rafmacalaba/datause-encoder-data huggingface dataset:
Among Vietnam’s wage workers,<sup>20</sup> the “informal wage” employers comprised the largest wage employment sector hiring about 6.8 million people in 2004.<sup>21</sup> Workers who worked for the state sector earned the highest hourly wage on average (VND 7,106) followed by workers working for the foreign (VND 6,783), private (VND 5,112) and informal wage sectors (VND 3,902).

Columns 2 and 3 of Table 2 report summary statistics by gender. The wage differential between men and women, known as the “gender gap”, is relatively low as women earn on average an 11 percent lower wage relative to men for the whole sample. At first glance, women working in foreign firms appear to be experiencing the greatest degree of gender wage inequality as women are found to be paid about 40 percent lower wages relative to male counterparts whereas the gap is narrowest in the state sector (8 percent).<sup>22</sup> Columns 4 and 5 of Table 2 disaggregate statistics by skill levels which are measured by whether or not the workers have upper-secondary educational attainment (12 grades completed with upper-secondary education diploma). The wage premium for skilled workers, defined as the relative wage of skilled to unskilled workers, is 176 percent for the whole sample. The skill premium is largest in the foreign sector (where skilled wages are 183 percent of unskilled wages) and lowest for those who work for the informal wage sector (132 percent).

# **3.2. Model Specification**

> 20 About 31 percent of Vietnam’s workers held a wage job as the first job in 2004. The remaining 69 percent were self-employed of which about 73 percent worked in agriculture/fishery/forestry activities.

> 21 The figure is computed using the household weights available in the VHLSS 2004. The corresponding figures are 0.7 million for “foreign”, 4.8 million for “state” (including government and SOEs), and 2.4 million people for “private” sector employers. The number of workers employed by foreign firms computed from the VHLSS 2004 is smaller than that taken from the _Enterprise Survey_ data (see Table 1), most likely because the VHLSS data exclude migrants who hold a temporary or no registration status.

> 22 The large gender gap in the foreign sector partly reflects the younger average age of female workers relative to male in this sector and the low-wage industries for which women work. Controlling for personal and other characteristics, the gender gap is found to be largest in the informal wage sector followed by foreign and domestic private sectors (see Sub-section 3.3 below).

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ratio has increased from 1.8 to 2.2. This evolution indicates a widening intra-generational inequality, given that the mean years of education also rose for the 25<sup>th</sup> percentile, only at a slower rate than for the 75<sup>th</sup> percentile. A similar pattern is observed across other Central American countries, suggesting an increase in intra-generation inequality, even as average years of education attainment increase across the board.

It is important to note that the existence and widening of intra-generation inequality will lead to the persistence or widening of inequality, as new generations replace older generations. Further, if intra-generation inequality is correlated with socio-economic status, this is cause for greater concern as it perpetuates socio-economic inequality.

|**Age **|**Table 2: D**<br>**Costa Rica**|**ifference**<br>**Panama**|**75 percent**<br>**Guatemala**|**ile-25 perce**<br>**El Salvador**|**ntile by ag**<br>**Honduras**|**e**<br>**Nicaragua**|
|---|---|---|---|---|---|---|
|||**Differenc**<br>|**e 75percentil**<br>|**e-25percentile**<br>|<br>||
|15-19|3|3|4|4|3|4|
|20-24|6|4|8|6|5|6|
|25-29|7|6|7|7|6|7|
|30-34|5|8|8|8|5|7|
|35-39|5|6|9|9|6|7|
|40-44|5|6|6|10|6|8|
|45-49|5|6|6|9|6|7|
|50-54|5|6|6|8|4|9|
|55-59|5|6|6|9|6|7|
|60-64|6|8|3|6|6|6|
|||**Ratio 7**|**5percentile/2 **|**5percentile**|||
|15-19|15-19|15-19|15-19|15-19|15-19|15-19|
|20-24|20-24|20-24|20-24|20-24|20-24|20-24|
|25-29|25-29|25-29|25-29|25-29|25-29|25-29|
|30-34|30-34|30-34|30-34|30-34|30-34|30-34|
|35-39|35-39|35-39|35-39|35-39|35-39|35-39|
|40-44|40-44|40-44|40-44|40-44|40-44|40-44|
|45-49|45-49|45-49|45-49|45-49|45-49|45-49|
|50-54|50-54|50-54|50-54|50-54|50-54|50-54|
|55-59|55-59|55-59|55-59|55-59|55-59|55-59|
|60-64|60-64|60-64|60-64|60-64|60-64|60-64|
|Source: Auth|ors’ calculations ba|sed on several h|ousehold surveys c|irca 2009.|||

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yellow (2.5 square meters between students) and orange (4 square meters between persons); and finally, municipalities classified in red were only allowed to conduct virtual schooling activities.

