rafmacalaba/datause-encoder-data huggingface dataset:
countries in 2015. In contrast, here enrollment rates below 100 percent are used at all levels, with the result that the maximum possible expected years of school is fixed at 14 years.

# **A2.2  Harmonizing Test Scores**

The school quality adjustment is based on a new large‐scale effort to harmonize international student achievement tests from several multi‐country testing programs. A detailed description of the test score harmonization exercise is provided in Patrinos and Angrist (2018). This paper updates and expands the dataset described in Altinok, Angrist, and Patrinos (2018). This earlier dataset harmonized scores from three major international testing programs (Trends in International Maths and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and Programme for International Student Assessment (PISA)), as well as three major regional testing programs (Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ), Program of Analysis of Education Systems (PASEC), and Latin American Laboratory for Assessment of the Quality of Education (LLECE)). Patrinos and Angrist (2018) subsequently update this dataset with more recent rounds of PIRLS, PASEC and SACMEQ, and also substantially expand the cross‐national coverage of the database by including Early Grade Reading Assessments (EGRA). The expanded dataset covers over 160 countries.

Test scores from these different testing programs can be converted into common units (or “harmonized”) using a variety of methodologies, as described in detail in Patrinos and Angrist (2018) and Altinok, Angrist and Patrinos (2018). In the version of the data used here, the numeraire units are those of the international standardized achievement tests. This includes TIMSS (for math and science) and PISA and PIRLS (for reading, at secondary and primary level, respectively). These tests are expressed in units with a mean of 500 and a standard deviation across students of 100 points. The harmonization method is based on the ratio of country‐level average scores on each program to the corresponding country‐level scores in the numeraire testing program, for the set of countries participating in both the numeraire and the other testing program. For example, consider the set of countries that participate in both PISA and TIMSS assessments. The ratio of average PISA scores to average TIMSS scores for this set of countries provides a conversion factor for PISA into TIMSS scores, that can then be used to convert all countries’ PISA scores into TIMSS scores. Altinok, Angrist and Patrinos (2018) and Patrinos and Angrist (2018) refer to the set of common countries as the “doubloon countries”, the resulting conversion factor as the “doubloon index”, and the test scores in common units as “harmonized learning outcomes (HLOs)”. In the version of the data used here, the doubloon index is calculated pooling all doubloon observations between 2000 and 2017 and therefore is constant over time. This ensures that within‐ 28
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# Annex 3: **Ihe** Electronic and hMnual Zone Fee Proposal of Stockholm, 1989 and 1992

# A. The Qllon _NW Maa_ Zone Fee Poosals of Stockholm. 1989

99. In order to combat environmental pollution and congestion, the City of Stockholm proposed that a wholly electronic fee system be imposed on automobiles entering Stockholm (City of Stockholm, 1989a). The inner city is marked by a circular cordon line around it with 32 control points. In addition, a screen line with 9 control points bisects the cordon, with vehicles crossing it in any direction being charged. (Through traffic can still pass through the city without charge via certain by-pass routes). Only one charge is made each day regardless - of origin or destination thus the concept of a day fee levied from 6 a.m. to 6 p.m., Monday through Friday. The proposed day fee of _SE125 [US$4.22]_ - which is much higher than a revenue-maximizing fee in order to combat congestion - is expected to curtail traffic by a fifth. In contrast to other systems such as the Trondheim system, in which transpondeii for local resdWents are given away free, each local motonst desiring to enter inner Stockholm is required to purchase a transponder at manufacturer's cost, estimated to be SEK100 [US$16.89] a piece. About nine tenths of motorists are estimated to be driving with purchased transponders, whereas a tenth of motonsts are enforced via the video recording system and charged by arrears if they are found to be without rental transponders. It was hoped that technological development would be able to verfy pictured license plate numbers electronically rather than manually. With avrage daily traffic of 195,000 cars and 260 opering days a year, the system cost per transaction is SEKO.69 [US12.7 cents] in 1990 figures, which is higher than that of a simple AVI system for toll roads but lower than that of a combination AVI and manual system.

