rafmacalaba/data-use-mentions-extended huggingface dataset:
Africa still represent an enormous improvement since 1998 when African net enrollment rates were only 58 percent. In Burkina Faso, the focus of this paper, the net enrollment rate was 35 percent in 2000 and improved to 55 percent by 2007 (UN Statistics Division). Based on this paper‟s data from Burkina Faso, only 24 percent of households with multiple primary school aged children enroll all of their children.

> 2 Based on the UNICEF Multiple Indicator Cluster Survey End of Decade Assessment, almost three-fourths of all rural primary school-aged children in developing countries were involved with some type of work that included helping with the family farm or business or providing domestic household services such as cooking, sweeping, doing dishes, or fetching water (Edmonds and Pavcnik 2005).

2
surveyed parents did not actively choose schools, even though nearly 70% do have other choices in the same village (Figure 3).

**Figure 3: Parental choice of schools**

<!-- Start of picture text --> How many other schools applied?<br><!-- End of picture text --> <!-- Start of picture text --> Is the school the only school in<br>village?<br><!-- End of picture text --> <!-- Start of picture text --> One other<br>school applied<br>9%<br>Yes<br>32%<br>Two other<br>Not applied any schools<br>other school 1%<br>90% More than two<br>other schools No<br>0% 68%<br><!-- End of picture text --> _Source: SBM Survey 2010, World Bank_ .

Parental involvement in school affairs tends to be low. The limited involvement is mainly interactions with teachers or principals on issues related to their own children, or attending regular parents’ meetings with schools. Over 80% of the parents have neither provided any inputs to school, nor volunteered in school activities, such as serving as a committee member, helping classroom activities, or raising funds for schools (Figure 4).

8
STEVEN PENNINGS 14 value of ρ = 10.5 from EMX, which implies η = 1.24 (similar to the values of ρ = 10 and η = 1.01 from AB). My model also does well in matching the sales-weighted demand elasticity of around 5 (targeted by Gopinath and Itskhoki (2010), based on Broda and Weinstein (2006)). The GE version of the model produces a trade elasticity in the range estimated in the literature (see the online appendix).

**Concentration** The value of ρ, combined with other parameters, yields a median sectoral Herfindahl index of around 0.165, in the “moderately concentrated” range of 0.1-0.18 according to the Department of Justice (as reported in AB). A much higher (lower) value of ρ leads to much higher (lower) concentration statistics, outside the DoJ’s moderate range. The level of concentration depends on the size of the sector one seeks to represent. In the empirical work I calibrate to 6 digit industries, so I would expect concentration statistics in between those of 4 digit industries of Amiti et al (2016) and 7 digit industries of EMX. The only concentration statistic reported by both papers is the mean share of the top domestic firm, which is around 0.45 in EMX and 0.12 in Amiti et al. In my calibration, the mean share of the top US firm is 0.27, which is almost exactly in the middle of EMX and Amiti et al’s estimates. My Herfindahl index is also lower than the 0.26 reported in EMX. As higher levels of concentration are associated with more variable markups, my calibration can be considered conservative.

# 5. Simulation Results (and comparison to data)

5.1. **PT of competitors’ RERs to simulated import and producer prices.** Estimates of exchange rate pass-through on simulated data are shown in Table 3. Using the preferred US+
# _CHAPTER 7. SEMI-PARAMETRIC AND MACHINE-LEARNING METHODS_ 58

in off-census years, as they are not based on census data. However, they seem to be all synthetic, similar to the traditional ELL method, in the sense of not accounting for area effects. If that is the case, their performance in an application is going to depend on the ability of the auxiliary information at hand to explain the between-area heterogeneity of the variable of interest. They might achieve great efficiency gains in an application where the covariates fully explain the between-area heterogeneity, but result in very poor estimates when the covariates are week in that sense. Note that to evaluate small area estimators, we need to look at their true MSE (accounting for both, bias and variance), since the produced MSE estimates might be biased low, giving a misleading picture of the true efficiency of the estimators. In real applications as those reviewed above, true MSEs are not available. The only way of having the true MSE is conducting simulation experiments.