We employ a difference-in-differences strategy given that we have administrative records for all registered students in the education system over time. Therefore, we construct a panel that tracks the same students over 2018-2022, providing two pre-COVID-19 and two post-COVID-19 years. These data contain information on students, their schools, and their municipality of attendance. We believe these records are thus well-suited to estimate the effects of differential municipal exposure to COVID-19 infections on student-level educational outcomes in the shortterm.

Results show that that locations with greater exposure to COVID-19 infections had greater educational losses. Higher values of the COVID-19 warning index increased dropout rates between 0.7-0.8 percentage points across the Guatemalan education system, an increase of about 8.1% compared to the pre-pandemic average. We also see a reduction of 2.4% in effective promotion rates<sup>6</sup> and an increase in movers from private to public schools of 10% compared to pre-pandemic levels, with a corresponding reduction in the other direction (public to private schools) of 6.3%.

We explore further heterogeneity in two dimensions: over time and by educational level. Effects were stronger during the first year of the pandemic: dropout increased by 10.5% in 2020 and 5.8% in 2021; promotion fell by 3.4% and 0.7%; switching from private to public schools increased by 0.2% in 2020 and was unchanged in 2021, while the reverse direction shows a decrease of 0.2% in both years. We reject the null hypothesis that estimated effects are equal in 2020 and 2021 across outcomes and specifications. We also find greater losses for younger children from higher COVID-19 exposure. Effects for most outcomes are significant for initial

> 6 Effective promotion is a binary indicator that is equal to one if a student is observed today in a higher grade than they were registered in the previous year and zero otherwise. By definition, this variable excludes dropouts.

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|||Full Sample||Tr|imming Tail 5|%|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Panel A: SOE|sales-revenue|share in the Cou|nty in 1998|||
|_Power Structure_|||||||
|YRD|-22.866***|-22.610***||-20.370***|-20.353***||
||(6.494)|(6.585)||(6.304)|(6.407)||
|Guerrilla||-2.547|||-0.282||
|||(6.196)|||(6.322)||
|YRD×GuerrillaNo [_b_0]|||-21.343***|||-19.706***|
||||(7.418)|||(7.271)|
|YRD×GuerrillaYes [_b_1]|||-25.320***|||-21.454***|
||||(7.441)|||(7.404)|
|_H_0 : _b_0 = _b_1|||0.597|||0.818|
||Panel B: S|OE Count Sha|re in the County|in 1998|||
|_Power Structure_|||||||
|YRD|-15.965**|-15.157**||-13.466**|-13.020*||
||(6.697)|(6.784)||(6.608)|(6.806)||
|Guerrilla||-8.046|||-6.805||
|||(5.295)|||(5.427)||
|YRD×GuerrillaNo [_b_0]|||-11.735|||-9.559|
||||(7.214)|||(7.180)|
|YRD×GuerrillaYes [_b_1]|||-22.782***|||-19.747***|
||||(6.947)|||(6.844)|
|_H_0 : _b_0 = _b_1|||0.071|||0.131|
|Obs.|56|56|56|52|52|52|
|Controls for Initial Conditions|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls for Geography|Yes|Yes|Yes|Yes|Yes|Yes|

Table 18: The Effect of Factions and Guerrilla Presence on the Share of SOE in the County Economy in 1998.

Note: The dependent variable in Panel A is the SOE share of sales revenue in the county in 1998; the dependent variable in Panel B is the SOE share in terms of the number of firms (count share) in 1998. In the right panel, the 5% tail observations are trimmed from the analysis. All regressions control for the initial conditions and geography variables as in Table 17.. The last row of each panel reports the p-values of the hypothesis that _b_ 0 = _b_ 1. White standard errors are in parenthesis.

