rafmacalaba/datause-encoder-data huggingface dataset:
recommend that rather than focusing on inputs, the focus should shift to other policies, including giving right incentives for teachers, and increasing school competition.

The importance of incentives is confirmed by a study of Woessman (2003) who estimated an education production function based on student-level data for 260 000 students from 39 developed and developing countries<sup>12</sup> . He finds that school autonomy (teacher incentives) and centrally controlled examinations (student incentives) do positively and significantly influence educational performance. He does not consider the school size variable, however, while _higher class size has a positive effect on education outcomes._ There is one cross country study that focuses on transition countries. Ammermuller et al (2003) estimate an education production function for seven Eastern European countries (excluding Russia) using the TiMMS 2003 wave. Their study finds that _class size has a positive influence on test scores, only if one does not control for selection effects_ . Once selection effects are controlled for the coefficient of class size becomes insignificant. Other included school characteristics, such as shortage of materials and measures of teacher and school autonomy were also found to be insignificant in most cases.

# _b. Single-country studies_<sup>_13_</sup>

Single transition countries studies so far focus on the Central European countries with, as far as we could find, no evidence being available about countries which belong to the Commonwealth of Independent States (CIS).

Three studies use Polish data to study the determinants of educational performance. Herczynski and Herbst (2005) find _a small positive effect of class size_ . They also find that increased school choice improves achievement but only up to a certain threshold, after which the opposite effect takes place. Bukowska and Siwińska-Gorzelak (2011) find _a significantly positive effect of school size_ in almost all their specifications. They also find a positive influence of school competition (and accordingly a negative effect of the Herfindahl index) on students‟ test scores.

> 12 This country included the same eight transition countries as the previous ones – the countries for which TIMSS data were available.

> 13 The few studies on Ukraine are covered in the next section which focuses on the educational system in Ukraine

9
# **1. Introduction**

Recent surveys document Ethiopia’s considerable progress in a number of dimensions. <mark>For example, from 2005 to 2013 the number of health posts increased by 159 percent from 6,191 to 16,048. Similarly, from 2006 to 2013 the number of health centers increased by over 386 percent from 668 to 3,245 (FMOH, 2013). Other reports also show that i</mark> mmunization coverage increased from 14 percent in 2000 to 24 percent in 2011, modern contraceptive use increased from 6 percent to 27 percent, and the percentage of women ages 15-49 years who received antenatal services increased from 27 percent to 34 percent (EDHS, 2011). Infant mortality declined from 97 deaths per 1,000 in 2000 to 59 deaths per 1,000 in 2010, and under-five mortality decreased from 166 deaths to 88 deaths per 1,000. There have also been encouraging results from the education side. For example, in <mark>2005 the primary net attendance rate for 7-12 year old children was 42.3 percent. In 2011, this increased by about 20 percentage points to 62.2 percent (EDHS, 2005 & 2011; Carranza and Gallegos, 2013).</mark> Despite apparent progress on many aspects of wellbeing, progress has not been observed to the same degree in the multi-dimensional poverty index (MPI). The recent Oxford Poverty and Human Development Initiative (OPHI) global MPI data report shows that, in 2011, 87 percent of the population was MPI poor, i.e. deprived of at least one-third of the weighted MPI indicators (OPHI, 2014). This sets Ethiopia as the second poorest country in the world. Other studies that explored the multidimensional aspect of poverty in the country find that the reduction in poverty measured by the MPI declined by only about 10 percent compared to the 33 percent decrease in monetary poverty during the same period (Carranza and Gallegos, 2013). Overall, with over 85 percent of the population deprived, the index suggests the country’s poverty is deep-rooted and complex.
35

