y.sub.2] + [X.sub.1][[beta].sub.1] + [u.sub.1] (1)

[y.sub.2] = [[alpha].sub.2][y.sub.1] + [X.sub.2][[beta].sub.2] + [u.sub.2] (2)

where [y.sub.1], is the post-training market attitude variable, [y.sub.2] is the post-training micro TUCE score, and [X.sub.1] and [X.sub.2] are regressors. To identify the system, the pre-training attitude is included in [X.sub.1] but not [X.sub.2] while the pre-training micro TUCE score is included in [X.sub.2] but not [X.sub.1]. We estimate the model using a two-stage procedure where [[??].sub.2] and [[??].sub.1] are estimated from the reduced form equations and then substituted into equations (l) and (2), respectively. Equation (1) is then estimated by probit and equation (2) is estimated by OLS. We compute the asymptotic covariance matrix following the procedure described in Maddala (1983, p. 245).

Descriptive statistics are included in Table 4. Seventy percent of the teachers are female and half live in Russia or Ukraine. Thirty-nine percent of the participants hold master's or doctoral degrees. Although the purpose of the Training of Trainers workshop is to better prepare high school teachers to teach economics, only 43 percent of the teachers have a degree specialization in economics. However, two-thirds received some economics training at a university. The average total years of teaching experience is 11.5 years--7.5 years for teaching economics. The average microeconomics TUCE score is 12 before the workshop and 16 after the workshop.

III. The Effects of Training on Market Attitudes

Table 5 gives results for the estimated probit model relating pro-market attitudes prior to training to the regressors. Because the coefficient estimates for the probit model do not give the marginal impact of changes in the explanatory variables, we include a column of marginal effects in addition to the coefficients and standard errors. The marginal effects for continuous variables are evaluated at the means, and the marginal effects of the dummy variables are evaluated as a change from zero to one, holding other variables constant at their means.

The results of this initial probit model indicate that prior to the workshop, specializing in economics at any degree level increases the probability of possessing positive attitudes toward markets by 26 percent. This finding is supported by U.S. research linking attitudes toward the market and economics knowledge (Allgood and Walstad, 1999; Becker, Walstad and Watts, 1994). However, having a master's degree or a doctorate reduces the probability of possessing positive market attitudes by 30 percent. For those with graduate degrees in economics, this finding implicates the instructors and content of their programs. Given the resistance to reform of many political economy faculty members and the difficulty university officials face in retraining or replacing them with faculty more sympathetic to Western ideas, it is not surprising that graduate studies foster more negative attitudes toward market reforms (Kovzik and Watts, 2002, 35-62). Finally, females are less likely to have positive market attitudes prior to training.

Table 6 gives results for the simultaneous equations model of post-training market attitudes and learning. This model controls for the initial attitudes and knowledge and allows for interactions between attitudes and learning throughout the training process. The standard errors for each model are calculated following the procedure described in Maddala (1983, p. 245) and the marginal effects for the probit model are calculated as before.

After training, the percentage of teachers with positive market attitudes increases from 58 percent to 69 percent. The Training of Teachers program seems to be meeting one of its ancillary goals: to improve economic understanding in order to make the transition to a market economy more acceptable. Of the 42 teachers who had a negative market attitude, 50 percent had a positive market attitude following the training. Recall that Table 5 has premarket attitude as the dependent variable and shows how factors influence the probability of having a pro-market attitude before training. However, the probit model in Table 6 has post-market attitude as the dependent variable and pre-market attitude as an explanatory variable. Thus, this model indicates some of the factors likely to result in a change of attitude.

Home country has an affect on whether pro-market attitudes change as a result of training. Participants in countries undergoing reform are less likely to develop pro-market attitudes as a result of training, even given their initial attitudes. This supports the hypothesis that even gradual reforms have created a backlash against transitioning to a market economy. An unexpected finding is that experienced teachers are more likely to hold positive pro-market attitudes following training than those teachers with less experience, holding initial attitudes constant. Perhaps more experienced (and older) teachers have first-hand knowledge of the problems associated with a command system, making them more open to the changes associated with market-based economies. This may also occur because older teachers were probably not taught market economics when they were in school and the training program is their first exposure to Western economic ideas. Thus, they are more likely to exhibit a change in attitude than someone who has already been exposed to these concepts.

