rafmacalaba/data-use-mentions-extended huggingface dataset:
bilaterally— involving engagements among multiple organized, armed factions, occasionally leading to collateral civilian harm— or unilaterally, wherein a group targets civilians deliberately.” Furthermore, for the most precise depiction of areas severely affected by conflict, fatalities stemming from protests, riots, and strategic development (as per ACLED data) have been excluded, maintaining consistency with the WBG Classification of Fragility and Conflict Situation’s (FCS) objectives and the scope of this study. Our analysis focuses on conflict records categorized as ‘Battles’, ‘Explosions/Remote violence’, and ‘Violence against civilians’. These types of conflicts are selected due to their violent nature.

# _Settlement data_

To determine the urbanization level, we use the Global Human Settlement Layer (GHSL) which combines gridded population data estimated by CIESIN GPW v4.11 GHS-POP R2023 and built-up surface information from Landsat and Sentinel-2 data GHS-BUILT-S R2023 (Schiavina et al., 2023).<sup>5</sup> The settlement data are available at the 1km resolution. We consider the data for the year 2020, which is the closest available to the time period of interest for both countries. In case of Nigeria, we defined ‘urban’ areas as cells defined as high-density cluster,<sup>6</sup> ‘suburban’ as moderate-density cluster,<sup>7</sup> ‘rural’ as rural and low-density clusters<sup>8</sup> and ‘Uninhabited’ as very low density rural and water covered areas (Figure 1).<sup>9</sup>

> 5 - In Google Earth Engine, this Image collection is accessible through <u>https://developers.google.com/earth engine/datasets/catalog/JRC_GHSL_P2023A_GHS_SMOD.</u>

> 6 The ‘urban’ category includes the classes 30: “Urban Centre grid cell”, 23: “Dense Urban Cluster grid cell”.

> 7 The ‘suburban’ category includes the classes 22:
combination of Poisson regression models along with IV estimation is proposed by Tenreyro (2009) within the GMM framework. This is relevant only for the size issues but it is nevertheless beyond the scope of this paper.

# 5 Results

After listing all the potential problems with the data, estimation methods and identification issues, we finally turn to the estimation of the three main equations listed above (2, 3 and 5) which correspond to analysis of the impact of diasporas on the size, skill composition and concentration of migration flows, respectively.

Table 1 reports the results for five different estimation techniques for the estimation of the impact of diaspora on migration flows. The techniques used are OLS (using lows as the dependent variable), Heckman two stage method with and without an instrument for the selection, Maximum likelihood Poisson and IV regression (on the flows as well) using the two above mentioned instruments. The results illustrate the strong robustness of the estimation of the key elasticity parameter which ranges between 0.62 and 0.76. This means that a 1% increase in the size of the migrant network present in the destination country in 1990 tends to increase the subsequent migrant flow from a given origin country over the next ten years by around 0.7%. This result is in line with some of the previous results in the literature using the US data. For instance, focusing only 13
# **Table 4: Ebola-Related Disruption Index**

## **Standard deviations in brackets, standard errors in parentheses**

|||**Mean**|**Between district**<br>**variation**<br>**(share of total)**|**Unconditional**<br>**coeff on ELA**|**Conditional**<br>**coeff on ELA**|**Conditional coeff on**<br>**ELA with distance**<br>**interaction**|**Conditional coeff on ELA**<br>**with distance interaction**<br>**and model selection**|
|---|---|---|---|---|---|---|---|
|**_A. Health Polic_**|**_y Components_**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**PHU Ever Closed**|.140|254|.001|-.000|.046|.076|
|||[.348]|.|(.049)|(.050)|(.075)|(.075)|
||**PHU Ever Disrupted**|.320|212|.000|-.006|-.077|-.069|
|||[.468]|.|(.065)|(.065)|(.098)|(.096)|
||**PHU Functioning Score**|89.6|485|-1.
digits make it possible to uniquely identify the firm.

