rafmacalaba/data-use-mentions-extended huggingface dataset:
when we control for domestic competition measured by the Herfindahl index (column (3)), the age of the firm and firm capital intensity (column (4)). The coefficient on input-tariff changes remains negative, significant and stable, however. It is very similar in size to the estimations with only industry-level controls shown in column (1).

If the availability of foreign intermediate goods induces firms to start importing capital goods, we would expect the effect of lower input-tariffs to be greater for firms that actually import intermediate inputs. Columns (5) and (6) carry out this test. First, we inlcude a dummy variable equal to one if the firm imports intermediate goods. Firms sourcing inputs from abroad are more likely to also import capital goods (column 5) confirming the previous descriptive evidence on technological complementarity between imported inputs and foreign technology. 30 Next, we introduce an interaction between input tariff and importer of intermediate goods status (column (6)).

Comparing the coefficients of tariffs on capital goods with those on intermediate goods (column (6)) reveals that the indirect effect of input tariffs cuts on the probability of upgrading capital goods is of a similar magnitude to the direct effect of reducing capital goods tariffs. The estimated coefficient of variable input tariff cuts implies that a 10 percentage point fall in input tariffs leads to 1.5% to almost 1.7% increase in the probability of importing capital goods for the average firm and for those actually importing intermediate goods. Between 1989 and 1997, input tariffs declined on average by 27 percentage points, with an associated implied increase in the probability of importing capital goods of about 4.6 for the average firm importing intermediate goods. These findings suggest that the additional gains from input-trade liberalization thanks to the complementarity channel of imported inputs and capital goods are non negligible.

> 30As previously mentioned, unfortunately the Indian firm level dataset does not allow us to test the quality upgrading channel of imported inputs or to distinguish the country of origin of
|**var0308_skills**|0.0015***|0.0010***|0.0013***|0.0004*|0.0013***|0.0025***|0.0011|0.0011|0.0010|0.0009|0.0011|0.0008|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**var0308_all_DB**|0.0052***||||||0.003**||||||
||(0.000)||||||(0.000)||||||
|**var0308_Busin. Oper**||0.0041***||||||0.002**|||||
|||(0.000)||||||(0.000)|||||
|**var0308_Permit**|||-0.0163***||||||-0.0024||||
||||(0.002)||||||(0.009)||||
|**var0308_Tax**||||0.0192***||||||
comprehensive cross-country database for 194,000 manufacturing and services firms in 155 countries. The surveys, however, include mostly formal establishments with five or more workers, and therefore exclude micro-enterprises and informal firms, with a few exceptions. This focus on small to large formal firms might lead to a potential bias in the representation of the demographics of the firm in Africa (Li and Rama, 2015).

Another source of data on firms is the World Bank Entrepreneurship Database (WBED), which was not used in this analysis. WBED provides information on the number of private formal companies with limited liability for developing and developed economies on a yearly basis. This data would, hence, form a lower threshold for the number of firms in these countries. The database also provides information on firm entry and exit for selected economies derived from national business registries. A comparison between WBED and OECD’s _Structural and Demographic Business Statistics database_<sup>6</sup> , where available for both, reveals that firm exit may not be accurately captured because it requires firms to file the legal closing of the establishment with government entities to be included in the number of exiting firms. This might cause an upward bias in the number of formal firms, the extent of which is unclear. WBED also lacks information about informal or unregistered establishments. For these reasons, we exclude the WBED from this analysis.

> 5Aggregate numbers also prevent breaking down by size category because of idiosyncratic thresholds used in every context. Harmonization was attempted but the proposed analysis ultimately required microdata, restricting further the number of countries with census microdata at our disposal.

> 6Source: https://www.oecd.org/sdd/business-stats/structuralanddemographicbusinessstatisticssdbsoecd.htm

6
# **1 Introduction**

Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entrylevel jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017).

We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. By randomly assigning similar fictitious applicants to advertised entry level jobs, we measure the extent of ethnicity-based (Malay/ Chinese/ Indian) and gender-based (male/female) discrimination in the Malaysian labor market. We also test whether employers respond to signals of two soft skills: teamwork and leadership, both randomly assigned to applications and resumes through the presence of statements that signal each skill (e.g. demonstrated ability to contribute to teams). Our primary contribution lies in testing how labor market competition and soft skills interact with ethnicity and gender to narrow or widen the discrimination gap.

