rafmacalaba/data-use-mentions-extended huggingface dataset:
## **Step 3. Tabulate summary statistics of individuals and dummy variables by nationality and state.**

The share and number of foreign workers are computed by main nationality and state, and by month from March 2017 through February 2018. We also take an annual average distribution.

## **Step 4. Make adjustments to the remittance statistics and compare the results obtained from remittance data analysis with the official numbers reported by MOHA** .

Based on the remittance analysis, we make additional adjustments to estimate the total number of foreign workers in Malaysia. As earlier in World Bank (2020), we recognize that not all foreign workers use MSPs to remit their earnings back home, and thus assume that the remittance data represent 77 percent of the foreign worker population, based on the World Bank Greenback 2.0 surveys of foreign workers in Johor, which finds that about 77 percent of foreign workers use MSPs to transfer money (World Bank, 2017). Then, the final estimated foreign worker population is compared with MOHA’s figures on work permit holders, using annual averages, to find the estimated number of irregular workers between March 2017 and February 2018.

## **Step 5. Robustness check to provide a lower-bound estimate of the number of foreign workers.**

As a robustness check of our initial estimations, we took the share of foreign workers by nationality in each state from the MOHA data, and then applied these shares to the total number of foreign workers at the state level we obtained from the remittance data (the figure prior to adjusting using the 77 percent remitter proportion in Step 4). After recalibrating the monthly numbers of foreign worker population by nationality, we calculated the differences in the number of foreign workers in each state between our results and the MOHA data. When the differences are negative (that is, the number of foreign workers in the remittance data set is smaller than in the MOHA data), we apply that the differences are zero. In other
not find evidence of border regions receiving more FDI from Germany (Figure A.8 of the Appendix). As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database. Since Romania acceded the EU in 2007, we would not expect to observe a significant effect in this case. Results in Figure A.9 of the Appendix show indeed no effect of our measure of pre-determined German specialization on the German acquisition of Romanian firms. The estimates show only a negligible increase in the probability of being acquired by a German shareholder after 2007, which is also not statistically significance. Hence, these estimates argue against the possibility of Germany investing abroad more 12
# **1. Cleaning of Raw Datasets**

The cross-country raw dataset contain six variables -country of origin, firm codes, country of destination, product, value, and year- that have been reformatted uniformly and subjected to the cleaning procedures explained below.<sup>10</sup>

- **a) Country of origin:** For each observation in the cross-country raw dataset, we define the country of origin as the country from which we received raw data from. It should be noted that Depending on the type of trade regime (general versus special) used by the country of origin, exports of a country might include goods originating from any geographical territory of that country or from any territory except free zones/customs. The implication of the trading regime in our cross-country raw database is discussed in more detail in the destination section c) below.

- **b) Firm codes:** Firm codes are the numbers that uniquely identify firms across time within each country. We received this information in different formats: a) the actual names of the firms, b) their tax identification number or c) artificial unique codes randomly created by our data providers. One peculiarity of the datasets for Albania, Burkina Faso, Cambodia, Cameroon, Mexico, Uganda, and Yemen is that the firm coding systems changed during the sample period. Hence, a specific firm is represented by different codes before and after 2007 in these countries’ datasets (2008 in Yemen’s dataset). As a result, it is not possible to calculate exporter dynamic measures such as firm entry and exit rates in 2007 and survival rates in 2006 and 2007 for those countries (firm entry and exit rates in 2008 and survival rates in 2007 and 2008 in Yemen).

> 10 The cross-country raw dataset at country-firm-product-destination-year level includes also quantities exported that will be used to calculate some of the measures in the Database.

