rafmacalaba/datause-encoder-data huggingface dataset:
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produces an index that is biased upward to some degree, as compared with that for the full list of developed countries, at least during the 1980s. For 1980-89, a unit value index with 1989 export value weights for the 8 countries rose by 2 per cent, or about .2 per cent per year relative to the 25-country unit value series.

# Conclusions

It seems likely that certain characteristics of the UN export unit value index for manufactures cause it to be biased upward as a measure of the prices of manufactured goods exported by developed countries to developing countries. It also appears that these biases could be large enough to offset the long-term deterioration in export prices of commodities relative to the UN unit value index for manufactured exports that has been calculated by proponents of the belief t-hat there has been such a deterioration. Thus, the case is weak that there has been a long-term deterioration in commodity prices in general relative to a price index, adjusted for quality change, of manufactured exports from developed countries to developing countries.

The main ingredient in this conclusion is the adjustment for quality change, which we estimate to be at least .5 per cent per year, just about enough by itself to offset the reported deterioration in relative coamodity prices. We find an additional "bias" of about .35 or .40 per cent per year from the difference between our unadjusted index for exports by developed countries to all countries and the UN Unit Value Index, possibly, but not.necessarily, due to the use by the UN of unit value data rather than specification price data. The country selection for our index might bias it upward relative to a more comprehensive index by about another' .2 per cent per year, although we have
# **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 candidates with Malay-sounding names, and 15 percentage points less likely to contact candidates with Indian-sounding names. These findings are persistent at all stages of the hiring process. Discrimination is more likely to occur in large companies, jobs that offer high wages, and companies that have lower average processing times. We present weak evidence that the 2
# **Figure 3: Comparison to January 2016 Fan Chart**

_OLS-based risk weights suggest lower risks to growth and a smaller downside bias than VARbased risk weights, especially in 2016. This reflects the failure of OLS estimates to take into account the persistence of growth shocks._

# **VAR-based estimates**

<!-- Start of picture text --> Percent<br>5 50 percent 5<br>80 percent<br>90 percent<br>Baseline<br>90 percent in JAN16<br>3 3<br>1 1<br>2014 2015 2016 2017<br><!-- End of picture text -->

# **OLS-based estimates**

<!-- Start of picture text --> Percent<br>5 50 percent 5<br>80 percent<br>90 percent<br>Baseline<br>90 percent in JAN16<br>3 3<br>1 1<br>2014 2015 2016 2017<br><!-- End of picture text --> Source: World Bank Staff estimates.

Note: “90 percent in JAN16” is the 90 percent confidence interval of a fan chart based on data available for the January 2016 _Global Economic Prospects_ report.

# **Figure 4. Probability of Growth Outcomes**

_The probability of global growth falling below the baseline forecast of [2.5] percent in 2016 and 2017 is 70 and 50 percent, respectively and, based on OLS estimates, 85 and 40 percent, respectively._

## **A. Probability of global growth below threshold: VAR-based estimates**

## **B. Probability of global growth below threshold: OLS-based estimates**

<!-- Start of picture text --> Percent<br>100<br>2016<br>80<br>2017<br>60<br>40<br>20<br>0<br>0.0 1.4 1.9 2.4 2.9 3.5 4.0<br>Threshold (percent)<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>100<br>80 2016<br>2017<br>60<br>40<br>20<br>0<br>0.0 1.4 1.9 2.4 2.9 3.5 4.0<br>Threshold (percent)<br><!-- End of picture text --> Source: World Bank staff estimates.

13
similarly, both younger and older adults decrease their participation in wage employment while increasing participation in self-employment. Likewise, both types of adults have similar responses in their intensive margins - lower weekly labor supply (Table 7).

Although outmigration of some family members has similar eect on the intensive margins for wage-work and own work, there is a signicantly dierent eect on the participation rate by household head status. The left-behind household heads are 41.4% less likely to participate in paid work compared to 23.1% of the other household members. Concurrently, outmigration of family members does not aect the household head's decision to participate in self-employment activities but signicantly increases other members' involvement, 29.8% (Table 8). This corresponds well with the intuition that the household heads are likely to have higher bargaining power within the household and are likely to receive remittances sent by the migrants, which in turn, may further increase their bargaining power.

# 6 Discussion and Conclusions

In this paper I use a unique source of nationally representative data during the period that Nepal experienced a boom in outmigration. The data set contains detailed information on time allocation of every individual in the household, which allows me to extend the analysis further and answer the question that previous studies on the topic could not answer in Nepal. Using the NLSS III data set, this paper explores the impact of migration on labor supply of the left-behind household members both on extensive and intensive margins for wage-employment, self-employment, and household activities. The paper also provides an answer to the question, what are the remaining members in the households engaged in instead of work employment?

