rafmacalaba/datause-encoder-data huggingface dataset:
In the Korea PISA data, girls’ schools were no more or less likely to be a school with tracking than boys’ schools or co-ed schools. Also, since we control for co-ed schools, it is unlikely that the measured association between achievement and tracking, as well as the gender difference in this association, reflects being at a girls school. Furthermore, schools where a student’s academic record is important for admission (our proxy for schools subject to equalization) were no more likely to be a school with tracking than a school where academic record was not important to admission. As a result, it is unlikely the association between achievement and tracking reflects equalization.

Alternatively, being in a tracking school might have an immediate effect on mathematics achievement through a student’s attitude or motivation towards mathematics. It is plausible that this effect would differ across the genders, and a follow-up analysis using PISA 2003 could gather more evidence relating to this.

Consequently, while we can not conclude from our analysis that tracking has an effect on student achievement, we do find a very interesting pattern in the 2003 dataset, and our results are especially interesting due to the gender difference aspect. Given the ongoing debate around the Korea’s equalization policy, these results expose the need for more research on tracking in Korea with an emphasis on gender differences.

References Ammermueller, A. and J. Pischke (2006), “Peer Effects in European Primary Schools: Evidence from PIRLS”, National Bureau of Economics R Working Paper 12180, Cambridge, MA.

Betts, J. and J. Shkolnik (2000), “The Effects of Ability Grouping on Student Achievement and Resource Allocation in Secondary Schools”, _Economics of Education Review_ 19 1 - 15 Duflo, E., P. Dupas, and M. Kremer (2008), “Peer Effects and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya” Educators without Borders (2007), “Are Korean Kids Smart or Working Hard? Some Reasons behind Top-level achievers of Korean Students”, Occasional Paper No. 1, Educators without Borders, Seoul, Korea Fertig, M (2003), “Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence from the PISA 2000 Study”, IZA Discussion Papers, 714, Bonn Gibbons, S. and S. Telhaj (2005), “Peer effects and pupil attainment: Evidence from secondary school transition”, London School of Economics, mimeo.

21
The total effect of fiscal decentralization is less sensitive to country-specificities than the partial effects. Korea, Switzerland, and Saudi Arabia spend significantly more than the USA; while the other countries spend about the same or less than the USA. It is interesting to note that country-specific fixed effects are more significant in the case of construction and administration than in the case of maintenance.

The other control variables of time and income have the same effect across road functions; time is significant and positive indicating that unit costs have been increasing over time and the income variable is not significant across all road functional specifications.

# Model 2: The Independent Impact of Decentralization on Preference Costs.

Model 2 investigates the country effects more thoroughly by looking, in addition to the impact of decentralization on road quality (a measure of preference costs); whether the countries which spent more or less than the USA achieved any better quality of roads than the USA. Tables 6a and 6b show the estimated results for the case of preference costs as measured by the percentage roads in bad condition. Decentralization of maintenance reduces preference costs initially (first order effect), but then they increase at a faster rate (second order effect), then decrease again at a slower rate (third order effect). Therefore, the overall effect of decentralizing maintenance functions is to reduce preference costs, and the optimal degree of decentralization of maintenance activities is 100 percent; where local governments are fully responsible for financing maintenance.

**Table 6a: Independent Impact of Decentralization on Preference Costs**

||Partial Effects|of Functional D|ecentralization|Total Effect of|
|---|---|---|---|---|
|Variable|Construction|Maintenance|Administration|Decentralization|
|fiscal<br>|_-0.15_|-0.55|0.25|-0.41|
|decentralization<sup>1</sup>|(-0.36)|(-2.17)-|(1.09)|(-1.33)|
|fiscal|0.002|0.D1|0.0006|0.004|
|decentralization|(0.26)|(244)<br>.|(0.10)|(0.71)|
|fiscal|-1.87e-06|-. 001|-0.00002|1.27e-06|
|decentralization<sup>3</sup>|(-0.03)|-2.29)|(-0.51)|(0.03)|
|R-squared|0.97|0.97|0.98|0.97|
|Observations|261|261|261|261|