We find evidence of ethnicity-based, but not gender-based, discrimination in Malaysia. Relative to individuals with Chinese-sounding names, companies in Malaysia are 13 percentage points less likely to contact
71.8|69.4|47.3|75.2|71.8|68.8|50.6|
|**Total**|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|

_Source:_ Authors’ calculations based on SEAD data.

_Notes_ : “VL”, “L”, “M”, and “H” respectively stands for “Very low”, “Low”, “Medium”, and “High”. The share of skills splits between digital, cognitive, and socioemotional, and other skills. Digital skills levels and cognitive and socioemotional skills subsets are subcategories counted in the total.

46
Turning to the empirical reason, inequality measures of total income are not available annually; we have inequality statistics for most countries only for a few years in a decade. The Deininger-Squire database, for example, gives on average an inequality statistic for 1 out of every 5 possible country/year combinations. In contrast, the two databases on wages that we use – Freeman and Oostendorp (2000) and the University of Texas Inequality Project (UTIP) database – have annual data for a large number of countries and years. This should increase the power of our empirical estimation and tests. Moreover, these databases provide information on inter-occupational and inter-industry wage inequality as required by the theoretical approach.

# **_A  model_**

We begin with the following simple 2x2 model with two sectors (protected and unprotected) and two types of labor (unskilled and skilled).<sup>7</sup> We shall make two different assumptions. First, that collective bargaining is skill-based (all unskilled workers are unionized, and only unskilled workers are unionized), and second, that collective bargaining is sector-based<sup>8</sup> so that the benefits of unionization are shared by all workers in a sector regardless of their skill level.

_Skill-based bargaining_ . Let us start with skill-based unionization and let the basic unskilled wage be w, and unskilled wages in protected (p) and unprotected sector (u) respectively, where the first subscript denotes type of labor (skilled= _s_ ) and the second subscript the sector, _t_ = tariff rate (protection level), _λ_ = share of increased protection that is “transferred” to wages, and _s_ =percentage by which wage is increased thanks to collective

> 7 Since labor does not move between the sectors, one can view it also as four types of labor.

> 8 The terms “industry” and “sector” are used interchangeably.

14
the sales of the services by the foreign-owned entities, generally termed foreign affliate trade in services (FATS), and not the value of the associated foreign direct investment flow or stock. The fourth mode of supply, mode 4 or presence of natural persons, refers to a process through which individual service suppliers temporarily move to the country of the consumer or client to provide the service. An example would be management consulting services provided by an employee of a firm based in the UK to a client in Russia. Although technology is making mode 1 increasingly feasible, for many services the need for provider and client or consumer to be in the same place (the proximity burden) remains so strong that delivery must be local, so that foreign ownership (establishment) or temporary movement of provider or consumer is required (modes 2, 3 and 4).

The reason why trade in services is defined to span these four modes reflects the characteristics of services. Most services are intangible and they are often difficult to store. As a result they are inherently more difficult to trade than tangible goods. Many services can be exchanged only if a provider and consumer – or client are in the same place at the same time which will imply that for trade to occur use will need to be made of modes 2, 3, and 4. An implication for modeling and analysis is that ideally account needs to be taken of all four modes, the factors that affect the costs of using alternative modes and the degree to which the modes are complements or substitutes.

Services trade data are available from a number of sources. The OECD, Eurostat and the UN all provide data in some form on bilateral services trade flows (both imports and exports) by partner for up to 24 sectors and subsectors. The most comprehensive coverage of reporting countries among the three sources is the UN, which at present count provides data on roughly 190 countries. Eurostat and OECD provide data for a more limited number of reporters, though with more sector detail than
that converts high-resolution marine-light intensity to actual boat counts. This algorithm enables us to obtain a monthly gridded panel dataset of fishing intensity (i.e. boat counts) for the entire Vietnam’s maritime Exclusive Economic Zone (EEZ) between 2013 and 2016.

# **3.1.1 Vietnam labor force surveys**

The Labor Force Survey is conducted annually by the General Statistics Office of Vietnam. The surveys in 2015 and 2016 include 689,747 and 814,611 individuals, respectively. LFS provides primary labor-market information including employment status, income and workload of workers at a quarterly basis. Household members are selected from a stratified random sampling method, which ensures representativeness by province and industry. The sample includes all family members of interviewed households. In addition, there is a representative sub-group of households randomly selected to be surveyed twice a year, with the first survey visit taking place in the first or second quarters, and the subsequent revisit taking place in the third or fourth quarters.<sup>7</sup> For the purpose of analysis, we restrict our sample to only working-age individuals between 18 and 70 years old.

