rafmacalaba/data-use-mentions-extended huggingface dataset:
and must return to their home countries after termination of their contract. In principle, therefore, the Ministry of Home Affairs (MOHA), which manages the work permit system, monitors the number of total foreign workers by sector, nationality, and state.

**But not all foreign workers are regular, posing challenges in grasping the actual number of foreign workers residing in Malaysia at a given time and thus in formulating relevant policies.** There exist three channels for a foreigner to become an irregular worker in Malaysia: (i) to enter Malaysia without a valid work permit and undertake paid jobs, (ii) to enter with a valid work permit, but fail the mandatory medical test and remain in Malaysia, or work with an employer who is unrelated to the valid work permit, and (iii) to work with an expired work permit (or overstayers).

**This paper and our earlier report respond to a call for a methodology to estimate the foreign worker population in a systematic and comprehensive manner.** MOHA estimates irregular foreign workers based on its sporadic and ad hoc enforcement activities. The Labour Force Survey (LFS) measures some of the foreign worker population but its underestimation is widely recognized, as the LFS sampling is householdbased and does not reach people in communal housing, which is common among foreign workers. Population census-based data include all people who were not born in Malaysia, but foreign workers are only a subset of the total foreign-born population. These official reports as well as other studies have estimated the number of foreign workers ranging widely from two million to five million. However, the

> † Irregular foreign workers are those who are either (i) undocumented or (ii) documented but whose actual employment is out of compliance with their documentation, such as working for a different employer or working with an expired work permit.

2
# **6 Empirical Strategy**

## **6.1 Survey Data Analysis**

### **Adolescent Outcomes**

Our main intention-to-treat (ITT) impact estimations for adolescent outcomes use the following analysis of covariance (ANCOVA) specification:<sup>37</sup> Where _Yi,t_ =1 _,_ 2 is the outcome variable for adolescent _i_ measured at midline ( _t_ = 1) and endline ( _t_ = 2), and _Yi,t_ =0 is the baseline value of the outcome variable.<sup>38</sup> _CHOICESi_ is a dummy variable that equals 1 if adolescent _i_ was randomly assigned to receive the _Choices_ training program. That is, this indicator equals 1 if adolescent _i_ was assigned to either _Choices only_ or the combined _Choices_ and _EMB_ treatment arms and 0 for the control group. _EMBi_ is a dummy variable that equals 1 if adolescent _i_ also had at least one adult in their household assigned to receive the _EMB_ training sessions. _Xi,t_ =0 is a vector of baseline control variables, _λd_ are district-level fixed effects, and _εit_ is the error term. Standard errors are clustered at the community cluster level.

In Equation 1 the coefficient _β_ 1 measures the impact of the _Choices_ training relative to the control group, and _β_ 2 measures the marginal impact of having your parents/guardians being trained in _EMB_ . We do not estimate a separate treatment effect for the treatment arm that combines the two interventions, instead, we are more interested in showing the marginal effect of involving parents directly in the program. Throughout the analysis, we control for whether there were multiple adolescents eligible for the program in the household, and the age of the adolescent. We do not pool the two follow-up survey rounds as we are interested in estimating the short-run effects (midline) and longer-run effects (endline) separately. We run the regressions on both the full sample of adolescents as well as conduct heterogeneity analysis by respondents’ sex.
61.0|61.7|-0.7|56.9|57.8|-0.9|
|N.E Nigeria|61.0|56.8|4.2*****|57.4|55.4|2.0|
|Somalia|63.3|58.7|4.5*****|62.3|63.2|-0.9|
|South Sudan|60.5|59.5|1.0|62.1|63.6|-1.6|
|Sudan|58.9|58.0|0.9|60.3|52.3|8.0*******|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). De facto - female-headed households whose head is a married female to a husband in the household or working away from the family. De jure - female-headed households whose head is a female widow, divorced or unmarried woman. H is the incidence of multidimensional poverty; A is the intensity of multidimensional poverty and MPI=H*A. Asterisks indicate statistical significance at 1% ***, 5% ** and 10% * levels.

