rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5879

# **Abstract**

Using a rich data set of almost the entire population of Ukrainian secondary schools, the authors estimate the effect of school size and class size on the performance of secondary schools on Ukraine’s External Independent Test. They find that larger schools tend to have somewhat better performance, both in terms of test scores and in terms of test participation. The size of this effect is relatively small, however, especially in rural areas for which the estimates are likely to be more clean estimates. Class size is found to be insignificant in most specifications and, if significant, of negligible size.

This paper is a product of the Human Development Sector Unit, Europe and Central 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 authors may be contacted at aolefir@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
- Dan-Glauser, E. S., . S. K. R. (2011). The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance. _Behavior Research Methods_ , 43 (2)(468).

- Danon, A., Das, J., De Barros, A., and Filmer, D. (2023). Cognitive and socioemotional skills in low-income countries: Measurement and associations with schooling and earnings 1. _Journal of Development Economics_ , page 103132.

- Davidson, R. J., Kabat-Zinn, J., Schumacher, J., Rosenkranz, M., Muller, D., Santorelli, S. F., Urbanowski, F., Harrington, A., Bonus, K., and Sheridan, J. F. (2003). Alterations in brain and immune function produced by mindfulness meditation. _Psychosomatic medicine_ , 65(4):564–570.

- Davis, J. M. and Heller, S. B. (2017). Using causal forests to predict treatment heterogeneity: An application to summer jobs. _American Economic Review_ , 107(5):546–550.

- de Chaisemartin, C. and Navarrete, N. (2019). The direct and spillover effects of a nationwide socio-emotional learning program for disruptive students. _Available at SSRN 3316325_ .

- Dinarte-Diaz, L. and Egana-delSol, P. (2023). Preventing violence in the most violent contexts: Behavioral and neurophysiological evidence. _Journal of the European Economic Association_ .

- Duckworth, A. and Gross, J. J. (2014). Self-control and grit: Related but separable determinants of success. _Current directions in psychological science_ , 23(5):319–325.

- Duckworth, A. L. and Quinn, P. D. (2009). Development and validation of the short grit scale (grit–s). _Journal of personality assessment_ , 91(2):166–174.

- Duckworth, A. L., White, R. E., Matteucci, A. J., Shearer, A., and Gross, J. J. (2016). A stitch in time: Strategic self-control in high school and college students. _Journal of educational psychology_ , 108(3):329.

39
Table A.10: The effect of Germany specialization by distance to the border

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. Var.:|TFPR|Revenues|Employment|TFPR|Revenues|Employment|
|Post _×_ border|0.010|0.074|0.033|0.002|0.008|0.048|
||(0.060)|(0.091)|(0.066)|(0.052)|(0.111)|(0.088)|
|Post _×_ export share|0.046**<br>(0.017)|0.075**<br>(0.034)|0.023<br>(0.024)||||
|Post _×_ export share * border|0.004<br>(0.017)|0.026<br>(0.024)|0.022<br>(0.017)||||
|Post _×_ output share||||0.040**<br>(0.018)|0.089**<br>(0.040)|0.056**<br>(0.026)|
|Post _×_ output share * border||||0.002|0.006|0.027|
|||||(0.015)|(0.032)|(0.024)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|102056|102056|102056|102056|102056|102056|
|Firms|11764|11764|11764|11764|11764|11764|
|R-squared|0.121|0.118|0.072|0.121|0.119|0.076|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Measures of Germany’s specialization refer to the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Border is a dummy variable that takes value 1 if a firm is in one of the three regions bordering Germany: Dolnoslaskie, Lubuskie and Zachodniopomorskie. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

