rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Common index−wide spline 52259<br>20<br>10<br>0<br>−10<br>0 100 200 300<br>Days since start of year<br>Temperature<br><!-- End of picture text --> Figure 1: An example of a common index-wide spline estimate This figure presents a common indexwide spline estimate for grid index 52259 (longitude: -1.5, latitude: 63.5) over the period from January 1, 2000, to December 31, 2019. The blue points represent the original daily temperature data, and the red line shows the spline fit, which is estimated across all 365 days using 20 years of observations. The x-axis shows the days since the start of the year, while the y-axis represents the predicted temperature.

1. Country, Grid, Year Level Aggregation: At this level, the unit of observation is a unique combination of country, grid, and year. For example, one such unit could be Spain, a specific grid index, and the year 2015. It allows for an examination of how weather patterns in a specific country, grid and year affect the firms located there. It is important to note that some grids are shared by multiple countries. In our dataset, roughly 5% of the total grids span across multiple countries. Despite this overlap, we retain the country-level grouping because the behavior of firms can vary significantly based on the country, even if they experience similar weather patterns.

2. Industry, Grid, Year Level Aggregation: The second level of aggregation employs the industry classification, using the NACE industry code (Statistical Classification of Economic Activities in the European Community). We use the highest level of aggregation (level 1), which includes 21 sectors. Unlike the country-grid level aggregation, where grids are nested within the country, the industry-grid level aggregation leads to a unique combination of industries and
the percentage of total loans in the worst two B.C.R.A. loan classifications The physical asset variable is the percentage of pre-privatization assets that ended up in residual entity balance sheets under the heading "bienes de uso." Many of the public provincial banks had an abundance of branches and buildings that purchasers might have preferred not to own (so they would not have to re-sell them later). The dependent variable is the percentage of pre-privatization assets shifted to the residual entity (see Table 2). B.C.R.A. changed its loan classification guidelines in 1994 so that the bottom three categories -- those with problems and deficient coverage, those with high risk of borrower insolvency and recovery difficulty, and those deemed unrecoverable -- are now considered non-performing ("bad credits"). The portfolio quality data used in the regressions discussed in this section are for loans classified under the old guidelines.

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presents the effects by geographical region showing zero impact of the scholarship in the 5 regions defined. Although not presented here, the lack of statistically significant effects on graduation rates by age, area, sex and region hold for math and Spanish test scores.

# **5 Why are PROBEMS scholarships ineffective?**

It is possible that the lack of effects of the scholarships on education outcomes is the result of poor targeting, poor students’ motivation, weak academic conditions among scholarship recipients or a combination of these. In this section, we perform further analysis to identify the effects of these three potential explanations behind the lack of impact of the scholarship program.

## **5.1 Targeting**

To test if poor targeting explains the lack of effects of the scholarships, we rely on information from the PROBEMS application portal to compute an index of self-declared household assets. The index of household assets (IHA) is the sum of four variables, indicating, respectively, the following basic characteristics of the dwelling: (1) concrete floors, (2) running water inside the house, (3) toilets connected to sewerage, and (4) concrete roof. A zero is used to indicate the absence of the amenity and a one its presence. The IHA index, therefore, has a maximum value of 4 and minimum of 0. The distribution of the IHA is shown in Figure 4. Few students self-declared that they live in a household with zero or only one of the basic dwelling characteristics; close to 70% of students have only two or three of the basic needs met, and 23% of students in our sample declared living in a household that meets all four basic needs.

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analyses in high income countries, higher discount rates of 4% and 5% are more appropriate for upper-middle income and lower-middle income and low-income countries. However, we choose to use a consistent discount rate for all countries of 3% so as not to penalize lower income countries in the global analysis.

We estimate the economy wide affect by aggregating the per-student-present-value effect on earnings over all students currently enrolled in pre-primary, primary, and secondary, in alignment with the HCI. We adjust this aggregate by the expected survival rate of the student cohort, using the HCI adult survival rate, and for the share of work-life that this student cohort is expected to be in gainful employment, this component is also referred to as Human Capital Utilization (Pennings, 2019). All these factors are available at the country level.

Ideally, we would like to rely on work-life tables for every country, unfortunately this is not available at a global scale.<sup>95</sup> We also assume that the current expected earnings, which reflect the prevailing structure of the labor market, prices, and discrimination, is on average, a useful aggregate proxy. We do, however, have concerns, to what extent this assumption would hold if we were to disaggregate results by gender, since both expected earnings and labor force participation are significantly lower for women, given prevailing discrimination, both of which are likely to improve in the next 45 years.

