rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables: Main Results**

Table 1: Summary statistics

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|Female|0.55|0.497|0|1|1492519|
|Age|14.256|1.665|9|21|1492520|
|Rural|0.881|0.324|0|1|1491692|
|CatchmentArea|0.187|0.39|0|1|1492520|
|CatchmentArea X Post|0.016|0.124|0|1|1492520|
|_Standardized Grade Points in_:||||||
|All subjects|-0.038|0.964|-1.459|5.367|1492516|
|<br>STEM subjects|-0.037|0.959|-1.05|6.503|1492519|
|Non-STEM subjects|-0.037|0.969|-1.678|4.419|1492515|
|_Unstandardized Grade Points in_:||||||
|All subjects|15.421|7.675|5|45|1492516|
|STEM subjects|4.867|3.144|2|18|1492519|
|Non-STEM subjects|10.554|4.818|3|27|1492515|
|_Standardized Test Scores in_:||||||
|Math|-0.035|0.959|-1.295|8.511|1492520|
|Science|-0.036|0.963|-1.542|7.121|1492520|
|Social studies|-0.037|0.966|-1.436|5.102|1492520|
|English|-0.044|0.948|-2.226|6.826|1492520|
|Kinyarwanda|-0.022|0.995|-3.406|3.801|1492520|
|_Baseline attributes_:||||||
|Road density (m/km2)|12.67|9.500|0|34.835|1492520|
|<br>Coffee density in 1999 (trees/km2)|3338.4|5034.6|0|33542.6|1492520|
|Distance to closest main city|24.119|17.328|0.544|91.796|1487362|
|_Health Data_:||||||
|Height-for-age (HAZ)|-1.71|1.43|-5.96|5.99|22794|
|Weight-for-Height (WHZ)|0.34|1.17|-4.91|4.97|22794|
|Weight-for-age (WAZ)|-0.74|1.11|-5.69|4.06|22796|
|Stunting|0.42|0.49|0|1|22794|
|Wasting|0.03|0.17|0|1|22794|
|Underweight|0.12|0.32|0|1|22796|

31
# **Table 18: DID Regression (4)**

||Bus for po|pulated area|||Bus for m<br>capital|unicipal|||
|---|---|---|---|---|---|---|---|---|
||Linearpro|bability|Probit||Linearpro|bability|Probit||
|D|0.046|(0.017) ***|0.296|(0.120) **|0.071|(0.024) ***|0.265|(0.094) ***|
|T|-0.017|(0.015)|-0.322|(0.180) *|-0.046|(0.024) *|-0.311|(0.126) **|
|D*T|-0.0006|(0.0213)|0.238|(0.199)|0.081|(0.032) **|0.442|(0.144) ***|
|lnHH size|0.001|(0.016)|-0.003|(0.119)|0.009|(0.023)|0.036|(0.090)|
|Adult share|-0.017|(0.033)|-0.132|(0.254)|-0.071|(0.048)|-0.272|(0.190)|
|HH head sex: Male|0.000|(0.017)|0.016|(0.133)|-0.008|(0.027)|-0.004|(0.105)|
|HH head education: Elementary|0.010|(0.013)|0.073|(0.097)|-0.017|(0.020)|-0.075|(0.075)|
|HH head education: Secondary|-0.028|(0.020)|-0.286|(0.231)|-0.093|(0.031) ***|-0.422|(0.158) ***|
|HH head education: University|-0.063|(0.015) ***|||-0.112|(0.052) **|-0.590|(0.371)|
|Appliance ownership: Refrigerator|0.004|(0.018)|0.053|(0.142)|-0.013|(0.029)|-0.042|(0.107)|
|Appliance ownership: Color TV|-0.040|(0.017) **|-0.334|(0.128) ***|-0.032|(0.027)|-0.121|(0.099)|
|Appliance ownership: Radio|-0.004|(0.012)|-0.028|(0.090)|-0.021|(0.018)|-0.077|(0.069)|
|Appliance ownership: Gas stove|0.003|(0.018)|0.025|(0.111)|-0.029|(0.026)|-0.102|(0.089)|
|Appliance ownership: Washing|||||||||
|machine|-0.003|(0.014)|-0.031|(0.132)|-0.041|(0.021) *|-0.177|(0.099) *|
|Gas use for cooking|0.021|(0.014)|0.213|(0.117) *|0.018|(0.021)|0.080|(0.090)|
|Power use for lighting|-0.041|(0.017) **|-0.297|(0.114) ***|-0.018|(0.028)|-0.065|(0.101)|
|Tap water for cooking|0.034|(0.012) ***|0.285|(0.089) ***|0.042|(0.018) **|0.172|(0.071) **|
|Constant|0.088|(0.050)*|-1.411|(0.368)***|0.264|(0.075)***|-0.659|(0.288)**|
|Obs.|2,064||2,036||2,064||2,064||
|R-squared|0.032||||0.044||||
|F-stat|5.53||||6.84||||
|Pseudo-R2|||0.065||||0.050||
|Wald-stat|||69.93||||90.86||
|Z-statfor interactioneffect (mean& s|td.dev.)||0.604|(0.107)|||2.761|(0.214)|
|Note: Standard errors are shown in par|entheses. *, *|*, *** indicate|the statistic|al significanc|e at 10%, 5|% and 1%, re|spectively.||