We use the detailed classification of occupations to identify those who switch jobs. The Brazilian Classification of Occupations changed in 2002 (CBO-2) and has been reported consistently since 2003. Although the RAIS data are available for earlier years, we restrict the analysis to the post-2003 period to ensure that this important variable is defined in a consistent way throughout the sample period. There are 2637 occupation codes at the 6-digit level during this period. We use the information on the establishment’s location (municipality) and industry, as well as worker-level data on gender, age, education, and December wage. While the RAIS data cover segments of the public sector, we restrict the analysis to the private sector. We focus on workers aged 16 to 64 years old.

Using RAIS, we define a _labor market_ as a pair of a region and a sector. For sectors, we aggregate industries up to three broad sectors: agriculture, manufacturing, and services.<sup>10</sup> For regions, we use the “microregion” concept of the Brazilian Statistical Agency (IBGE) to define regional boundaries as in Dix-Carneiro and Kovak (2017). This definition groups economically integrated contiguous municipalities with similar geographic and productive attributes. We consider a set of 558 consistently

> 10We combine agricultural and natural resources industries into the agriculture sector.

6
Figure 5. Outlines of our automated urban EAs in different locations over Hargeisa and Mogadishu in Somalia.

# **3.2.2 Comparison with PESS manual urban EAs**

We compared our automated urban EAs to the manually digitized PESS 2014 urban EAs in terms of the population and area covered (Figure 6). The total number of PESS EAs is 1,380 and the total automated EAs is 1,775. If we consider the maximum urban population as 2,000 people per an EA and the preferred target is less 1,000 people per an EA, the automated EAs might have the same performance or even better in some cases compared to PESS urban EAs. While we cannot directly present the PESS urban EA data here, but we have noticed some issues when we have reviewed and generated the histograms. For the most part, PESS urban EAs follow roads well but we have found some examples where this is not the case as they cut the houses, likely due to changes in building layouts since the construction of the PESS EA data set. The minimum population size per PESS urban EA in Mogadishu, which was obtained from high gridded population data sets, ranges from zero to 17,000 and the minimum area ranges 5 m<sup>2</sup> to around 7,000,000 m<sup>2</sup> . The zero values in population size and small area indicate the presence of gaps in the data sets. The large population and area size for some of the EAs indicate that the EAs may not be practical for a surveyor in the urban context as it may either cover a high-populated area or cover a large space. In the automated process, these constraints can be tuned based on user requirements. The minimum population size for the automated EAs was 150 and the 14
in schools different than those surveyed in the baseline, so the team felt it unnecessary to survey non-randomly selected schools for which there was no baseline information.. In addition, there were some difficulties with non-response in some of the schools, so we have complete information on 508 schools and partial information on seven schools.

> 27 In the original sample selection, schools with multiple parallel classes were excluded (so all sample schools started out with one grade 4 class), and thus we did not assign student IDs; but in the endline survey, it was discovered that several schools had more than one grade six class, due to schools merging.  We only have data on the number of

11
On the contrary, the brain drain is strongly underestimated in countries such as the Gambia, Madagascar, Gabon, Brunei, Papua New Guinea. These countries send a relatively small number of emigrants to non-US areas. They are usually not identi...ed in the OECD migration statistics. Consequently, their emigration rates are largely underestimated.

The last ...gure (...g 3.C) represents the percentage of emigrants on the basis of which educational attainment is extrapolated (let us call this proportion a ”reliability rate”). On average, our method is based on 92.7 percent of observations. A method relying on US educational attainment is based on 44.1 percent of observations. However, the reliability rate is heterogenous bewteen countries. Figure 3.C represents the number of reliable country estimates as a function of the reliability criterion required by any user of the database. The function is obviously decreasing: the number of reliable observations decreases with the degree of reliability imposed by the user. For example, setting the reliability rate at 80 percent, our database provides 175 reliable country observations in 2000 (160 reliable observations in 2000). Transposing the US immigration structure to the total stock of immigrants, we would only provide 23 reliable observations in 2000 (22 countries in 1990) at the same reliability rate. By extending the number of observations and their degree of reliability, our method provides useful material to examine the changes in the international distribution of migration rates, to test for the (push and pull) determinants of migration per skill group or to evaluate the macroeconomic consequences of migration on source and destination countries.