One important point is to how to best benchmark our returns to education assumption. This is critical since much of the literature on Mincerian regressions<sup>96</sup> uses years of schooling, which are computed using a quality unadjusted measure of years of schooling. We are comfortable with our assumptions for two main reasons. First, one could argue that labor markets should be able to price years of education, taking into consideration their quality. And second, if not, a quality adjusted

> 94 We have checked the robustness of this assumption using Patrinos et al (
Goal<br>Not Attrited Attrited Not Attrited Attrited<br><!-- End of picture text -->

**Figure A2** Baseline outcomes by attrition status: IPV

_Notes._ This figure shows the mean of each specified outcome among females who attrited and those who did not by treatment status. The left-side column presents these means for each community-level treatment arm, specified on the x-axis. Above the mean bars are _p_ -values for tests of equality between the means of attrited and non-attrited females. Below the mean bars are _p_ -values for tests that attrition differed between treatment arms, first comparing the _Supply_ arm to ELA only communities and then comparing the _Boys_ arm to the ELA only communities. The right-side column presents the outcome means by attritor and non-attritor status for females assigned to the _Goal_ treatment and those who were not, separately. Above the mean bars are _p_ -values for tests of equality of means between attritors and non-attritors within each arm. Below the mean bars is a _p_ -value from a test that attrition differed between females who were assigned to _Goal_ and those who were not.

_Source._ Female baseline data.

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ex-combatants spent a large share of their allowance in nonproductive ways: food, drinks or clothes. They are therefore likely to have experienced a consumption boom similar to ex-FNLs as of 2005. From our survey, we find that 50% of CNDD+ ex-combatants spent part of their allowance in consumption goods and 33.3% in clothing; only 5.6% reported having saved part of the allowances. Ndayiziga et al (2008) underlines the tendency of ex-combatants to “overspend the demobilization indemnities in an unorganized way, quickly putting them back into hardship”, while Willems et al (2010) conclude that “although the total sum of the benefits given to ex-combatants was rather large, many of them found it difficult to effectively use it for their reintegration because they did not have the capacity to handle money.” Third, our results are also consistent with the fact that the indicators related to the stocks of productive assets exhibit a constant trend, while consumption indicators exhibit a downward trend. According to our data, a substantial fraction of ex-combatants invested in a plot of land (39%), in a small shop (28%), in working material (22%) or in livestock (11%). These investments are more durable and hence still observable in 2010 (table 4, columns [4] and [8]). On the contrary, the spike in food and nonfood spending was short-lived and vanished when ex-combatants ran out of demobilization money (table 4, columns (2) and (6)). Fourth, our interpretation is consistent with the evolution of total consumption percentile 22
for recognized violent conflicts. We transform the number of battle-related deaths using the inverse hyperbolic sine transformation. Data for both violent conflict variables are derived from UCDP/PRIO (see Pettersson & Wallensteen, 2015).

> 12 A Hausman test does not find evidence of systematic differences between the fixed effects and random effects model and hence the random effects model is statistically most appropriate. For reasons discussed in our methodology section, we still have more confidence in the results obtained in the more conservative fixed effects model.

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# **Box 2: Examples of how organizations and structures involved in community responses have been categorized**

The DFID‐funded program Strengthening the HIV/AIDS Response in Zambia, which has supported civil society participation in the national response, categorizes organizations as (1) grassroots and community organizations; (2) national and international non‐governmental organizations (NGOs), faith‐based organizations (FBOs), and other more formalized civil society structures; (3) civil society networks, umbrella bodies, and associations; and (4) civil society platforms and forums. Around 75 percent of civil society organizations fall into the first two categories; over 22 percent have a religious affiliation (Collins, Simwanza and Mumbi 2009).

An analysis of the National HIV/AIDS Database in South Africa found that 162 of the 1,582 entries identified themselves as FBOs, which were categorised as (1) networks or coalitions (associations of churches or FBOs that facilitate coordination and communication among members); (2) national or provincial structures (for example, dioceses); (3) social services agencies (welfare and charitable wings); (4) faith‐based NGOs (NGOs with a religious orientation); (5) congregations; and (6) projects (initiatives such as children’s homes) (Birdsall 2005).

A large number of different typologies and nomenclature have been used in the literature to describe _types_ of civil society organizations and initiatives. The _type_ of entity often informs the basis for research and evaluation processes – and has resulted in a varied body of information that makes cross‐analysis and comparison difficult. Differing classifications of type thus remains a challenge to any systematic analysis of community response which seeks to be inclusive of both formal organizational response (such as that by non‐governmental organizations who are named in national databases), as well as informal community _initiatives_ which might not have any organizational (or physical) structure and be significantly more fluid in nature.

