rafmacalaba/datause-encoder-data huggingface dataset:
expanded investment while containing fiscal pressures. Thus access to infrastructure services can expand - a key goal of development policy.

At the same time experience serves some warnings. Some governments have used PPPs to postpone the day of reckoning by not accounting for future fiscal obligations. Expected cost advantages have not always materialized. Rather than exploiting the respective advantages of public and private parties, some ventures have hidden costs at the expense of taxpayers and consumers.

The discourse around PPPs emphasizes ownership form and financing approaches. This paper argues that such an emphasis risks cloaking the real issues and may undermine sensible approaches. Instead of ownership and finance the prime issues are the choice of market structure for an infrastructure sector, which determines the way prices are set, and the level at which prices are set. Financial and ownership considerations derive from the primary choice about price formation processes be they market-based or regulated.

# **Demand**

_“When high roads, bridges, canals etc. are in this manner made_ [i.e. based on user fees] _and supported by the commerce which is carried on them by means of them, they can be made only where that commerce requires them, and consequently where it is proper to make them. Their expence too, their grandeur and magnificence, must be suited to what that commerce can afford to pay. They must be made consequently as it is proper to make them. A magnificent high road cannot be made through a desart country where there is little or no commerce, or merely because it happens to lead to the country villa of the intendant of the province, or to that of some great lord to whom the intendant finds it convenient to make his court. A great bridge cannot be thrown over a river at a place where nobody passes, or merely to embellish the view from the windows of a neighbouring palace: things which sometimes happen, in countries where works of this kind are carried on by any other revenue than that which they themselves are capable of affording.”_

# _Adam Smith (1776)_

In many countries infrastructure services are woefully deficient. Complaints, for example, about lack of water or electricity service, low quality service or infrequent service are commonplace. At the same time it is not unheard of that a new road serves the estate of powerful people or that water or power systems reach them first. Roads to nowhere and prestige projects recur all too often. The demand and supply of infrastructure services may thus both be deficient. Dealing with the issue brings out the central role of prices – as determinants of demand and as incentives to supply.

Demand or “need” is often determined with reference to some “norm”. Policymakers may come to believe that development requires a certain percentage of GDP to be invested in infrastructure. Such a norm may be derived from cross-country studies on past levels of investment. Or one may set stretch 4
# **Psychometrics as a Tool to Improve Screening and Access to Credit**<sup>**1**</sup>

Irani Arráiz,<sup>2</sup> Miriam Bruhn,<sup>3</sup> Rodolfo Stucchi<sup>4</sup>

## **JEL Classification:** D82, G21, G32

**Keywords:** Asymmetric information, psychometric tests, credit risk, access to credit.

> 1 We thank the Entrepreneurial Finance Lab, the implementing institution, and the Superintendencia de Banca y Seguros de Peru for sharing their data and information. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank or the World Bank, their Boards of Directors, or the countries they represent.

> 2 Inter-American Development Bank; e-mail: iarraiz@iadb.org

> 3 World Bank; e-mail: mbruhn@worldbank.org

> 4 Inter-American Development Bank; e-mail: rstucchi@iadb.org
Policy Research Working Paper 8165

# **Abstract**

The paper introduces a Bayesian cross-entropy forecast (BCEF) procedure to assess the variance and skewness in density forecasting. The methodology decomposes the variance and skewness of the predictive distribution accounting for the shares of selected risk factors. The method assigns probability distributions to baseline-projections of an economic or social random variable—for example, gross domestic product growth, inflation, population growth, poverty headcount, among others—combining ex-post and ex-ante market information. The generated asymmetric density forecasts use information derived from surveys on expectations and implied statistics of predictive models. The BCEF procedure is applied to produce world GDP growth forecasts for three-year horizons using information spanning the period of October 2005–August 2015. The scores indicate that the BCEF density forecasts are more accurate and reliable than some naïve—symmetric and normal distributed confidence interval—predictions, illustrating the value-added of the introduced methodology.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 author may be contacted at fmendezramos@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
# **Table A3.5. The Labor Channel in the Extended Model**