100. Because the electronic tee system was estimated to take a couple of years from implementation to oration, the City of Stockholm proposed a manual zone fee system, i.e., supplementary licesing, in June 1989 to serve dunng the transitional period (City of Stockholm, 1989b). The splementary license was set at the same rate as that proposed for the electronic system, that is, _SE25_ [US$4.22] a day. In addition, a monthly car card of SE300 [US$50.68]
- Fields, G. (2001). _Distribution and development: a new look at the developing world_ . MIT Press.

- Fishburn, P. and I. Lavalle (1995). Stochastic dominance on unidimensional grids. _Mathematics of Operations Research 20_ (3), 513–525.

- Fleurbaey, M. (2015). Equality versus priority: how relevant is the distinction? _Economics and Philosophy 31_ (2), 203–217.

- Foster, J., M. McGillivray, and S. Seth (2012). Rank robustness of composite indices: Dominance and ambiguity. OPHI Working Paper 26b, University of Oxford.

- Foster, J. E., J. Greer, and E. Thorbecke (1984). A class of decomposable poverty measures. _Econometrica 52_ (3), 761–6.

- Foster, J. E. and A. K. Sen (1997). _On Economic Inequality_ . Oxford University Press.

- Foster, J. E., S. Seth, M. Lokshin, and Z. Sajaia (2013). _A unified approach to measuring poverty and inequality: Theory and practice_ . Washington DC: The World Bank.

- Foster, J. E. and A. F. Shorrocks (1988a). Poverty orderings. _Econometrica 56_ (1), 173–7.

- Foster, J. E. and A. F. Shorrocks (1988b). Poverty orderings and welfare dominance. _Social Choice and Welfare 5_ , 179–98.

- Gravel, N., B. Magdalou, and P. Moyes (2015). Ranking distributions of an ordinal attribute. Technical report, halshs-01082996v2.

- Gravel, N., B. Magdalou, and P. Moyes (2018). Inequality measurement with an ordinal and continuous variable. https://doi.org/10.1007/s00355-018-1159-8.

37
**Table 4: Correlations between government support and firm characteristics**

|||Governm<br>|ent support<br>||
|---|---|---|---|---|
||Round 1 or 2|Round 1 only|Rounds 1 and 2|Round 2 only|
|Log(labor productivity)|0.015|0.028*|0.030*|-0.060***|
|Log(number of employees)|0.050***|0.005|0.064***|-0.002|
|Log(firm age)|-0.003|0.032*|-0.013|-0.039**|
|Top manager female|0.001|-0.004|-0.010|0.021|
|Innovated during 2017-19|0.060***|0.031*|0.025|0.025|
|State ownership (10%)|-0.022|-0.006|-0.008|-0.020|
|Foreign ownership (10%)|0.028*|-0.011|0.053***|-0.001|
|Has line of credit or loan|0.056***|0.019|0.038**|0.019|
|Owns a website|0.086***|0.039**|0.079***|-0.013|
|Local market dummy|-0.076***|-0.072***|-0.019|0.005|
|National market dummy|0.029*|0.041**|-0.001|-0.013|
|International market dummy|0.056***|0.035**|0.025|0.011|
|Manufacturing sector|0.032*|0.042**|-0.008|0.001|
|Retail sector|-0.059***|-0.035**|-0.022|-0.021|
|Other services sector|0.015|-0.016|0.027*|0.017|

_Sources_ : Enterprise Surveys COVID-19 Follow-up Surveys and Enterprise Surveys. _Notes_ : Correlations matrix based on 3,647 observations. *** p < 0.01, ** p < 0.05, * p < 0.1.