I nd that having international migrants in the family discourage members staying behind from participation in wage-employment. This is true for both male and female members. However, female members increase participation in self-employment, almost entirely through subsistence farming. The paper also nds that both self-employed and wage-employed adults decrease weekly hours of labor supply and only women in the migrant-sending household increase time in household activities. Findings presented in the paper suggest that male-dominated migration forces women to realign priorities and reallocate time from market-work to farming and household activities. In contrast, 17
movement for a large number of people between health facility catchment areas at the daily level. I leverage mobile phone metadata for 9.5 million SIM cards in Senegal in 2013 to extract patterns of movement between dierent areas from the approximate locations of 15 billion calls and texts. For each month and health facility area, I measure the number of incoming travelers from other regions weighted by the incidence of malaria in these regions and the length of time spent in the origin and destination to calculate "expected imported malaria cases." I study an area of Senegal close to elimination to focus on reintroduction eects. I use a panel data strategy to estimate the impact of imported incidence on total malaria incidence in this low-malaria setting using a linear dynamic panel-data model and controlling for time xed eects. If infected travelers only lead to a malaria case being detected in the destination rather than the origin of the traveler, but do not generate any externality in the form of additional malaria cases, then a standard model would predict for each expected imported case one more additional case reported in the destination. Instead, I nd that one additional expected imported case of malaria in a low malaria area leads to 1.7 cases of malaria reported, indicating an externality of .7 new cases.

Given that migration has numerous economic and social benets, policymakers face trade-os between economic growth and improving public health in designing policies to reduce travel-linked malaria cases. This paper provides a useful framework for strategic targeting of high-risk populations in low-incidence areas to reduce negative externalities from travel with minimal interference to travel patterns. There are two categories of targeting considered: (1) targeting high-risk travelers entering a low malaria area from a high malaria area and (2) targeting all travelers in only specic areas of low-malaria regions that are likely to receive many high-risk travelers.<sup>5</sup> Within each type, I compare a strategy that incorporates daily information on origins and destinations of travelers from mobile phone data with strategies that only use information on incidence in the previous year that could be implemented by the government in the absence of mobile phone data. The most cost-eective

> 5This paper does not focus on the type of targeting, but examples can include information campaigns targeted to travelers via mobile phone and strategically setting up testing sites.

3
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employment. Using estimates from the World Bank's later study of the demand and supply of labor in Colombia's formal and informal sectors suggests that the addition could be in the order of 70-75,000 jobs. The reduction of 0.5-1.1 percentage points on a 13% unemployment rate implied by these studies is not trivial and suggests that finding different ways of financing these activities merits serious consideration.<sup>5 2</sup>

# V. SUMMARY AND RECOMMENDATIONS

69. Over the past two decades, earmarking in Colombia has trended upward and the sources of earmarking have become more varied. The rise in overall earmarking (in the "traditional" sense) from 21% to about 35% of revenues at all levels of government is the product of a substantial rise in national government revenues earmarked from 11% to 28% and falling proportions of revenues earmarked at the departmental and municipal levels from 94% to 77% and 30% to about 20% respectively. During this period the share of the national government in earmarking at all levels rose from under one half to over three quarters. In 1970, departmental taxes on alcohol/tobacco/gambling were the largest source of earmarking followed by the coffee export tax and gasoline tax at the naticnal level and the valorization tax at the municipal level. During the past two aecades, revenue sharing (the tax allowance and sales tax transfer), import duties for PROEXPO (et. al.), and, to a lesser extent, a number of new minor funds have increased the variety of earmarking.

> 52/ **See** H. Gom6z Buendia **and** A. Cifuentes **Noyes** "Seguridad Social Y Empleo" in El Problema Laboral Colombiano, Tomo 2, J. Antonio Ocampo y M. Ramirez (eds.), (Servicio Nacional de Aprendizaje, Departmento Nacional de Planeacion, Contraloria General de la Republica; Bogota, 1987), pp. 115-139; and IBRD op. cit., Annex 3.
# **Table 6 – Local banks: Adverse reform spillovers affecting financial sector structure and development**

_Please indicate the reform areas where you have a reasonable indication that their adoption in other jurisdictions has already had, or is expected to have, a significant adverse impact on the activity in and development of the domestic financial sector (you can select more than one reform area)._

|**N = 20**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing**<br>**with OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't**<br>**know**|
|---|---|---|---|---|---|---|---|---|
|Foreign financial institutions’ participation<br>in local markets (e.g. withdrawal or<br>deleveraging)|13|4|7|6|4|1|5|1|
|Overall competition in the financial sector|12|6|13|8|3|1|6|0|
|Shadow banking (i.e. the rise of potentially<br>risky credit intermediation outside of the<br>regular banking system)|5|2|5|2|1|0|8|4|
|Non-bank financial institutions such as<br>insurance companies, pension funds, and<br>mutual funds (e.g. size, stability, pricing,<br>product offering, and efficiency of these<br>sectors)|5|2|4|2|0|0|7|7|

23
workers to the total number of workers in the household.<sup>28</sup> They are estimated by Ordinary Least Squares and the results are summarized in Table 17.