A first useful distinction
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# Previous Real Product Estimates for Manufacturing

The present study is not thc' first to use the industry of origin approach to derive PPPs and measure real output levels in manufacturing. Table 1.3 lists 14 other international comparisons of levels of output or productivity in manufacturing which have appeared over the past four decades. They all use information derived from production censuses, and they are all restricted to two or three countries. This type of study was given its initial impetus by Rostas (1948), and the studies of Maddison (1Q52), Galenson (1955), Frankel (1957) and Yukizawa (1978) more or less replicated his method for measuring real output, which concentrates on comparisons of "physical" gross output of different countries (with or without coverage adjustments for non-sampled products within an industry). Paige and Bombach called this the "single indicator" approach, and they themselves devoted considerable space to discussing an alternative "double indicator" method, which would involve separate estimation of inputs across countries as well as gross output. This approach, if fully implemented. involved a double deflazion procedure, i.e. separate calculation of PPPs for output and inputs, to arsive at a true comparison of value added in real terms. In fact, Paige and Bombach did not achieve their goal of double deflation for manufacturing (though they did it for agriculture and part of transport), and were able only to make a very partial input PPP for fuel use. Subsequent researchers have also failed to achieve the goal of double deflation even for countries which provide a substantial amount of detailed information on inputs, because the structure of inputs is so heterogeneous.

The most ambitious studies in terms of sample size were those by Paige and Bombach (1959), Kudrov (1969), West (1971), Smith, Hitchens and Davies (1982), Smith (1985)1, and Davies and Caves (1987). Table 1.3 shows their coverage so far as we could determine
learning algorithms.

These findings are also policy relevant, particularly regarding providing more accurate inputs into designing cost-effective poverty targeting and budget transfers. For example, recent World Bank guidelines strongly encourage the adoption of EBP models where census microdata is available (Corral et al. 2022). These results offer more nuanced insights into the performance of EBP and other methods when applied to geospatial data, which presents policy makers with different modeling options for the best decision-making outcomes. If additional information is available for within-community targeting (Elbers et al., 2007), more accurate poverty estimates would translate into significant poverty reduction. Furthermore, since small area estimation methods are generally less costly than additional data collection, they offer appealing policy choices in a poorer country context.<sup>4</sup> The rest of the paper is organized as follows. In section 2, we provide a brief overview of the data, the estimation methods, and the method utilized to validate estimates for accuracy and uncertainty. Then, in section 3, we review detailed results of simulations, including distinguishing between in and out of sample performance, performance in poorer or richer areas, using alternative metrics, and different specifications. Section 4 concludes.

# **2 Methods**

This paper evaluates four different methods for generating predictions of district-level poverty rates: linear empirical best predictor (EBP) models (Battese et al., 1988; Jiang and Lahiri, 2006; Molina and Rao, 2010); cubist regression models (Quinlan et al., 1992; Wang and Witten, 1997), which we refer to as Cubist throughout the results section; extreme gradient boosting (Chen and Guestrin, 2016), more commonly known as XGBoost; and boosted regression forests, or BRF (Friedberg et al., 2020; Tibshirani et al., 2018). Table 2 summarizes key features of these methods, which we describe in greater detail below. Importantly, we evaluate these methods in the context of developing countries, where census data is often
# 3.4 Gender preference in job postings

Hiring practices based on gender are not uncommon in Pakistan, and gender preferences of employers can be explicitly signaled in job postings in Rozee.pk. Every job posting has the option to indicate gender preference, i.e., male or female. While 80% of jobs do not indicate any gender preference, 14% and 6% of job postings indicate that they prefer, respectively, men and women (Figure 19). This indication of gender preference is effective in attracting preferredgender applicants, as shown in Figure A1.3. The proportion of male applicants is 97% for malepreferred job vacancies, while the proportion of female applicants is 51% for female-preferred vacancies.

**Figure 19. Percentage of genderearmarked vacancies**

_Source_ : Authors’ calculation using the data from Rozee.pk.

## **_Q. What type of jobs prefer male and female workers? Are there salary differences associated with gender preference?_**

**_A. Jobs that require longer professional experience and are at higher occupation levels are more likely to prefer males. Job postings that indicate gender preference, irrespective of which gender is preferred, advertise lower salaries than the job postings that do not indicate a preference._**

We examine what types of job postings indicate gender preferences. We also examine how gender preferences are associated with advertised salaries (Figure 20). Key findings are summarized below.

- _Finding 1: Female workers are more likely to be preferred in jobs requiring higher education._ 7% of the jobs requiring bachelor and graduate-level degrees prefer female workers, whereas only 3% of the jobs requiring the secondary and less education level prefer females.