Corral, Henderson and Segovia (2023) conducted design-based simulations based on a real census that included an income variable, by drawing multiple samples from it. They showed that, in the case of weak auxiliary information, such as the geo-referenced area-level data in their study, gradient boosting techniques applied at the area or cluster (PSU) level perform very poorly compared to other SAE procedures, both in terms of design bias and MSE. However, when using much more powerful census auxiliary area-level information, the same technique performs well in terms of design bias and MSE, close to the gold standard EB method based on the richer unit-level census data and including area effects. The EB method has a more consistent performance in terms of design bias and MSE than the machine learning procedures, even when the auxiliary information is week. Hence, their results point out the need to include area effects in modern machine learning procedures as well.

Moreover, the methods for aggregated data in certain locations that use survey estimates as a response variable
in dynamic panel estimations and rely on "internal instruments" to control for a weak form of exogeneity of all explanatory variables. We consider the explanatory variables of interest as predetermined, i.e. instrumented using their own one-period and further lags, in order to use a relevant number of instruments for e¢ ciency reasons and at the same time keeping the number of instruments lower than or equal to the number of country groups in all speci...cations.<sup>18</sup> In column (3) example, Surinamese emigrants mainly live in the Netherlands, with just 3 percent of Surinamese emigrants living in the six receiving countries in Defoort’s sample. We will therefore conduct a sensitivity analysis to check the robustness of the results to the exclusion of low-coverage countries in the Defoort dataset.

> 18A problem of the GMM estimator is that too many instruments can over ...t the endogenous variable. As rule of thumb, the number of instruments should be less or at least equal to the number

12
.37|9.44|

_Notes._ Henceforth, “NO2” or “Lights” means per area. “GDP” is real GDP at constant 2017 prices. *3 countries out of the full sample of 178 appearing in Panel A are ungraded, and therefore do not appear in Panel B: Cura ̧cao, Palestinian Territory, and Sint Maarten. Country lists by-grade are reported later in Table 7.

fine-grained analysis is of interest.<sup>7</sup> The lesser average growth rate of NO2 is at least in part due to heterogeneity in decarbonization efforts across countries, as we now discuss.

**Data Quality Grades.** Table 1 Panel B breaks down the mean growth rates in Panel A by-country data quality grade. These grades are provided in the Penn World Tables on the basis of countries’ capacity for compiling accurate statistics. For consistency, we use Chen and Nordhaus (2011)’s expanded version with grade “E” meaning those countries having basically no statistical organizations.<sup>8</sup> In general, higher grades are associated with more developed economies. It is no mistake that grade A and B countries have trend NO2 declining over time, which is primarily due to decarbonization efforts, akin to the theory of an environmental Kuznets curve. This is unlike lights, which are associated with upwards trends over time across all grades, and with conditional convergence. So, when considering economic growth i.e. trend changes in output, night lights are most useful. Critically, this conclusion flips when we consider

> 7The resolution of lights is 30 arc-seconds for the DMSP-OLS database and less for VIIRS. An arc-second is one sixtieth of an arc-minute, which is one-sixtieth of a degree of latitude or longitude. Thus, OMI’s resolution of 0.1° _×_ 0.1 is about 360 arc-seconds, or 12 times as coarse as night lights. An alternative European Space Agency NO2 instrument, TROPOMI, with data beginning
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 13 Productivity America for the year 2010 and extracted the urban extents for the same locations from the processed NTL images of the years 1996 and 2000 (Figure 2). According to (Chuvieco, 2016), an object must be several times larger than the pixel size to be delineated properly from a remote sensing image. Since the pixel size of the DMSP-OLS NTL RC images is 30 arc seconds (almost 1 by 1 Km near the Equator), we feel justified to exclude those urban extents that were smaller than 3 Km<sup>2</sup> in 1996. That resulting final sample therefore includes 919 urban extents in each year (Figure 2).