|**Region**|**Country**|**Source**|**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|
|---|---|---|---|---|---|---|---|
||Burundi|MICS3|100|100|100|100|98|
||Cameroon|MICS3|98|95|94|72|22|
||Central African<br>Republic|MICS3|100|100|99|99|97|
||Côte d’Ivoire|MICS3|100|100|99|90|41|
||Ethiopia|DHS|100|100|100|100|81|
||Gabon (2000)|DHS|99|57|11|2.6|0.2|
||Gambia, The|MICS3|100|98|95|86|79|
||Ghana|MICS3|100|98|95|89|49|
||Kenya|HH surveys|99|98|93|88|56|
||Malawi|MICS3|100|100|100|100|94|
||Mali (2001)|DHS|100|98|94|91|96|
||Mauritania|MICS3|100|96|71|36|9.9|
||Mozambique (2003)|DHS|100|100|100|88|17|
||Namibia (2000)|DHS|100|80|53|30|0.8|
||Nigeria|MICS3|99|97|94|67|22|
||Rwanda (2000)|DHS|100|100|100|98|45|
||São Tomé and|MICS3|100|95|79|63|31|
||Principe|||||||
||Somalia|MICS3|100|100|100|99|99|
||South Africa|Statistics SA|25|23|15|4.6|0.8|
||Togo|MICS3|100|100|99|98|92|
||Uganda|HH surveys|100|100|99|99|95|
||Zambia (2001–02)|DHS|100|100|99|98|21|

_Sources:_ The sources are identical to those for Table 1. _Note:_ See the notes for Table 1.
statistics are presented in Appendix Table A4, while Appendix Table A6 provides details on the definition of leading metropolitan areas. Given its large size, Brazil’s leading metropolitan area includes three of its largest urban agglomerations – Rio de Janeiro, São Paulo, and Belo Horizonte. In all other countries, except Ecuador and Panama, the largest city is the leading metropolitan area. In Ecuador, Quito is the leading area, while urban Panama province, which includes Panama City, is the leading metropolitan area in Panama.

**Table 1. The urban gradient: definitions of locality types**

|**Locality type**<br>**Population size**|**Share of urban**<br>**residents (%)**|**Share of metropolitan**<br>**residents (%)**|
|---|---|---|
|**Mexico**|||
|Urban-Metro (large)<br>≥ 300,000|> 50%|> 50%|
|Urban-Metro (small)<br>< 300,000|> 50%|> 50%|
|Urban (large)<br>≥ 20,000|> 50%|≤ 50%|
|Urban (small)<br>< 20,000|> 50%|≤ 50%|
|Rural (large)<br>≥ 10,000|≤ 50%|≤ 50%|
|Rural(small)<br>< 10,000|≤ 50%|≤ 50%|
|**Brazil**|||
|Urban-Metro (large)<br>≥ 300,000|> 50%|> 50%|
|Urban-Metro (small)<br>< 300,000|> 50%|> 50%|
|Urban (large)<br>≥ 50,000|> 50%|≤ 50%|
|Urban (small)<br>< 50,000|> 50%|≤ 50%|
|Rural (large)<br>≥ 30,000|≤ 50%|≤ 50%|
|Rural(small)<br><30,000|≤ 50%|≤ 50%|
|**Colombia**|||
|Urban-Metro<br> <br>|All designated as metro<br>|areas in 2011<br>|
|Urban (large)<br>≥ 20,000|> 50%|Nonmetro areas|
|Urban (small)<br>< 20,000|> 50%|Nonmetro areas|
|Rural (large)<br>≥ 10,000|≤ 50%|Nonmetro areas|
|Rural (small)<br><10,000|≤ 50%|Nonmetro areas|
|<br>**All other LAC countries**|||
|Urban|> 50%||
|Rural|≤ 50%||

We explore the variation in productivity across different types of 2<sup>nd</sup> level administrative units, defined using an urban gradient that varies with the size of the country and territorial coverage of the data. In the cases of Brazil and Mexico, using the rich information in the IPUMS population censuses, it is possible to distinguish between six types of municipalities: large metropolitan areas; small metropolitan areas; large urban areas; small urban areas; large rural areas; and small rural areas. Table 1 lists the criteria for defining the different types of localities in each country case. For example, in Mexico, large metropolitan areas are municipalities with 300,000 or more people of whom more than 50 percent reside in urban households (as of 2000) and more than 50% are metropolitan residents. In Brazil, metropolitan municipalities are defined as in the case of Mexico, but urban and rural municipalities are considerably larger (Table 1). In Colombia, official aggregates of value added at the second administrative level are used to define five types of municipalities. In this case, metropolitan municipalities are those designated as such as of 2011. In all other countries, municipalities where the 10
Table 1 Uni-variate Statistics

|||for Variab|les Averaged|over the Period 198|4-1990||
|---|---|---|---|---|---|---|
|Variables|N|Mean|Std Dev|Coe. of Variation|Minimum|Maximum|
|MCY|32|0.137|0.175|1.277|0.004|0.680|
|M1Y|32|0.159|0.106|0.667|0.049|0.472|
|QMY|32|0.269|0.188|0.699|0.044|0.752|
|DCPY|32|0.306|0.201|0.657|0.063|0.924|
|GNPCAP|32|2055|2746|1.336|168|15177|