Fiscal decentralization measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

** significant at the 80% level of confidence.

20
**Page** 18

**Livestock Insurance in Mongolia**

The matrix of mortality rates is 30 by 22 by 5 (30 years, by 22 aimags, and<sup>5</sup> animal species). The matrix for animal values is simply a 1 x 22<sup>x5(1= 1999</sup> values that are imposed over the 30 years, by 22 aimags, by 5 animal species). Value at risk is calculated as follows: Value at riska, = Number of animals in 1<sup>9 9 9</sup> a, * Average value of animal in 2000 where a = aimag and s = animal species

|**AnimalValuesUsedintheAnalysis**|
|---|
|**Species**<br>**Value(Tg)**|
|Cattleandyak<br>100,000|
|Sheep<br>22,000|
|Goat<br>14,000|
|Camel<br>100,000|
|Horse<br>70,000|

Given these calculations, the estimated value for all livestock in Mongolia is about Tg 1.1 trillion (see table 3). If the policy were made available at an average premium<sup>rateof</sup> 3%, the total premium would equal about Tg 32.8 billion. This also makes the strong assumption that all livestock would be insured at the full value. The analysis will<sup>makethis</sup> assumption to give the full scope of this insurance. However, it is quite simple to<sup>factorall</sup> numbers down with any assumption one wishes to make about the rate<sup>of participation.For</sup> example, at 10% participation the premium would equal Tg 3.3 billion.

To begin the analysis it was assumed that the premium rates would<sup>needtobe</sup> loaded at least by about 40%6. Further, a target of about 3% average premium rates was used. Therefore, the contracts needed to set the coverage or trigger mortality rates for each aimag and species so that the average pure premium (before loads) would equal roughly 2.15%. There will be variations around this rate and these are reported<sup>insummary statistics</sup> below to give some idea of the relative risk across aimags.

The trigger values were set by each animal species so that the average pure premium rates would equal the target of 2.15%. To accomplish this task the following strike percentiles were used by species; 1) cattle = 86%; 2) sheep and goats = 80%; 3)<sup>camel=</sup> 76%; and 4) horses = 70%. The contract design used the principle of paying at the mortality rate for values above the strike. The strike mortality values are reported in Table 4. Corresponding values of the insurance or the pure premium rates are reported<sup>in Table5.As</sup> is quite evident there are large differences across Mongolia for these risks. The pure premium rates are each multiplied by 1.4 to load for some administrative costs and for reinsurance. Should a mortality index insurance be constructed one would want to put the structure of parent distributions in place and not have as large of differences in the<sup>premium</sup> rates across Mongolia. Still, the differences are important as they do reflect the risk profile of the country.

**6** This load is likely low. However, a lower number is used with the assumption that the factors that<sup>cause</sup> this work to over estimate the spatial risk have already loaded the rates to some extent.
**Policy Research Working Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**for ,paper**|
|---|---|---|---|---|
|WPS883|Malaria: The Impact of Treated Bed-<br>Netson Childhood Mortality in the<br>Gambia|Pedro L. Alonso<br>Allan G. Hill<br>Patricia H. David<br>Greg Fegan<br>Joanna R. M. Armstrong<br>Andreas Francisco<br>K. Cham<br>Btian M. Greenwood|April 1992|0. Nadora<br>31091|
|WPS884|Intercommodity Price Transmittal:<br>Analysis of Food Markets in Ghana|Harold Alderman|April 1992|C. Spooner<br>30464|
|WPS885|The Impact of EC-92 on Developing<br>Countries' frade: A Dissenting View|A. J. Hughes Hallett|April 1992|A. Kitson-Walters<br>33712|
|WPS886|Factors That Affect Short-Term<br>Commercial Bank Lending to-<br>Developing Countries|Sudarshan Gooptu<br>Maria Soledad Martinez Pe|April 1992<br>ria|L. Dionne<br>31426|
|WPS887|Power Sharing and Pollution Control:<br>Coordinating Policies Among Levels<br>of Government|William Jack|April 1992|P. Pender<br>37851|
|WPS888|Transformation of Agriculture in<br>Certral Eastern Europe and the Forme<br>USSR: Major Policy Issues and<br>Perspectives|Csaba Csaki<br>r|April 1992|C. Spooner<br>30464|
|WPS889|On-th-Job Improvements in Teacher<br>Cornpetence: Policy Options and<br>Their Effects on Teaching and<br>Learning in Thailand|Stephen W. Raudenbush<br>Suwanna Eamsukkawat<br>Ikechuku Di-lbor<br>Mohamed Kamali<br>Wimol Taoklam|April 1992|C. Cristobal<br>33640|
|WPS89O|Population, Health, and Nutrition:<br>Fiscal 1991 Sector Review|Denise Vaillancourt<br>Janet Nassim<br>Stacye Brown|April 1992|0. Nadora<br>31091|
TABLE 8: Composition of the Constrained International Portfolio Without Commodity Assets [Emerging Markets = 10%1