# _4.3 Measuring urban productivity_

The use of NTL in socioeconomic studies emerged as a way to address the lack of economic measures at disaggregated scales. The pioneer contribution by Henderson et al (2012) shows that NTL data can be used as a proxy of GDP within and across countries. Since then, other authors have used light density from NTL imagery to measure economic performance and welfare (Bleakley and Lin, 2012; Michalopoulos and Papaioannou, 2013; Lowe, 2014; Storeygard, 2016; Pinkovskiy, 2013).

In this paper we follow Tewari et al. (2016) to calculate a measure of productivity ( _Y_ ) from NTL data. We use the density of radiance within the urban extent, _dr_ntl_2010_ , as our measure of productivity, computed as the sum of the NTL DN values in 2010 divided by area, in square kilometers, of the urban extent in 2010.
Over the same period, “traditional” services industries have increased their employment share across all income levels. In contrast to manufacturing, the relationship between income level and employment in the sector has not shown a distinct trend in changes in its slope over time.

Figures 3 and 4 use sectoral productivity figures to investigate trends in unconditional convergence across countries over the past four decades. Although these data are available only for a subsample of economies, we find several trends:

- while convergence in manufacturing was a clear and strong trend some decades ago, it may no longer be as strong in recent decades,

- recent trends in convergence suggest that (traditional) services show more evidence of convergent pressures than manufacturing,

- modern services (particularly FIRE industries), but also the catch-all of “other services” (including primarily traditional hospitality/restaurants industries), show stronger convergence in recent decades than traditional trade services.

6
Francisco Santini. 2021. “How Research Affects Policy: Experimental Evidence from 2,150 Brazilian Municipalities.” _American Economic Review_ 111, no. 5 (May): 1442–80.

- Holden, Stein T., and Mesfin Tilahun. 2020. “Farm size and gender distribution of land: Evidence from Ethiopian land registry data” [in en]. _World Development_ 130 (June): 104926.

- INDEC. 2022. _Censo Nacional de Población, Hogares y Viviendas, República Argentina._ Brief. https: //www.indec.gob.ar/indec/web/Nivel4-Tema-2-41-165.

- Kapon, Samuel, Lucia Del Carpio, and Sylvain Chassang. 2024. “Using Divide-and-Conquer to Improve Tax Collection.” _The Quarterly Journal of Economics_ 139 (4): 2475–2523. https://doi. org/10.1093/qje/qjae018.

- Kleven, Henrik Jacobsen, Martin B Knudsen, Claus Thustrup Kreiner, Søren Pedersen, and Emmanuel Saez. 2011. “Unwilling or unable to cheat? Evidence from a tax audit experiment in Denmark.” _Econometrica_ 79 (3): 651–692.

- Komatsu, Hitomi, Alemayehu A. Ambel, Gayatri Koolwal, and Manex Bule Yonis. 2021. _Gender Implications of Rural Land Use Fee and Agricultural Income Tax in Ethiopia_ [in English]. Brief. Accepted: 2021-07-28T17:17:34Z. Washington, DC: World Bank, June. https://openknowled ge.worldbank.org/handle/10986/36029.

17
achievement of durable solutions, births and deaths) so that data on the size and composition of refugee populations can quickly become outdated; and (d) data protection and privacy issues.

# **_IDPs_**

Data on IDPs are compiled, analyzed and disseminated by a number of organizations, including IDMC, UNHCR,<sup>89</sup> OCHA, ICRC, WFP, IOM, JIPS, as well as NGOs and research institutes. IDMC is the lead organization responsible for aggregating sources and establishing estimates of new and cumulative displacements that are as reliable as possible. IDMC relies on a wide range of secondary sources of information, including national governments, international organizations, national and international NGOs, human rights organizations, the media and IDPs themselves (IDMC 2015). It publishes estimates of internal displacement caused by conflict, generalized violence, and human rights violations in over 60 countries.