37
children who enroll and the proportion who drop-out of school in the first 5 years."<sup>1</sup> The pattern is again consistent. Ghana is the only Western and Central African country which does not have a large gender gap, and Bangladesh is the only South Asian one which does not. Among countries outside of Western and Central Africa, North Africa, and South Asia, the same set of countries who performed poorly with respect to gender equality reappear. An exception is Malawi where there is a relatively large gap. Malawi has two surveys separated by four years and a comparison of enrollment rates of 12 to 14 year olds in school between the survey dates reveals that although there was a gender disadvantage in 1992, it had vanished by 1996. The gap in the percentage of 15 to 19 year olds who have completed grade 5 is therefore most likely a reflection of a gender disadvantage which existed some time ago.

Although we are focused here in female _disadvantages_ in education, it should not go unnoticed that in several countries there is a female _advantage._ Of the 41 countries analyzed (counting only the most recent survey in countries where there are two) 16 have a female advantage in the enrollment of 6 to I I year olds, 10 have a female advantage in the enrollment of 12 to 14 year olds, and I 1 have a female advantage in the completion of grade 5. The fact that the countries for which these data are drawn were not randomly selected makes it hard to draw strong conclusions, however it is indicative that a large disadvantage of girls in education may not be a worldwide problem, but is quite localized in certain regions or countries.<sup>12</sup> _Comparison with other data sources_ At this point it might be useful to digress and compare the findings based on these (generally) nationally representative household surveys to those reported in standard crosscountry tables. Table 4 reports the primary net enrollment rate for girls as derived from the DHS

> " In a subsequent section, the properties of the entire "attainment profile" of this cohort are investigated. **12** Filmer, King, and Pritchett (1998) and Filmer and Pritchett (1999b) disaggregate the data within India and find substantial heterogeneity across the different states.

12
Policy Research Working Paper 6219

# **Abstract**

This study provides estimates of social and financial costs of environmental damage in India from three pollution damage categories: (i) urban air pollution; (ii) inadequate water supply, poor sanitation, and hygiene; and (iii) indoor air pollution. It also provides estimates based on three natural resource damage categories: (i) agricultural damage from soil salinity, water logging, and soil erosion; (ii) rangeland degradation; and (iii) deforestation. The estimates are based on a combination of Indian data from secondary sources and on the transfer of unit costs of pollution from a range of national and international studies. The study estimates the total cost of environmental degradation in India at about 3.75 trillion rupees (US$80 billion) annually, equivalent to 5.7 percent of gross domestic product in 2009, which is the reference year for most of the damage estimates. Of this total, outdoor air pollution accounts for 1.1 trillion rupees, followed by the cost of indoor air pollution at 0.9 trillion rupees, croplands degradation cost at 0.7 trillion rupees, inadequate water supply and sanitation cost at around at 0.5 trillion rupees, pasture degradation cost at 0.4 trillion rupees, and forest degradation cost at 0.1 trillion rupees.