_<u>Table 17: Regression for logarithms of income changes</u>_

||<br>**(segme**<br>**Full**|<br>**nted)**<br>**lib.**|**(segme**<br>**CAF**|**nted)**<br>**TA**|
|---|---|---|---|---|
|||**Robust**||**Robust**|
||**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|
|**_Household head's characteristics_**|||||
|**age**|0.019|0.012|***0.030|0.011|
|**age squared**|-1.20E-04|1.14E-04|**-2.20E-04|1.04E-04|
|**female**|-0.035|0.061|***-0.174|0.057|
|**education**|||||
|**(no education)**|||||
|**primary or less**|***0.323|0.065|***0.414|0.063|
|**secondary**|***0.407|0.095|***0.423|0.093|
|**technical**|***0.428|0.149|***0.369|0.128|
|**university or above**|0.271|0.288|***0.994|0.146|
|**_Demographic variables_**|||||
|**logarithm of hhsize**|***-0.363|0.071|***-0.235|0.064|
|**ratio of no. of infants (0-5) to hhsize**|-0.283|0.244|-0.245|0.221|
|**ratio of no. of children (6-12) to hhsize**|0.278|0.198|0.199|0.190|
|**_Geographic variables_**|||||
|**urban**|0.021|0.092|*-0.140|0.079|
|**regions**|||||
|**(Managua)**|||||
|**Pacific**|***-0.296|0.070|***-0.381|0.065|
|**Central**|***-0.415|0.084|***-0.333|0.076|
|**Atlantic**|-0.142|0.102|0.059|0.098|
|**_Employment variables_**|||||
|**Ratio of wokers to household size**|0.164|0.140|***0.894|0.132|
|**Ratio of agri. workers to total workers**|***-2.470|0.115|***-2.536|0.098|
|**Ratio of skilled workers to total workers**|**0.212|0.088|***0.405|0.083|
|**Constant**|***5.063|0.309|***3.735|0.285|
|**R2**|0.4|3|0.4|7|
|**No. of observations**|416|9|416|9|

Note: The dependent variables are the percentage change from the initial income. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively.

As expected, the two scenarios display similar results. Education variables unambiguously show that the more educated the household head, the larger the percentage gains of the household, which seems to coincide with the positive coefficient

> 28 A similar regression analysis is found in Chen and Ravallion (2003).

42
**<u>Table 7: Type of remarriage for widows by union and husband’s characteristics.</u>**

||Co-resides<br>with husband|Polygamy|Husband works in<br>the formal sector|
|---|---|---|---|
|_Ref: dissolution after age 40. _||||
|Dissolution before age 25|0.379<sup>***</sup>|-0.261<sup>***</sup>|0.035|
||(0.090)|(0.101)|(0.101)|
|Dissolution between ages 25-39|0.283<sup>***</sup>|-0.134|-0.017|
||(0.081)|(0.099)|(0.091)|
|Last dissolution; rural resident|0.009|0.128<sup>*</sup>|-0.097|
||(0.077)|(0.074)|(0.075)|
|No children born in last union|0.309<sup>**</sup>|0.156|-0.294<sup>+</sup>|
||(0.138)|(0.214)|(0.179)|
|Had a son at widowhood|-0.208<sup>***</sup>|-0.031|-0.000|
||(0.072)|(0.086)|(0.080)|
|Number of marital dissolutions|0.128|0.021|0.029|
||(0.093)|(0.085)|(0.078)|
|Ever been to a French school|-0.192<sup>+</sup>|-0.097|0.400<sup>***</sup>|
||(0.117)|(0.088)|(0.083)|
|Polygamous father|-0.151<sup>*</sup>|-0.058|-0.146<sup>*</sup>|
||(0.079)|(0.077)|(0.077)|
|Fostered before age 15|-0.129|-0.014|0.152<sup>+</sup>|
||(0.087)|(0.083)|(0.095)|
|_Ref: Wolof/Lebou._||||
|Serere|-0.399<sup>***</sup>|0.015|0.249<sup>**</sup>|
||(0.127)|(0.124)|(0.118)|
|Poular|-0.155|-0.065|-0.015|
||(0.100)|(0.107)|(0.104)|
|Other ethnicity|<br>-0.161<sup>*</sup>|<br>-0.139|<br>0.019|
||(0.098)|(0.092)|(0.103)|
|Mean of dep. var|0.51|0.74|0.33|
|N|133|132|123|
|Pseudo R2|0.265|0.106|0.223|

Note: Logit model, marginal effects shown. Sample of ever-widowed women.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1, + _p_ < 0.12.