Table A.11: Effects of EU accession on firm performance across sectors

|Dep. variable: Log TFPR|(1)<br>All|(2)<br>Low labor-intensity|(2)<br>High labor-intensity|
|---|---|---|---|
|Post 2000 # Exports share Ger (log)|0.017**|-0.003|0.028**|
||(0.008)|(0.015)|(0.011)|
|Pos 2004 # Exports share Ger (log)|0.083***|0.071*|0.090***|
||(0.015)|(0.032)|(0.024)|
||Yes|Yes|Yes|
||Yes|Yes|Yes|
|N (obs.)|102736|51217|51519|
|N (frms)|14131|6901|7230|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Germany’s export shares are measured in the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Labor intensity is based on the share of wages over value added from UNIDO. Sectors are ranked based on these shares and then divided into two equal groups. We control for input and output Tariffs for Poland and for EU external tariffs. We exclude the Chemical and food sectors. The standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

38
16 significantly lower than the volatility of an export basket consisting mainly of crude oil.'<sup>2</sup> Diversification into refined products has led to the reduction of the overall volatility of export and tax revenues. However, this reduction was not found to be statistically significant. We also calculated the volatility of the 1976 and 1991 export baskets of PDVSA, based on an estimate of the weights (shares) for each of the products plus crude oil. The volatility of this basket was 28.3% for the 1991 expon basket and 32.39% for the 1976 export basket over the period 19811991 (compared to 31.1 % for crude oil only). This shows that volatility declined somewhat with the diversification of exports away from crude oil to products, but not significantly.

Table 3: Correlation Between Crude Oil and Products (

||1981-91|
|---|---|
|Crude oil|100.00|
|Residual oil (low sulfur)|95.8|
|Residual oil (high sulfur)|94.6|
|Gasoline|96.8|
|Diesel|9W.4|
|LPG|79.1|
|Bunker|62.8|

Source: International Trade Division, The World Bank.

# B. Basis Risk

One important reason why one may not want to use established futures/options markets "<sup>2</sup> Using monthly data for the pe.riod 1988-90, the correlation coefficients were found to be somewhat lower, ranging from 75% to 85%. But these correlations using monthly data are still high enough not to expect a statistically significant reduction in the overall volatility of a diversified oil export basket compared to exporting crude only.
<!-- Start of picture text --> 12000<br>10000<br>8000<br>j 6000<br>4000<br>2000<br>0<br>0.01 0.0101 0.0102 0.0103<br>Beta(r)<br><!-- End of picture text --> Figure 2: Equilibrium L as Buyer Management of Loan <!-- Start of picture text --> 10<br>8<br>p< 4<br>2<br>0<br>0.001 0.001001 0.001002 0.001003<br>Kin<br><!-- End of picture text --> Figure 3: Equilibrium Residual Entity as Gov't Fiscal Situation 19
Chapter 1 2

# The Data

_5._ Information on country area (in kim<sup>2</sup> ) is readily obtained and presumably quite accurate. The area variable nevertheless presents us with a special problem, to be discussed in the section immediately following.

6. GDP data are adjusted for purchasing power and expressed in international dollars, as provided by the International Comparison Project.<sup>3</sup> ' The exceptions are Bulgaria, Czechoslovalda, and the USSR for which there are no corresponding ICP GDP figures. As a best estimate, for Bulgaria and Czechoslovakia we have used data provided by L.W. International Financial Research, Inc. From PlanEcon (as quoted in Th E omist) we took the figure for the USSR though the country was omitted from the regression analysis for reasons to be given later.

7. Measuring GDP in a common currency according to the ICP methodology rather than by methods that involve market exchange rates obviously affects the regression results. In Annex A we discuss the effect of this approach.

8. Dollar GDP numbers were taken for the year 1989. Where ton-kilometers data were only available for another (earlier) year, an adjustment was made (via GDP in national currency and the general GDP deflator) so as to represent GDP for that earlier year at 1989 prices.

9. Our ton-kilometers refer to domestic (i.e. non-transit) transport perfonned by modes other than air and pipeline. As a statistic, this is unlikely to be better than GDP estimates, and probably rather more problematic. Rail transport data are presumably relatively accurate. The difficalty of estimation affects road and water transport for which sources and method differ between countries. The definitions and reporting of "transit" also vary among countries.

10. Transit traffic is neither a function of the transit country's GDP, nor of its area. We have therefore sought to exclude the transit component from the total ton-ldlometers of countries with a substantial amount of transit traffic. The available data allowed all or part of transit traffic to be removed from the data for Austria, Belgium, Finland, Hungary, Netherlands, Norway, and Yugoslavia.