|||Employme|nt ratio (ln)||
|---|---|---|---|---|
||(1)<br>Pooled|(2)<br>FE|(3)<br>RE|(4)<br>GMM|
|(t-1)|1.002***|0.534***||1.032***|
||(364.54)|(6.66)||(38.97)|
|**Pillar 1**|0.0043|-0.0269*|0.0013|-0.0119|
||(0.61)|(-1.42)|(0.06)|(-0.99)|
|**Pillar 2**|0.00245|-0.0043|-0.0057|-0.0017|
||(0.87)|(-0.55)|(-0.63)|(-0.27)|
|**Pillar 3**|-0.0000|-0.0000|-0.0000|0.0000|
||(-0.22)|(-0.09)|(-0.22)|(0.06)|
|**Pillar 4**|||||
|Output Gap|0.0018**|0.0041***|0.0068***|0.0042***|
||(1.92)|(3.30)|(4.92)|(3.23)|
|(t-1)||||-0.0042***<br>(-5.27)|
|Change ToT|-0.0181|0.0090|0.0356|-0.0188|
||(-0.50)|(0.33)|(1.13)|(-0.72)|
|**Pillar 5 and 6**|||||
|(t)|0.0015|0.0504|0.139***|0.398***|
||(0.26)|(0.77)|(2.10)|(2.36)|
|(t-1)||||-0.368***<br>|
|||||(-2.25)|
|**Pillar 7**|||||
|Tax rate (avg)|-0.0473***|-0.0162|-0.0181|-0.0963*|
||(-1.97)|(-0.19)|(-0.18)|(-1.46)|
|Mean tariff rate|-0.0000|0.0433***|0.0585***|0.0041|
||(-0.02)|(2.17)|(2.66)|(0.32)|
|**Pillar 8**|||||
|Women (%pop)|0.0017|0.0447|-0.0087|-0.0036|
||(0.81)|(0.94)|(-0.25)|(-0.42)|
|CBR|-0.0098|-0.0211|0.0263|-0.183***|
||(-1.20)|(-0.23)|(0.26)|(-2.29)|
|**Pillar 9**|-0.0094***|-0.0113|-0.0040|-0.0277**|
||(-2.13)|(-0.40)|(-0.12)|(-1.75)|
|**Pillar 11**|0.0000|0.0003|0.0001|0.0003*|
||(0.58)|(1.19)|(0.42)|(1.46)|
|**Pillar 12**|0.0029|-0.0015|0.0004|0.0092|
||(0.44)|(-0.14)|(0.03)|(1.00)|
|EMS dummy<br>|||<br>-0.0637<br>(-0.56)||
|Year Effect|||||
|Constant|Yes|Yes|Yes|Yes|
|Obs|195|195|195|195|
|Wald Test|318026.8||63.13|10898.6|
|p-value|0|0|0|0|
|LogLik||612.9|||

37
# ANNEX 1: CASE **STUDIES**

5.01 The nine case studies include the countries from each World Bank region (Africa, Asia, EMENA, LAC) for which data coverage is most complete: Chile, Costa Rica, Ghana, Korea, Malawi, Morocco, Philippines, Turkey, and Zambia. Each case study compiles available education sector data for a specific country and examines possible links to adjustment programs. A table summarizes available evidence on education indicators for each country from all sources. Because sources often disagree over exact figures, but rarely over general trends, the tables are intended to indicate only likely trends.

# CHILE

5.02 Chile introduced major education reforms during the early 1980s, concurrent with other reforms to increase privatization and reduce the role of state, but did not receive any Bank adjustment loans until 1986. Severe economic dislocations began in the early 1970s, accompanied by socio-political conflicts. Chile's response to the 1975 world recession and terms of trade problems was to increase foreign borrowing. Chile received World Bank SALs in 1986, 1987, and 1988. SAL conditionality focused on reducing tariffs and ending wage indexation (that is, controlling the growth of civil service salaries). Unfortunately, little data is available for the period after World Bank-assisted adjustment began.

5.03 Chile is often cited as an example of a country that protected the poor without utudermining the adjustment process, particularly with respect to child nutrition (Nicholas, 1988). This protection did not extend to the education sector. After 1974, Chile reduced the share of government resources devoted to education, although public spending was simultaneously redirected from universities to the pre-school, primary and intermediate levels. The increase in per student expenditures after 1980 was aided by declining primary enrollment rates.