|Rate of<br>Return<br>(Re)|USA|UK|JAP|GER|SWZ|FRA|HKG|TAI|KOR|MEX|THI|BRZ|IND|Sum of<br>Weights|Portfolio<br>Standard<br>Deviation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1.00|.100||.527||.286||||||||.087|1|4.57|
|1.25|.467||.264|.013|.155||||||||.100|1|3.99|
|1.28*|.493*|.011*|.238*|.021*|.135*||.003*||||||.100*|1*|3.98*|
|1.50|.515||.184|.039|.038|.001|.123||||||.100|1|4.08|
|1.75|A76||.098|||.085|.241|.001|||||.099|1|4.39|
|2.00|.429||.001|||.136|.334|.009||.016|||.074|1|4.87|
|2.25|.287|||||.159|.454|||.059|||.041|I|5.49|
|2.50|.143|||||.1-19|.578|||.096|||.004|1|6.24|
|2.75||||||.121|.779|||.100||||1|7.12|

# NOTE: Weights may not sum exactly to I because of rounding up.

*Starred values rapresent return, weights and standard deviation of the minimum-risk portfolio.

Data sources: See Tables I and 2.
Table 3: Change in gun crime charges after 2016

||Gun charges|Narrow gun charges|Assault charges|Agg. assault charges|
|---|---|---|---|---|
|T above 90F=1 _×_ Post 2016=1|0.144|0.391|0.009|0.003|
||(0.033)|(0.095)|(0.024)|(0.066)|
|T 80-90F=1 _×_ Post 2016=1|0.017|0.129|0.018|0.022|
||(0.044)|(0.125)|(0.013)|(0.043)|
|T 70-80F=1 _×_ Post 2016=1|-0.009|-0.001|-0.017|-0.058|
||(0.030)|(0.056)|(0.015)|(0.036)|
|N|709,307|601,940|734,151|702,732|
|Fixed Efects:|||||
|County|Yes|Yes|Yes|Yes|
|Month|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|DOW|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a Poisson fixed effects specification. Errors are clustered at the county level and are reported in parenthesis. All regressions are weighted by the total population in each county-year. Coefficents for bins below 70<sup>_◦_</sup> F are suppressed for parsimony but all regressions include the full set of temperature bins and the full set of precipitation bins. 100 _×_ the coefficient estimates indicate the percent change in the impact of a day in each temperature bin on arrests relative to a day in the omitted bin after 2016 relative to pre-2016. “Gun charges” refers to all charges that we categorize as involving guns based on there National Crime Information Center (NCIC) and Texas Uniform Offense Classification codes. “Narrow gun charges” refer to those charges that are specifically related to possessing, discharging, or displaying a gun. Assault and aggravated assault charges are categorized based on their NCIC and Texas Uniform Offense Classification codes.

32
Since there are so many unobserved variables and the theoretical determination of interest rates is so complex, it may be useful to approach the question of modeling the entire set of world interest rates as being determined by a small number of unobserved common factors plus idiosyncratic local factors. This approach has been employed, for example, by Koedijk and Kool (1994), in order to analyze European interest rates, and the method is potentially even more effective when data. on a larger number of countries is available.<sup>4 0</sup> It can allow both identification of a core world interest rate for comparative purposes (measure mean deviation in real rates), and also allow the experience of different developing countries to be grouped. The interesting issues include: (a) How many significant common factors are there in real interest rates across the world? (b) What are the characteristics of countries have low correlations with the main common factors? In particular, what is the role of financial repression; and of national inflation vol]atility in determining low correlations.

