rafmacalaba/data-use-mentions-extended huggingface dataset:
as many less educated immigrants relative to highly educated ones.

Another important observation is that the recent ‡ows are usually more educated when compared with the stock of immigrants already present as of 1990 (see table A4). Hence, if the impression on immigrants is based on the stock, rather than recent ‡ows, there may be a perception bias towards less educated migrants who possibly migrated long ago. Third, in spite of the fact that census data are better than o¢ cial immigration data, they may miss some undocumented migrants, especially in Western Europe. If those undocumented migrants that are missing from the census data are mainly less educated, the actual numbers of the less educated are understated by our data. We address this issue in section 5.1 by using estimates on the extent of undocumented migration in di¤erent destination countries.

Finally the perception of the skills of immigrants may be based on the occupations and the labor market performance of immigrants (rather than their formal education) as analyzed by Mattoo et.al. (2008) in the US labor market. In many cases, college educated immigrants are less productive and take less skilled occupations than college educated natives since their education quality, mostly obtained at home, might be less easily transferable or lower than those of the natives in the destination. The lower quality/downgrading of education levels for immigrants matter for our exercise as well. We address quality adjustment in the extensions of our simulation exercise in section 5.2.

In terms of the overall picture of migration across countries in the world, the nonEuropean Anglo-Saxon countries (US, Canada, Australia) attract highly educated immigrants, from all over the world and their native-born citizens tend not to emigrate. Western European countries seem to attract highly educated immigrants (from other European and

> 10 See our discussion below on undocumented migration, which tends to be less skill intensive, and how we try to account for this in our analysis.

11
a percentage of gross fixed capital formation). The sample correlation of both variables is high, 0.87. The variable is logged and labeled _lFDIstock_ip_ .

For **export diversification** , we rely on the export diversification index of the IMF (Papageorgiou et al., 2015), which is a Theil index. A higher value hence indicates lower diversification. Since this index stops in 2014, we interpolate the missing years with the Herfindahl-Hirschmann Product Index from UNCTAD. The correlation between both series in the sample is high, 0.90. The variable is logged and labeled _lEDI_ipol_ . Moreover, we take the square of the variable because a non-linear or U-shaped relationship is expected (see Imbs and Wazciarg, 2003, and Papageorgiou et al., 2015: Fig. 3).

Institutions are widely acknowledged as a key ultimate source of cross-country income differences (e.g., Acemoglu et al., 2001; 2005). However, their measurement problems have also been highlighted (e.g., Glaeser et al., 2004; Voigt, 2013) and deep institutional aspects barely vary over time, which is the key dimension we use for identification of our model parameters (due to the presence of country fixed effects). Moreover, most common measures of institutional quality are not available for the whole sample we aim to investigate. Instead of using a fine measure of institutional quality, we hence rely on data about major **episodes of political violence** from Systemicpeace.org., which has the advantages of wide coverage and considerable variation over time. In the regression models, we rely on _actotal_ , which aims to capture the magnitude of major events. Our dataset further contains a breakdown of this variable for interstate and societal violence ( _inttot_ and _civtot_ ), as well as a dummy variable, _dum_mepv_ , which equals 1 for any period that contains a major episode of political violence. Note that Systemicpeace.org data does not include data for 2019.<sup>
(range) of monthly wage earnings). ‘Received transfers from an employer’ is a dummy variable equal to 1 if respondent is working for an employer who lives in the same community and who is also reported as a source of transfers for the respondent’s household. Sample includes baseline observations from ultra-poor and non-ultra-poor households. Each observation is weighted using sampling weights calculated as the fraction of households surveyed from each wealth class (lower, middle and upper) relative to the number of households from the relevant wealth class in the community census. Columns 1 - 3 refer to terms of labor contracts of primary female in the household and columns 4 - 6 refer to terms of labor contracts of the male household head. All regressions control for the following characteristics of the relevant individual [primary female in columns 1 - 3 and male household head in columns 3 - 6] : whether the individual is literate, individual’s age (in years) and age squared, total household wealth (in BDT) and female respondent’s height (in centimeters) – male respondent’s height was not available, thus we control for female respondent’s height as a proxy. In columns 1 and 4, the dependent variable is wage per hour (in BDT), which is the sum of earnings from wage labor divided by hours spent in wage-labor during the year preceding the survey. In columns 2 and 5, the dependent variable is the range of monthly wage earnings, which is the difference between the maximum and minimum monthly earnings of the respondent from wage employment during the year preceding the survey. In columns 3 and 6, Ithe dependent variable is the range of monthly wage earnings divided by the wage per hour (in BDT). In 2007, 1USD=69BDT. *** (**) (*) indicates significance at the 1% (5%) (10%) level.

