rafmacalaba/data-use-mentions-extended huggingface dataset:
_Fixed capabilities_ include proximity to markets and natural capital. Proximity to markets is given by a GDP‐ weighted measure of distance between countries, which was then indexed and inverted to become a measure of closeness. The value of natural capital<sup>5</sup> in dollar terms was provided by the World Bank Wealth of Nations data set, with a custom update provided for Botswana. We use natural capital in aggregate terms rather than per‐capita terms as the total amount of an extractable resource is likely to influence investors’ decisions.

_Long‐term policy variables_ include human capital, physical capital and institutional capital. Human capital is measured as average years of schooling of the population ages 15 and up. This is the most comprehensive set of human capital data available, as international test scores (PISA) are not available with similar country coverage. Physical capital stock per capita is based on gross fixed capital formation estimates from the World Bank, where initial capital stock is set by the rate of depreciation, growth rate and population rate, and accumulation is done using a perpetual inventory method. We elect to put physical capital in per‐capita terms as it can serve as a proxy for productivity. Institutional capital is given by the World Governance Indicators (WGI) rule‐of‐law rating. This is admittedly an incomplete measure of institutional strength, as the WGI itself also covers voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality and control of corruption. In practice, however, these measures are highly correlated and we chose the measure that may best relate to the strength of the business environment.

_Short‐term policy variables_ include logistics (connectivity), wage competitiveness (labor costs), market access and access to inputs. The logistics measure is from the World Bank Logistics Performance Index (LPI), a widely cited international rating system. The measure is influenced by proximity to markets, so there is some overlap between the two indicators. Wages are measured by the
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Air transport

## **Achievements**

Angola’s air transport seat capacity grew significantly between 2001 and 2007 (figure 15, table 12). The decline in capacity for 2009 may be more of a function of missing domestic capacity figures than a true slowing of growth, since both intercontinental traffic and international traffic within Africa still show growth in spite of the global recession. Angola’s restructured national airline, TAAG Angolan Airlines, seems to be expanding its routes and fleet, which includes several Boeing 777s. Reflecting cultural ties, flights to Portugal and Brazil feature prominently in the country’s pattern of intercontinental connectivity.

**Figure 15. Evolution of seats and city pairs in Angola**

<!-- Start of picture text --> a. Seats b. City pairs<br>2,500,000 45<br>40<br>2,000,000<br>35<br>30<br>1,500,000<br>25<br>20<br>1,000,000<br>15<br>500,000 10<br>5<br>- 0<br>2001 2004 2007 2009 (Est)<br>2001 2004 2007 2009 (Est)<br>Total<br>International Total International<br>Intercontinental excluding flights between NA and SSA<br>Intercontinental Domestic<br>Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems. NA = North Africa; SSA = Sub-Saharan Africa.

26
14 The Brazilian data configuration is such, therefore, that the expenditure effect is computed for all households from one sample (POF), while the (market and transfer) income effects are computed for all households from another sample (PNAD). Although both are large and nationally representative surveys, they do not sample the same households. To combine the three effects in (6), we therefore look at their average values at each centile π of the pre-shock income distribution. Denote by the set of households in centile π of that distribution. Let each centile have _n(Π)_ households. We can then define: as the (proportional) expenditure, market income, and transfer income effects of the food price increases, for centile π of the Brazilian income distribution. Whereas equation (7) uses POF and INPC data, (8) and (9) are computed using PNAD data. Given (6), and the above definitions, we can also define the net price effect for each centile as: Equation (10), which is the centile-average equivalent of (6), describes our estimates of the proportional money-metric welfare effect of the price changes along Brazil‟s income distribution. It is closely analogous to the growth incidence curve of Ravallion and Chen (2003), which describes the proportional income growth for each percentile of the original income distribution. We accordingly denote the “price change incidence curve” (PIC). Equations (7), (8) and (9) define its three components: the expenditure effect incidence curve; the market income effect incidence curve, and the transfer income effect incidence curve.

In the following section we present our empirical results in two ways. First we graphically present the price change incidence curves, building on equations (7), (8), (9) and (10). The visual clarity and degree of distributional disaggregation of these figures are, in our view, important improvements in the analysis of the distributional consequences of
less biased fitted measure. Thus, for each survey (1996/97, 2002/03, and 2008/09) an identical consumption model has been estimated (Equation 1), the dependent variable for which is household per capita consumption deflated using the national consumer price index (CPI). Then, out-of-sample predictions are made for both 2002/03 and 2008/09 by replacing the original coefficients with coefficients obtained by applying the 1996/97 model parameters. Normally distributed errors are randomly assigned (Equation 2).