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.029|18,718|
||_(2.314)_|_(2.332)_|_(2.317)_||||
|Present Bias|4.208|4.207|4.221|0.001|-0.013|18,718|
||_(0.987)_|_(1.015)_|_(0.999)_||||
|S/E Skills Index|0.0529|0.072|0.083|-0.019|0.031|18,718|
||_(0.997)_|_(1.005)_|_(1.010)_||||
|GPAs|3.324|3.304|3.311|0.019|0.012|33,454|
||_(1.150)_|_(1.141)_|_(1.157)_||||

18 Normalized differences are calculated as Δx = (xt - xc) / √(σt2 + σc2), where xt and xc is the sample mean of the covariate for the treatment and control group, respectively. σ2 denotes the respective variance. The normalized difference between our first treatment group (“student self-learning”) and our control group is -0.13, that between our second treatment group (“teacher delivery”) and our control group is -0.19.

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most prevalent reasons were not being called back for the registration (13 percent), not enough information about the course (11 percent), studying (6 percent), too expensive/couldn’t afford it (6 percent), distance to the training center (4 percent), difficulty in finding accommodation (4 percent), and not interested in the training subjects (4 percent).

The lesson for future attempts to provide vocational training as an alternative to irregular migration is that it may not be enough to make such training free – potential migrants may need help covering transportation and subsistence costs, as well as the opportunity cost of their time while participating in such a program.

# **4. Data and estimation methods**

## **4.1 Baseline data and estimating short-term effects**

Our baseline survey, in April-May 2019, contained questions about the knowledge, beliefs, and intentions individuals had about backway migration and migration to Senegal. Our knowledge questions asked factual statements (e.g. how much does it typically cost to migrate from The Gambia to Italy through the backway; True or False: Some backway migrants are forced to work for others without being paid; True or False: Gambians do not need a passport to travel to Senegal), and were used to construct pre-specified knowledge measures defined as the number of correct responses (see Appendix B for the full list of questions).

Our baseline beliefs and intentions questions then attempted to ask for subjective probabilities (Delavande et al, 2010). We attempted to make it easier for individuals with no schooling to answer percent chance questions on a 0 to 100 scale by using a dynamic slider, as suggested by Maffioli and Mohanan (2018), and went through demonstration questions asking about the chances of rainfall and going to market to illustrate the use. Beliefs about the riskiness of migration were then assessed by asking questions such as “Out of every 100 Gambians who try the backway to Europe, use the slide (
hypothetical unleveraged financial structure. (They are then called _unleveraged_ or _unlevered betas._ ) All the betas in Table 2 are unleveraged. To account for the extra risk embedded in companies’ leveraged capital structure (making them _leveraged_ or _levered betas_ ), they must be releveraged using the formula (3): where _ßL_ is the leveraged beta, _ßU_ is the unleveraged beta, _D_ is outstanding long-term debt, _E_ is total equity, and _T_ is the corporate income tax rate. In this analysis, unleveraged betas were transformed into leveraged betas using a capital structure typical for each sector estimated as the average leverage of our sample companies which are summarized in Table 3.

**Table 3: Average leverage by sector**

||**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|
|**Electricity**|87%|87%|87%|86%|97%|
|**Water & Sanitation**|103%|112%|83%|79%|145%|
|**Railroad**|43%|45%|46%|50%|76%|
|**Ports**|104%|93%|106%|122%|90%|

Source: own calculations based on sample data The **country risk premium** (Crp) is the extra return that investors require to invest in stocks of companies in a country deemed riskier than a less risky country used as benchmark (often the United States). The premium reflects the potential volatility of investments in a given country due to defaults associated with political or other events. Country risk premiums are usually estimated as the average spread over U.S. Treasury bonds (assumed to be riskfree) of U
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis the dataset based on a likely explanatory variable, which in this case is country income level.

# 4.2 Controlling for Heterogeneity between Income Groups

The model specification presented below attempts to differentiate the impact of resource dependence on higher- and lower-income countries. The model has been adjusted to include a dummy variable equal to 1 for low- and lower-middle-income countries and to 0 for high- and upper-middle-income countries. Within this framework, the coefficient associated with the interaction variable will measure the association between resource dependence, GDP per capita and the Gini index among lower-income countries, while the coefficient associated with the non-interaction variables permits to isolate the average relationship for higher-income countries. Table 8 summarizes the results obtained from the recursive model.