16
POLICY **RIESEARCH** WORKING **PAPER** 1404

# Summary findings

Kim and Nugent evaluate the effectiveness of private and collecrive technical, marketing, and financial support systems for the Republic of Korea's small and mediumsize enterprises (SMEs) and enitrepreneurs. Their report draws on a survey of 122 SMEs that make woven textilcs (42 firms), auto parts (20 firms), electronic parts (20 firmns), and facory-automation products (40 firms). Although Korea has a dense network of public technology-support institutions, Korean SMEs tend to turn to private sources of technical support (especially from buyers, suppliers, and moonlighting engineers) more often than to public institutions. Even so, some collective support was used often and valued highly, especially in technologically dynamic sectors.

For technical assistance to be timely and relevant, its delivery must increasingly be decentralized - to industry-specific institutes and to geographic clusters of SMEs in the same industryn Among various collective providers of technical support for Korean SMEs, at least one was used by more than half of the SMEs surveyed. Collective marketing support was used much less than private sources and was - not considered vcry useful except by firms in the initial stages of exporting or in the pioneer stage of a new export industry. Generally, networks of local agents and foreign traders developed as firms gained in export experience.

Financial assistance is the most critical form of support for SMEs. The Korean government has made extensive use of parastatal finance institutions, targeted credit, and credit guarantee schemes. Gavernment intervention in finance was so pervasive that the line between private and collective support bluried. Most SMEs sampled used at least one collecdive institution of financial support, and rated them very positively.

- - This paper a product of the Finance and Private Sector Development Division, Policy Research Department is part of a larger effort in.the department ro examine the impact of proactive intervention on SME performance. Copies of the paper are available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please conta Daniele Evans, room N9-OSS, extension 38526 (78 pages). December 1994.

_The Poleif Reseac Workiog Paper Sries dissmmates the fmndhgs of waok_ n progress _to encourage the exchange_ of _ias about development issues. An objective of the series is to get the findings_ **_oid_** _quicly, even if the presentions are less than fidly polisbed. The papemr cay the nams of the authors and should be used and cited accorfingly. The fmdings, interpetations, and conclusions are tbe authors' own and sbould not be attributed to the World Bank, its Execute Board ofDrectors, or any of its member countrie_ Produced by the Policy Research Disseminaton Ccntcr
# **APPENDIX B: Stationarity Properties and Parameter Estimates of Equation (1) and (2) for the three External Price Indicators**

**Table B1: Stationarity Properties**

||**-- Log-levels**|**without trend --**|**-- Log-levels**|**with trend --**|**-- First differ**|**ences of logs --**|
|---|---|---|---|---|---|---|
||_ADF_|_PP_|_ADF_|_PP_|_ADF_|_PP_|
|_U.S. Gulf_|-1.59|-1.46|-1.83|-1.76|-3.04|-9.65***|
|_S. Africa_|-2.00|-1.70|-2.58|-2.21|-3.54**|-8.88***|
|_Nairobi_|-1.14|-2.01|-3.25*|-3.22*|-3.91***|-10.04***|
|_Arusha_|-1.66|-2.23|-3.51**|-3.01|-4.53***|-7.94***|
|_Bukoba_|-1.10|-1.83|-3.91**|-3.53*|-4.01***|-11.24***|
|_Dar es Salaam_|-1.23|-1.30|-3.37*|-3.03|-4.28***|-8.87***|
|_Dodoma_|-1.57|-1.96|-3.90**|-2.89|-4.47***|-6.81***|
|_Iringa_|-1.35|-2.12|-3.45**|-3.13*|-4.47***|-8.72***|
|_Lindi_|-1.16|-2.45|-3.92**|-4.38***|-4.19***|-14.77***|
|_Mbeya_|-1.11|-1.37|-3.78**|-2.87|-4.23***|-8.09***|
|_Morogoro_|-1.85|-2.05|-3.36*|-2.78|-4.07***|-7.50***|
|_Moshi_|-1.47|-1.70|-3.35*|-2.79|-3.67***|-8.91***|
|_Mtwara_|-1.41|-2.99|-3.17*|-4.74***|-3.22**|-16.58***|
|_Musoma_|-0.89|-2.21|-4.20***|-3.52*|-4.56***|-9.34***|
|_Mwanza_|-1.24|-2.27|-4.28***|-3.57*|-4.52***|-10.65***|
|_Shinyanga_|-1.26|-2.91|-4.18***|-4.16***|-4.04***|-8.70***|
|_Singida_|-1.41|-2.48|-4.19***|-3.17*|-4.43***|-9.83***|
|_Songea_|-1.65|-2.11|-3.83**|-3.59**|-4.19***|-11.08***|
|_Sumbawanga_|-1.22|-1.65|-3.36*|-3.23*|-4.18***|-9.98***|
|_Tabora_|-1.54|-2.34|-4.23***|-3.30*|-3.98***|-11.09***|
|_Tanga _|-1.62|-2.39|-3.22*|-2.91|-3.94***|-7.58***|