3
</sup><sup>_δ_</sup> _i_<sup>_G_,</sup> where _ξ_<sup> ̄</sup><sup>_G_</sup> is calibrated to match the share of investment in electricity (71.88%),<sup>15</sup> and following Lipscomb et al. (2013), _δi_<sup>_G_is given by</sup>

_δ_<sup>_G_Φ (</sup><sup>_β_1 log(Max Flow) +</sup><sup>_β_2Average Slope in River +</sup><sup>_β_3Max. Slope in River +</sup><sup>_β_4Forest Cover)</sup> _i_<sup>=</sup> where Φ( _·_ ) denotes the CDF of the standard normal distribution, and the coefficients are taken from Table 1 in their paper. Their estimates provide the geographic potential for hydroelectric energy production. We assume that locations with smaller potential have a higher cost per unit of electricity investment because they are more dependent on the external electricity supply.

**Calibration of Available Grid Supply of Electricity.** To calibrate _Ei_ , we use the IBGE continuous cartographic database (Bases Cartográficas Contínuas), which provides data on the location and load of the main sources of energy generation. We use the load from thermal and hydroelectric stations to compute a distance-weighted average of the total available load for each region. We subtract the average electric power transmission losses.<sup>16</sup> Finally, we normalize this index to be in the range [1, 100].

**Calibration of Local Quality of Electricity.** The observed quality of electricity _Gi_ is calculated based on two collective indicators of continuity provided by ANEEL: the Equivalent

> 15This number comes from the World Bank PPI database: `ppi.worldbank.org` . Other sources (Paiva, 2010; BNDES, 2014)
development, verbal skills, culture and spirituality, social and emotional development, perseverance, approaches to learning, numbers and concepts, and literacy skills. It is a rating type assessment where information about a child’s abilities and behaviors is obtained from individuals (including caregivers or teachers) that know the child well. The instrument is not designed to provide a diagnostic of a child’s development but rather to make inferences about developmental outcomes of a population.

The survey also collected data on the frequency of children being exposed to six different types of activities at home. These variables provide some insight into the home environment, though they do not provide information about the duration or quality of these caregiver-child interactions. The average was used as an index of home activities for the analysis.

The survey was conducted in 2014 prior to the interventions and in 2017 near the end of the interventions. In both rounds, all communities in Tonga were sampled and all children aged 3 to 4 who were identified by local informants including district nurses were sampled. All 5-yearolds were also sampled in 2014, but only those who were not in primary school were sampled in 2017. In this latter sample, the number of 5-year-olds is approximately 40 percent of the number of 3- or 4-year-olds. As a result, the 3- and 4-year-old sample is used to estimate the impact of the community school readiness interventions on school readiness outcomes and support for school readiness at children’s homes. Figure 1 presents a density plot of the average TEHCI score and the average score for the literacy sub-domains in standard deviations.

For estimates using both types of data, data are weighted by the inverse of communities’ selection probabilities, and standard errors are adjusted using a finite population correction to account for the small number of communities in Tonga from which the treatment and control communities were randomly drawn. Because the TEHCI survey samples all children in each community, there is no sampling variation within communities.