We find differences, with _de facto_ female-headed households being worse off in Ethiopia and _de jure_ being worse off in Nigeria. In Ethiopia, refugee individuals in _de facto_ female-headed households (MPI of 0.392) are significantly more likely to be poor than _de jure_ female-headed households (MPI of 0.238). Looking at the incidence of poverty, there is a 25.7 percentage points difference among female heads, with 64.2% of those in _de facto_ and 38.5% of those in _de jure_ households being MPI
How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the Learning-Adjusted Years of Schooling Metric Noam Angrist, David K. Evans, Deon Filmer, Rachel Glennerster, Halsey Rogers, Shwetlena Sabarwal<sup>∗</sup> Originally published in the Policy Research Working Paper Series on _October 2020_ This version is updated on _May 2023._ To obtain the originally published version, please email prwp@worldbank.org.

# _JEL Codes_ : H43, H520, I2

_Keywords_ : Education; Cost-Benefit Analysis; Government Policies; Impact Evaluations

> ∗Angrist: Blavatnik School of Government, University of Oxford; Youth Impact. Evans: Center for Global Development; Filmer: The World Bank; Glennerster: University of Chicago; Rogers: The World Bank; Sabarwal: The World Bank. The authors are grateful to Abelardo De Anda Casas who provided excellent research assistance. Amina Mendez Acosta also provided valuable research assistance and Radhika Bula codified and structured data from the Abdul Latif Jameel Poverty Action Lab (J-PAL) database; Yilin Pan compiled data from the World Bank Strategic Impact Evaluation Fund; and Alaka Holla provided guidance on inclusion of studies from the SIEF database and input into categorizations of interventions and cost-effectiveness implications. The views expressed here are those of the authors and should not be attributed to their respective institutions. This work has been supported by a World Bank trust fund with the Republic of Korea (TF0B0356), acting through the Korea Development Institute (KDI) on the KDI School Partnership for Knowledge Creation and Sharing. Data availability statement: the data used in this article will be made available.
.<sup>9</sup> Although changes in height undoubtedly reflect nutrition, the quantitative relationship between net caloric surplus (intake minus TEE and any caloric cost of disease) and height is not well known. Changes in population height may be more reflective of micro-nutrients, macro-nutrients composition, and the timing of life-cycle nutrition from conception to adolescence rather than changes in the average caloric surplus of the population. However, the NFHS allows for measurement of weight gain over time, which does have a well known quantitative relationship with caloric surplus. While a single cross-section

> 9In Appendix Table S.7, even larger increases in REE for adults and children and hence smaller decreases (or even small increases) in TEE over the 1983-2012 period are found when including a linear time trend in the REE prediction equation. This may be reasonable in later period but is likely to be a heroic assumption when extrapolating a linear trend observed between 2005 to 2015 back 22 years.

16
_Segment overview:_ There appears to be no restriction on telemarketing to banks’ customer bases. This is likely to be the major focus of the newly acquired insurance subsidiaries. Past sales data, which saw Bancassurance account for a peak of 60 percent of Life insurance sales in 2010, are not a good indicator of future performance, following the banning of insurers’ staff and agents from bank branches. Accounting changes that will encourage insurers to focus on regular premium contracts also pose a challenge to the previous Bancassurance business model.

_Consumer protection:_ Extensive requirements have been introduced in recent years to protect bank customers purchasing insurance products:

- Branch Bancassurance agreements must have a term of at least one year

- and be signed at the head office level. Respective responsibilities must be clearly defined.

- A bank branch may not have agreements with more then three insurers.

- Staff selling insurance products must hold Insurance Agent Professional

- Qualification Certificates. Insurance staff are not allowed to operate out of bank branches.

- Investment-linked contracts (including variable annuities) may not be sold

- at deposit, and counters and the sales staff involved must have at least 12 months experience and 40 hours of special training.

- Insurers must separate the accounting for their Bancassurance business.

- The skill level of the bank staff involved should match product complexity.