**Notes:** All variables are expressed in logarithms. ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The ADF statistic was based on 12 lags, while the spectral estimation for the PP statistics was based on the Bartlett kernel method. Significance level of stationarity: * = 10 percent, ** = 5 percent, *** = 1 percent.

44
# Table 5: Micro simulation: marginal tax reform

|**Product**|**Tax**|ε =|0|0<br>ε =|.5|ε =|1|ε =|2|
|---|---|---|---|---|---|---|---|---|---|
|**Group**|**Equivalent**|_w_<br>_w_<br>ε %|λ|_w_<br>_w_<br>ε %|λ|_w_<br>_w_<br>ε %|λ|_w_<br>_w_<br>ε %|λ|
|Wheat|0.04|1.00|1.04|1.19|1.24|1.34|1.39|1.37|1.42|
|Maize|0.13|1.00|2.04|1.40|2.91|1.84|3.77|2.25|4.60|
|Alcohol Tobacco|0.40|1.00|2.35|1.03|2.43|1.07|2.52|1.38|3.24|
|Meat|0.25|1.00|1.16|1.13|1.32|1.18|1.37|0.94|1.09|
|Legumes|0.20|1.00|0.79|1.52|1.21|2.17|1.72|3.27|2.60|
|Dairy|0.30|1.00|1.23|1.15|1.42|1.23|1.52|1.08|1.33|
|Oil Fats|0.06|1.00|0.49|1.37|0.67|1.78|0.87|2.43|1.19|
|Vegetables|0.00|1.00|1.18|1.24|1.47|1.46|1.72|1.74|2.05|
|Fruit|0.20|1.00|1.04|1.03|1.07|0.99|1.03|0.70|0.73|
|Sugar|0.40|1.00|0.51|1.45|0.75|2.02|1.04|3.26|1.68|
|Other Goods|0.00|1.00|0.99|0.93|0.92|0.87|0.86|0.83|0.83|

Note: The table reports relative budget share and cost benefit rations calculated for four different levels of the inequality aversion parameter.

Source: Author’s calculation using INEGI households surveys 1984-2000.

25
# 4. The Population Component of PHN Proj cts_and the Role of IDA

Of the 60 PHN projects listed in Table A-4, Table III-4 lists those non-population projects for which been have been given an estimation of the percentage of population activity. Among these 18 health and population/health projects, the population component is less than 10% in eight projects, and 1025% in six projects.

There is a feeling that willingness to accept loans for population is influenced by the lending terms, although we lack sufficient evidence to support t;lis. The issue may require further investigation by the Bank. The data available are presented in Table A-5.