Table 2 Correlation Analysis for the period 1984-1990

|Pearson Correlation Coefficients / Prob|>IRIunder Ho: RhO=0 / N=32|
|---|---|
||MCY|
|**M1Y**|0.397<br>**0.0245**|
|**QMY**|0.704<br>0.0001|
|**DCPY**|**0.839**<br>**0.0001**|
|**GNPCAP**|**0.552**<br>**0.0010**|

**17**
**Convexity and Sheepskin Effects in the Human Capital Earnings Function: Recent Evidence for Filipino** Men

Norbert R. Schady*i Economist, Latin America and the Caribbean Region, Poverty Reduction and Economic Management, World Bank.

I I would like to thank Harold Alderman, Jere R. Behrman, Benu Bidani, Gaurav Datt, Olivier Deschenes, Emanuela Galasso, Hans Hoogeveen, Emmanuel Jimenez, Homi Kharas, David Lam, Erzo Luttmer, Tamar Manuelyan Atinc, Peter Moock, Lant Pritchett, and Martin Rama; participants at the LACEA Conference held in Rio de Janeiro on October 12-14, 2000; and participants at a seminar held at The World Bank for many useful comments. I also thank Josie Perez and the staff of the Philippines National Statistics Office (NSO) for help with the data. Please address correspondence to the author at nschady@worldbank.org.
# **Policies and Investment Priorities** :

- To lower transport costs in rural areas, the government may want to explore options for encouraging the development of a domestic small vehicle transport sector. As international truckers are unlikely to find these routes profitable, alternate modes of transport need to be identified. It may be useful to consider options for promoting appropriate intermediate means of transport (IMTs) for connecting rural areas to market towns. Related work by the World Bank’s transport department suggests that IMTs offer considerable opportunities in relation to the short distance travel and transport demands of rural communities with regard to efficiencies in terms of cost and time savings. IMTs are seen as important in reducing travel and transport burden and increasing overall rural mobility in terms of reduced effort and time and high carrying potential. Efforts on identifying appropriate IMTs are already underway in Malawi.

- To enhance agriculture led economic growth, public investments in transport infrastructure should be prioritized towards improving feeder roads that connect rural areas offering a combination of rich natural and economic geographies - favorable agronomic potential and high population densities - to major domestic and international markets. Most of these locations are in the South and Central regions of the country.

- For manufacturing to take-off, infrastructure investment should support emerging clusters in the country’s main urban agglomerations – Blantyre and Lilongwe. As firms in Blantyre specialize in activities requiring reliable power supply and benefit from scale economies and international market access –public investments should be designed to amplify these market linkages. On the other hand Lilongwe specializes in agro processing - priority should be enhancing market linkages for adding value to local production and to transport local products at lower costs.

- Transport improvements in regions with good agronomic potential will also help in poverty reduction. However, when these improvements are targeted on spatial equity grounds to support remote regions with poor agronomic potential, they are likely to impose a trade off with national economic performance and aggregate poverty reduction.

6. The rest of the paper is organized in three sections. Section 2 discusses the determinants of transport costs in Malawi and includes the spatial analysis of the transport network as well the survey of transport provides. In Section 3, we examine the implications of geographic variations in transport costs on economic specialization and productivity. Section 4 highlights priorities for public policies and investments to maximize the economic gains from transport improvements.

- 3 -
_Gender Equality and Economic Development: The Role for ICTs_ 18

## <u>Regression for Gender Inequality in Employment</u>

1. _Per Capita Real GDP (Trend Value):_ HP Filtered Chain Weighted Per Capita Real GDP.  As above.