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focusing on cancer, the Young Lives study in 4 LMICs (https://www.younglives.org.uk) and HELTI - (https://cihr <u>irsc.gc.ca/e/49510.html) with linked studies in four middle and high-income countries, among</u> others. It might be worth exploring some of these regarding cancer risks.

Prospective cohort studies do have strengths and limitations: they may aim to measure “usual” consumption of food groups, rather than a 24-hour recall as in most cross-sectional surveys; however, unless the cohort is recruited in early adulthood, current diet may not be a good reflection of previous diet. Prospective cohorts have advantages over cross-sectional studies, where an individual’s diagnosis with elevated blood pressure or blood glucose may have led the individual already to change diet and thus attenuate the estimated link between diet and NCD risk.

# 9. Data Limitations

While we propose a life cycle approach, we acknowledge that certain age groups are not adequately covered in terms of evidence and dietary data. These are: children from 2 to 5 years, primary school children (6 to 10-12 years) and older adults (over 65 years). Evidence and data for adolescents is limited but growing. Dietary data for adult males (15 and above) are significantly less covered than for women.

Data on MDD-W are growing both through DHS and the Diet Quality Project (2022) but there appear to be some differences in the national prevalence estimates, some of which may reflect different sampling and data collection methods (face-to-face, as compared to by telephone). Few countries currently have opendata access for DHS MDD-W. MDD-W was not designed to measure risks of obesity and NCDs, and Herforth et al (2021) advise that caution is needed in using this in high income countries to indicate nutrient adequacy. The two GDRS subcomponent scores (NCD-Protect or NCD-Risk) perform better than MDDW in relation to compliance with WHO dietary recommendations (Herforth et al
(EBRD 2023). The survey covers the transition economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa.<sup>2</sup> The 2010 round includes 35 countries, the 2016 round 34 countries, and the 2023 round 39 countries. The survey is based on a nationally representative sample of around 1,000 households per country in the second and fourth rounds and around 1,500 households per country in the third round.

We capture support for tax-financed investments in education by examining responses to the question “ _would you be willing to give up part of your income or pay more taxes if you were sure that the extra money was spent on education?_ ” This dummy variable is the main dependent variable of our analysis.

Our measure of expectations of future mobility is based on the two social ladder questions: “ _Please imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10 percent people in [COUNTRY], and on the highest step, the 10th, stand the richest 10 percent people in [COUNTRY]. On which step of the ladder is your household today?_ ” and “ _Where on the ladder do you believe your household will be four years from now?_ ” We take the difference between the expected future and current income ladder positions as our measure of expectations of future mobility, which ranges from –9 to +9. We also conduct a sensitivity analysis that relies only on the direction of the expectations by creating a variable with three categories (downwardly mobile, static, and upwardly mobile) based on the difference between the ladder position today and the ladder position four years from today.

The LiTS data also allow for assessment of the expectation of intergenerational mobility, which is captured by responses to the following statement: “ _Children who are born now will have a better life than my generation_ ,” with respondents answering on a five-step Likert scale ranging from strongly disagree to strongly agree
(0.01|
|c.<br>Hours last week|43.39|1.14|-0.06|43.05|-2.35|-0.1|
||(18.15)|(1.09)|(0.69)|(18.53)|(1.37)*|(0.76)|
|Controls|||||||
|Municipality fixed effects||Yes|Yes||Yes|Yes|
|Timefixed effect||Yes|Yes||Yes|Yes|

_Notes:_ Each entry in panels A–B, rows a–c, columns 2–5 represents a coefficient from a different regression. All the regression include municipality fixed effects, year fixed effects, a vector of maternal background, and a vector of municipality characteristics. All the regressions control for cluster by municipality, and standard error are consistent for heteroskedasticity and error correlation within municipalities over time.