## **Equation 1**

> 13 “Auto-consumption” refers to the consumption of goods (in this case food) produced by the household.  Autoconsumption prices should, in principle, reflect the opportunity cost of consuming household produce rather than selling it.

8
extreme impact that water shortages can have on informal firms, which are an important source of employment in the developing world. On the other hand, Davis et al. (2001) find a small effect of water infrastructure improvement on expenditures of small enterprises. Escribano et al. (2010) use earlier Enterprise Surveys for 26 African countries conducted between 2002 and 2006 to explore the effect of infrastructure quality on firm productivity and only find an effect for slower growing African economies. Typically, studies using firm-level data have been relatively silent on the possibility of endogeneity of firmlevel infrastructure measures, pointing out instead that disaggregated infrastructure measures do not suffer several endogeneity concerns faced by aggregated public capital measures found in macro-level studies (Escribano et al., 2010; Moyo, 2011). Macro-level studies have not found robust results with regards to infrastructure and growth, although a few studies employing different tools to address endogeneity have found some positive and significant effects (Roller and Waverman, 2001; Shioji, 2001).

4
We tested the validity of the economic and demographic effects found in the decomposition that explains the increase in FLFP over the last decade. We predicted the propensity of women being employed by studying the marginal effects using a simple probit model (detailed information about the specification in Appendix 1).

Figure 6 shows the estimated propensity of females being employed according to their age and educational attainment, compared to a male with middle-school education in the 26-to35 age group. We estimated coefficients and their respective confidence intervals for 2005 (light-gray bars) and 2017 (black bars). Only where confidence intervals do not overlap can we conclude statistical significance regarding a change in the propensity of women being employed between 2005 and 2017.

For males, no characteristic has a statistically significant effect on the change of employability over the period, similar to the previously explained disaggregated labor indicators. For women, on the other hand, the propensity to be employed does change throughout the years for the 36-to-65 age brackets with basic education. For these three groups of females, the propensity to be employed increased by approximately 5 percentage points.

Results reinforce our previous findings that older women (36-to-65) account for the demographic component explaining the increase in female labor market participation and employment in the last decade. They also suggest that specific characteristics, such as education, explain these changes, despite stagnant or declining wages.

14
While outlined indicators are presented in context of the DIa variable, which describes discrepancies in import reporting, they can be applied to any of the six variables discussed above, including the general DI distribution outlined in Figure 1. This report uses indicators related to DIa , DIb , DIc variables. Thus, we supply country level indicators that can describe both import and export reporting practices of a country and product level indicators that describe the quality of reporting on products.

# 4. Data description

This analysis relies on UNSD’s Comtrade annual database, the largest and most widely used resource in empirical investigations of international trade. It covers all trade transactions recorded in the Harmonized System (HS) and Standard International Trade Classification (SITC) product nomenclatures. See Appendix 6 for details on the database tables. The Annual Discrepancy Index and Trade Reporting Indicators below currently provides discrepancy measurement in HS Nomenclature, while SITC was used for nomenclature comparison. The Annual Discrepancy and Reporting index data can be downloaded from the World Bank Data Catalog using the links below. See Appendix 6 for details on the database tables.

- <u>Discrepancy Index Annual for H5 nomenclature</u>

- <u>Discrepancy Index Annual for H4 nomenclature.</u>

- <u>Discrepancy Index Annual for H3 nomenclature</u>

- <u>Discrepancy Index Annual for H2 nomenclature</u>

- <u>Discrepancy Index Annual for H1 nomenclature</u>

- <u>Discrepancy Index Annual for H0 nomenclature</u>

- <u>Trade reporting Indicators all nomenclatures</u>

- <u>Trade reporting Indicators by commodity all nomenclatures</u>

Country and product level trade reporting indicators provide coverage for quality of reported trade across the various versions of the Harmonized System nomenclatures (H0 to H5). The number of countries included in our database for each year and nomenclature version is detailed in Appendix tables 3 through 14. We provide both import and export reporting indicators for each country and
Policy Research Working Paper 9428