We focus on the labor outcomes specifically in saltwater fishery industry, and examine the changes in these outcomes before and after the _Formosa_ incident. Our main sample includes all individuals who worked in the saltwater fishery industry before the disaster in 2016 (i.e. in Q1-2016). Thus, the fishers living in Ha Tinh, Quang Binh, Quang Tri, and – – Hue the four _Formosa_ -affected provinces serve as the treatment group. As we will elaborate further, we exploit both the location-specific and industry-specific characteristics of _Formosa_ to obtain the two separate groups of counterfactuals. First, because _Formosa_ took place strictly in the central provinces, we form a “geographic” control group consisting of non-affected fishers located in other coastal provinces. To avoid the concerns related to contaminated counterfactuals, we restrict to only fishery workers living in the southern provinces distant from Formosa, specifically
Assignment of the program is based on a clear rule. Of the 34 districts in Punjab at the time of its launch in 2004, the program was offered to the 15 districts with the lowest adult literacy rates, which results in a cutoff literacy rate of 40 percent. Data from before the program’s conception were used for the assignment variable. Thus, there is no risk of manipulation of the assignment variable by program administrators or beneficiaries. And to our knowledge, this variable (or correlated variables) and, importantly, the cutoff have not been used in assigning any other programs.

Exploiting the program assignment process that generates a cutoff, we evaluate program effects on the basis of a sharp regression discontinuity (RD) design. Because the program assignment variable is not continuous but discrete, with 34 mass points (given 34 districts), local randomization-based RD (Cattaneo, Idrobo, and Titiunik, forthcoming a, forthcoming b) is a better fit than the parametric and continuity-based nonparametric methods typically used in RD applications. Local randomization-based RD uses the smallest window that yields two consecutive mass points (two districts), one on either side of the cutoff. We apply this approach to government administrative data from annual censuses of government schools. These data are independent of those used by the government to administer the CCT program. Constructing a school-level panel with the census data, we examine program effects in the RD program district on school-level changes in girls’ enrollment in secondary grades across different subperiods from 2003 (before the program) until 2015.

The Punjab Female School Stipend Program has been evaluated in multiple studies (Population Council 2007; Hasan 2010a, 2010b; Chaudhury and Parajuli 2010; Alam, Baez, and Del Carpio 2011). These evaluations have used various sources of data, including annual government school censuses, covering periods extending as far as 2009. The evaluations tend to find significant, positive effects on girls’ secondary school enrollment, among other 4
- Wu, J., He, S., Peng, J., Li, W., and Zhong, X. (2013). Intercalibration of DMSP-OLS night-time light data by the invariant region method. _International Journal of Remote Sensing_ , 34(20):7356–7368.

- Zhang, Q., Pandey, B., and Seto, K. C. (2016). A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data. _IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING_ , 54(10).

32
Fonseca, 2015; Sorsa et al., 2015; Klasen et al., 2021; Lopez-Acevedo et al., 2021a; LopezAcevedo et al., 2021b). In contrast, our paper explores different data sets for Guatemala and is thus able to differentiate the impact of a wide set of factors.

Second, we explore nationwide data and within-country dispersion by relying in the two most recent population census collected by the Guatemala National Institute of Statistics (INE, in its Spanish acronym). Differences across more than 1.5 million female household heads or spouses in active age in 333 municipalities and 22 departments in Guatemala, between 2002 and 2018 are empirically explored. Furthermore, the 2018 census is combined with unique survey and administrative data at the subnational level. In contrast, most studies exploring within-country variation in FLFP typically rely on household or labor force surveys alone.

Third, our empirical specification accounts for differences across the country in migration and rates of crime and violence. These are well known drivers of FLFP and development more broadly in the Central America region. On the one hand, the number of Guatemalan who live abroad in 2019 reached 6.9% of the overall population (Instituto Guatemalteco de Migración, 2021). Remittances are, therefore, a very important driver of consumption and growth in the country and likely impact the women’s decision to participate in the labor market (Amuedo-Dorantes and Pozo, 2006; Antman, 2015). On the other hand, Guatemala is a country with high levels of crime and violence, and particularly of gender-based violence. In 2015, female homicides reached 7.6 per 100,000 females, well above the averages of 2.2 worldwide, 3.8 in Mexico or 4.4 in Colombia (World Bank, 2021). On the other hand, the percentage of women aged 15-49 years old subject to physical or sexual violence in the last 12 months in Guatemala was 8.9 in 2015, below the rates of countries such as Mexico (
Heterogeneity by Development Level and Country**

The 25 Asian countries included in the analysis are at distinct stages of development. It is possible to examine whether the relationship between agglomeration effects and firm innovation varies with the level of development by dividing the overall sample into upper-middle income countries and low and lower-middle income countries based on the latest World Bank country classifications.<sup>24</sup> Probit regressions are estimated for both subsamples and results are reported in Table 5.