Source: Authors’ estimations using PSF1.

38
female sectoral participation (Zone I) in 2019 and the growth of employed persons between 2019 and 2021, indicating that sectors with high female participation were more affected by the contraction in labor demand than sectors with a high concentration of men (Zone II). This pattern is also evident in the job vacancies offered by the public employment services, even though formal demand for the health and financial sectors increased during the same period (Figure 10).

In 2022, DANE changed its expansion factors, making it difficult to compare information for years before 2021. However, a review of the trend of participation and unemployment for the 1st semester of 2021 and 2022 (with the 2018 factors) shows that the recovery trend is positive for both men and women (Table 1).<sup>7</sup> The participation rate has increased in both groups, especially for women, and the unemployment rate has contracted in both groups,<sup>8</sup> with the reduction being more significant in women. Therefore, it can be deduced that there was a gap reduction for both indicators. In 2022, the growth of women's labor demand has been driven by growth in the service sector, such as professional services, accommodation, food and entertainment, commerce, and industry, with more than 10% growth rates pared to 2021. Among these groups, only the industry grew more rapidly among men.

Table 1. Labor Market Indicators 2021-2022

||||1er semester<br>||Chang<br>|e (p.p.)<br>|
|---|---|---|---|---|---|---|
||Indicator|2021<br>(1)|2021<br>(2)|2022<br>(3)|(3)-(1)|(3) - (2)|
|Mn|GPR|72.0%|75.7%|76.6%|4.6|0.9|
|e|UR|12.0%|12.9%|9.6%|-2.4|-3.3|
|Women|GPR|49.1%|48.2%|51.5%|2.4|3.3|
||UR|20.1%|19.4%|15.6%|-4.5|-3.8|
|Expansi|on factors|2005|2018|2018|||
|||S|ource: GEIH.||||

# **The lack of recovery can be attributed to persistent structural barriers that impede access for women with children and those with lower educational levels, who face fewer economic opportunities.**

Women with children under 12 years of age and those with lower levels of education face persistent structural barriers that limit their economic opportunities. Higher education is a protective factor against job losses for women compared to those without it, although there is still a significant gap between women with and without higher education (as shown in Figure 11). In terms of labor participation (Figure 12), there is a wide gap between women with and without higher education, which can be attributed to the latter being more concentrated in the informal sector (Figure 13).

> 7 Columns (1) and (2) compare the result of the 1st semester of 2021 with both expansion factors, where the gap generated by the change in the projections can be seen. It is particularly strong over the GPR given the change in the working-age population decision (10 to 15 years).

> 8 In this comparison, all ages are used because the aggregate annex of the GEIH is used.
# _Individual-level skills and industrial emissions_

To test the hypothesis that skills of employees in an industry are associated with lower emissions per unit of output of the industry, the first data set was created by merging the OECD’s Programme for International Assessment of Adult Competencies (PIAAC) data sets with industry-level data from the EU’s air emissions accounts (EUROSTAT 2021a) and national accounts aggregates and employment by industry (EUROSTAT 2021b, 2021c). PIAAC is a nationally representative household survey of individuals aged 15 to 64 which collected data on individual literacy, numeracy and problem solving skills based on a standardized test as well as background data on employment including earnings, education and on other demographics. Included in the employment data is the industry employment for those employed using the ISIC Rev 4 coding. PIAAC data for the EU countries used in this study were conducted in 2012. The EU emissions accounts data set provides data on various types of emissions by industry based on the NACE rev 2 coding system. There were values for approximately 60 industries per country, depending on the country. For this study, carbon emissions were used. The EU’s national account aggregates and employment data sets provide data on output in terms of value-added and employment numbers for each industry also coded using the NACE rev 2 system. Both the level for 2012, to match the PIAAC year was used as well as the annualized growth from 2010 to 2019.

Merging the EUROSTAT data to the OECD PIAAC data by industry was conducted by aggregating the EUROSTAT data to ISIC Rev 4 coding using a NACE rev 2 and ISIC Rev 4 mapping data set provided by European Commission (2021a). This was used to map values for the variables of interest derived from the EUROSTAT datasets for the four types of ISIC Rev 4 coding, the 4-, 3-, 2- and 1- digit levels. In cases where NACE Rev 2 value mapped to more than one ISIC code, particularly at the 4- and 3- digit levels, the same value was used for each ISIC code. In cases where more than NACE Rev 2 value mapped to a single ISIC code, then the average of the values from the NACE Rev 2 codes was applied. Values at the 4-, 3-, 2- and 1- digit ISIC coding were then merged with PIAAC country datasets based on the finest ISIC coding reported by the individual (e.g., in some cases the 4- and 3- digit ISIC code was missing for an individual but a 2- digit level was reported). Table 1 presents summary statistics from the resulting skills and industrial emissions data set.

11