- Unit-linked sales must be accompanied by a ―know your customer

- exercise,‖ and the resultant analysis must be signed by the customer and the sales person.

- Contracts with a term of more than one year sold by banks must be

- accompanied by a ―bring to attention letter‖ laying out relevant product risks and a signed document in the consumer’s own handwriting confirming that they were appropriately informed.

- Insurers are required to follow up within the cooling off period to ensure

- that the contract is acceptable.

- Commission caps apply in practice.

A complaints function exists within the insurance association, and consumers can file complaints with a supervisor.

# **Malaysia**

_Regulation:
on analysis of laws without collection of information on practice are, at best, seriously problematic; and, at worst, may be biased and misleading.

# **3.3.2 The 2002 Indian Ministry of Power Study**

There are other studies (including the econometric studies of regulatory effectiveness that we will discuss below) which rely largely or wholly on studies of laws and formal regulatory governance properties but which provide little or no detailed examination of regulatory practice. We will not discuss these here as they add little additional information. There is, however, a study for Indian electricity regulation, which contrasts interestingly with the Prayas study and which shows the limitations of primarily paper-based studies (as well as the issue of the relative assessment/marking standard).

The Ministry of Power in India commissioned (apparently in 2002) an appraisal of power sector reform and a performance rating for 26 Indian states by CRISIL/ICRA. The study was primarily from paper records, but supplemented by face-to-face meetings with SERC members, heads of utilities, etc.

> 28 Unfortunately, the paper does not explicitly record the data collection methods for the EBRD/WB study of regulators.

21
appears in both household and area-level models, this bias is not a sufficient justification for preferring area-level models over unit-context models.

There are two other reasons why the omitted variable bias identified in Corral et al (2021) may not be important in practice. First, if sampling error is exogenous, as one would expect with a random sample, both the household and area level models are consistent, implying that this bias will tend towards zero as the number of AGEBs included in each area becomes large. Furthermore, in both

> 9 Auxiliary data in area-level models is typically drawn from administrative data or other sources that represent the population rather than the sample.

10
- 70 -

ALTHOUGH RELIABLE DATA ARE AVAILABLE FOR BOTH EMPLOYMENT AND WAGES, LEBANON IS NOT INCLUDED IN THE REGIONAL AVERAGES SHOWN IN THE TEXT TABLES BECAUSE ITS UNIQUE SITUATION, COMBINED WITH A RELATIVELY SMALL NUMBER OF COUNTRIES IN THE MENA REGION, WOULD MAKE SUCH INCLUSION HIGHLY MISLEADING.

Data on wages and salaries is taken from EMTPM Background paper in EMTPM files and relates to 1991. In Lebanon, consolidated Central Government includes education and Health services. Accordingly employment figure includes education and Health employment.

Morocco Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Central Government, Education and Health employment are taken from Note on Public Administration as of December 1995 and refer to 1995. Non central government is taken from Morocco Public Expenditure Review of August 30, 1994 and relates to 1993. Military employment data include conscripts (100,000), but do not include personnel of paramilitary units, i.e., Gendarmerie Royale (12,000), the Force auxiliaire (30,000), and the Customs/Coast Guard.

Average wage estimate comes from Anne Marie Leroy's report "La Fonction Publique Marocaine au ler Decembre 1995: Constats et elements de reforme". Data on wages in manufacturing (monthly basis) is taken from a World Bank Report 'Kingdom of Morocco: Country Economic Memorandum: Towards a Higher Growth and Employment" and refers to 1995.

# Syria

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991.

Education, Health, Central Government estimates are taken from Algerian Background report and are for 1992 (see reference for Algeria). Non Central government in Syria is based on a staff estimate. Military employment data do not include paramilitary personnel, i.e., Gendarmerie (8,000), and the Ba'th Party Workers Militia.

GDP, and data on Consolidated Central Government wages and salaries are taken from IMF Senior economist from Syria Leigh Alexander and
# **II. Previous literature**

Our analysis is inspired by three strands of the literature. The first strand compares the characteristic of entrepreneurs in developing countries to those of wage and salary employees and other workers. The second strand attempts to measure the extent to which the selfemployed are self-employed by necessity (and would rather be wage and salary employees) or are potentially successful entrepreneurs, while the third attempts to identify and measure the characteristics of those self-employed who have the potential to be successful but are constrained by lack of access to capital or other reasons.