# 4 Key indicators

Let us summarize the results by a few ...gures.

Immigration structure in OECD areas. The intensity of the brain drain clearly depends on the structure of immigration in OECD countries. Figure 4 describes that structure. In ...gure 4.A, it clearly appears that the US is the major destination country for skilled workers
jobs had rebounded, producing a predicted cumulative 5.9 percent rate of loss relative to December 2019. Permanent firm closures were 43 CMMHH (2020). Just prior to the pandemic, 20.6 percent of employed individuals of working age in Jordan were employed in PFPS jobs, representing 42 percent of private sector wage workers. In addition, 37.6 percent of employed individuals worked for a public or government entity;2.2 percent worked irregularly but in formal work arrangements. Many (26.1 percent) worked informally, whether in a regular or irregular job, and 12.9 percent were self-employed. Recall that the survey excludes “other” nationalities. In 2016, this percentage was 22.5 (LMPS 2016), and slightly more when all nationalities are included.

The numbers reported in this paragraph differ from those reported in Section 3.2 above, because these do not include “other” nationalities, for the purposes of comparability with the CMMHH.

29
Executive Board (EB) for consideration through December 2010 is obtained from the CDM/JI Pipeline Analysis and Database of the United Nations Environment Programme (UNEP) Risoe Center. The dataset includes information about individual CDM project, including the project name, type, registration or validation status, approved

> 9 This solves the missing price-information problem, but the associated parameter on the dummies likely pick up other attributes of time and place. This is a mixed blessing, since the net effect is to round up otherwise unobserved effects; however it does confound the interpretation of the estimated fixed effects.

9
that technology dissemination and support from MNC affiliates enables local suppliers to introduce new and more complex products. Using the product sophistication index by Hausman, Wang, and Rodrik (2007), Eck and Huber (2016) obtain similar evidence for India. For Romania, Bajgar and Javorcik (2019) combine firm-level data on output and inputs with customs data containing information on annual exports at the firm-product-destination level, allowing them to calculate indicators of within-product quality improvements. Their estimations reveal that both forward and backward linkages of MNC affiliates generate significant product quality improvements among domestic firms.

# **4. Backward Linkages and Productivity Spillovers: The Scope for Policy Making**

10 Other studies that report findings of such wider dissemination effects include Kee (2015) and Jordaan (2017).
by 5 percentage points. At the same time, the share of manufacturing dropped dramatically by 13 percentage points.<sup>5</sup> Transition economies differ substantially as regards the sectoral structure of employment. In CEE the economy is dominated in equal measure by manufacturing and market services (jointly about 60 percent of all jobs). Public services (health, education, administration, etc.) provide additional 25 percent of jobs. The share of agriculture is relatively small (15 percent). Outliers in this group are Bulgaria and Romania, where the share of agriculture is well above the average for the group. If the group is limited to current EU member transition economies, then the share of agriculture is correspondingly lower, and that of manufacturing is higher.

In Belarus, Russia and Ukraine relatively more jobs are provided by agriculture and public services at the cost of significantly smaller market services sector. Belarus, Russia and Ukraine apparently move towards similar sectoral employment structure as CEE, however the pace of structural changes is slower and thus the economic structure seems “less advanced”.

In the other CIS countries the employment structure looks dramatically different. It is heavily dominated by agriculture, which accounts for close to half of total employment. The manufacturing sector tends to be small, providing on average less than 15 percent of all jobs. The market services sector is also substantially smaller than in other ECA sub-regions, accounting for less than 20 percent of all jobs. Only the share of public services (which is the least varying component of the employment structure) is comparable to that in other ECA transition economies.