This paper is a product of the Disaster Risk Management and Climate Change, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mmani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(Vagstad, 1995; Krasnokutskaya and Seim, 2011; Nakabayashi, 2013). Branco (2002) explores the effects of favoritism in procurement on technology adoption. Strands of the literature have focused on other elements of public procurement such as audits (Di Tella and Schardgordsky, 2003), competition (Estache and Iimi, 2008; Ohashi 2009), and reputational mechanism (Spagnolo 2009). Very few studies have explored the effects of the overall public procurement system.

The study builds on two recent articles in the literature that study the overall public procurement system. We use the public procurement score created by Djankov et al, (2017), which explored the effects of public procurement on road-quality outcomes at the country-level. The data source is the World Bank’s Benchmarking Public Procurement database (BPP). The public procurement score adopts a lifecycle approach. Main stages of the procurement lifecycle are identified - bid preparation, bid and contract management, and payment of suppliers. An aggregated score of public procurement (PP Overall Index) is crafted that incorporates important aspects of these stages. A higher score implies a higher quality of the public procurement system. The second closely related study is by Knack et al. (2017) that also uses country-level measures of public procurement and links it to firm-level engagement and corruption. We build on this study in two ways. First, we explore a larger range of outcomes capturing infrastructure quality and innovation. Second, Knack et al. (2017) utilize the public procurement data from the Public Expenditure and Financial Accountability (PEFA) assessments.

PEFA assessments are built on the likelihood of accomplishing desirable outcomes based on seven pillars of performance. There is some thematic overlap between the BPP and PEFA assessments, particularly with regards to two of PEFA’s pillars: predictability and control in budget execution and accounting and reporting. The BPP database is far more extensive in covering specific details of the public procurement system, going beyond the aggregate assessments in PEFA. Whereas BPP presents an assessment of the regulatory framework applicable to public procurement, PEFA does not measure the legal framework nor institutional capacity affecting public financial management. The PEFA has some comparability challenges 3
prospects.” School attendance in Chile is consistently lower for migrants than non-migrants aged 6–13 (92 percent vs. 76 percent) and 14–17 (74 percent vs. 60 percent). Globally, forced migrants’ enrollment in primary school is estimated to be 63 percent vs. 91 percent of natives, and 24 percent vs. 84 percent of natives in secondary school (UNHCR, 2020)). Many migrants remain out of school due to conditions associated with recently arriving in the country, thus generating an educational lag with potentially disastrous consequences for their future (Servicio Jesuita a Migrantes, 2019)

# **Literature review and hypotheses**

The literature at the intersection of migration and education has tended to emphasize the effects of large population flows in the educational performance of native children. Overall, the evidence is mixed and mostly concentrated on case studies in developed countries or historical studies in Europe. Recent studies from Chile (Contreras and Gallardo, 2020) and Colombia (Rozo and Vargas, 2020) have shown a negative effect of migrants on native-born children’s school performance. Studies conducted elsewhere in the developing world have found positive or null effects of migrants in native schooling (Assaad et al., 2019; Jensen, 2015; van der Werf, 2019). However, little is known about how migrant status affects educational attainment, grade advancement, and enrollment in higher education.

The large inflow of migrants has also been associated with selective school sorting in some European countries (Demintseva, 2020), which has been linked to lower test scores and enrollment in higher education for migrant children (Entorf and Lauk, 2008). This could be attributed to overcrowding in certain schools. However, selective sorting could be more difficult in developing countries’ public education sectors due to differences in the number of available schools for migrants. In Latin America, there is some evidence that migrants and refugees tend to arrive in already overcrowded public school systems, which makes learning even more difficult. A series of studies in developed and developing nations explains some of these differences through variables related to the education sector, such as the disruptive effects of moving schools (Hanushek et al., 2004; Contreras and Gallardo, 2020), congestion of educational resources (i.e. teachers, classrooms, etc.) (Rozo and Vargas, 2020), the perceived returns to education (Jensen, 2010), and missing school due to natural disasters and migration (Pane et al., 2008; 7
**Table 4a: Pre-Reform Averages of Individual Level Variables**