5.04 Increasing resources to primary education may be responsible for improving repeater rates at this level. However, primary enrollment rates worsened as a result of deterioration in economic and nutritional status at the household level. Families could not afford materials, and the school feeding program was reduced in 1975-76. Education coupons replaced state-sponsored education. But in times of recession, poorer households cashed in their coupons rather than spend them on schooling (United Nations, 1988), suggesting that the opportunity cost of education rose. Therefore, despite continuing public subsidies, the emphasis on private schools may have been detrimental to school attendance by poorer students.

_5.05_ Beginning in 1980 schools were transferred to municipalities. They continued to receive government subsidies, although subsidies were difficult to maintain during the austerity period from 1982 to 1986. Quality declined in municipality-operated private schools because teacher salaries were lower and resources were limited, especially in lowincome communities. Test scores declined during the 1980s in all subjects. The allocation of state subsidies encouraged private schools to be more concerned with pupil numbers than 36
# **Labor Productivity and Employment Gaps in Sub-Saharan Africa**

Ellen B. McCullough Charles H. Dyson School of Applied Economics and Management Cornell University

*Author email: ebm68@cornell.edu

**JEL:** J21, J22, J24, O12, Q10, Q12

**Keywords:** Structural transformation; Sub-Saharan Africa; Agricultural labor productivity; Sector labor shares; Productivity gaps.

## **Acknowledgements**

I am grateful for the extraordinary effort by the LSMS-ISA team of the World Bank to collect the innovative data sets used in this analysis, under the leadership of Gero Carletto and with funding from the Bill & Melinda Gates Foundation. Each country’s National Statistics Office, including survey supervisors and enumerators, played a central role in implementing large scale, high quality, nationally representative surveys. And, of course, many respondents across SubSaharan Africa were very generous with their time. My time was funded through the _Food Systems and Poverty Reduction_ IGERT traineeship of the National Science Foundation. Additionally, support from the _Agriculture in Africa: Telling Facts from Myths_ project, led by the World Bank with cofounding from the African Development Bank, is gratefully acknowledged. I especially thank Chris Barrett for providing overall guidance and Megan Sheahan for research support. I would like to thank the entire “Myths and Facts” team, in particular Luc Christiaensen for his leadership and Amparo Palacios-Lopez for reducing the transactions costs of data use. For valuable comments on this manuscript and its earlier iterations, I thank Paul Christian, Brian Dillon, Walter Falcon, Bob Herdt, Linden McBride, John Nash, Prabhu Pingali, Abebe Shimeles, Erik Thorbecke, and members of Chris Barrett’s research group. Any remaining errors are mine alone.
Table 3. Impacts of Extremism on Refugees’ Integration

|Variables in SD.|Integration<br>Index<br>(1)|Feel Comfortable if<br>your Child Socialize<br>with Host Children<br>(2)|Feel Welcomed<br>in this<br>City<br>(3)|Feel Part<br>of this<br>Village<br>(4)|Feel that<br>Tajikistan is<br>your Home<br>(5)|Number<br>of Refugee<br>Friends<br>(6)|Number of<br>Tajikistan<br>Friends<br>(7)|
|---|---|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_||||||||
|_ETi_|-0.566***|-0.500***|-0.189*|-0.261*|-0.366**|-0.335**|-0.451***|
||(0.160)|(0.174)|(0.160)|(0.161)|(0.164)|(0.164)|(0.138)|
|FDR q-values|[0.001]|[0.007]|[0.074]|[0.056]|[0.027]|[0.034]|[0.004]|
|R-squared|0.027|0.026|0.030|0.024|0.046|0.060|0.037|
|_Panel B. Contested Territories Exposure_||||||||
|_ECi_|-0.086|-0.090|-0.113|-0.091|0.034|-0.029|-0.051|
||(0.062)|(0.067)|-0.062|(0.063)|(0.064)|(0.063)|(0.054)|
|FDR q-values|[0.468]|[0.468]|[0.468]|[0.468]|[0.468]|[0.468]|[0.468]|
|R-squared|0.025|0.025|0.030|0.024|0.045|0.059|0.035|
|_Panel C. U.S.- Allies Exposure_||||||||
|_EAi_|0.103|0.098|0.084|0.079|0.012|0.047|0.071|
||(0.048)|(0.052)|(0.048)|(0.048)|(0.049)|(0.049)|(0.041)|
|FDR q-values|[0.167]|[0.167]|[0.167]|[0.167]|[0.299]|[0.167]|[0.167]|
|R-squared|0.025|0.025|0.030|0.024|0.045|0.059|0.036|
|Mean Dependent Variable|0.002|-0.027|0.026|0.038|0.074|-0.007|-0.099|
|Observations|5,592|5,285|5,535|5,469|5,592|5,592|5,589|
|_Controls in All Panels_||||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The Integration Index is constructed using the outcome variables of columns (ii) to (vii) using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again. See Appendix B for details of variable’s constructions. Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
# **Population, Poverty, and Sustainable Development: a review of the evidence**<sup>**1**</sup>