A recent and growing literature studies the characteristics of entrepreneurs in developing countries. Djankov, Qian, Roland and Zhuravskaya (2005) collected data on the personal, family and business characteristics of approximately 1500 entrepreneurs and nonentrepreneurs in 2004 in China. Djankov, Qian, Roland and Zhuravskaya (2006) use similar data (from 2003-2004) to examine the characteristics of entrepreneurs in Russia.<sup>1</sup> They find that compared to non-entrepreneurs, entrepreneurs in China and Russia are more mobile, more willing to accept risk, have parents who are more educated, are more likely to have parents and other family members who were entrepreneurs, and are more willing to trade away leisure for more money. Djankov, Qian, Roland and Zhuravskaya (2005 and 2006) further distinguish between entrepreneurs and “failed entrepreneurs” (who at one point were entrepreneurs but are not now). Failed entrepreneurs score worse on aptitude tests compared to entrepreneurs, but have the best self-reported performance in school.

De Mel, McKenzie and Woodruff (2008) perform a similar analysis using data from surveys carried out in Sri Lanka between 2005 and 2007 of employers in small and medium sized firms, own account workers and wage and salary employees. Although they do not find that entrepreneurs are more willing to accept risk, they do confirm other patterns from China and Russia. Compared to own account workers and wage and salary employees, employers are older, more educated, have parents who
obtained by aggregating the small area estimates to the national and regional level using population weights. The results are shown in Table 5. The model-based estimates from both approaches are seen to line up with the survey-direct estimates. ELL and SEM both identify the North and South as the regions with the highest rates of poverty.<sup>9</sup> While the two approaches disagree slightly on the ordering of these two regions, the observed difference in the poverty estimates lies well within the 95 percent confidence interval.

More striking is the difference in statistical precision between the two approaches. ELL achieves slightly greater statistical precision than the survey-direct estimates (by virtue of combining the survey with unit record population census data), while the standard errors obtained by the SEM approach are notably larger (between two and three times the ELL standard errors). This difference in precision is in large part due to divergence in number of observations used to estimate the model parameters: 12,271 households in the ELL approach versus 767 PSUs in the SEM approach. The variance of the poverty estimates are largely driven by the variance of the model parameter estimates, which in turn scales with the number of observations used in the respective regression model. As the model adopted by ELL is at the household level, it has 16 times more observations to work with compared to the SEM approach.

||survey-direct|ELL||SEM||
|---|---|---|---|---|---|
|Malawi|0.507 (0.009)|0.498|(0.006)|0.492|(0.023)|
|North|0.543 (0.021)|0.562|(0.018)|0.540|(0.022)|
|Central|0.445 (0.015)|0.438|(0.009)|0.434|
especially countries with weak environmental policies. In this section we review a range of these standards, from binding requirements from international lending institutions to voluntary standards that may be advantageous to follow when considering net economic benefits or reputational enhancement. This section also reviews the environmental standards of Chinese policy banks. Critically, while as just noted often international practices go beyond the standards of host countries, if those practices are not implemented in ways that actually constrain the impacts of all BRI investments, including with significant consequences for failing to carry out improved and agreed practices, then their benefits are not realized. Consequential monitoring and the enforcement of agreed conditions is a necessary part of all projects.

## A. International Agreements

Though there are many fewer international environmental policies than the myriad national laws, there are still far too many that potentially bear on BRI transportation infrastructure to describe individually here (and see Monteiro and Trachtman _in review_ for provisions within trade agreements). These international agreements are not likely to affect BRI transportation projects directly but through their work with governments, international institutions, and NGOs to create appropriate relevant policies. Appendix 11 provides an overview of some of the critical agreements with the greatest overlap. For more detailed information, the Green Growth Knowledge Platform meta‐analysis of environmental databases is a good source (Booth 2017). The Ecolex (www.ecolex.org/), Environmental Treaties and Resource Indicators (http://sedac.ciesin.columbia.edu/entri/index.jsp), and International Environmental Agreements Database (https://iea.uoregon.edu/) also provide useful references along these same lines.