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Our results shed light on whether large bank size is desirable. In practice, bank managers have taken the decisions that have led to the considerable growth of banks around the world in the last two decades or so. This is prima facie evidence that such growth was in the interest of bank managers. Bank managers can benefit from size, as their status and pay may be positively affected by bank size.

It is less clear that large bank size is in the interest of bank shareholders. Our results suggest that bank growth may increase a bank’s rate of return in relatively large economies, but even then at a cost of more bank risk. In smaller countries, bank growth may have reduced bank return on assets, and increased bank risk. These findings suggest that bank growth has not been in the interest of bank shareholders in smaller countries, while there are doubts whether shareholders in larger countries have benefited. Inadequate corporate governance structures at banks may have enabled managers to pursue high-growth strategies at the expense of shareholders.

This paper builds on the literature dealing with bank size, business models and market discipline. Berger and Mester (1997) estimate banking returns to scale using US bank data for the 1990s to find an optimal banking size of around $25 billion in assets. In line with this, Amel, Barnes, Panetta and Salleo (2004, Table 2) report that commercial banks in North America with assets in excess of $50 billion have higher operating costs than banks in smaller size classes. This suggests that today’s large banks, with assets in some instances exceeding $ 1 trillion, are far beyond the technologically optimal point. Demsets and Strahan (1997) further find that large bank holding companies have a diversification advantage, as evidenced by lower idiosyncratic risk. Large bank holding companies, however, are not less risky as they tend to have lower capital ratios. These contributions on the scale and risk effects of bank size cannot distinguish between absolute and systemic size as they use data for only the US.

Several studies consider the risk and return implications of combining traditional banking with other financial activities. Among these, Stiroh (2004) considers how the impact of the activity mix of US banks, proxied by the share of interest income in total income, affects bank 4
Table 9: Response to Soft Skills

||(1)<br>Rejected|(2)<br>Profle<br>viewed|(3)<br>N of profle<br>views|(4)<br>Contacted|(5)<br>Interview<br>requested|
|---|---|---|---|---|---|
|Teamwork|-0.045**|-0.000|-0.013|0.000|0.011|
||(0.021)|(0.021)|(0.063)|(0.012)|(0.010)|
|Leadership|-0.004|-0.018|-0.012|0.000|0.001|
||(0.021)|(0.021)|(0.065)|(0.012)|(0.010)|
|Constant|0.343***|0.334***|0.714***|0.083***|0.050***|
||(0.015)|(0.015)|(0.045)|(0.009)|(0.007)|
|_R_<sup>2</sup>|0.002|0.000|0.000|0.000|0.000|
|N|2,995|2,995|2,995|2,995|2,995|
|_Teamwork = _|_Leadership_|||||
|_p-value_|0.052|0.379|0.979|0.995|0.361|
|_F-statistic_|3.789|0.776|0.001|0.000|0.833|

_<u>Note:</u>_ <u>This table presents the results of specifcation 6. The outcomes are: In column</u> 1 Rejected takes the value of 1 if the company disregarded the application as notsuitable. Column 2 Profile viewed takes the value of 1 if the profile was visited by the company at least once. Column 3 is the number of visits to the website profile. In column 4 Contacted takes the value of 1 if the Company contacted the candidate through email. Finally, column 5 Interview requested takes the value of 1 if the Company contacted the candidate requesting an interview. Robust Standard errors. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01 Table 10 presents the result of specification 7. In this specification we assess the differential effects of ethnic discrimination when candidates signal soft skills. We find that signaling teamwork skills improve the hiring chances of both Malay and Indian candidates in the hiring process. Having a signal of teamwork increases the likelihood of being contacted in 7 percentage points in comparison to the baseline category. In other words, signaling a collaborative soft skill attenuates the discrimination against Malay by 43%. For Indian-sounding names the teamwork soft skill attenuates the discrimination gap in 34%, but this result is only significant at the 10% level. Interestingly, the number of rejections decrease and the number of profile views increases for both ethnicities. However, these results are not statistically significant at the conventional levels. These results have important implications, hiring opportunities improve when candidates signal teamwork skills. We cannot reject the hypothesis of equality of these effects across Malay and Indian candidates.