> 4   This does not necessarily imply that labor has moved directly from agriculture to services.  An alternative scenario is also possible that agricultural labor moved to manufacturing and manufacturing labor moved to services. Data which would allow one to discriminate between those two scenarios is absent.

> 5  These figures come from an employer based survey, and thus probably underestimate the growth in the employment share of the services and agriculture sectors
preferential treatment provisions have strong and significant effect on protectionism in trade data, as measured by the difference between internal and international trade. The estimates suggest that for importers whose laws have preferential treatment for domestic bidders in government procurement, domestic purchases are around 16 times bigger on average than international ones, whereas for importers without a preferential treatment clause domestic purchases are 7 times bigger than international ones. Lacking a national treatment provision or having a domestic sourcing provision increases the border effect, but not significantly so.

17
## **3.4 Irrigation data & crop data**

We use data on irrigation statistics from the ICRISAT District Level Data to examine farmers’ responses to changes in the groundwater depth. The data provide information on areas irrigated by source: canals, tanks, tube wells, and other wells. We also access data on crop-wise irrigated areas for water-intensive crops.<sup>8</sup>

## **3.5 Soil moisture**

We use data on soil moisture from the European Space Agency’s Climate Change Initiative (Dorigo et al., 2017; Gruber et al., 2019).<sup>9</sup> This provides a gridded reanalysis product that uses satellite observation to estimate soil moisture (measured as m<sup>3</sup> of water per m<sup>3</sup> of soil). The ESA CCI SM algorithm combines active scatterometer data and the passive radiometer data to estimate the moisture content of the first few centimeters of the top soil. Environmental studies have shown soil moisture to be a good indicator of irrigation water use globally (Zohaib and Choi, 2020) and drought conditions in India (Modanesi et al., 2019). The data is provided on a 0.25<sup>_◦_</sup> _×_ 0.25<sup>_◦_</sup> grid. We aggregated to districts by averaging over all gridpoints within a district’s boundaries.

# **4 Empirical Approach**

In our primary empirical approach we take advantage of both the staggered roll-out of MNREGA and the heterogeneity in implementation to estimate a difference-in-differences model with a true control. Our approach is similar to that taken in García (2022) and relies on the fact that MNREGA implementation in non-Star states was limited compared to Star states to estimate the impact of MNREGA by comparing outcomes in Star and non-Star states after implementation.<sup>10</sup> This
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey relative the median wage that it barely affects the wage distribution, since most of the population makes more than the minimum wage. Brazil is an intermediary case in which we observe a large hike in the distribution of wages around the minimum wage, which demonstrates its importance in determining the wage distribution.

With respect to inequality, most studies in the previous decade find a negative relationship between increases in the minimum wage and inequality. For the period between 1981 and 1999, Menezes-Filho and Rodrigues (2009) use the semiparametric approach of DiNardo et al. (1996) and find that decreases in the real minimum wage during the period were related to increases in inequality. Covering a period of real increases in the minimum wage, Firpo and Reis (2007) find that they were associated with lower wage inequality between 2001-2005. Neumark et al. (2006) arrive at a different conclusion for the period between 1996 and 2001. They use a measure of bindingness to assess the impact of the minimum wage on the distribution of family income. The minimum wage is related to small increases in wages for the bottom of the distribution only in the short-run, that is, when they consider the contemporaneous share of earners below the minimum wage. However, when they consider the effects of this share in previous quarters, i.e., long-run effects, it becomes negative.