- _Finding 2: Jobs that require longer years of experience and managerial jobs tend to prefer men._ 30% of the jobs that require 10 or more years of experience prefer males, whereas 13% of the jobs requiring zero to two years of experience do so. 27% of manager jobs
its Debt Sustainability Framework for Market Access Countries.<sup>4</sup> Fourth, they are used – together with many other indicators – to inform environmental, social and governance (ESG) investment strategies and corporate social responsibility policies by firms in the private sector.<sup>5</sup> The rest of this paper is organized as follows. Section 2 defines the six governance dimensions, provides an overview of the 35 WGI data sources, and describes how the individual variables from each

> 2 The top five most cited WGI methodology papers (with hyperlinks to their Google Scholar listings) are Kaufmann, <u>Kraay and Mastruzzi (2011), Kaufmann, Kraay and Mastruzzi (2009), Kaufmann, Kraay and Mastruzzi (2004), Kaufmann, Kraay and Zoido-Lobaton (1999b) and Kaufmann, Kraay and Zoido-Lobaton (1999a).</u>

> 3 For information on MCC eligibility criteria, see htps://www.mcc.gov/who-we-select/scorecards/.

> 4 For example, for information on the Fitch sovereign rating methodology, click here and for Moody’s, click here. See also IMF (2021)

> 5 For example, the WGI are included in the World Bank’s Sovereign ESG Data Portal, they are used in the Morgan <u>Stanley Capital Internatonal (MSCI) ESG Government Ratngs Methodology</u> and the FTSE-Russell Sustainable <u>Sovereign Risk Methodology, and they are used by the Disney Corporation to inform its Permited Sourcing Countries policy.</u>

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calibrated) using data from developing countries. The modern approach exploits microdata as much as possible. Fortunately, micro-data have become increasingly available across the developing world and it is now easy to find firm surveys or household surveys for many countries. However, there are limitations. Studies such as Dix-Carneiro’s rely on individual records of workers who are tracked along their working life. This type of administrative data is not, unfortunately, so widespread. Instead, the method in Artuc, Chaudhuri and McLaren (2010) requires panel data to compute flows of workers between sectors and interindustry wage differentials. While this type of data is generally more readily available, sufficiently large panels to estimate the model with a decent degree of precision are probably harder to come by than one would a priori think. In fact, in Artuc, Lederman and Porto (2011), while working on creating a mapping of labor mobility costs in the developing world, we have encountered enormous difficulties in finding the needed panel data. To overcome this limitation, we develop a modified version of the labor mobility cost model that could be estimated with repeated cross-section (instead of panels). One of the goals of the project is to facilitate applied research in developing countries on the topic of adjustment costs to trade and, if possible, to green growth.

For some questions such as for example the role of capital adjustment costs or firm’s costs of adjusting employment, firm-level data is also needed. The rich data used by Bloom (2009) or Cooper and Haltiwanger (2006) is seldom available in developing countries. Many middle-income countries do have rich panels of firms (Argentina, Chile, Mexico, are examples). The investment climate data, which covers most countries of interest, is perhaps not suitable for this type of analysis.

# **6. Other Relevant Adjustments**

There is a large literature on responses to trade reforms/shocks. Clearly, firms adjust labor and capital and also output and prices. Harrigan and Barrows (2009
variant event (like a shock pulling or pushing someone into migration), or change in individual characteristic (like coming of age, achieving higher levels of education or winning a lottery), which causes one to both migrate and take on the special role in the insurance network.

# **9. Robustness**

We conducted an array of robustness checks to verify our findings.<sup>29</sup> First, we find that the results are robust to an alternative migrant definition where also households that moved to a nearby village are defined as non-migrants.

Second, the results are not driven by the configuration of the data. The shock data were initially defined at individual level while our outcome variable is measured at household level. Conducting the empirical analysis at individual level does not affect our main findings.

Third, defining household consumption per adult equivalent instead of per household member yields close to identical results in all specifications.

Fourth, changing the way we isolate the shocks that occurred before the households split does not change our results either.

Fifth, we also checked whether the potential endogeneity of some our control variables is driving our results. Instrumenting the lagged consumption variable does not affect the shock coefficient. In addition, when the 2010 household level control variables are omitted one-by-one, the estimated shock coefficients remain stable across all specifications.

# **10. Conclusions**

Starting from the household rosters of a representative household survey conducted nearly two decades ago in Kagera, we find that over half of the original household members had moved internally, while very few moved internationally. We document how this powerful current of migration, an integral part of development, interacts

> 29 The results of these robustness checks are available upon request.

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