Table 8: Estimated Results from the Recursive Model Specification (income-group heterogeneity)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.082***|-0.006|-0.190***|0.124|
|Lower income|dropped|dropped|4.386*|47.628***|
|Lower income* Nonrenewable resource dependence|0.068|-0.370***|0.115**|-0.720***|
|High-low education ratio|-0.622***|-|-0.179|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (
42% of these wells were recorded to contain As concentrations >10 μg/L (WHO drinking water standard). Our analysis applies interpolated As concentrations (Figure 2a) for developing groundwater risk maps at the national scale in Bangladesh. Arsenic concentrations at geographic points ( _n_ =3,207) were interpolated using the Inverse Distance Weighting (IDW) algorithm and resampled at 2.5-km × 2.5-km grid resolution using the ‘raster’ package in the ‘R’ programming language (R Core Team, 2017).

# **2.2 Groundwater salinity: Electrical conductivity**

Elevated groundwater salinity is common at shallow depth (<150 mbgl) in coastal aquifers of southern Bangladesh and is generally defined by total dissolved solids (TDS), or Electrical Conductivity (EC) or chemical constituent such as chloride (Cl) (Zahid et al., 2013). There is no national-scale, frequent (e.g. monthly) monitoring of groundwater salinity in Bangladesh. Over the years, a few studies (see Supplementary Texts for further details) have generated some contour maps of groundwater EC at the national scale but there is little detail on the data sets used for mapping. Here, we have generated a groundwater EC map at the national scale using data primarily from two sources. First, we collated groundwater EC data from 461 newlyinstalled monitoring boreholes that were installed recently under a regional-scale hydrogeological study conducted in 19 coastal districts by the Bangladesh Water Development Board (BWDB) (BWDB, 2013). Second, we have digitized and georeferenced the contoured map of groundwater EC (Rahman and Ravenscroft, 2003) and extracted point data of EC at 102 locations throughout the country, predominantly in the northern part where there is limited groundwater EC measurement as groundwater is generally fresh. Finally, we interpolated the point data ( _n_ =563) at the national-scale using the IDW algorithm in ArcGIS environment. The interpolation error was 50 μS/cm compared to the data range of 27 to 43,950 μS
Rank each topic on a scale of 0 to 5.

VXAMPLK

|Tooic<br>Fre<br>Diarrhoea<br>in children|ouency<br>5|x Severitv<br>4|Ability to<br>x Interveng<br>2|<br>Need for info<br>x to iustify<br>**oro**<br>2|- Tota<br>80|
|---|---|---|---|---|---|
|Brain cancer|1|5|0|5|0|
|Anaemia|3|2|3|0|0|
|Infant formula<br>use|3|3|4|4|144|

Questions on cause-specific morbidity will typically follow questions on general morbidity. For certain diseases, questions on symptom/sign complexes have high sensitivity and specificity in most settings. These diseases tend to be easily recognised by the population and have clear symptoms eg. measles and diarrhoea. Kroeger (in WHO et al 1986) presents groups of diseases which can be elicited by means of interviews directly, indirectly or after having received a medical diagnosis. It is particularly important that questions on specific diseases are pre-tested for cultural appropriateness. In Bangladesh for example, it was discovered that an evaluation of an ORS programme was using a word for diarrhoea that meant cholera in certain parts of the country (Chowdhury 1986).

A problem that many developing countries are particularly interested in is long term, or chronic, disability. However, measuring disability through health interview surveys is fraught with difficulties. The main problems arise from the fact that both the I= and severitv of disability need to be defined and measured. In defining 'disability' much confusion arises from the casual use of different terms such as impairments, disabilities and handicaps. Previous surveys have been incomparable since different definitions were used. The International Classification of Impairments, Disabilities and
in the region, El Salvador is marked by high levels of vulnerability.** At a subnational level, several of El Salvador's departments report high vulnerabilities compared to Latin America and the Caribbean (World Bank Group, 2021b). El Salvador's subnational vulnerability index varies between 17.4 and 33.7 on the subnational Socioeconomic Vulnerability Index (SEVI).<sup>6</sup> Twelve of the country’s 14 departments are located among the most vulnerable half of subnational

> 5 The ND-Gain index is a summary index about a country’s vulnerability to climate change and other challenges as well as its resilience to tackle them.