Our results are interesting given that the signal is not endorsed by any certificate or institution. The soft skill signal is subtly displayed in three parts of the resume: 1) a sentence in the description of each of the previous job positions stating a collaborative role in a single task, 2) a bullet point stating teamwork and collaboration in a section describing personal skills, and 3) A sentence in the 27
# **_A profile on labor informality in Senegal_**

Labor informality is a widespread phenomenon in Senegal. The percentage of workers that can be classified as informal with the definition and data used in this study reaches the 93.7%, representing almost 98% of the employed population in rural areas and 89.5% among urban regions. A similar pattern can be seen across the eight WAEMU member countries using their respective 2018-19 EHCVM surveys, with average informality rates of 98.6% and 88.9% in rural and urban areas, respectively (Figure 3).<sup>9</sup> Informality is not only spread across rural and urban areas in Senegal, but also among different working conditions. Table 1 shows that the informality rate at the national level is almost 100% among unpaid workers, 97.5% within the group of employers and self-employed workers and lower but still very high for wage earners, with informality rates of about 84% nationwide and 80% in urban areas. In rural areas, the proportion of unpaid workers is much higher than in urban areas (32% and 12%, respectively), but even among salaried workers, informality is much higher.

In the Senegalese labor market, most of the workers are men, without any formal education and strongly concentrated in the Midwest and Capital regions, with important differences in the composition of the formal and informal populations. Table 2 shows that around 57% of all workers are male, 60% has no formal education (42.5% in urban areas) and most of them live either in the Midwest region (31%) or in the Capital (27%). Formal workers are even more predominantly male than their informal counterparts, as nearly 72% of formal workers are men. Moreover, formal workers are older, much more educated and typically married. While their mean age is almost 42 and more than 80 percent of them have a formal education and are married, the average age within the informal population is 35.6, and only 37% and 59% are formally educated and married, respectively. These socio-demographic differences between the group of formal and informal workers are greater in urban areas than in rural regions — where these two groups are slightly more similar— smaller in the sample that will be used to estimate

> 9 Each survey was carried out independently across WAEMU member countries. The sample size of the surveys is comparable in magnitude and the questionnaire has been harmonized, allowing for comparability as well as the standardization of variables across countries. These estimates differ from the informality rates per the ILO, which are based on a slightly different definition of informality and less-recent data.

7
**Table 5. Physical Access by Region**

||**Branches**<br>**per 100,000**|**Branches**<br>**per 1,000**|**ATMs per**<br>**100,000**|**ATMs per**|**POSs per**<br>**100,000**|**POSs per**|
|---|---|---|---|---|---|---|
|**Region**|**adults**|**km2**|**adults**|**1,000 km2**|**adults**|**1,000 km2**|
|SSA|5.1|5.8|11.7|12|5,695.1|1,801.1|
|SA|6.5|18.1|5.2|11.2|39.6|80.8|
|EAP|11.8|6.5|25.3|21.3|352.3|185.2|
|**MENA**|**14.3**|**11.8**|**31.1**|**21.4**|**641.3**|**774.7**|
|GCC|17.1|7.6|63.3|28.3|739.3|627.9|
|non GCC|13.1|13.7|16.8|18.4|608.6|823.6|
|LAC|15.8|15|44.1|32.6|881.6|871.2|
|HI: non OECD|19.8|254.4|78.9|511|1,833.2|22,227.1|
|ECA|21.4|13.8|43.6|24.3|667.6|405.5|
|HI: OECD|33.4|35.9|103.2|101.5|2,293.9|2,243.3|

Source: CGAP and World Bank Financial Access 2010

# _Mobile Banking_

**Mobile banking has the potential to be transformational** because it: i) uses existing mobile communications infrastructure which already reaches unbanked people, ii) may be driven by new players, such as telecommunications companies, with different target markets from traditional banks, iii) may harness the power of new distribution networks for cash transactions, such as airtime merchants, beyond the conventional merchant POS or ATM networks of banks, and iv) may be cheaper than conventional banking, if the offering is competitive. (Porteous, 2006) CGAP research comparing service provides between banks and branchless banking service providers indicates that mobile banking services can be up to 50 percent cheaper (see Annex 3.4 for cost comparisons).