As expected, the market attitude before training is highly significant in explaining the market attitude after training. The probability of a positive post-training attitude is 37 percent greater when the teacher has a positive pre-training attitude than when she has a negative pre-training attitude. Finally, there is some evidence that increasing knowledge leads to an increase in positive attitudes toward market economies. In the simultaneous equations model, each one point increase on the TUCE leads to a 3.6 percent increase in the probability of having a positive market attitude. This result is significant at the 15 percent level (one-tail test). Recall that in the initial probit model, the pretest TUCE score is highly insignificant and a one point increase leads to only a 0.2 percent increase in the probability of a positive market attitude. When we consider the feedback between learning and attitudes there is at least some additional evidence that learning affects attitudes. Intuitively, these regressions imply that while knowledge did not necessarily increase pro-market attitudes prior to training, those with greater knowledge were better able to follow the training and thus increase their pro-market attitudes.

From the OLS equation we find that the only significant variable in predicting the post-test TUCE score is the pre-test TUCE score. Thus, while knowledge did increase as evidenced by the rise in average test scores, none of the regressors are able to explain why the scores increased. Of note is the negative (but insignificant) sign on attitude after training. Thus, the evidence from this trial shows that while learning increases market attitudes, the reverse is not true.

IV. Conclusions

The evidence shows that teacher attitudes do become more pro-market after participating in the Training of Trainers workshop. Furthermore, 50 percent of the teachers with a negative predisposition to market reforms initially became pro-market after training. These teachers tend to be more experienced, possess a graduate degree with a specialization in economics and score well on the microeconomics TUCE exam administered after the workshop. This indicates that increased knowledge leads to a change in pro-market attitudes. These results need to be treated with some caution because we do not know the attitudes of teachers not participating in the program. If the participants who select themselves into the program differ systematically from teachers not participating in the program, these results may be biased due to sample selection. Unfortunately, data on non-participants are simply not available.

The Training of Trainers program seems to be an effective tool in changing the attitudes of teachers in transitioning economies--even those who begin the program with negative attitudes. Furthermore, Grimes and Millea (2001) find that teacher attitudes toward market economies influence the attitudes of their students--positive teacher attitudes lead to positive student attitudes. Although the primary goal of the Training of Trainers program remains to better prepare teachers to teach economics, increasing their acceptance of market reforms can only aid the transition process, especially when they take both their knowledge and positive attitudes back to their own classrooms.

References

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Allgood, S. and W. B. Walstad (1999). The longitudinal effects of economic education on teachers and their students. Journal of Economic Education, Vol. 30 (2), pp. 99-111.

Aslund, A., P. Boone and S. Johnson (1996). How to stabilize: Lessons from post-Communist countries. Brookings Papers on Economic Activity, 1, pp. 217-314.

Becker, W. E., W. B. Walstad and M. Watts (1994). A comparison of the views of economists, economic educators, teachers, and journalists on economic issues, in W.B. Walstad (ed.), An international perspective on economic education. Boston: Kluwer Academic Publishers, pp. 65-87.

Breeden, C. H. and N. E. Lephardt (2002). Student attitudes towards the market system: An inquiry and analysis. Journal of Private Enterprise, 17(2), pp. 153-171.

Clarke, S. (1993). Popular attitudes to the transition to a market economy in the Soviet Union on the eve of reform. Sociological Review. Vol. 41 (4), pp. 619-52.

Education Development Center, Inc. (1996). International Economic Education Exchange Program: Program Report. New York: EDC, International Programs.

Education Development Center, Inc. (1997). Final Program Evaluation: International Economic Education Exchange Program, 1996-1997. New York: EDC, International Programs.

Grimes, P. W. and M. J. Millea (2001). An outcomes evaluation of the International Economic Education Exchange Program in Kazakhstan. A report prepared for the National Council on Economic Education, pp. 1-78.

Kovzik A. and M. Watts (2002). Reforming undergraduate economics instruction in Russia, Belarus, and Ukraine: Curriculum, personnel and clientele issues, in M. Watts and W. B. Walstad (eds.), Reforming Economics and Economics Teaching in the Transition Economies: From Marx to Markets in the Classroom. Northhampton, MA: Edward Elgar, pp. 35-62.

Lopus, J. S. (1996). An international comparison of teacher attitudes on economic issues: Azerbaijan, Russia and the United States. Journal of Private Enterprise, Vol. 12 (1), pp. 169-83.

Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge University Press: Cambridge. ORC Macro (2002). Cooperative Education Exchange Program Database, 2002-03. Calverton, Maryland.

Saunders, P. (1991). Test of understanding in college economics. 3rd edition. Joint Council on Economic Education: New York.