*** _p_ < .01   ** _p_ < .05  * _p_ < .1

_Source:_ Underlying data are from the Chilean socioeconomic household survey (CASEN) and administrative data from the Ministry of Education for 1990–2011.

33
# **1 Introduction**

_Labor market outcomes are a critical determinant of household well-being during an economic crisis, especially in developing countries where labor is usually the main source of income._ Understanding how labor market outcomes changed for different groups during the most recent downturn is therefore an important first step in crafting appropriate and well-targeted policies to respond to future crises. This paper examines _which groups of workers in developing counties were most affected by labor market retrenchment during the great recession_ . The analysis sets aside other important dimensions affecting income and household well-being, such as changes in transfers, remittances and consumption patterns, and focuses only on changes in labor market outcomes.

_The vulnerability of different types of workers to economic downturns remains subject to debate, and targeted policies in response to the crisis were typically based on ad-hoc or institutional considerations._ Labor market assessments and anecdotal evidence typically emphasizes the vulnerability of young, unskilled, or female workers. Although it is widely accepted that youth suffered disproportionate increases in unemployment during the latest downturn, less is known about youths’ adjustment along other dimensions, and there is no similar consensus regarding the relative vulnerability of female and unskilled workers. Furthermore, conclusions are often inferred from past crises, data from advanced countries, or data from particular developing countries. Few studies have examined the impact of a recession on different types of workers across a wide range of developing countries, and this is the first to our knowledge that compares disparities in labor market outcomes across several potentially vulnerable groups.

_This study examines changes in labor market trends during the crisis for different types of workers, defined by their gender, age, education, and urban or rural residence._ Data are taken from 17 middleincome countries that field household surveys at least once per year. The outcomes measure aspects of individuals’ main labor market activities and job quality. In particular, the indicators capture labor market activity, as measured by the ratios of employment, unemployment, and non-participation to total population, and unemployment among the active labor force
# **<mark>References</mark>**

<mark>Adams-Prassl, A., T. Boneva, M. Golin, and C. Rauh (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys.</mark> _<mark>Journal of Public Economics</mark>_ <mark>, 189: 104245.</mark> <mark>Amin, M. and D. Viganola (2023). Does better access to finance help firms deal with the COVID19 pandemic? Evidence from firm-level survey data.</mark> _<mark>Journal of International Development</mark>_ <mark>, 1– 31. https://doi.org/10.1002/jid.3787</mark> <mark>Ayyagari, M., A. D</mark> emirguc-Kunt, and V. Maksimovi <mark>c (2014). Who creates jobs in developing countries?</mark> _<mark>Small Business Economics</mark>_ <mark>43 (1): 75-99. https://doi.org/10.1007/s11187-014-9549-5</mark> <mark>Ayyagari, M., A. D</mark> emirguc-Kunt, and V. Maksimovic (2011). Small vs. young firms across the world: Contribution to employment, job creation, and growth. Policy Research Working Paper No. 5631, World Bank, Washington, DC. <u><mark>https://doi.org/10.1596/1813-9450-5631</mark></u> <mark>Bak, O., S. Shaw, C. Colicchia and V. Kumar, "A Systematic Literature Review of Supply Chain Resilience in Small–Medium Enterprises (SMEs): A Call for Further Research," in</mark> _<mark>IEEE Transactions on Engineering Management<
Variables are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

**Robustness checks** We perform a number of robustness tests to check the validity of our results. To begin, we take care of selection issues by employing Heckman-type selection models (Heckman, 1976; Lewis, 1974; Gronau, 1974). In essence we model a firms’ probability of belonging to the panel (i.e., being interviewed in two consecutive waves of the WBES survey) adding as an excluded regressor firms’ belonging stratum (the intersection of sector and country). We then augment the original specification with the inverse Mill’s ratio of the selection regression. As shown in Table A4, our main conclusions are largely unchanged.