||Early adopters<br>average|Late adopters<br>average|Difference<br>in averages|Coefficient on<br>quarter of<br>implementation|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Outcome Variables|||||
|Employed dummy|0.6617|0.6605|0.0012|0.0002|
||(0.4731)|(0.4735)|(0.0118)|(0.0025)|
|Low-risk employed dummy|0.4463|0.4402|0.0062|0.0003|
||(0.4971)|(0.4964)|(0.0211)|(0.004)|
|High-risk employed dummy|0.2154|0.2203|-0.0049|-0.0001|
||(0.4111)|(0.4145)|(0.0208)|(0.0032)|
|Wage earner dummy|0.5014|0.4941|0.0074|-0.0021|
||(0.5000)|(0.5000)|(0.0119)|(0.0024)|
|Low-risk wage earner dummy|0.2998|0.2869|0.0129|-0.0019|
||(0.4582)|(0.4523)|(0.0170)|(0.0029)|
|High-risk wage earner dummy|0.2017|0.2072|-0.0055|-0.0002|
||(0.4012)|(0.4053)|(0.0207)|(0.0032)|
|Registered business owner dummy|0.0842|0.0839|0.0003|0.0008|
||(0.2776)|(0.2772)|(0.0064)|(0.0010)|
|Low-risk registered owner|0.0737|0.0734|0.0003|0.0007|
||(0.2612)|(0.2608)|(0.0058)|(0.0010)|
|High-risk registered owner|0.0105|0.0105|0.0000|0.0001|
||(0.1019)|(0.1019)|(0.0019)|(0.0002)|
|Log monthly income|8.0686|7.8917|0.1769**|-0.0298|
||(0.7450)|(0.8117)|(0.0812)|(0.0197)|
|Background Variables|||||
|Primary education dummy|0.2286|0.2265|0.0020|-0.0007|
||(0.4199)|(0.4186)|(0.0141)|(0.0021)|
|Secondary education dummy|0.2287|0.2289|-0.0001|-0.0004|
||(0.4200)|(0.4201)|(0.0127)|(0.0018)|
|High school education dummy|0.2416|0.2348|0.0067|-0.0025|
||(0.4280)|(0.4239)|(0.0167)|(0.0024)|
|University education dummy|0.1475|0.1371|0.0104|-0.0017|
||(0.3546)|(0.3440)|(0.0148)|(0.002)|
|Female dummy|0.5310|0.5432|-0.0121**|0.0018|
||(0.4990)|(0.4981)|(0.0052)|(0.0014)|
|Age|36.9033|37.1139|-0.2106|0.0305|
||(11.9884)|(12.0612)|(0.1598)|(0.0221)|
|Married dummy|0.6694|0.6848|-0.0154|0.0041**|
||(0.4704)|(0.4646)|(0.0108)|(0.002)|

_Note:_ Standard errors in parentheses (clustered at municipality level). Data is from 2000 & 2001 ENE. The employed include wage earners and self-employed/business owners. Column 4 presents the coefficients of a separate regression for each variable on quarter of implementation. Significance levels: * 10%, ** 5%, *** 1% 37
Policy Research Working Paper 7281

# **Abstract**

Africa is estimated to have great potential for agricultural production, but there are a number of constraints inhibiting the development of that potential. Spatial data are increasingly important in the realization of potential as well as the associated constraints. With crop production data generated at 5-minute spatial resolution, the paper applies the spatial tobit regression model to estimate the possible impacts of improvements in transport accessibility in East Africa. It is found that rural accessibility and access to markets are important to increase agricultural production. In particular for export crops, such as coffee, tea, tobacco, and cotton, access to ports is crucial. The elasticities are estimated at 0.3–4.6. In addition, the estimation results show that spatial autocorrelation matters to the estimation results. While a random shock in a particular locality would likely affect its neighboring places, the spatial autoregressive term can be positive or negative, depending on how fragmented the current production areas are.