_Monica Das Gupta_<sup>_2_</sup> _, John Bongaarts_<sup>_3_</sup> _, and John Cleland_<sup>_4_</sup> JEL codes: Demography, Poverty, Economic Growth, Environment & Development, Public Policy

> 1 **_Acknowledgements:_** We thank the Hewlett Foundation for grant support through Trust Fund TF070424 given to the World Bank.  We are also very grateful for inputs from Shareen Joshi, and for valuable feedback from Martha Ainsworth, Jere Behrman, Ed Bos, Partha Dasgupta, Shanta Devarajan, Andrew Foster, Manny Jimenez, Elizabeth King, David Lam, Peter Miovic, Vijayendra Rao, T. Paul Schultz and Adam Wagstaff, as well as feedback received from participants in the Population and Poverty Conference held by the Hewlett Foundation, Marseille January 2011, the Population Association of America annual meeting April 2011, and a seminar at the World Bank April 2011.

> These are the authors‘ personal views and should not be attributed to the World Bank or any affiliated organization or member country.

> 2 Development Research Group, The World Bank, Washington DC, USA. Email: <u>mdasgupta@worldbank.org, mdasgupta@gmail.com</u> (Corresponding author)

> 3  The Population Council, New York, USA.  Email: jbongaarts@popcouncil.org

> 4  London School of Hygiene & Tropical Medicine, London, UK. Email: John.Cleland@lshtm.ac.uk
**Table 8: Average TFPC by airport and sub period (Annual %)**

|**Country**|**Airport**|**1995-1999**|**1999-2003**|**2003-2007**|
|---|---|---|---|---|
||**AEP**|-|-7.0|-3.0|
|**Argentina**|**EZE**|-|-18.9|4.0|
||**FTE**|-|-|22.9|
||**BSB**|10.0|5.4|2.9|
||**CGH**|13.8|2.6|-4.0|
|**Brazil**|**GIG**|7.4|-5.5|16.3|
||**GRU**|3.5|-0.9|2.7|
||**MAO**|-2.3|0.3|6.8|
||**VCP**|0.9|-7.6|-0.8|
|**Chile**|**SCL**|-|1.3|2.0|
|**Colombia**|**BAQ**|-23.0|-8.4|1.5|
||**CLO**|-|-6.2|-5.1|
|**Costa Rica**|**SJO**|-|22.1|0.0|
|**Ecuador**|**GYE**|-|-|8.1|
|**El Salvador**|**SAL**|-|2.7|1.4|
||**CUN**|-|6.6|-0.3|
|**Mi**|**GDL**|-|-6.1|9.5|
|**exco**|**MEX**|-|1.1|4.9|
||**MTY**|-|5.8|4.7|
|**Panama**|**PTY**|-|-|7.4|
|**Peru**|**LIM**|-|-|9.7|
|**Dominican Rep.**|**SDQ**|-|-|-3.7|
|**ALL**||-2.7|- 1.2|3.9|

A relevant policy question is whether private operated airports in LAC, a region that has experienced with a wide variety of private sector participation schemes for the operation of airports, have higher productivity gains. Table 9 sheds some light to this question. As the exercise of estimation of explanatory variables of TE scores at the international level showed, private operation is a relevant variable to explain differences in productivity. Table 9 also presents changes in productivity dividing airports by size and then uses the Work Load Unit measure to weight airports to avoid reaching a conclusion on public/private operated airports biased by the size of airports.