**Mobile banking**<sup>**12**</sup> **has been used primarily for payments and transactions so far** , and not for loans or savings, although pilots are underway to develop credit and deposit products through mobile phones. Using a mobile phone for more complex transactions than payments and transfers requires a different pricing structure (customers are unlikely to want to pay transferlevel fees to make a loan repayment), and also implies that regulators need to understand any risks that are involved, and to adapt supervisory practices and legal frameworks as appropriate. The functionality of using a small keyboard and screen can also be seen as less convenient than using a branch or computer, even if mobile phones are much more easily accessible.

> 12 Mobile phone payments are financial transactions undertaken using a mobile device such as a cellular phone. Mobile banking includes mobile payments but involves access by mobile device to the broader range of banking services, such as account-based savings or transactions products offered by banks.

16
conclusions are presented in section V. An appendix with additional results follows.

# I. EMPIRICAL LITERATURE ON FINANCE AND GROWTH

We briefly survey the empirical literature on the effect of financial development on growth. In addition, we discuss certain issues regarding the measurement of financial development. We refer readers to Levine (2005), Ang (2008), and Valickova et al. (2015) for more comprehensive surveys of this literature.<sup>2</sup>

# _Empirical Evidence_

Focusing on the period between 1960 and 1989, King and Levine (1993) show how the initial levels of various financial indicators, such as the liabilities to the financial sector, bank ratios, credit to nonfinancial private sector/total domestic credit, and credit to the private sector to GDP, explain the real growth of GDP per capita, capital accumulation, and efficiency of capital utilization in the following period. Atje and Jovanovic (1993) examine the stock market’s effects on economic growth and find that more active stock markets induce growth. The conclusion regarding stock market activity is subsequently confirmed by Levine and Zervos (1998). In addition to providing evidence on stock market effects, Levine and Zervos (1998) simultaneously control for banking sector development by including credit to the private sector. Interestingly, both the banking sector and stock markets are significant in fostering growth. This leads the authors to conclude that each of the sectors has a different function in the economy and a different financial function. Furthermore, they add that the mere size of the stock market as measured by total capitalization is irrelevant to growth and that the relevant factor is the activity of the stock market. Nevertheless, this link may be an outcome of an unobserved third factor that stimulates both trading activity and economic growth.

> 2. There is also literature on the determinants of financial development, see Ang (2013) and Ang and Kumar (2014).

6
On the one hand, this measure is comparable to value-added statistics, like GDP, and it is useful to link the ‘net’ production with the specific market of final absorption; on the other hand, the **VAX** _s_ is only a fraction of the country _s_ ’s GDP embedded in its exports since it does not consider the portion that is later re-imported and absorbed at home; moreover, this approach does not allow us to identify the trade linkages through which the value produced in _s_ reaches the market of final destination _l_ . The latter aspect could be relevant in many cases, including the analyses of international supply networks and evaluation of trade policies.

Koopman et al. (2014) combine the measures of the content of exports (i.e. (2)) with the index of ‘Value Added Exports’ proposed by Johnson and Noguera (2012). More generally, their accounting framework allows to single out the entire domestic and foreign value-added embedded in the aggregate exports of country _s_ , as well as the double counted items originally produced at home and abroad. In particular, they highlight that some trade flows are purely double-counted, such as when intermediate inputs cross a country’s borders several times during the different stages of production. The complete decomposition proposed by Koopman et al. (2014) is reported in Appendix B whereas Figure 1 shows a simplified scheme of their breakdown of aggregate exports. Notice that **VAX** _s_ is a subcomponent of the domestic value-added embedded in gross exports, the remaining part being the value-added that is finally absorbed by the exporting country itself (labeled ‘Reflection’ by Koopman et al., 2014).

<!-- Start of picture text --> Gross Exports ( E s∗ )<br>Domestic Content Foreign Content<br>( DC s∗ ) ( FC s∗ )<br>Domestic Domestic Foreign Value Foreign Double<br>Value Added Double Counted Added Counted<br>( DVA s∗ ) ( DDC s∗ ) ( FVA s∗ ) ( FDC s∗ )<br>Value Added<br>Reflection<br>Exports<br>( REF s∗ )<br>( VAX s∗ )<br><!-- End of picture text -->

**Figure 1:** A basic scheme of the Koopman et al. (2014) decomposition of total exports

8