This paper is a product of the Transport and ICT Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 25 -

**<u>Table 10. Instrumental variable estimation with limited samples where no actual works took place</u>**

||Battese's speci<br>|fication<br>|Small-positi<br>|ve-number mod<br>|el|
|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.||
|_OPRC_*_t_|-14.283|(14.719)|-19.312|(17.122)||
|_OPRC_|7.306|(7.479)|10.176|(8.852)||
|_t_|6.157|(6.450)|8.214|(7.515)||
|ln_POT_|0.044|(0.093)|-1.251|(0.913)||
|ln_L_|0.502|(0.535)|0.714|(0.650)||
|ln_H_|0.324|(0.298)|2.103|(1.389)||
|ln_S_|0.566|(0.317)|*<br>-0.131|(0.271)||
|ln_F_|-0.176|(0.382)|-0.043|(0.059)||
|ln_P_|0.640|(0.756)|-0.006|(0.032)||
|ln_TR_|-0.109|(0.185)|0.101|(0.073)||
|d (_S_)|6.477|(3.608)|*|||
|d (_F_)|-0.409|(1.253)||||
|d (_P_)|7.378|(8.963)||||
|d (_TR_)|-2.607|(2.710)||||
|ln_SIZE_|-0.345|(0.502)|-0.529|(0.693)||
|d (_MALE_)|0.156|(0.392)|0.120|(0.460)||
|ln_AGE_|-0.215|(0.442)|-0.277|(0.594)||
|ln_EDU_|-0.007|(0.206)|-0.002|(0.033)||
|_CONC_|0.607|(0.704)|0.715|(0.682)||
|_TOIL_|-0.260|(0.511)|-0.310|(0.605)||
|_MOTO_|-0.724|(1.165)|-1.044|(1.580)||
|_BIKE_|0.233|(0.320)|0.352|(0.446)||
|_CHIPATA_|-0.168|(0.465)|-0.398|(0.616)||
|Constant|-2.523|(7.780)|14.447|(7.231)|***|
|Obs|828||2049|||
|R-squared|-10.247||-19.669|||
|Wald chi2|46.76||17.72|||
|Exogeneity test:||||||
|C statistic (chi2)|20.394|***|31.318|***||
|Overidentifying restriction:||||||
|Hansen's J statistic<br>(chi2)|0.108||0.245|||

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Finally, the impact of OPRC on market sales is examined: The total value of crops sold in the market is regressed on the same independent and instrumental variables as above. No significant impact of OPRC is found. The coefficient of _OPRC_ * _t_ is not significant.
concerns tend to outweigh the inflation concerns and deflation worries may even make depreciations more likely.

The political willingness to engage in a devaluation may be captured to some extent by the past experience of exchange rate misalignment. Finally, there must be a strong enough incentive to engage in expansionary policies in the first place, which may be captured by the expected growth slowdown.

# **3.1. Evaluation of incentives**

The outlook matters a lot as authorities evaluate their options. Since the onset of the crisis, economic forecasting has proven to be extremely difficult, if not impossible. Output and inflation forecasts have constantly been revised, by large margins, within very short periods and always downward. This is normal as the forecasting models either rely on econometric estimates from sample periods that do not include a crisis of the kind that is unfolding, or are based on theoretical models that do not allow for any of the on-going events (bubbles, financial meltdown, seizure of financial markets) and the policy responses so far. In Figure 2, we use forecasts provided by the Economist Intelligence Unit as an illustration of the issues at stake, while warning that these forecasts can prove to be as unreliable as the recent ones. For half the countries in our sample of exporters, output is predicted to contract by more than 5 percentage points. Only seven countries are expected to register a non-negative growth rate.