# An Adjustment **to Country Area**

11. GDP and Area are obviously not the only explanations that one tiinks of for the volume of a country's freight transport. They may be so for some countries, but not for all. More comprehensive formulations might yield better (or more widely tsed and applicable) regression 3/ For the latest set of these estimates, see Summers, R. and Alan Heston, "The Penn World Table (Mark 5): An expanded set of intemnational comparisons, 1950-1988," Ouarterly Journal of Economics, Vol. CVI, No. 2, May 1991.
projections for inflation, the exchange rate, and commodity prices are based on several dependable sources,<sup>28</sup> but they still include an inevitable degree of uncertainty. More details on data sources and methodology are provided in Annexes 1 and 2.

# **RESEARCH QUESTIONS AND RESULTS**

**The wealth accounting analysis conducted for this report is designed to address three primary research questions.** (i) What is the estimated existing stock of capital in the country, including produced, intangible, and both renewable and nonrenewable natural capital? (ii) What is the current rate of adjusted net national savings in Mauritania, and what are its implications for the sustainability of the country’s stock of wealth? (iii) What would sustainable growth require in terms of the management of both renewable and nonrenewable resources?

**The findings of the analysis indicate that the value of Mauritania’s total stock of national wealth**<sup>**29**</sup> **is approximately US$60 billion, or roughly US$16,000 per capita.** To put this in context, in low-income countries total wealth per capita averages approximately US$8,000, while it averages about US$30,000 in middle-income countries and about US$400,000 in high income countries.<sup>30</sup> Mauritania’s figures put it squarely between averages for low- and middle-income countries, roughly double the average of the former and about half the average of the latter. This would seem reasonable for a country which has just entered the ranks of lowermiddle income countries. Breaking down Mauritania’s total wealth into its components (natural capital, produced capital and intangible capital) presents a similar pattern to that found in most low-income countries (see Figure 2 and Figure 3, below).

> 28 These include World Bank commodity-price projections, IMF projections and JP Morgan reports on future mining prices, among other sources.

> 29 For details on the methodology for calculating national wealth, see: World Bank (2011) “The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium,” Washington DC: The World Bank. 30 The study refers to 120 countries. For more details, please see World Bank (2006) “Where is the Wealth of Nations? Measuring Capital for the 21st Century,” Chapter 1, Washington DC: The World Bank

11
**Table 5:  Likelihood and growth rates of economic acceleration and deceleration in Africa,  1975-2005**

||**All country**<br>**in the pe**|**-years**<br>**riod**|<br>**Country-ye**<br>**growth acc**|**ars with**<br>**eleration**|**Country-ye**<br>**growth dec**|**ars with**<br>**eleration**|**Country-**<br>**years with**<br>**trend**<br>**growth**|
|---|---|---|---|---|---|---|---|
|**Period**|**Observations**<br>**(country-**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of**<br>**country**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of**<br>**country-**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of**<br>**country-**<br>**years)**|
|1995-2005 (after trend break)|494|1.88|0.42|3.76|0.12|-1.29|0.46|
|1975-1994 (before trend break)|749|-0.07|0.14|3.39|0.29|-3.14|0.57|
|1985-1994|433|-0.23|0.21|3.21|0.36|-3.18|0.43|
|1975-1984|316|0.13|0.04|4.61|0.18|-3.06|0.78|
|1975-2005 (allyears)|1,243|0.7|0.25|3.64|0.22|-2.74|0.53|

