rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1**

# **Course characteristics**

||2008|2009<br>Panel|2010<br>A. Stock T|2011<br>rading Cou|2012<br>rses|2013|2014|2015|2016|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|N. of Courses|21|78|141|200|178|83|56|71|48|876|
|Average students|17.14|17.81|18.02|17.61|12.92|16.08|12.14|17.45|16.17|16.14|
|Average hours|10.00|10.00|10.00|11.20|12.57|15.53|25.34|24.34|23.23|14.19|
|Tuition (USD)|82.72|94.28|92.16|113.86|169.26|174.07|240.98|286.66|400.99|162.70|
|Age > 30 years (%)|61.04|70.32|72.40|59.49|53.86|44.60|40.80|31.55|23.36|54.58|
|Females (%)|43.93|29.51|27.94|31.32|31.20|32.13|31.95|34.74|28.90|31.15|
|||Pane|l B. Experi|enced Stud|ents||||||
|% per class|20.61|16.16|14.92|17.51|14.48|11.54|7.02|3.24|4.3|13.31|
|10th percentile|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|90th percentile|46.15|34.48|31.82|37.27|33.33|28.57|25.0|12.5|14.29|30.68|
|Before the course|||||||||||
|Peer Returns (12 months %)|8.57|-1.36|14.36|7.89|-2.8|-4.81|-5.8|-7.51|-6.47|1.84|
|10th percentile|-8.83|-14.24|-3.85|-5.69|-10.74|-12.49|-14.63|-15.96|-12.82|-9.7|
|90th percentile|22.53|18.21|33.63|26.36|10.92|3.28|.41|-4.07|-.89|15.77|
|Number of Trades|19.92|19.02|12.6|12.27|6.88|10.38|4.18|3.1|8.35|10.36|
|Peer Volatility (%)|6.66|5.87|5.26|4.38|3.56|2.6|1.8|1.52|1.82|3.84|
|After the course|||||||||||
|Average number of trades|26.84|53.35|42.34|27.17|17.06|28.59|21.64|9.9|18.82|29.64|
|Average 12-month Returns (%)|-1.98|6.94|0.16|-6.51|-5.53|-6.41|-7.77|-7.69|-5.28|-3.35|
|||<br>Panel|<br>C. Inexper|<br>ienced Stud|<br>ents||||||
|% of market participants|7.2|13.4|20.5|16.|12.6|6.|3.4|3.|2.2|12.05|
|Average number of trades|22.4|38.5|29.5|27.2|25.1|19.5|29.2|42.1|7.3|28.58|
|Average 12-month returns (%)|2.12|10.1|4.12|-4.92|-6.19|-11.53|-7.77|-10.31|-6.77|-0.86|

41
particularly appealing as it is more readily comparable across different group definitions as a result of the normalization by maximum BGI.<sup>23</sup>

# **3.  Evidence**

Expressing observed between-group inequality as a fraction of the maximum possible BGI can provide additional insight in the analysis of inequality. Figures 4-6 decompose inequality on the basis of three different sub-group definitions for a number of developed and developing countries. Our data come from nationally representative household surveys from each of the countries and all refer to a year during the 1990s. We consider three ways of breaking down the population in each country: by social group membership, rural-urban location of residence, and education of household head (not all countries’ data permit a breakdown along all three dimensions, however). Of these groups, only social group membership (which loosely refers to racial, ethnic, or caste breakdown relevant to each country) can be truly considered as a ‘circumstance’ or a predetermined characteristic, and as such consistent with measuring inequality of opportunity. In the rest of our empirical work, we would have ideally used _place of birth_ instead of _rural-urban residence_ , and _parents’ education_ in place of _education of household head_ . However, many of the household surveys in our database do not permit us to break the population down by these circumstance variables, and hence the variables we use instead can be viewed at best as crude proxies for an individual’s circumstances.<sup>24</sup> For each country and for each sub-group definition, the between-group share is calculated

> 23 We are grateful to Sam Bowles for suggesting to us this interpretation of _Rb ́._

> 24 Such data on individual circumstances, of course, exists for an increasing number of countries.  For example, drawing on the distinction between ‘circumstance’ and ‘effort’ variables in Roemer’s work on equality of opportunity, Bourguignon, Ferreira, and Menéndez (2003) uses a different method to decompose earnings inequality in Brazil into a component due to unequal opportunities and a residual term.