In practice, we find that on the larger samnple of 24 developing country wholesale rates<sup>4</sup> ' the picture that emerges is of a diverse and volatile interest rate experience, with conisiderable persistence in real interest rate rnovements. The degree to which the movements are common across countries was assessed by computing the eigenvalues and principal components of the real interest rate data. With the first principal component accounting for only some 28 per cent of the total variance it is clear that the degree of commnonality is rather modest. Indeed, a three-factor model explains only 61 per cent of the total variance, and a six factor model is required to reach 80 per cent). With such modest explanatory power, it is not surprising that no evident economic interpretation could be placed on these principal components, nor are they highly correlated with inclustrial country interest rates.

A itechnical difficulty that emerges in using factor models is that high volatility countries tend to dominate the overall variability to be exp:lained, and a natural normalization of the dala that would reduce this dependency on outliers is not obviously available.

> 40 The model of unobserved common factors has also been applied to the term structure of interest rates in one country; see for example, Jegadeesh and Pennacchi (1996).

> 41 This is the monthly sample (excluding Argentina and BTazil) discussed in the text.

48
- Horvath, F. W. 1982. “Forgotten Unemployment: Recall Bias in Retrospective Data.” _Monthly Labor Review_ 105 (3): 40–43.

- Hussmanns, R., F. Mehran, and V. Verma. 1990. _Surveys of Economically Active Population, Employment, Unemployment and Underemployment: An ILO Manual on Concepts and Methods._ Geneva: International Labour Office.

- ILO (International Labour Organization). 2015. _World Employment and Social Outlook: Trends 2015_ . Geneva.

- Juster, F. T., and F. P. Stafford. 1991. “The Allocation of Time: Empirical Findings, Behavioral Models, and Problems of Measurement.” _Journal of Economic literature_ 29(2): 471–522.

- Langhaug, L. F., L. Sherr, and F. M. Cowan, 2010. “How to Improve the Validity of Sexual Behaviour Reporting: Systematic Review of Questionnaire Delivery Modes in Developing Countries.” _Tropical Medicine and International Health_ 15 (3): 362–81.

- Mathiowetz, N. A., 1986. “The Problem of Omissions and Telescoping Error: New Evidence from a Study of Unemployment” _Proceedings of the Section on Surveys Methods Research_ (American Statistical Association, Alexandria, VA.)

- Mathiowetz, N. A., and G. J. Duncan. 1988. “Out of Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment.” _Journal of Business and Economic Statistics_ 6 (2): 221–29.

- Neter, J., and J. Waksberg, 1964. “A Study of Response Errors in Expenditures Data from Household Interviews” _Journal of the American Statistical Association_ 59 (305): 18-55.

- Pierret, C. 2001. “Event History Data and Survey Recall.” _Journal of Human Resources_ 36: 439– 66.

- Robinson, J. P., and A. Bostrom. 1994. “The Overestimated Workweek? What Time Diary Measures Suggest.” _Monthly Labor Review_ 11–23.

- Robinson, J. P., S. Martin, I. Glorieux, and J. Minnen. 2011. “The Overestimated Workweek Revisited.” _Monthly Labor Review_ 134 (6): 43–53.

- Stewart, J. 2014. “The Importance and Challenges of Measuring Work Hours.” _IZA World of Labor._ Available at https://wol.iza.org/articles/importance-and-challenges-of-measuringwork-hours/long.

- Sudman, A. and N. M. Bradburn. 1974. _Response Effects in Survey._ Aldline Publishing

Company, Chicago.

- Sudman, A. and N. M. Bradburn. 1982. _Asking Questions. A Practical Guide to Questionnaire Design._ San Francisco.