2. _Urbanization Ratio:_ Urban population is the share of the total population living in areas defined as urban in each country, expressed as a percentage. To account for the possibility of endogeneity, this variable was lagged by one year when used as an independent variable in the regressions. (Source: WDI) 3. _Unemployment Rate:_ Unemployment refers to the share of the labor force that is without work but available for and seeking employment, expressed as a percentage. Definitions of labor force and unemployment differ by country. (Source: WDI) 4. _Average Years of Schooling:_ As above.

5. _Female to Male Ratio of the Average Years of Schooling:_ This ratio is constructed as the ratio of the average years of schooling for females to that of male, expressed as a percentage. As with average years of schooling of the total population, the 5-yearly data for female and male average years of schooling were linearly interpolated to obtain annual observations, before the ratio was taken. (Source: Barro and Lee, 2001).

# **6.2 Econometric Methodology**

Both OLS and IV panel regressions with country fixed-effects were estimated to investigate the effects of ICT infrastructure on gender inequality. Variables that proxy gender inequality were used as dependent variables, while ICT variables, along with other control variables, acted as independent variables. White’s heteroskedasticity consistent standard errors were also employed.

## <u>Ordinary Least Squares (OLS) Estimation</u>

To investigate the effects of ICT on the gender inequality in education, as measured by the ratio of female to male students in primary and secondary school, the following regression specification was estimated: _Student Ratio it =_ β _0 +_ β1 log( _per capita real GDP i,t-_ 1 ) _+_ β _2_ ( _Youth Sex Ratio it_ ) _+_ where _i_ denotes country and _t_ denotes year.
<!-- Start of picture text --> Policy Reuorh<br>and Programs<br><!-- End of picture text --> <!-- Start of picture text --> WPS 1100<br><!-- End of picture text -->

- This paper a product of the Environmental Assessments and EFrograms Division, Environment Department-is part of alargereffort in the departmentto seek ways of incorporating indigenous peoples and theirtraditional cultural knowledge into biodiversity conservation and natural resources management. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please. cor.tact Shelton Davis, room S5-109, extension 33413 (Febniary 1993, 31 pages).

Using data from Latin America, Davis and Wali argue that fundamental changes must take plac> in the legal recognition and demarcation of indigenous territones if indigenous peoples are to fulfill their potential as resource managers for threatened tropical forest ecosystems.

Davis and Wali compare different national land tenure models for forest-dwelling indigenous peoples (contained in national Indian, agrarian, and protected-ara laws in Lain America) and a model proposed by indigenous organizations in Latin America.

The conventional models emerged during an era when most governments were more concemed with the rapid occuprtion and exploitation of frontier zones and & . assimilation of indigenous peoples. Recent attention to the environmental degradation of these areas and the need to create altemative models of land use and development have directed attention to the potential contribution of indigenous peoples to the conservation and management of the vast tropical forests of Latin America.

Davis and Wali find that indigenous peoples must be given some degree of control over their territories and resources. They contend that for successful management of tropical forests there must be a new type of partnership between indigenous peoples, the scientific community, national govenmments, and intemational development agencies. This relationship should be a contractual one, in which indigenous peoples are provided with juridical recognition and control over large areas of forest in exchange for a commitmen **t** o conserve the ecosystem and preserve biodiversity.

The Policy ResearchWoking PaperSeriesdissemirates the findings of work under way intheBank Anobjectiveof die series is to get these findings out quickly, even if prestations are less than fully polished. The findings, interpretations, and conclusioDs in ihese papers do not necessarily represent officiai Bank policy.

Produced by the Policy Research Dissemination Center
# **An Expansion of a Global Data Set on Educational Quality: A Focus on Achievement in Developing Countries**

Noam Angrist, Harry Anthony Patrinos, and Martin Schlotter<sup>*</sup> August 13th, 2013

**Abstract**

In this paper, we assemble a panel data set which measures cognitive achievement for 128 countries around the world from 1965 to 2010 in 5-year intervals. This data set is constructed from international achievement tests, such as PISA and TIMMS, which have become increasingly available since the late 1990s. We link these international assessments to regional ones such as SACMEQ, PASEC and LLECE in order to produce one of the first globally comparable datasets on student achievement. In particular, our dataset is one of the first to include achievement in developing countries, including 29 African countries and 19 Latin American countries. This data set is an extension of an earlier data set constructed by Altinok and Murseli (2007). We provide a first attempt at using this dataset to identify causal factors that boost achievement. Our results show that key drivers of global achievement are civil rights and economic freedom across all countries, and democracy and economic freedom in a subset of African and Latin American countries.