Walstad, W. B. (2002). The effects of teacher programs on student economic understanding and market attitudes in transition economies, in M. Watts and W. B. Walstad (eds.), Reforming Economics and Economics Teaching in the Transition Economies: From Marx to Markets in the Classroom. Northhampton, MA: Edward Elgar, pp. 63-96.

Walstad, W. B. and K. Rebeck (2002). Assessing the economic knowledge and economic opinions of adults. Quarterly Review of Economics and Finance. Vol. 42 (5), pp. 921-935.

Watts, M., W. B. Walstad, and A. Skiba (2002). Attitudes toward markets and market reforms in the former Soviet Union and Eastern Europe, in M. Watts and W. B. Walstad (eds.), Reforming Economics and Economics Teaching in the Transition Economies: From Marx to Markets in the Classroom. Northhampton, MA: Edward Elgar, pp. 8-34.

Notes

(1.) A simple t-test for difference in means yields t = 1.63, which is significant at the 10 percent level of significance (one-tail test).

(2.) Eight respondents to the survey answered only 18 or 19 of the 20 questions. In an effort to reduce the total number of missing values in the final analysis, we calculate the dependent variable for these individuals as the number of pro-market responses on the ATM survey divided by the total number of questions answered. Nevertheless, we lose 6 observations from the full sample due to missing information on the PIE

(3.) Models using alternative thresholds greater than 75 percent yield similar results to those reported. The results are available from the author upon request.

(4.) One might also define pro-market as "Agree" or "Strongly Agree" with positive statements and "Disagree" or "Strongly Disagree" with negative statements. The only difference between this definition and ours is that we classify an "Uncertain" response to a negative statement as pro-market while this definition classifies these responses as not pro-market (there is no difference for positive statements). Therefore, fewer individuals would be classified as pro-market under this approach. A model with this structure yields similar results although we lowered the threshold for being classified as pro-market to 60 percent due to the lower number of pro-market responses for the negative questions.

(5.) The results from models controlling for the macroeconomics TUCE score and for the combined microeconomics and macroeconomics TUCE scores are quite similar to those reported with the microeconomics TUCE score. The results are available from the author upon request.

by M. Kathleen Thomas * and Randall C. Campbell **

* Corresponding author: Mississippi State University; Box 9580; Mississippi State, MS 39762; (662) 325-2561; kthomas@cobilan.msstate.edu

** Mississippi State University

Support for this research project was provided by the National Council on Economic Education through the Cooperative Education Exchange Program funded by the U.S. Department of Education in coordination with the U.S. Department of State. We would like to thank Paul W. Grimes and an anonymous referee for helpful comments and suggestions. Any errors that remain are our own.

TABLE 1.
Attitudes Toward the Market Survey

 Pro-market Pro-market
 responses responses
 before after
In my opinion, a market system ... training (%) training (%)

 1. leads to an unfair distribution 78 77
 of income (N)
 2. encourages a maximum of personal 93 96
 freedom and choice (P)
 3. encourages unethical (immoral) 89 88
 business behavior (N)
 4. leads to quality and technological 97 97
 advancement (P)
 5. leads to insufficient provision of 81 76
 important public services (N)
 6. provides opportunities and 96 95
 incentives for success (P)
 7. leads to inflation (N) 61 57
 8. raises the living standard for 64 77
 the average person (P)
 9. leads to monopolies (N) 57 55
10. leads to an efficient allocation 85 91
 of resources (P)
11. encourages the abuse of natural 88 89
 resources (N)
12. leads to excessive unemployment 55 65
 risk for workers (N)
13. leads to excessive risk of business 60 63
 failure (N)
14. requires a great deal of government 66 79
 control to be efficient (N)
15. allows unfair foreign competition (N) 87 93
16. maximizes consumer choice of products 93 93
 and services (P)
17. provides consumers the goods and 87 94
 services they want (P)
18. provides employment opportunities for 42 51
 all who desire (P)
19. rewards people fairly for their 73 78
 productivity and hard work (P)
20. encourages innovation and 96 98
 entrepreneurship (P)

Source: (Breeden and Lephardt 2002); Responses based on 106
participants.
(P) = positive towards a market economy
(N) = negative towards a market economy