IDMC candidly acknowledges the challenges associated with data compilation and the corresponding limitations in terms of the completeness, quality, reliability and comparability of aggregate numbers. IDMC’s aggregation and analysis is constrained by the scope and quality of its source data. Significant challenges include:

- (a) The lack of consistent definitions and methodologies (across locations and over time), in particular the lack of consensus on when internal displacement ends (IDMC 2015). Consequently, data may be aggregated across situations and countries even though the source data were generated for different purposes and using different methodologies.

- (b) Very few national governments collect or report up-to-date data on IDPs. Political will as well as the resources and capacities to carry out effective and timely data collection vary across countries. In 2015, only five governments responded to IDMC requests for data.<sup>90</sup> Consequently, data on internal displacement is outdated in several countries and is at risk of becoming outdated in others, including countries like Afghanistan with large IDP populations (IDMC 2016).<sup>91</sup> Problems of outdated
# Knowledgeable Bankers?

The Demand for Research in World Bank Operations

## Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank_

### **Keywords:** Research, incentive to learn, absorptive capacity, operations, development aid

> 1 This is a background paper for the 2011 _Report on the World Bank Research Program._ The paper draws on a survey of the views of World Bank staff carried out by Sharon Felzer and Jessica Cameron. The overall task manager for the Report was Jean-Jacques Dethier and the director responsible was Asli Demirguc-Kunt. The author is grateful to Asli for encouraging him to write this paper and to Asli, Marianne Fay, Aart Kraay, Tamar Manuelyan Atinc, Will Martin, Berk Ozler, Mike Toman, Dominique van de Walle and Adam Wagstaff for useful comments. However, the author alone is responsible for the interpretations of the survey data provided in this paper. In particular, the findings, interpretations and conclusions of this paper should not be attributed to the World Bank or any affiliated organization.
This paper attempts to contribute by assessing the impact of aquatic salinization on the spatial distribution of fish species that are significant for the livelihoods of poor fishing communities in southwest coastal districts and the Sundarbans region.<sup>3</sup> In absence of comprehensive data on spatial distribution of fish abundance by species, the focus of our analysis is on expected impact of changing aquatic salinity on the extent of fish habitats. Although the importance of Sundarbans mangroves as fish habitats and nursery grounds is recognized in the literature, this paper does not consider the indirect impact that climate-induced changes in the location and composition of mangroves will have on fish species.

The remainder of the paper is organized as follows. Drawing on prior work by Dasgupta, et al. (2015), Section 2 develops high-resolution digital maps of aquatic salinity in the Sundarbans region for 2012, and salinity in 2050 projected for 27 combinations of IPCC climate change scenarios, global circulation models, and assumptions about rates of land subsidence in the Ganges Delta. In Section 3, we develop a database for 83 fish species that are important for the livelihoods of poor households in the Sundarbans region. Section 4 combines our salinity projections with information on fish species salinity tolerances to produce maps of projected changes in habitats and local species populations by 2050. In Section 5, we combine projected changes in species populations with _upazila_ -level information on poverty to assess the potential impacts of aquatic salinization on poor households in the Sundarbans region. Section 6 summarizes and concludes the paper.

> 3 Examples of prior research on climate change and fisheries in coastal Bangladesh can be found in Ali (1999); World Bank (2000); Sarwar (2005); Hassan and Shah (2006); UK DEFR (2007); Chowdhury et al. (2010); World Bank (2011) and Nicholls et al. (2013). However, the bulk of this research makes inferences from descriptive statistics.

5
welfare due to the loss of varieties and show why agreements with large, technologically advanced partner countries are more likely to lead to gains.