19
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Fourth, the IES makes explicit allowance for own-consumption. For each of the 14 expenditure categories considered (food, transportation, clothing, housing, medicines, etc.) data was gathered to evaluate the monetary value of consumption from household own production, and from non-monetary payments and gifts. The sum total of monetary expenditures plus the monetary equivalent of own-consumption and gifts was considered as total expenditure. Unfortunately, I had no information to divide the total monetary value of own-consumption into its components. Thus I am able to rank households on the basis of total 25 expenditures, but not on the basis of total food expenditures.

Fifth, as is generally the case with most IES, income seems to be 26 underreported. This, together with consumption smoothing considerations, propelled me to use total expenditures rather than reported income as the relevant variable (including, as just mentioned, the monetary value of ownconsumption). Thus, while for expositional convenience in the paper I refer to the variable y1 as total income, the reader should have in mind that total expenditures is the proxy variable used.

Two adjustments were made to the data. The first concerns inflation. 1984 was a period of substantial inflation in Mexico (49.5X according to the CPI), implying that average nominal values of income and expenditure for the beginning of the year were lower than for the end of the year. This generates the need for a correction. Fortunately, the IES included information on the date on which a household was surveyed, allowing me to apply appropriate deflators to express all monetary flows in prices of January of 198427.

25Differently put, if yi and yf is total expenditures and total food expenditures (including own-consumption) of the ith housnhold, respectively, I can obtain f(y) but not f(yf). While clearly f(y) ; f(yy), the difference between the two increases with income given Less than unitary food expenditure elasticities. 26 In particular, total expenditur **e** xceeds total income for the lowest 16 out of the 20 household groups (ordered by per capita household income; see below).

27The IES divided the year into 36 intervals of 10 days, and recorded the interval in which a household was surveyed. I divided the Banco the Mexico national monthly consumer price index for 1984 into three components (assuming a linear trend within each ten day period), constructed a price series for the year with 36 observations, and made the base for mid-January 1984 - 1.00. All nominal variables were then converted into prices of mid-January 1984 using the respective date and corresponding price index.
report the number of sources on which that country’s score does not change or moves in the opposite direction to the aggregate indicator. For each country we also summarize the extent to which changes in the individual sources agree with the direction of change in the aggregate indicator by calculating the “Agreement Ratio”, or “Agree” / (“Agree” + “Disagree”).

The agreement ratio is quite high for countries with large changes in governance. Averaging across all countries and indicators, we find an average agreement ratio of 0.8 for the period 2002-2005, as reported in Table 3. This provides some confidence that for countries with large changes in our governance estimates, these changes are most often being driven primarily by changes in underlying sources rather than by the addition or deletion of sources. In fact, for these large changes there are only two cases where the agreement ratio is 50 percent or less. These cases are small Caribbean island economies that had only two data sources in 2002, and the addition of a new data source in 2005 resulted in a substantial change in these countries rankings. As can be seen from Table 3, such cases where the addition of a data source for a country significantly affects a country's score are quite rare.<sup>14</sup> It is also worth noting that the agreement ratios for large changes in governance are substantially higher than the agreement ratios for all changes in governance. This can be seen in Table 4 which computes the same agreement ratio, but for all countries over the period 2002-2005. The agreement ratio averages 62 percent, compared with 80 percent for large changes, suggesting that for the more typical smaller changes in our governance estimates, there is relatively more disagreement across individual sources about the direction of the change than there is for large changes. These examples underscore the importance of carefully examining the factors underlying changes in the aggregate governance indicators in particular countries. In order to facilitate this, on our website users can now retrieve the data from the individual indicators underlying our aggregate indicators and use this to examine trends in the underlying data as well as changes over time in the composition of data sources on which the estimates are based.

> 14 The only other cases for which this occurs are the Solomon Islands and Vanuatu, where the addition of these countries to our data source WMO resulted in a large changes in these countries scores (as can also be seen in Figure 2)

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Policy Research Working Paper 5016

# **Abstract**

This paper analyzes the impact of banking crises on manufacturing exports exploiting the fact that sectors differ in their needs for external financing. Relying on data from 23 banking crises episodes involving both developed and developing countries during the period 1980-2000 the authors separate the impact of banking crises on export growth from that of other exogenous shocks (i.e. demand shocks). Their findings show that during a crisis the export of sectors more dependent on external finance grow significantly less than other sectors.