**Figure 2: GDP growth**

**Figure 3: Interest rates and inflation**

<!-- Start of picture text --> GD P gro wth pro spects Interst Rate and Inflation<br>Drop in perentage points Growth in %<br>-30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 30 20 10 0 -10 -20 -30<br>Kuwait Japan<br>Ukraine Singapore Inflation<br>SingaporeVenezuela United StatesSwitzerland Interest<br>Korea, Hong Kong<br>Russian Canada<br>Hong Kong UK<br>Argentina Sweden<br>Thailand Israel<br>Indonesia Kuwait*<br>Japan Ireland<br>Brazil Thailand<br>Greece Portugal<br>Germany Germany<br>M alaysia Belgium<br>Cz ech Rep. Spain<br>Vietnam France<br>Turkey Luxembourg<br>Saudi Italy<br>Finland Austria<br>Lux embour Greece<br>Phili ppines Finland<br>Net herland Netherlands<br>H ungary Denmark<br>S weden Czech Re p.<br>M exico Norw ay<br>South M alaysi a<br>Poland Saudi Arabi a<br>United Australi a*<br>Spain Korea, R ep.<br>U nited. C hile<br>B elgium Pol and<br>De nmark China<br>Po rtugal India<br>Chile M exico<br>A ustria Phili ppines<br>Swit zerlan Ind onesia<br>Israel Vietnam<br>China Hungary<br>No rway Sou th Africa<br>AustFrIrel anceItalyraliaand Drop2009* ArgentinaUkraineTurkeyBrazil<br>I ndia 2008 Russian<br>Can ada Venezuela<br>-15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00 25.00 -30 -20 -10 0 10 20 30<br><!-- End of picture text --> Source: EIU (Apr. 2009), no forecast is available for Kuwait .

Source: Interest Rate is the policy rate as of Apr. 2009 (Central Banks) and inflation is the latest figure from EIU (Apr. 2009) 6
# **Table 2**

# **Stratification checks: Comparing courses by peer experience**

|FullSample|Below<br>Median|Above<br>Median|t-stat|
|---|---|---|---|
|Panel A. Sorted by share|of experienced s|tudents||
|Female<br>0.31|0.32|0.29|(2.12)**|
|(0.16)|(0.15)|(0.16)||
|Age > 30y<br>0.51|0.50|0.52|(-1.62)|
|(0.25)|(0.25)|(0.25)||
|Earnings > 5MM COP<br>0.36|0.36|0.36|(-0.04)|
|(0.39)|(0.39)|(0.39)||
|Graduate schooling<br>0.46|0.45|0.47|(-1.51)|
|(0.40)|(0.40)|(0.41)||
|Teacher experience (log hours)<br>5.26|5.21|5.30|(-1.05)|
|(1.59)|(1.60)|(1.57)||
|Teacher returns (%)<br>0.06|-0.10|0.25|(-1.49)|
|(2.62)|(2.19)|(3.01)||
|Panel B. Sorted by average ret<br>|urns of experien<br>|ced students<br>||
|Female<br>0.31|0.31|0.31|(0.37)|
|(0.16)|(0.16)|(0.16)||
|Age > 30y<br>0.51|0.49|0.52|(-1.48)|
|(0.25)|(0.25)|(0.23)||
|Earnings > 5MM COP<br>0.36|0.36|0.38|(-0.50)|
|(0.39)|(0.39)|(0.40)||
|Graduate schooling<br>0.46|0.46|0.47|(-0.09)|
|(0.40)|(0.40)|(0.42)||
|Teacher experience (log hours)<br>5.26|5.31|5.18|(1.10)|
|(1.59)|(1.67)|(1.47)||
|Teacher returns (%)<br>0.07|-0.01|0.19|(-0.85)|
|(2.62)|(2.30)|(3.05)||

The table shows mean (and standard deviation in parentheses) of different course characteristics. The sample consists of the 876 courses on stock trading. The last column shows t-statistics for the test of difference in means between the subsamples. The courses are sorted by the share of experienced students (Panel A) and by the average returns (Panel B). All comparisons exclude students with trading experience, except teacher experience and teacher returns. * denotes significance at the 10% level; ** at 5%; and *** at 1%.

42