> 6 The SEVI Index is a subnational index measuring the socioeconomic vulnerability of subnational regions in LatinAmerica and the Caribbean in 2019. It ranks from 0 to 100, with 100 being the highest possible exposure to vulnerability. In 2019, the highest obtained value on the SEVI Index was 50.1 by Alta Verapaz in Guatemala. The lowest value was 8.6  for Antofagasta in Chile.

4
recent past also increases the probability of _work only_ for both men and women, but by a smaller amount (between 9.2 for women and 34 p.p. for men).

Figure 6. Proportional Change in the Probability of Being Nini to a One-Unit Change in the Corresponding Index of Past Decisions, Youth Aged 15-18 y.o., by Gender (a) Male (b) Female <!-- Start of picture text -->
1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>Study Only Work Only Nini Study Only Work Only Nini<br>Index Index Index Index Index Index<br><!-- End of picture text -->

_Notes_ : The value of the bars represent the marginal effects on the probability of being Nini for the corresponding variables from the underlying estimation of multinomial logit models (one for each gender). The dependent variable in the multinomial models takes three states (Nini, Work Only, and Study Only). Only marginal effect for the outcome of Nini is presented in this graph. The models include controls for history of past decision indices, youth’s own education level, parental education level, household income quintiles, a set of demographic variables, and state economic condition variables. Complete results are presented in Appendix Table A1. Indices take values from 0 to 1. _Source_ : Authors’ calculations based on model estimation using data from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

Having been enrolled in school for longer in the recent past, as reflected by the Study index, increases the probability of current _study only_ (for both men and women, but more for women), reduces
# **4 Analysis**

## **4.1 Empirical Strategy**

To estimate the impact of big sisters on child development, we assume that child gender is plausibly exogenous.<sup>18</sup> We estimate the regression equation: where _Sisteri_ is an indicator equal to one if the older sibling in household _i_ is female. Parents cannot control the sex of any given child, and households in our study area have little access to sex-selection technologies — so gender is not explicitly endogenous. Nevertheless, our estimates of the treatment effect of older sisters will be biased if _Sisteri_ is correlated with any (observed or unobserved) covariates that also predict outcomes. For example, if adolescent girls were more likely to live at home in wealthier households, _β_<sup>ˆ</sup> would not capture the causal impact of having an older sister on child development. We test for this by comparing the observable characteristics of households with and without an older sister.

Summary statistics comparing households with an older sister to households with an older brother are presented in Table 2. Households are broadly similar in terms of family structure, parental characteristics, and living conditions; and younger children are similar in terms of gender, age, and school enrollment. Older sisters and older brothers are also similar in age, suggesting comparable patterns of fertility and birth spacing in the two types of households. Since families with older sisters and older brothers look similar in terms of observable characteristics, we treat the gender of the older child as plausibly exogenous in our subsequent analysis.<sup>19</sup>

> 18See Washington (2008) for a similar estimation approach.

> 19Our sample is also demographically similar to families sampled in the 2014 Kenya Demographic and Health Survey (DHS) (Kenya National Bureau of Statistics 2015). For example, in the DHS, among women age 15-49 in the former Nyanza province who had given birth at least once, the average number of years of schooling was 7
Figure 1: Mean IT use by sector <!-- Start of picture text --> .6 .4<br>.3<br>.4<br>.2<br>.2<br>Mean computers−per−worker<br>Mean of share of labor with internet .1<br>0 0<br>Food Beverages and Tobacco Textile Mills Textile Product Mills Apparel Leather and footwear Wood Products Paper Printing and Related Chemical Products Plastics and Rubber Nonmetallic Mineral Products Primary Metal Manufacturing Fabricated Metal Manufacturing Machinery Computer and Electronics Electrical Equipment, Appliances Transportation Equipment Furniture and Matresses Miscellaneous Manufacturing Food Beverages and Tobacco Textile Mills Textile Product Mills Apparel Leather and footwear Wood Products Paper Printing and Related Chemical Products Plastics and Rubber Nonmetallic Mineral Products Primary Metal Manufacturing Fabricated Metal Manufacturing Machinery Computer and Electronics Electrical Equipment, Appliances Transportation Equipment Furniture and Matresses Miscellaneous Manufacturing<br><!-- End of picture text --> Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI As Figure 2 shows, intensive IT-use is located in the Northern and Central region of the country. This is consistent with what we observe regarding welfare measures in Mexico. As the South-Eastern region is the region with highest level of deprivation, it is also where the lowest IT-use is observed.