# **JEL Classifications** : I2, J24, O15

**Keywords** : Quality, Education, International, Achievement, PISA, TIMSS

> * The authors are associated with the World Bank, the University of Munich, and the Massachusetts Institute of Technology. The views expressed here are those of the authors and should not be attributed to the World Bank Group or to the author’s institutions.

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**<u>Table 4. Decentralization Ratio by Location and Affiliation</u>**

||Ratio of De<br>|centralization<br>|
|---|---|---|
||SOEs located in the<br>SAME city with the seat<br>of thegovernment|SOEs located in a<br>DIFFERENT city with the<br>seat of thegovernment|
|Central SOEs|5.53%|15.63%|
||(253)|(1945)|
|Province SOEs|5.33%|15.35%|
||(2231)|(1726)|
|Municipal SOEs|4.17%|11.38%|
||(7298)|(967)|

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35
of men ages more than 15; the age, gender, religion, and educational attainment of the household head, and whether the household is in a rural or urban area. The results are robust to the inclusion of additional demographic and geographic controls. After the parameters are estimated, the _quaids_ postestimation commands allow the estimation of own-price and cross-price elasticities for all goods and different population groups. Uncompensated price elasticities are estimated for individual observations in the data and are summarized by decile using the household survey weights.

Table A1 presents the estimated coefficients from which the elasticities presented in table 3 have been calculated.

# **Table A1. Quadratic AIDS model**

|Number of observations|29,971||||||
|---|---|---|---|---|---|---|
|Number of demographics|7||||||
|Alpha_0|1.5||||||
|Log-likelihood|16517||||||
||**Coefficient**|**Standard**<br>**Error**|**z**|**P>|z|**|**[95**<br>|**% Conf.**<br>**Interval]**|
|**Alpha**|||||||
|Cigarettes|−0.19|0.03|−5.57|0.00|−0.25|−0.12|
|Bidis|−0.03|0.03|−1.02|0.31|−0.08|0.03|
|Betel leaf|0.09|0.02|5.41|0.00|0.06|0.13|
|Betel nut|0.18|0.02|12.13|0.00|0.15|0.21|
|Rolled betel leaf|0.94|0.04|22.92|0.00|0.86|1.02|
|**Beta**|||||||
|Cigarettes|0.16|0.01|20.18|0.00|0.15|0.18|
|Bidis|0.16|0.01|20.78|0.00|0.14|0.17|
|Betel leaf|−0.08|0.01|−14.30|0.00|−0.09|−0.07|
|Betel nut|−0.06|0.00|−13.19|0.00|−0.07|−0.05|
|Rolled betel leaf|−0.18|0.01|−21.97|0.00|−0.20|−0.16|
|**Gamma**|||||||
|Cigarettes-cigarettes|−0.15|0.01|−16.62|0.00|−0.17|−0.13|
|Bidis - cigarettes|0.00|0.01|0.60|0.55|−0.01|0.02|
|Betel leaf - cigarettes|0.03|0.00|7.06|0.00|0.02|0.03|
|Betel nut - cigarettes|0.00|0.00|1.21|0.23|0.00|0.01|
|Rolled betel leaf - cigarettes|0.12|0.01|15.13|0.00|0.10|0.13|
|Bidis-bidis|−0.10|0.01|−14.63|0.00|−0.11|−0.09|
|Betel leaf - bidis|0.01|0.00|1.94|0.05|0.00|0.01|
|Betel nut - bidis|0.03|0.00|9.93|0.00|0.02|0.03|
|Rolled betel leaf - bidis|0.06|0.01|9.74|0.00|0.05|0.08|

22
# Agglomeration, Urbanization and Employment growth in Ghana: Evidence from an industry‐district panel Andrew Kerr<sup>1</sup> and Bruce McDougall<sup>2</sup>

JEL codes: R11; O12; E24 Keywords: Ghana; Agglomeration economies; Employment Growth

> 1 DataFirst, University of Cape Town. andrew.kerr@uct.ac.za

> 2 DataFirst, University of Cape Town

> We thank Lasse Møller‐Jensen at the University of Copenhagen for making available to us his data on the 1970 and 1984 district level populations for Ghana.