36
The period shifts indicate that international conditions for growth have deteriorated over time, resulting in considerably poorer conditions in the 1980s and 1990s than in the previous decades. Finally, both the Sargan and serial-correlation tests indicate that the null hypothesis of correct specification cannot be rejected, lending support to our estimation results. This is

> 8 The ICRG governance index is available since the mid 1980s and shows some time variation.  Given that we are forced to assume that its value was the same in the 1960s and 1970s as in the mid 1980s, we take the conservative assumption that its growth effect cannot be estimated separately from that of the unobserved fixed effect, as is the case with the other institutional variables that are completely constant over time.

22
three global circulation models (IPSL-CM4, MIROC3.2, ECHO-G) and three assumptions about the rate of subsidence in the Ganges Delta (2, 5 and 9 mm/year).

Our results cover salinity-induced migration patterns for 14 mangrove types: both sole species -- _Avicennia alba, marina, officinalis_ (Baen), _Excoecaria agallocha_ (Gewa), _Ceriops decandra_ (Goran), _Bruguiera gymnorrhiza_ (Kankra), _Sonneratia apetala_ (keora), _Xylocarpus mekongensis_ (Passur), _Heritiera fomes_ (Sundri) -- and combinations of those species.

Using the most recent digital maps of aquatic salinity and mangrove distributions, we estimate incidence probabilities by unit salinity interval for each mangrove type. Then we combine estimated incidence probabilities with information on current species locations and our 27 salinity scenarios to project the distribution of mangrove types in 2050. We find significant overall losses for _Heritiera fomes_ (Sundri) and _Xylocarpus mekongensis_ (Passur) _Bruguiera gymnorrhiza_ (Kankra), along with substantial gains for _Excoecaria agallocha_ (Gewa), _Excoecaria agallocha_ (Gewa) _Ceriops decandra_ (Goran) and _Xylocarpus mekongensis_ (Passur) _Bruguiera gymnorrhiza_ (Kankra) _Avicennia alba, marina, officinalis_ (Baen). Projected gains are modest for _Ceriops decandra_ (Goran) and _Ceriops decandra_ (Goran) _Excoecaria agallocha_ (Gewa).

We overlay the results with an administrative map of the Sundarbans region and compute changes in mangrove types for the five _upazila_ s in the region. To assess potential poverty impacts, we combine our results with upazila poverty populations identified using two bounds: an upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics; and a lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line. Our calculations cover 27 salinity scenarios for 2050.

25
that might affect cross-country regressions. Evidence that uses such data is scarce. Exceptions include Ferriani and Oddo (2019), who use data on official remittances from institutions or other authorized intermediaries (MTOs, banks, and post offices) at the provincial level in Italy. They find a strong positive correlation between remittance outflows to other countries and the cost of travel to such countries, which is heavily driven by distance. The authors interpret this finding as evidence of the importance of informal remittances when the distance between recipients and senders is shorter. Similarly, Simpson and Sparber (2020) use the US Current Population Survey (CPS) to estimate the determinants of household remittances abroad using household surveys, which include both channels. They find that a 10 percent increase in distance reduces the probability of remitting by 4.7 percentage points. In other words, these findings accord with the notion that distance increases the total costs of sending remittances but reduces the relative cost of sending them through formal channels relative to informal ones.

> 9See Table A2 in the Appendix. Before the pandemic, the elasticity of formal (official) remittances and distance was -0.50 and the elasticity between total (formal and informal) remittances and distance was -0.75. Although the difference between the two coefficients is not statistically significant at the conventional levels ( _p-_ value = 0.14), the magnitude of the difference is economically relevant. 10See Figure A2 for migration patterns during the 2002–2020 period. 11See Figures A3, A4, A5, and A6 in the Appendix.

5