20
# **A2:  Education**

# **A2.1  Data on Expected Years of School**

**E** xpected number of years of school ( EYS ) that a child who starts school at age 4 would attain by her 18<sup>th</sup> birthday is calculated using the methodology described in UNESCO et. al (2014), Section 2.2 and Annex 2.2.  Conceptually, expected years of school achieved by age A is simply the sum of age‐specific enrollment rates over all ages in this age range, i.e.

where ENR� is the enrollment rate of children aged a . Unfortunately however, age‐specific enrollment rates are not systematically available for a broad cross section of countries. Instead, more readily‐ available data on enrollment rates by level of school are used to approximate enrollment rates in different age brackets. Specifically, pre‐primary enrollment rates approximate the age‐specific enrollment rates for 4 and 5 year‐olds; the primary rate approximates for 6‐11 year‐olds; the lower‐ secondary rate approximates for 12‐14 year‐olds; and the upper‐secondary rate approximates for 15‐17 year‐olds. Naturally, cross‐country definitions in school starting ages and duration of different levels of school imply that these will only be approximations to the number of years of school a child can expect to complete by age 18.

The ideal measure of enrollment rates for this calculation is the “total net enrollment rate” (TNER), which measures the fraction of children in the theoretical age range for a given level of school, who are in school at any level. For example, if the theoretical age range for lower secondary school is 12 to 14 years, then the TNER measures all children aged 12 to 14 who are enrolled in any level of school as a fraction of all children aged 12 to 14. In this way, the TNER best approximates the age‐specific enrollment rates for ages 12 through 14 since it captures the enrollment status of all 12 to 14 year‐olds, irrespective of what level of school they are in. Unfortunately however, data on TNER are missing for many countries and years in the UNESCO database, and, depending on the country and year, one or more of three other enrollment rates are more widely available. These are (i) the “adjusted net enrollment rate” (ANER), measuring the fraction of children in the theoretical age range for a given level 25
l~~~~~~~~~~~~~~~~~~~~~~~

# Table 3. Hungarian Exports to the World and the EC-12, 1983, 1987.

||Product||19<br>World<br> <br>|83<br>EC12<br> <br> <br>Sh of|Wor<br>Sm|1<br>ld<br>Share|987<br>EC<br>in<br>$m|12<br>Share of|
|---|---|---|---|---|---|---|---|---|
|||Sm|Share<br> <br>Total<br>|n<br>$m<br>are<br>%<br>Exports<br>to World||<br>Total<br>|<br> <br>%|Exports<br>to World|
||Total|8,722<br>|100.01<br>|3,384<br>15.9<br> <br>2|9,571<br>1529|100.0<br>160|1,912<br>476|20.0<br>31.2|
|0|Food & Live Animals|1,636<br>|18.8<br>|421<br>5.7<br> <br>74|,<br>151|.<br>16|<br>17|11.4|
|1|Beverages<br>& Tobacco|216<br>|2.5<br>|16<br>.<br> <br>317|<br>411|.<br>43|<br>153|37.1|
|2|Crude Matla<br>Excl Fuels|349<br>|4.0<br>|110<br>.<br> <br>|<br>402|.<br>42|<br>69|17.2|
|3|Mineral<br>Fuels Etc|806<br>|9.2<br>|47<br>5.8<br> <br>124|<br>80|.<br>08|<br>9|11.0|
|4|Animal,<br>Veg. Oil, Fat|105<br>|1.2<br>|13<br>.<br> <br>230|<br>1112|.<br>116|<br>262|23.5|
|5|Chemicals|883<br>|10.1<br>|203<br>.<br> <br>198|,<br>1311|.<br>137|<br>351|26.8|
|6|Basic<br>Manufactures|1,090<br>|12.5<br>|216<br>.<br> <br>57|,<br>3249|.<br>339|<br>243|31.6|
|7|Machines,Transp.Equip.|2,650<br>|30.4<br>|151<br>.<br> <br>|,<br>1152|.<br>120|<br>292|25.4|
|8|Misc Manuf Goods|839|9.8|183<br>21.3|,|.|||
|9|Goods Not Classfd|||<br>188|175|18|40|23.0|
||By Kind|127|1.5|24<br>.||.|||

',urce: COMTRADE Note: Percentage calculations and totals are based on<sup>more detailed</sup> data which are<sup>rounded</sup> off in this tab:e.