# **Abstract**

Free college proposals have become increasingly popular in many countries, yet cross-country evidence indicates that higher college subsidies raise enrollment but not graduation rates. To capture the evidence and evaluate proposals, this paper develops and estimates a dynamic model of college enrollment, performance, and graduation. A central piece of the model, student effort, has a direct effect on class completion and an indirect effect mitigating the risk of performing poorly or dropping out. The model is estimated using rich student-level data from Colombia, and multiple free college programs are simulated. Among them, universal free college expands enrollment the most but does not affect graduation rates, thereby helping explain the evidence. Performance-based free college, in contrast, raises graduation rates yet has a smaller enrollment impact.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean 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://www.worldbank.org/prwp. The authors may be contacted at mferreyra@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
Tanzania’s national CPI reflects the prices surveyed in urban areas in 21 regions across the country. Meanwhile, Madagascar’s national CPI reflects the prices of goods and services in the capital, Antananarivo, only. Similarly, before 2011, the National Statistical Institute of the Democratic Republic of Congo did not collect prices in any administrative province except Kinshasa, the capital. When price data in rural areas are not available, CPIs are based on urban price data only, with the (arguably strong) assumption that urban and rural prices move in parallel. What is more, even when HBS data are nationally representative, some countries compute budget shares for urban households only. In Tanzania, for instance, CPI weights until 2009 were based on consumption patterns among urban households in the 2001 HBS. In 2010, as part of the process of rebasing the index to the HBS 2007, the reference population for the weights was broadened to include rural households (Tanzania NBS 2010).

Finally, for the purpose of poverty analysis, concerns arise over the fact that the weights are not representative of poor households’ consumption patterns. CPI weights are computed as the consumption shares of an aggregate reference population, which attaches a weight to each household in proportion to its total expenditures. Because of this plutocratic bias, CPI weights reflect consumption patterns of households at the upper end of the distribution (Nicholson 1975, Deaton 1998). In times of changing relative prices, such as during food price shocks, inflation measured by the CPI can then differ from the inflation experienced by poorer population groups (Günther and Grimm 2007).

# _The Engel curve method_

The Engel curve method attempts to address some of the above biases, mostly the commodity and outlet substitution biases.<sup>4</sup> The method takes as a starting point Engel’s Law and the idea that movements in food budget shares that cannot be explained by changes in relative prices and household characteristics reveal changes in real incomes. As a result, any systematic difference
provides a simple framework for combining the two features in a linear mixed model, in addition to offering a well-established parametric bootstrap method for estimating uncertainty (Gonzalez-Manteiga et al, 2008). Official estimates for municipalities developed by the government based on the household-level intercensus were used as the reference for comparison. The R<sup>2</sup> of the estimates was 0.74 for sampled municipalities, but only 0.49 for non-sampled municipalities. Because this is only based on one sample, the paper also performed design-based simulations using a measure of per capita labor income taken from the intercensus. In the simulations, the R<sup>2</sup> was 0.86 for sampled areas and 0.64 for out-of-sample areas. I further discuss the difference in accuracy between sampled and non-sampled areas below.

Steele et al. (2017) predict a wealth index and per capita expenditure in Bangladesh using a Hierarchical Bayes Structural Equation Model, allowing for spatial covariance. The paper differs from many others by also incorporating call detail record (CDR) features from mobile phones in addition to satellite features. Results were validated both using cross-validation and using poverty estimates derived from the 2012 census. The results showed that it is much easier to predict wealth than per capita consumption, a finding consistent with Jean et al. (2016). When using cross-validation for evaluation, out-of-sample R<sup>2</sup> for village-level estimates was 0.76 for wealth as opposed to 0.36 for consumption. However, when comparing Upazilla-level (sub-district level) estimates with previous estimates derived using traditional small area estimates from the 2010 census, R<sup>2</sup> was a much higher 0.95.

Similarly, Pokhriyal and Jacques (2017) combine CDR and satellite data from Senegal with census data to predict non-monetary poverty across communes in Senegal. They use Gaussian process regression, a non-parametric machine learning method. They evaluate their estimates using 10-fold cross-validation. The estimates for non-monetary poverty across
# **<mark>References</mark>**

1. ONUSIDA/09.36S. 2009. “Situación de la epidemia de sida, diciembre de 2009”. <u>http://data.unaids.org/pub/Report/2009/jc1700_epi_update_2009_es.pdf</u>