As pointed out by Saltiel and Urz ́ua (2018), the effects of the minimum wage may depend on the macroeconomic situation, with increases being beneficial only when a growing economy can support them. Ferreira et al. (2017) provide evidence suggesting this mechanism. They show that raises in the minimum wage increased inequality in the period between 1995 and 2002, marked by smaller growth and higher unemployment, while they find the opposite effect between 2003 and 2012. Using data from the Brazilian formal sector, Engbom and Moser (2018) and Haanwinckel (2018)<sup>
# TABLE A4: ITT effects on attrition

||(1)<br>Child|(2)<br>Household|(3)<br>Mother|(4)<br>Father|
|---|---|---|---|---|
|Panel A|||||
|ITT|0.022<sup>_∗_</sup>|0.015|0.010|0.016|
||(0.013)|(0.012)|(0.014)|(0.015)|
|Observations|2803|2803|2803|2803|
|Mean control|0.089|0.071|0.115|0.199|
|Panel B|||||
|Nutrition & Stimulation|0.018|0.012|0.000|0.006|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Nutrition|0.039<sup>_∗∗_</sup>|0.025<sup>_∗_</sup>|0.029|0.035<sup>_∗_</sup>|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Stimulation|0.017|0.009|0.015|0.025|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Observations|2803|2803|2803|2803|
|Mean control|0.089|0.071|0.115|0.199|
|**P-values Statistical Signifcance Test**|||||
|Nutrition & Stimulation = Nutrition
tables overlap, but they seem close and consistent in their method of construction. Miguel et al. (2004, 730–31, and table C1) seem to use primarily the union of the X, I, O, and F data at intensities 1–3 (corresponding to the concepts in row 2 of table 1), but they also present some results based on intensity 3 (row 1 of table 1). In a study of the onset of civil war, Gleditsch (2007, 300–01) seems to do the same but considers only states in which conflict has erupted to have had an onset of civil war, seemingly excluding our F coding. Buhaug and Gleditsch’s (2008, 223) study of civil war onset also seems to use the union of X, I, and O data for intensities 1–3. Earlier quantitative studies using different data also code a civil war by a battle-death count, usually of at least 1,000 per year (Licklider 1995), or more than 1,000 battle-related deaths in any given year (Wallensteen and Sollenberg 1999, 595). These rules are close to those used for row 1 in table 1. Thus, the most recent studies seem to favor the lower threshold of row 2, but the earlier studies favored a higher threshold, similar to that of row 1. As argued below, both approaches have serious shortcomings.

Deaths related to a conflict are difficult to estimate, a point well made by Cramer (2007, table 1). Furthermore, “battle” is a strange modifier of deaths when most of the violence that is considered part of civil wars in these data sets does not involve set-piece battles, making it difficult to know what a battlefield death is. This modifier is especially problematic because these episodes often involve civilian deaths. These deaths hardly ever seem to occur on a battlefield, and certainly not in a case like Sierra Leone, where terrorizing civilians in their villages and homes was a prevalent
discounting method, includes direct private costs assuming that they are the only cost of education. Using this method, the social rate of return to investment in education is estimated when the social costs of education are included in the estimation.

This paper uses the discounting method to estimate the private and social rate of return to investment in college education. According to the discounting method, the private and social rate of return to investment in college is estimated by finding the rate of discount ( _r_ ) that equalizes a stream of discounted benefits to the costs at a given point in time. In the case of university education lasting five years, for example, the formula for the social rate of return is: where ( _Wu-Ws_ ) is the earnings differential between a university graduate (subscript _u_ ) and a secondary school graduate (subscript _s_ ). _Cu_ represents the direct costs of university education (tuition, fees, books), and _Ws_ denotes the student's foregone earnings or indirect costs. To be even more precise about the private costs and the social costs, one can define them as follows: Private costs include foregone earnings while studying and tuition, fees, books and incidentals (referred to as direct private costs). Social costs include teacher and other personnel salaries, rentals for buildings (referred to as direct social costs) plus foregone taxes (referred to as indirect social costs). Ideally, one would use the total private costs in computing the private returns and the total social costs in computing the social returns to education. A similar calculation can be made for the other levels of education.

One limitation to this methodology is that the discounting method requires specific information that is not easily accessible or available in surveys or censuses. For example, earnings surveys do 7