Table 6 shows the frequency of growth accelerations by country for the three periods of our analysis. Most countries experienced a higher frequency of growth accelerations after 1995 compared with 1975-1994. Burkina Faso and Ghana were in a growth acceleration during the entire period 1995-2005, and Mali, Namibia, Nigeria, Sao Tome and Principe, Sudan, and Tanzania accelerated at a frequency above 70 percent. Burundi, Congo, Guinea-Bissau, Kenya, Lesotho, Mauritius, and Swaziland in contrast had a reduction in the frequency of growth accelerations relative to earlier periods. Table 7 presents information on growth rates by country on average and during acceleration episodes for the relevant time periods. The last two columns show the deviation of the growth rate during accelerations from trend (1975-2005).<sup>23</sup> We could attempt to use the increase in the relative frequency of growth accelerations as a criterion for identifying potential leopards. Using that criterion alone would lead us toward picking such dramatically improved performers as Burkina Faso, Ghana, Mali, Sudan and Tanzania, for example. It would, however, also lead us to exclude such historically strong economies as Botswana, Lesotho and Mauritius. This is partly an artifact of our method of identifying growth accelerations. Because we define an acceleration (or deceleration) relative to an economy’s own long run trend growth rate,

> 23 Burundi, Cote d’Ivoire, Malawi and Mali show negative average growth rates during accelerations in 1975-1994. This is due to negative growth rates in the first year(s) of the acceleration episodes.

23
rate. Therefore, poverty accuracy and leakage will approximately be equal to the household poverty rate used to determine beneficiaries.

Table 4. Evaluation of a National Random Sample (Uniform Allocation Strategy for Baseline of Comparison)

||**Poverty Line/ PMT**<br>|**Cutoff**<br>||
|---|---|---|---|
|**Evaluation Criteria**|**Bottom 20%**|**Bottom 40%**|**Bottom 60%**|
|Povertyheadcount(observed)|20.00%|40.02%|60.00%|
|Householdpovertyrate(observed)|19.08%|37.20%|55.50%|
|Household poverty rate (predicted)|19.42%|37.31%|55.53%|
|Total accuracy|68.64%|53.35%|50.62%|
|Povertyaccuracy|18.69%|37.44%|55.54%|
|Undercoverage|81.31%|62.56%|44.46%|
|Leakage|19.59%|37.23%|55.52%|

_Notes:_ The predicted household poverty rate is determined by randomly selecting a proportion of households equal to the observed household poverty rate.

Table 5 presents targeting results based on the Sudan national PMT. Here program beneficiaries are determined by the PMT and a PMT cutoff score based on the observed household consumption that identifies the bottom percent of the distribution. We see that choosing this poverty line often underpredicts true poverty at low levels and overpredicts at higher levels. However, compared to randomly accepting beneficiaries, we see that poverty accuracy and leakage are significantly improved. For example, poverty accuracy with the PMT increases by over 15 to 30 percentage points depending on the targeted population living in poverty, with larger increases in performance associated with a larger population living in poverty and/or eligible for the social program. Likewise, leakage falls by a similar magnitude, again depending on the targeted population living in poverty. However, in this case, leakage increases with stricter PMT score cutoffs. It is also worth noting that in a random sample the total accuracy is much lower (50% to 68%) to the total accuracy of the PMT, which ranges between 78% and 83% over various PMT cutoff scores.

Table 6 presents the results of a rural and urban PMT. The same modeling process as the national PMT was used to create individual rural and urban PMT models. The results are similar for both rural and urban areas. The urban model has a higher adjusted R-squared (0.65) than the rural model (0.52), although parameter estimates between the two models do not vary much where they coincide. The main difference between the rural and urban models is that several variables, conditional on the other variables, were estimated to not be statically significantly related to the PMT score in one of the areas.

15
Florida, Richard 2002. _The Rise of the Creative Class and How It's Transforming Work, Leisure and Everyday Life_ . New York, NY: Basic Books.

- ----- 2005a. _Cities and the Creative Class_ . London, U.K.: Routledge.

- ----- 2005b. _The Flight of the Creative Class. The New Global Competition for Talent_ . New York, NY: Harper Business, Harper Collins.

Florida, Richard, Gary Gates, Brian Knudsen, and Kevin Stolarick. 2006. "The University and the Creative Economy.".www.creativeclass.org.

Gerybadze, Alexander and Guido Reger. 1999. "Globalization of R&D: Recent Changes in the Management of Innovation in Transnational Corporations." _Research Policy_ 28(2-3): 251-274.