# Table III-4

# Percentage of funding devoted to population activities in non-population projects

|:=__________________________________________________|Fiscal<br>Year<br>Approved<br>___|Project<br>Total<br>US$m|Proportion<br>devoted to<br>population|
|---|---|---|---|
|Botswana I (H)|1984|26.60|7.5%|
|Burkina Faso I (H)|1985|28.00|2.0%|
|Comoros I (H)|1984|3.12|14.0%|
|Ghana I (H)|1986|15.60|5.0%|
|Ivory Coast I (H)|1986|29.70|22.0%|
|Jordan I (H)<br>|1985|30.50|25.0%|
|Lesotho I (P/H)|1985|7.42|19.0%|
|Malawi I (H)|1983|8.70|9.0%|
|Mali I (H)|1984|29.30|<10%|
|Morocco I (H)|1985|47.60|10.0%|
|Niger I (H)|1986|29.30|6.0%|
|Nigeria I (H)|1985|53.00|<5%|
|Rwanda I (H)|1986|14.45|(1.18)8|
|Senegal I (H)|1983|16.70|<10%|
|Sierre Leone I (P/H)<br>|<br>1986|5.70|20.0%|
|Tunisia II (P/H)|1981|41.00<br>|(1.13)|
|Yemen AR I (H)<br>|1983<br>|n/a<br>|0.0%<br>|
|Yemen, PDR I (H)|1983|n/a|0.0%|

Source: Responses to a survey circulated by Lauren Chester

> 8 Dollar estimates are given where percentages are not available.

27
- 15 -

An important specification question in using the VAR-type model is how many lags should be included in the system. When removing periodicities on a unilateral variable basis, the correlograms of seasonally adjusted hydrologic series may suggest that in the Vishnugad Pipalkoti case, for instance, the first three or four orders might be autocorrelated (Figure 6). Formally, the Akaike’s information criterion (AIC) lag-order selection statistics is estimated (Akaike, 1973). As shown in Table 5, the maximum number of lags to be included in the model is 2 and 3 for the Vishnugad Pipalkoti and Upper Kotmale cases, respectively. In the Thac Mo hydro case, only one lag may need including. These results are robust, regardless of whether or not a time trend component is introduced in the model.

Figure 6. Correlograms of Seasonally Adjusted Hydrology: Vishnugad Pipalkoti, India <!-- Start of picture text --> 0 10 20 30 40<br>Lag<br>Bartlett's formula for MA(q) 95% confidence bands<br>0.80<br>0.60<br>0.40<br>0.20<br>(mil. m3 per month)<br>0.00<br>Autocorrelations of seasonally adjusted hydrology<br>-0.20<br><!-- End of picture text --> Table 5. Lag Selection Criteria

||<br>W/o t<br>Vi|<br>rend<br>shnugad Pipa|<br>lkoti (India)<br>With t|<br> <br>rend|<br>U<br>W/o tr|<br>pper Kotmale<br>end|<br>(Sri Lanka)<br>With t|<br> <br>rend|<br>W/o tr|<br>Thac Mo (<br>end|<br>Vietnam)<br>With t|<br>rend|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Log|AIC|Log|AIC|Log|AIC|Log|AIC|Log|AIC|Log|AIC|
|Lag<br>|likelihood|statistics|likelihood|statistics|likelihood|statistics|likelihood|statistics|likelihood|statistics|likelihood|statistics|
|0|-349.9|3.820|-339.0|3.743|110.1|-0.370|184.0|-0.723|673.8|-5.405|696.3|-5.570|
|1|-215.8|2.582|-209.1|2.545|256.8|-1.056|279.7|-1.155|737.8|-5.871 <sup>**</sup>|747.0|-5.924 <sup>**</sup>|
|2|-203.2|2.546 <sup>**</sup>|-197.5|2.520 <sup>**</sup>|286.9|-1.162|299.4|-1.209|741.7|-5.828|749.5|-5.869|
|3|-197.5|2.579|-192.3|2.558|303.5|-1.199 <sup>**</sup>|311.2|-1.223 <sup>**</sup>|751.2|-5.832|756.6|-5.852|
|4|-191.3|2.607|-185.9|2.583|307.1|-1.172|315.0|-1.197|756.3|-5.799|761.0|-5.814|