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_50_

# **6.1. Analytic Methodology**

Several lessons can be learnt from the methodology in this paper. First the results have demonstrated that there is a difference between the poor and the extreme poor. It has also been established that while the headcount declined for the poor and extreme poor, depth and severity actually increased for the extreme poor. This would have been entirely missed if the analysis had used only one poverty line. Second, it is important to check how robust the results are, to changes in the poverty lines and poverty measures. Sensitivity analysis showed that all major patterns remained unchanged with small changes in the poverty lines. Dominance analysis permitted us to broaden that conclusion, and did indicate that caution was needed because for values up to almost half of the present poverty line, the conclusions would be reversed, in the sense that, it would find an increase in poverty over the seven year period. Third, the decomposition analysis of the over-time changes in poverty into growth and redistribution, and further into intra-sectoral and population shifts, was particularly useful to understand the dynamics of poverty and its implications for a continuously changing scene.

The analysis of targeting indicators is very useful to identify vulnerable groups. This is particularly useful to policy makers who always ask the question, who are the vulnerable groups and who should be targeted. Lastly, our use of a multi-dimensional approach in combination with decomposable poverty indices proved to be a very effective tool in linking economic change to welfare of households and individuals within the household. We do not claim in any way to have proved causality, as perhaps we could have done with a comprehensive economy-wide model, but what we do claim is that this collaborative effort has broken ground in the field of poverty in Nigeria. However, a lot of research still needs to be done to explain many of the findings in this paper.

# 6.2. **Beyond the Poverty Profile**

Considering the severe constraints imposed on the poverty profile by the difficulties in
Focusing on the other ones, reach _×_ effectiveness is 27% for online platforms, 53% for the television, and 79% for take-home packages. We could roughly assume _c × e_ = 50% on average.

However, the estimates are provided by ministries of education that may have imperfect information, or incentives to over-estimate the effectiveness of their preferred modalities. For a few countries in late 2020, (pre-college) teacher surveys show much lower reach _×_ effectiveness rates. For seven HICs with a large population, McKinsey (2020) finds an average rate of 4.2 out of 10, hence 42%. Given an effective closure rate of 44% in year 1 in the HICs, _ν_ = 0.44 (1-0.42) = 0.26, or a loss of 26%. Likewise, McKinsey explains that pre-college students were two months behind by November 2020, suggesting a 29% loss. Were we to use the ministries’ estimate, we would have 0.44 (1-0.75) = 0.11, hence a 11% loss only. Thus, we assume that reach _×_ effectiveness = 40% in year 1 and 45% in year 2, thus allowing for improvement over time.

> 39Azevedo et al. (2020) writes: “While mitigation strategies in the time of COVID-19 are often referred to as remote learning [...] in reality what many school systems rolled out was emergency response teaching.” 40Take-home packages are less than 50% efficient in LICs given their lack of resources.
on Learning-adjusted years of schooling and health (stunting and adult survival rates). Human capital embedded in each age cohort, but only boosts productivity for cohorts of working age. Human capital data is taken from the components of the World Bank Human Capital Index, with historical years of schooling (for older generations) taken from the Barro-Lee educational dataset or Cohen-Soto-Leker when Barro-Lee is missing.

> 18We report the adjusted GEGI for ARE, BHR, KWT, OMN, QAT and SAU.

9