2. UNAIDS/WHO. 2002. “Initiating Second Generation HIV Surveillance System: Practical Guidelines. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance”. - <u>http://data.unaids.org/Publications/IRC pub02/jc742 initiatingsgs_en.pdf</u> 3. UNAIDS/WHO. 2004. “Second Generation Surveillance for HIV: The Next Decade”. - <u>http://data.unaids.org/publications/irc pub01/jc370 2ndgeneration_en.pdf</u> 4. Rodríguez-García R, World Bank Group. 2007. “Planning and Managing for HIV/AIDS Results. A Handbook”. <u>http://siteresources.worldbank.org/INTHIVAIDS/Resources/375798 1103037153392/Results_Handbook_31Oct2007.pdf</u> 5. Atkins D, Best D, Briss PA, et al. 2004. "Grading quality of evidence and strength of recommendations". BMJ 328 (7454): 1490 6. World Health Organization. 2009. “HIV Triangulation Resource Guide”. <u>http://data.unaids.org/pub/Manual/2009/20090915_hiv_triangulation_resource_guid e_en.pdf</u> 7. Downs S, Black N. 1998. “The feasibility of creating a checklist for the assessment of the methodological quality both of randomized and non-randomised studies of health care interventions”. J Epidemiol Community Health 1998; 52; 377-384.

8. World Health Organization. 1999. “Sexually Transmitted Infections Prevalence Study Methodology. Guidelines for the implementation of STI prevalence surveys”. <u>http://www.wpro.who.int
Drawing on Autor et al. (2003) and Acemoglu and Autor (2011), we define the skill content of occupations using the following categories: non-routine analytical, non-routine interpersonal, routine cognitive, routine manual, and non-routine manual. Analytical skills are measured by the number of different types of documents read, length of longest document typically read, writing activities, use of math, frequency of thinking for at least 30 minutes, and frequency of learning new things. Interpersonal skills involve supervising coworkers, making presentations, contact with clients, and frequency of collaborating with coworkers. To capture routine cognitive skills, we use a measure of structured versus unstructured work captured by a scale indicating the lack of freedom a worker has to decide the way he works. Routine manual skills are defined as driving, operating machines, and repairing electronic equipment, along with a scale indicating how physically demanding the work is. Finally, non-routine manual skills are measured by the frequency of doing repetitive tasks. Table A1 in the Appendix provides more details on these variables.

We mapped the <mark>Philippines Standard Occupational Classification, that follows closely the International Standard Classification of Occupations (ISCO) 2008, to the Indonesian Classification of Occupation 1982 that is used in the Sakernas survey. We work with six occupational groups: administrative and managerial workers; professionals and technicians; clerical workers; sales and services workers; agriculture, animal husbandry, forestry workers, fishermen and hunters; and production workers, transport and equipment operators, and laborers. We combine the variables capturing each type of skill in the STEP survey into a single skill measure for each occupation following the methodology applied by Acemoglu and Autor (2011). Details are provided in the Appendix. Finally, using Sakernas information on the s</mark> hare of each occupation in total provinceindustry-year employment, we link the skill measures of each occupation obtained from STEP to the Sakernas database: � <mark>In equation (1), ω</mark> ��� <mark>
# **2.2 A cautionary note: issues regarding the reliability of poverty estimates for geographical targeting in Azerbaijan**

As discussed above, the imputation of the welfare estimates in the census relies upon parameters derived from the 2002 Household Budget Survey. The new sample design that was fully implemented in the 2002 survey has several characteristics which have the potential to affect the reliability of spatially disaggregated estimates of welfare. Amongst the most important of these are issues related to the choice of Primary Sampling Units (PSU) and the allocation of the households to a specific interview area of fieldwork.

The sample design of the HBS of Azerbaijan selects 84 PSU from master sampling frame of 484 PSU, each of which are based on areas with a population size of about 15,000. Taking such large areas as a PSU has the potential of jeopardising the selection of a random sample of the population which forms the basis for the design of a nationally representative survey, and can potentially affect the reliability of the spatially disaggregated estimates of welfare.

Furthermore, with the redesigning of the new survey, the SSC has increased the number of interviewers from 64 to 84. Each interviewer has been assigned one of the 84 selected PSU for the survey data collection. This has the potential of creating an ‘interviewer effect’ in the collection of the data as each interviewer will tend to interpret the questionnaire and collect the data in his or her own ‘personal’ way. As each interviewer works exclusively in one location, it is not possible to separate out the effect of the interviewer from the impact of the location itself. Unfortunately, it is not possible to estimate the size of the error due to the interviewer effect and thus to statistically account for this specific sort of error associated with the disaggregated estimates.

In the light of these shortcomings, it is suggested to interpret results with caution and not to exclusively rely on poverty maps for geographical targeting. Rather, they should be used with caution and together with other targeting approaches. We will graphically