TABLE 2.
Descriptions of Variables

Variable Name Description

Dependent Variables
 Attitude Before Training Equal to one if attitude is pro-market
 before the workshop; zero otherwise
 Attitude After Training Equal to one if attitude is pro-market
 after the workshop; zero otherwise
Independent Variables
 Female Equal to one if respondent is female;
 zero otherwise
 Reformers Equal to one if country is rapidly or
 gradually reforming; zero otherwise
 Advanced Degree Equal to one if Master's degree or
 Ph.D.; zero otherwise
 Economics Field Equal to one if degree specialization
 is economics; zero otherwise
 University Equal to one if an economics course was
 taken at a university; zero otherwise
 Experience Number of years teaching
 Teaching Economics Number of years teaching economics
 Micro Pre-test Score on Microeconomics TUCE before
 training
 Micro Post-test Score on Microeconomics TUCE after
 training

TABLE 3.
Degree of Market Reforms in Transitioning Countries

Teacher's Home Country Degree of Reform Participants

Albania Radical 1
Armenia None 2
Belarus Delayed 3
Bulgaria Gradual 4
Croatia (a) Gradual 1
Estonia Radical 1
Georgia None 4
Kazakhstan Delayed 4
Kyrgyzstan Gradual l
Latvia Radical 2
Lithuania Gradual 4
Moldova Delayed 1
Poland Radical 2
Romania Delayed 4
Russia Gradual 30
Slovakia Radical 4
Tajikistan None 2
Turkmenistan Delayed 3
Ukraine Delayed 20
Uzbekistan Delayed 6
Mongolia (b) Gradual 1

Sources: Aslund, Boone and Johnson (1996) and CEEP Data 2002-03

Notes: (a). This is our characterization. Aslund, Boone and Johnson
(1996) do not place Croatia in one of the four categories of reform.
However, they do report that Croatia has a high liberalization index,
but battled inflation over 50 percent until 1994. (b). See footnote 11,
p. 225 from Aslund, Boone and Johnson (1996).

TABLE 4.
Descriptive Statistics

Variable Full Sample Means

Attitude Before Training 0.58
 (0.49)
Attitude After Training 0.69
 (0.46)
Female 0.70
 (0.46)
Reformers 0.51
 (0.50)
Advanced Degree 0.39
 (0.49)
Economics Field 0.43
 (0.50)
University 0.67
 (0.47)
Experience 11.5
 (7.70)
Teaching Economics 7.5
 (6.09)
Micro Pre-test 11.6
 (4.77)
Micro Post-test 16.4
 (4.90)
Number of Observations 100

Source: CEEP Data 2002-03

Note: Standard deviations are in parentheses.

TABLE 5.
Probit Model of Attitudes Toward the Market (Before Training)

Variables Parameter Estimates Marginal Effects

Constant 0.373
 (0.53)
Female -0.379 * -0.144
 (0.30)
Reformers -0.249 -0.097
 (0.27)
Advanced Degree -0.776 *** -0.299
 (0.32)
Economics Field 0.698 *** 0.264
 (0.32)
University -0.00003 -0.00001
 (0.36)
Experience 0.015 0.006
 (0.02)
Teaching Economics 0.002 0.001
 (0.03)
Micro TUCE Pre-Test 0.005 0.002
 (0.03)
N 100

Source: CEEP Data 2002-03

Standard errors are in parentheses.

*** p [less than or equal to] 5 percent; ** 5 percent < p [less than
or equal to] 10 percent; * 10 percent < p [less than or equal to] 15
percent (one-tailed significance)

Table 6. Simultaneous Probit-OLS Model of Market Attitudes and Learning

 Probit Model OLS Model

 Parameter Marginal Parameter
Variables Estimates Effects Estimates

Constant -2.192 9.794
 (2.07) (1.98)
Female 0.318 0.109 -0.014
 (0.33) (1.00)
Reformers -0.402 ** 0.132 -0.078
 (0.30) (1.01)
Advanced Degree -0.200 -0.067 0.266
 (0.35) (1.14)
Economics Field -0.346 -0.115 0.629
 (0.39) (1.07)
University 0.277 0.094 -1.001
 (0.41) (1.22)
Experience 0.059 *** 0.019 -0.01
 (0.03) (0.09)
Teaching Economics -0.035 * -0.012 -0.039
 (0.03) (0.10)
Attitude Before Training 1.113 *** 0.372
 (0.34)
Post-Micro TUCE 0.108 * 0.036
 (0.09)
Attitude After Training -0.876
 (0.94)
Pre-Micro TUCE 0.681 ***
 (0.13)
N 100 100

Source: CEEP Data 2002-03

Standard errors are in parentheses.

*** p [less than or equal to] 5 percent; ** 5 percent < p [less than
or equal to] 10 percent; * 10 percent < p [less than or equal to] 15
percent (one-tailed significance)