20
—the difference in wages between skilled and unskilled workers— which dictates that higher educational attainment should lead to higher compensation and more secure employment. In the MENA region, the opposite has happened: university graduates are far more likely to be unemployed than are workers with only a basic education. The report argues that two factors work against the region’s young people. First, schools are still geared toward channeling graduates into large public sectors, which means they place less emphasis on fields such as mathematics and science. Second, bloated public sectors are crowding out the private sector, which would otherwise be a larger provider of high‐skill, high‐wage jobs.<sup>10</sup>

**Figure 2.9 Morocco: Unemployment rates by skill level and age group, 2002, 2013 and 2016 (Percentage)**

<!-- Start of picture text --> 30<br>26.5<br>25<br>22.2 22<br>19<br>20<br>15.1<br>14.1<br>15<br>11.3<br>9.2 9.3<br>10<br>5.5<br>4.5<br>5 3.7<br>0<br>Without Diploma Average level of Higher levelof National<br>education education<br>2002 2013 2016<br>25<br>22.5<br>19.3<br>20<br>17.1 17.3<br>15 13.213.5<br>10<br>6.1 5.6<br>4.8<br>5 2.1 2.3 2.7<br>0<br>15-24 25-34 35-44 45 and older<br>2002 2013 2016<br><!-- End of picture text --> _Source_ : High Commission for Planning (HCP).

A key reason for high and persistent unemployment in Morocco is the fact that job creation has been weak. While the working age population grew in net terms, by 270,000

> 10 MENA Economic Monitor. A New Economy for the Middle‐East and North Africa. World Bank. October 2018.

12
scale of activity’.

The Upper-Bound estimates can be heavily affected by the presence of some extreme outliers in the matched group. To eliminate this impact, Upper Bound Inter-Quartile Mean intensities (known as Inter-Quartile Mean (IQ) hereafter) were calculated for the matched group. This involved calculating the unweighted mean of the plant intensities after dropping those which are below the first quartile or above the third quartile.

The ratio of total EPA emissions reported in a sector (from groups (1) and (2)) to the total level of economic activity in that sector reported by the LRD (from all three groups) was calculated as the Lower Bound Weighted Mean pollution intensity (known as Lower-Bound (LB) hereafter). This intensity measure assumes an emissions level of zero for group (3) plants (those which report to the LRD but not to the EPA). To the extent that these facilities have some emissions, this LB estimate is biased downward)*.

All three intensity measures were compiled with respect to each of the denominators - total value of output, value added and employment. We recommend the use of LB intensities {especially for non-toxic air and water pollutants) because of 3This intensity is equivalent to: [total pollution in group (1)]\[total activity in group (1)]

*If the plants in the matched data set had lower than average sectoral pollution intensities compared to all the plants in the entire EPA dataset, IQ for those sectors could be lower than the LB.

17
With these limitations in mind, we were able to whittle the challenges down to the sector-specific issues. Relying on the IMF data for health and education is not a major problem because they explicitly identify these two categories. The problem is more serious for infrastructure, an expenditure category that does not exist in the GFS, which uses instead the categories of transport and “fuel and energy.” The sum of these two categories would be the closest to what is widely known as infrastructure but still would exclude the water and sanitation sectors as well as the telecom sector. The omission of the latter sector in this analysis is not a major problem because in most countries the associated expenditures are now broadly the private sector’s responsibility. The omission of water and sanitation is much more problematic, but it is not the only issue. Fuel and energy combines anything that would have to do with fuel exploration costs and leaves out the very significant role of utilities in the sector. The upshot is that the data allow only for a very partial view of the infrastructure sector.

The time span is 1990 to 2002, although data are not available for every year of this period for every country. The data coverage available for each infrastructure subsector that can be matched with outcome data during that period is shown in table 1. The table distinguishes between general government and central government only to show that any effort to account for all expenditure sources has an overwhelming cost in terms of country coverage. It also shows how much more the coverage would drop if we wanted to cover the same specific countries for the two subsectors. Working with the central government data is a problem because the data on outcomes are poor. These data series are not ideal, but they are the only ones available to maintain a relatively large data sample at this stage.

6