The paper is organized as follows. We begin in section II with a brief review of the applied general equilibrium literature of regional arrangements. In section III we discuss the welfare economics of preferential trade liberalization in services. In section IV, we provide an overview of the Tanzanian services sectors. We discuss how we estimated the tariff equivalents of the barriers in services in section V. We provide an overview of the model in section VI and a discussion of the data in section VII. The central results are presented in section VIII and sensitivity results are presented in section IX. Given the initiative of the East African Customs Union to begin to include services in their agreements by negotiating mutual recognition agreements in professional services, we focus on a range of possible policy options in professional services. These are presented in section X. Conclusions are presented in section XI. In appendix A, we discuss the trade and tariff data in some detail. We document the calculation of ownership shares by sector and region in appendix B. How we obtained estimates of the DixitStiglitz elasticities in goods is described in appendix C. Our estimation of the ad valorem equivalents of barriers in professional services in explained in appendix D and our calculations of the research and development intensity by sector are in appendix E.

# **II. Review of the Applied General Equilibrium Literature on Regional Agreements**

The formation of the Canada-US free trade agreement led to the path-breaking work of Harris (1984) and of Cox and Harris (1986) in incorporating imperfect competition into a small open economy applied general equilibrium model. They showed that if the agreement leads to a more competitive pricing strategy by Canadian firms, there would be substantial welfare gains 7
following a mean-variance approach. The results are robust to many types of estimations that take into account expected returns, variances, and benchmark effects.

We also use the simulated global funds to look beyond a mean-variance framework and explore the possibility that there is an insurance premium in the returns of global funds. Because global funds, unlike specialized funds, can secure gains by moving away from troubled countries during crisis times, investors might be willing to pay for this benefit by accepting lower expected returns. Our results indicate that global funds do not seem to better shield investors from tail risk. We find that the skewness and kurtosis of global fund returns are similar to those of the simulated global funds. Higher moments of the distribution of returns are important if global funds were to minimize losses during bad times instead of following standard mean-variance models. Moreover, conditional on large negative returns on either specialized funds or the MSCI Emerging Market Index, we find that returns of the simulated global funds are broadly similar to those of global funds. In sum, the lack of diversification does not seem to be explained by a better performance of global funds during turbulent times. It is nevertheless possible that global funds provide some other unobservable benefits that are not captured by the meanvariance and other analyses performed in this paper.

Our findings contribute to several strands of the literature. First, our paper relates to a large literature on the extent of investor international diversification. Most of this research has concentrated on aggregate measures to explain the determinants of international investments and on country portfolios to study the degree to which countries hold assets abroad.<sup>5</sup> More recently, researchers have begun to exploit asset-level data based on institutional investor portfolios, although the evidence remains scarce.<sup>6</sup> Our findings on mutual funds with global reach reinforce the existing evidence of growing but still limited international diversification. Furthermore, our results provide evidence that some of the usual explanations for these international investment patterns, such as the relevance of transaction
0.1037)|(0.4317)|(0.2560)|(0.3473)|(0.2964)|(0.3361)|(0.2909)|
|Observations|36,184|36,184|36,184|36,184|36,184|36,184|36,184|
|R-squared|0.373|0.155|0.106|0.139|0.273|0.139|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

81
.398***|-0.573|0.009|-0.718|
||(0.053)|(0.504)|(0.126)|(0.509)|(0.092)|(0.449)|
|Herfindahl-Hirschman Index|-10.670***|1.930|-5.828|-5.922|-0.835|-1.032|
||(3.836)|(7.299)|(3.648)|(3.654)|(4.327)|(4.324)|
|Starting a Business (DTF)|0.076*|0.075*|0.120***|0.147**|0.070*|0.074*|
||(0.039)|(0.039)|(0.045)|(0.061)|(0.040)|(0.039)|
|Female Dependence of Sectors|||||48.382***|41.236***|
||||||(6.366)|(9.237)|
|Exports (%, cell average)*GDP||0.120**||0.116**||0.081|
|per capita (logs)|||||||
|||(0.055)||(0.055)||(0.053)|
|No. of workers||-4.336**|||||
|(logs)*Herfindahl-Hirschman|||||||
|<br>Index|||||||
|||(1.726)|||||
|No. of Workers (logs)*