Note: *** 1 % significance level, ** 5 % significance level, and * 10% significance level.
The major reason to prefer DEA over SFA is that DEA can be used even when conventional cost and profit functions that depend on optimising reactions to prices can not be justified. Since it is likely that regulations and other market imperfections distort prices in the East Asian banking sectors complicating the application of SFA to price and quantity data, it seems that DEA is more suitable for examining the efficiency of East Asian banks. A practical consideration to use DEA instead of SFA is that it avoids having to measure output prices, which are not available for transactions services and fee-based outputs.

Most bank efficiency studies look at the US or other developed countries. Bhattacharyya et al. (1997) have applied such techniques to Indian banks in the first study using data of a developing country. The only bank efficiency studies so far that have used East Asian banking data are Leightner and Lovell (1997), Leightner (1997), Gilbert and Wilson (1998) and Hao et al. (1999). Leightner and Lovell (1997) use linear programming techniques and Thai banking data to show that Thai banks experienced high growth rates in production during the years of 1990-94. They argue that these high growth rates indicate an unusual level of success of the banking system. Leightner (1997) uses identical linear programming techniques on data between 1990-95 for Thailand's finance and securities companies. He shows that, although the finance and security companies enjoyed tremendous profitability and rapid growth, these firms were not fully efficient, i.e. that profits could have been increased even more by changing the output and/or input mix. He also finds strong economies of scale and argues that the finance and security companies are too small to compete with the larger banks. Gilbert and Wilson (1998) use linear programming techniques to investigate the effects of privatisation and deregulation on the productivity of Korean banks over the years 1980-94. They find that Korean banks responded to privatisation and deregulation by altering their mix of inputs and outputs, yielding large changes in productivity. Hao et al. (1999) use the stochastic frontier approach to explain differences in efficiency scores for 19 Korean banks over the period 1985-95. They find that banks with faster growth rates, banks with a countrywide branch network, and banks which made extensive use of deposits in funding their assets were most efficient.

# **2. Efficiency and Risk-Taking**

In the previous section we have already pointed out that bank efficiency models have the disadvantage that they do not take into account risk factors. Firstly, they assume that all banks are risk neutral. Secondly, they ignore risk-taking by banks. Efficiency models, for example, 7
**Table 3: Patent Output by Company**

|Preliminary<br>Rank<br>in 2003|Preliminary<br>number<br>of patents<br>in 2003|Organization|(Final<br>Rank in<br>2002)|(Final<br>number of<br>patents in<br>2002)|
|---|---|---|---|---|
|1|3,415|International Business Machines<br>Corporation|(1)|(3,288)|
|2|1,992|Canon Kabushiki Kaisha|(2)|(1,893)|
|3|1,893|Hitachi,Ltd|(5)|(1,601)|
|4|1,786|Matsushita Electric Industrial<br>Co.,Ltd (a)|(6)|(1,544)|
|5|1,759|Hewlett-Packard Development<br>Company,L.P. (b)|(9)|(1,385)|
|6|1,707|Micron Technology,Inc.|(3)|(1,833)|
|7|1,592|IntelCorporation|(15)|(1,077)|
|8|1,353|Koninklijke Philips Electronics<br>N.V.|(16)|(842)|
|9|1,313|SamsungElectronics Co.,Ltd.|(11)|(1,328)|
|10|1,311|Sony Corporation|(7)|(1,434)|
|11|1,302|FujitsuLimited|(12)|(1,211)|
|12|1,243|Mitsubishi Denki Kabushiki<br>Kaisha|(10)|(1,373)|
|13|1,184|Toshiba Corporation|(14)|(1,130)|
|14|1,181|NEC Corporation|(4)|(1,821)|
|15|1,139|General Electric Company|(8)|(1,416)|

Source: U.S. Patent and Trademark Office 46