For low and lower-middle income countries, encompassing about 75% of firm observations in 345 cities, the agglomeration effects for product innovation and R&D are positive and comparable to the estimates from the full sample with 1% statistically significance. For process innovation, the marginal effect is estimated at 0.019, around half of that for the full sample, with 5% statistical significance.

> 23 The sensitivity of estimates to the exclusion of small cities which have only a small number of firms surveyed by the WBES is also considered using thresholds of 10 and 20 firms per city. The former yields 264 cities and around 20,000 firms and results are close to baseline estimates; the city size coefficients for all three innovation categories are positive and significant at 1% level. The more aggressive exclusion results in a subsample of 193 cities and around 19,000 firms. The estimate for product innovation remains almost identical to the full sample estimate. For process innovation and R&D, however, both coefficients halve in size and are marginally significant.

> 24 The World Bank defines low income economies as those with a gross national income (GNI) per capita of $1,025 or less, lower-middle income economies between $1,026 and $3,995, and upper-middle income economies between $3,996 and $12,375.

15
# Never Too Young to Dream Big: Measuring Youth Aspirations in the Time of the COVID-19 Pandemic

Valentina Costa (World Bank)<sup>1</sup> Ivette Contreras (World Bank) Amparo Palacios-Lopez (World Bank) Key words: Youth, aspirations, education, employment, migration, COVID-19, Sub-Saharan Africa JEL Codes: I24, J13, J16

> 1 <mark>Corresponding author: vcosta@worldbank.org . This work has been supported by the World Bank Multi-Donor Trust Fund for Integrated Household and Agricultural Surveys in Low and Middle-Income Countries (TF072496). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Calogero (Gero) Carletto (Development Data Group, World Bank), Alemayehu Ambel (Development Data Group, World Bank), Kevin Robert McGee (Development Data Group, World Bank), Vanya Slavchevska (FAO), and Alberto Zezza (Development Data Group, World Bank) and all the participants to the IFAD Conference Jobs, Innovation and Value Chains in the age of Climate Change (June 2022) for their valuable comments on an earlier draft. Professional English editing was provided by Olutola Oni Jordan. Any remaining errors are the authors' sole responsibility.</mark>
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

CDR data to track short-term mobility in the hours and days after Cyclone Mahasen hit Bangladesh in May 2013 (Lu et al., 2016).

# **B. Methodological innovations and CDR data**

The most commonly used methods for processing CDR data are traditional data mining techniques. These include frequency-based analysis, data clustering using unsupervised machine learning, and geovisualization techniques by mapping geolocation (Calabrese, Ferrari, & Blondel, 2014). In recent years, researchers have used de-identified CDR data to compute Origin-Destination matrices in order to better map patterns in travel behavior (Calabrese, Di Lorenzo, Liu, & Ratti, 2011). In combination with additional administrative or survey data, supervised machine learning methods can inform the prediction of outcomes of interest-based solely on patterns in the cell network (Sundsøy, Johannes, Reme, Iqbal, & Jahani, 2016).

The pre-processing of the raw CDR data is essential to accommodate positioning errors in data collection and the first step for processing. The oscillation problem of the user’s location is the leading cause of noise in position data collected from the cellular network as they transfer calls to the nearest base station for traffic management, creating imprecise and overlapping Voronoi polygons (Chen, Ma, Susilo, Liu, & Wang, 2016). The time-based filter is used to ignore oscillation and agglomerative (hierarchical clustering) in methods to extract truthful location data from raw CDR.

However, handling such sensitive data requires appropriate protocols to address concerns around data privacy. While anonymizing data is necessary, Kondor et al. (2015) show that it is theoretically possible to identify users based on their mobility patterns alone (Dániel Kondor et al., 2015). It is, therefore, best practice to restrict access to individual observations and use aggregated indicators for the purpose of analysis.

# **C. Country context: The Gambia**

The Gambia is a West African country