One possible concern with our results is that firms that report to be constrained most by informal competition are the ones that operated as informal firms in the past and only recently switched to a formal form of business. If this were the case, the lower connectedness with the banking sector may only capture a matter of timing whereby constrained firms are those having had a shorter periods to establish a banking connection. To assuage this concern we augment our baseline specification with a direct indicator about whether the company was originally operating informally, together with a measure for number of years since the formal registration. Results for both loan availability (reported in Table A5 column 1) and loan 13
demonstrate that discrimination did not take place. Vietnam’s new Civil Proceedings Code adopts a hybrid approach. According to the new law, the burden of proof shifts to the employer when the employer is the repository of the evidence or, in other words, holds the documentation related to the employment claim.<sup>64</sup> At present, Morocco has no similar forms of protection for its employees.

Discriminatory hiring practices, unfair labor treatment, and prejudicial attitudes toward certain minority groups may hinder their full participation in the labor market, wasting their potential and lowering the competitiveness of the economy. Survey results suggest that legislation in the field of nondiscrimination labor law is fairly well developed in all six economies. Interestingly, despite the fact that such economies have profoundly different legal traditions, they are adopting similar forms of enforcement mechanisms to implement the laws. Certainly, that trend is a reflection of the work of international organizations such as the International Labour Organization that have tirelessly worked for many years on the promotion of labor standards and harmonization of the law in that field. As mentioned, however, discrimination against minorities in the workplace is still a reality, which suggests that stronger enforcement mechanisms must be put in place to ensure equal opportunity in the labor market. Figure 3 shows that, as with the results of the surveys related to the previous indicators, the weakest body of law protecting access to the labor market is that related to LBGTI communities. In Morocco and Tanzania—where homosexuality is a crime—all existing protective legislation in the workplace applies only to ethnic and religious minorities.

> 64 Decree 92/2015/QH13 on Civil Procedures Code (2015), Art. 91(1)(b), effective July 1, 2016.

Anti-Discrimination Law and Shared Prosperity 21
# **Measuring Social Norms About Female Labor Force Participation in Jordan**

# **Varun Gauri, Tasmia Rahman, and Iman Sen**

It is widely understood that social norms constrain women’s labor force participation worldwide (Alesina, Giuliano, & Nunn, 2013; Bertrand, Kamenica, & Pan., 2015; Chamlou, Muzi, & Ahmed, 2016; Fernandez, 2013; World Bank, 2012). The role of women in childcare and domestic tasks, their visibility in public and mixed gender environments, segregation into particular jobs and industries, and women’s decision making power relative to men are all thought to bear on women’s labor force participation. What is not as well understood are the salience and importance of these various behaviors and beliefs, the relative influence of importance of behavior versus normative views, and the influence of husbands and fathers in comparison to the views and behavior of the society at large.

This study develops methods for measuring and validating social norms and beliefs regarding women’s access to and participation in labor markets. Using a large-scale quantitative survey conducted across three governorates of Jordan, the study measures the components of social norms in a sample of 1,000 households; conducts tests of the consistency, validity, stability, and reliability of the measures; and examines the influence of inter-personal expectations within the household on women’s beliefs and choices.

Jordan’s female labor force participation is the lowest in the world of a country not at war. Though Jordanian women are among the most educated in the MENA region (53% of university graduates are women), female labor force participation (FLFP) was 14 percent in 2008, compared to 64 percent among men (The World Bank, 2018). Our study finds that social norms play a key role in these labor market outcomes for women. But social norms are not so deeply internalized that there is little scope for interventions – most non-working women would like to work. More than 95% of
use vehicle ownership data included in the World Road Statistics database produced by IRF. These data are panel data covering 194 countries from 2000 to 2017. More specifically, we use data on vehicle in use including: Passengers cars, Buses and Motor coaches, Vans, Pick-ups, Lorries, Road Tractors, and Motorcycles.

> 4The standard LSMS household survey questionnaires in Nepal even involve a question on whether the vehicle was purchased by the household or received as a gift, payment for services, or inheritance.

9