However, this result holds only for sectors depending more heavily on banking finance as opposed to inter-firm finance. Furthermore, sectors characterized by higher degree of assets tangibility appear to be more resilient in the face of a banking crisis. The effect of the banking crises on exports is robust and additional to external demand shocks. The effect of the latter is independent and additional to that of a banking shock, and is particularly significant for sectors producing durable goods.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to understand the implications of the global economic crisis for international trade flows. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Liacovone@ 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
**<u>Table 26: IV Estimation (1)</u>**

||Distance t|o:|||||||
|---|---|---|---|---|---|---|---|---|
||Populated|area|Municipal|capital|Hospital||School||
|D|-2.414|(1.004) **|-0.536|(0.628)|-0.631|(0.671)|2.550|(0.727) ***|
|lnHH size|-0.334|(0.313)|0.361|(0.196) *|0.286|(0.209)|-0.141|(0.227)|
|Adult share|0.072|(0.657)|0.270|(0.411)|0.220|(0.439)|0.069|(0.475)|
|HH head sex: Male|-0.605|(0.378) *|-0.655|(0.236) ***|-0.680|(0.252) ***|-0.484|(0.274) *|
|HH head education: Elementary|0.035|(0.271)|0.103|(0.169)|-0.171|(0.181)|-0.117|(0.196)|
|HH head education: Secondary|-0.483|(0.442)|0.194|(0.276)|0.153|(0.295)|-0.278|(0.320)|
|HH head education: University|-0.927|(0.894)|0.552|(0.559)|0.086|(0.598)|-0.823|(0.647)|
|Appliance ownership: Refrigerator|-0.195|(0.410)|-0.288|(0.256)|-0.299|(0.274)|0.097|(0.296)|
|Appliance ownership: Color TV|0.129|(0.407)|0.473|(0.254) *|0.038|(0.272)|0.133|(0.294)|
|Appliance ownership: Radio|0.096|(0.252)|-0.194|(0.158)|-0.232|(0.168)|-0.106|(0.182)|
|Appliance ownership: Gas stove|0.520|(0.423)|0.334|(0.265)|0.133|(0.283)|0.637|(0.306) **|
|Appliance ownership: Washing<br>machine|-0.580|(0.295) **|-0.410|(0.184) **|-0.470|(0.197) **|-0.187|(0.214)|
|Gas use for cooking|-0.601|(0.285) **|-0.127|(0.178)|-0.458|(0.190) **|0.082|(0.206)|
|Power use for lighting|-0.878|(0.432) **|-0.330|(0.270)|-0.316|(0.289)|-0.495|(0.313)|
|Tap water for cooking|0.666|(0.274) **|0.074|(0.171)|0.193|(0.183)|-0.237|(0.198)|
|Constant|2.228|(1.499)|2.834|(0.937)***|3.685|(1.002)***|-1.159|(1.085)|
|Obs.|911||911||911||911||
|R-squared|||0.016||0.017||||
|Wald-stat|2.90||2.14||2.49||3.75||
|Sargan overidentifying restriction test|0.75||0.05||2.03||0.93||
|Hausmanexogeneity test|21.79*|**|23.78*|**|10.40*|**|45.83|***|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

45
Policy Research Working Paper 9185

# **Abstract**

Air pollution from vehicular traffic is a major source of health damage in urban areas. The problems of urban traffic and pollution are essentially geographic, because their incidence and impacts depend on the spatial distribution of economic activities, households, and transport links. This paper uses satellite images to investigate the spatial dynamics of vehicle traffic, air pollution, and exposure of vulnerable residents in the Dar es Salaam metro region of Tanzania. The results highlight significant impacts of seasonal weather (temperature, humidity, and wind-speed factors) on the spatial distribution and intensity of air pollution from vehicle emissions. These effects on the metro region’s air quality vary highly by area. During seasons when weather factors maximize pollution, the worst exposure occurs in areas along the wind path of high-traffic roadways. The research identifies core areas where congestion reduction would yield the greatest exposure reduction for children and the elderly in poor households.

This paper is a product of the Development Research Group, Development Economics and the Urban, Disaster Risk Management, Resilience and Land Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@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
Industry Restructuring and Regulation - Building a base for sustainable growth The way in which these results should be interpreted is as follows. In the case of Tierra del Fuego, income per capita is 32.5% below the Argentinean average. Of this, almost 80% of the difference, i.e. a 27.4% difference in income per capita, can be explained by the three infrastructure factors. Access to sewers and education levels are only slightly below the national average, 1.5% and 2.5% respectively, while road infrastructure is almost 30% below the national average. As can be seen in some of the examples, the relative position _vis-a~-vis_ the national average is not always consistent.