Harrigan, James and Anthony J. Venables. March 2006. "Timeliness and Agglomeration." _Journal of Urban Economics_ 59(2): 300-316.

Henderson, J. Vernon. 2004. "Issues Concerning Urbanization in China." Prepared for the 11th Five Year Plan of China. Washington, DC: World Bank.

Hillberry, Russell and David Hummels. 2005. "Trade Responses to Geographic Frictions: A Decomposition Using Micro-Data." NBER Working Paper 11339. Cambridge, MA: National Bureau of Economic Research.

Howe, Christopher. September 1983. "Growth, Public Policy and Hong Kong's Economic Relationship With China." _The China Quarterly_ 95(September): 512-533.

Humphrey, John. 2003. "Opportunities for SMEs in Developing Countries to Upgrade in a Global Economy." SEED 43. Geneva: International Labour Office.

"Johor". 2007. "Johor". Columbia Electronic Encyclopedia, Jones, Gavin W. 2002. "Southeast Asian Urbanization and the Growth of Mega-Urban Regions." _Journal of Population Research_ 19(2): 119-136.

Jovanovic, Boyan and Chung-Yi Tse. 2006. "Creative Destruction in Industries." NBER Working Paper 12520. Cambridge, MA: National Bureau of Economic Research. Kenney, Martin and Richard Florida. 2000. "Venture Capital in Silicon Valley: Fueling New Firm Formation," in _Understanding Silicon Valley_ . Stanford, CA: Stanford University Press.

Kenney, Martin and Donald Patton. April 2005a. "Entrepreneurial Geographies: Support Networks in Three High-Tech Industries." _Economic Geography_ 81(2): 201-228.

20
However, domestic financing to the private sector expanded at a much smaller pace than domestic financial savings, which grew considerably but were largely channeled to public debt. As can be seen in figure 5, there was a significant deepening of the domestic financial system during 2000-2005 as indicated by the evolution of a broad monetary aggregate such as M4. However, much of the increase in domestic financial savings has been channeled – mostly indirectly via the growth in the size of institutional investors such as Afores – to the public sector<sup>9</sup> .

Figure 5: Evolution of M4 Components (2000Q1-2005Q3) <!-- Start of picture text --> Increase 00-05<br>60% (% of GDP)<br>50% + 1.4%<br>+ 1.8%<br>40%<br>+ 11.2%<br>30%<br>20%<br>- 3.7%<br>10%<br>+0.9%<br>0%<br>Currency Outside the Banking System Bank Deposits Public Securities Other Private Securities<br>% of GDP<br>Mar-00 Jul-00 Nov-00 Mar-01 Jul-01 Nov-01 Mar-02 Jul-02 Nov-02 Mar-03 Jul-03 Nov-03 Mar-04 Jul-04 Nov-04 Mar-05 Jul-05<br><!-- End of picture text --> Source: BOM.

The supply of debt financing to the private sector has shifted toward domestic nonbank providers. There has been a switch from foreign funding sources (bond issuance and cross-border bank lending) towards domestic credit providers (see figure 6). The increase is particularly pronounced for Sofoles, whose portfolio more than tripled in real terms over this period, albeit from a low base. Lending by other non-bank financial institutions such as credit unions, savings and loans associations (“ _Sociedades de Ahorro y Crédito Popular_ ” or SAPS) and leasing and factoring companies (“ _Organizaciones y Actividades Auxiliares de Crédito_ ” or OACs), as well as domestic private sector bond issuance, have increased considerably, again from a small base. The mortgage portfolio of Infonavit (“ _Instituto del Fondo Nacional para la Vivienda de los Trabajadores_ ”)<sup>10</sup> has also expanded significantly over this period.

> 9 This does not necessarily imply crowding out; in fact, a recent paper by Moissinac V. (December 2005) refutes the hypothesis that bank credit to the public sector – primarily in the form of FOBAPROA notes used to restructure the banking sector following the 1994 crisis – has crowded out private sector lending in recent years.

> 10 Infonavit is an autonomous, publicly administered pension fund for private sector employees that receives mandatory payroll contributions and is required by law to provide housing finance to affiliates.

9