These results, although tentative, due to the quality and quantity of the information available, show that infrastructure is clearly linked to the growth of an economy in general and it significantly explains divergences in per capita income across regions within a country. So, when formulating policies to address regional growth it is vital to address the infrastructure question.

# _2.1.2 Single country studies_

## **Argentina1**<sup>**2**</sup>

An attempt to investigate the general equilibrium impact of the reforms in Argentina was recently undertaken. It considers the changes that have happened in four key sectors and how they interact with the economy as a whole. The starting point is the impact of the reforms on the industries themselves, which is summarized in Table 4. An overview of some of the actual reforms that occurred is provided in Section 2.2.

The way that these changes in performance affect the whole economy depends on several factors, including the importance of each of the utilities to:

- other productive sectors;

- household consumption; and

- the allocation of factor incomes.

Establishing the overall impact of these changes in performance also depends on the way in which the gains are shared. This is basically a question about the effectiveness of regulation and can be modeled through considering the two extreme cases:

- e the private operator gains all the benefits, implying that utility service prices are unchanged even though improvements in performance have occurred; or

- 'consumers' gain all the benefits and prices reflect the changes in performance.

The first option provides a lower bound for the impact of reform, and then, depending on the effectiveness of the regulation, there would be a move to the second option.

Table 5 summarizes the impact of a general equilibrium calculation on the levels and distribution of gains across income classes from the efficiency and quality improvements due to the privatization process and those that could be achieved from effective regulation. To provide a perspective, the gains are presented in terms of the annual expenditures of each income class on utilities in 1993.

- 12 Information on the impact of the reforms in Argentina is drawn from _Winners and Losers from Utilities Privatizations: Lessons from a General Equilibrium Model of Argentina_ by Chisari, Estache and Romero (1997).

**8**
B - -5.65 - 0.803 FG (6') (16.3) (9.2) RSQ-0.605 # of obs - 108 (reduced sample) 2SLS Relying especially on the reduced data set, we conclude that FG is exogenous to the household and business sector surpluses, and that the - response of the business sector is much larger five times as large or more - than that of the household sector.

In order to discover whether the response of the business surplus to foreign and government shocks was achieved by a reduction in investment or an increase in savings, regressions on each component were run for a smaller sample of countries where savings and investment were separately available (equations 7 and 8). The results were unambiguous. A fall in foreign sector or government lending is associated with a reduction in business investment and not in an increase in business savingA5.

BBI - 13.53 + 1.111 FG (7) (14.0) (3.7) RSQ-0.073 # of obs - 104 (full sample) 2SLS BBS - 7.41 + 0.268 FG (8) (8.9) (1.0) RSQ-0.005 # of obs - 104 (full sample) 2SLS The conclusion of this analysis is that exogenous swings in the availability of foreign finance or in the government's surplus are absorbed almost entirely by the business sector. The household sector does not, in other words, come forward to any large extent with additional financial 45 Curiously, we also found that, while household surplus responds little to FG, there is a correlation between FG and each of household savings and investment separately. The size of the estimated impact on each Is about tha same resulting in a negligible impact on the household surplus.

42
Table 4 estimates the ultimate outcome of the attrition process by comparing the non-attriting students using the baseline characteristics. The table layout is identical to Table 2. As one would expect from the previous tables, the students remaining in the study after the follow-up survey are similar in their baseline characteristics. In fact, the attrition pattern was so similar, that the results in column three of Table 4 are almost exactly the same as those in column three of Table 2 despite having lost almost a third of the sample. Focusing on the differences in column three, all of the differences are small, and only two differences are statistically significant. This includes an 8.5 percentage point difference in the probability that a child reports seeing a teacher outside of class and a 3.4 percentage point difference in the probability that a child stays after school.

# **V.  Results**

Given that the sample of students completing the survey in both research groups is comparable, we can estimate the causal effects of the program by directly comparing the average responses in the treatment and control groups. First, we check to ensure that the treatment was implemented as planned and that the implementation created the expected treatment differential between the two groups – it did. Second, we estimate the differences in outcomes. Overall, the program seems to have had little effect on students’ test scores and other outcomes. These results are consistent across grades, subjects, and different types of students. The main reason for these results may be the implementation of the program. Surveys of both teachers and students suggest that the program increases computer use among students and teachers by a surprising small amount, and most of the use of computers by students is for the purposes of learning to use a computer rather than

- 16 -