rafmacalaba/datause-encoder-data huggingface dataset:
Table 2. Logit model (eq. 8) estimates STEP countries country

||Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|---|
|literacy achievement|0.04|‐0.29***|‐0.08|‐0.05|‐0.35***|‐0.16*|‐0.21***|
||(0.09)|(0.11)|(0.11)|(0.09)|(0.1)|(0.08)|(0.07)|
|educational attainment (r|elative to ge|neral post‐s|econdary)|||||
|none|1.56|2.28***|1.5***|0***|2.52***|1.45***|2.16***|
||(1.31)|(0.36)|(0.44)|(0)|(0.4)|(0.3)|(0.33)|
|primary|1.29|2.81***|2.15***|1.6|2.26***|1.39***|1.65***|
||(1.25)|(0.63)|(0.36)|(1.12)|(0.42)|(0.23)|(0.2)|
|lower secondary|1.83***|2.44***|1.69***|1.03**|1.93***|0.99***|1.31***|
||(0.45)|(0.28)|(0.41)|(0.44)|(0.36)|(0.22)|(0.15)|
|vocational upper sec.|1.41***|1.92***|1.74***|1.42***|1.97***|0.55**|0.45|
||(0.29)|(0.25)|(0.39)|(0.36)|(0.41)|(0.24)|(0.29)|
|general upper sec.|1.13***|2.06***|1.46***|0.88***|1.3***|0.82***|0.95***|
||(0.2)|(0.25)|(0.29)|(0.26)|(0.32)|(0.17)|(0.13)|
|technical post‐sec.|0.93***|1.28***|0.94***|1.28***|0.79**|‐0.92**|n.a.|
||(0.2)|(0.26)|(0.28)|(0.23)|(0.39)|(0.37)||
|age|0.01*|0|0|0|0|0.01**|‐0.02***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0)|
|female|‐0.26*|‐0.07|0.13|‐0.41**|0.27*|0.2*|0.18*|
||(0.15)|(0.13)|(0.17)|(0.19)|(0.14)|(0.11)|(0.11)|
|constant|‐0.95***|‐0.93***|‐0.63|‐0.42|‐0.94**|‐0.58***|0.19|
||(0.3)|(0.34)|(0.39)|(0.36)|(0.42)|(0.22)|(0.21)|
|observations|956|1760|1702|888|2094|2340|2305|
|gen ‐ voc upper|‐0.284|0.14|‐0.278|‐0.535|‐0.663*|0.272|0.503*|
|secondary|(0.297)|(0.26)|(0.335)|(0.391)|(0.356)|(0.194)|(0.287)|

Authors' calculations using World Bank STEP data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

Non-cognitive skill measures are available for the 7 STEP countries; estimates of model (9) are presented in Table 3. Openness to experience is found to have a negative, statistically significant association with being in an automation-prone occupation in three countries; extraversion is found to have a positive statistically significant association in one country and a negative statistically significant association in one country, and conscientiousness has a negative association in one country.
- The focus of long term strategies for cities must be first and foremost to create opportunities for all its inhabitants and to ensure the sustainability of the city’s economy.

- This will, almost certainly, make new investments or reinvestments necessary in many areas ranging from mobility to culture and to the green economy. Cities must be managed with flexibility so as to adapt to unforeseen new circumstances and today's investment decisions should not lock cities out of options tomorrow.

- In cities where public finances have deteriorated and where it is essential to restore their credit, urban decision-makers must make sure that their decisions are financially sustainable in the long term.

- While there has been a lot of talk about "smart cities" and new technologies among urban specialists and urban planners, it is ultimately the focus on basic economic principles, good governance and good institutions that will create sustainable, dynamic and livable cities.

# **Creating Opportunities for All**

Dark clouds gather on the horizon today for most cities in developing countries. The future is uncertain and risky. But the future is also full of opportunities. Cities, rich and poor, face risks but they also have opportunities to manage their city well, ensuring that growth is inclusive, greener and more sustainable when the crisis subsides and growth resumes.

Creating opportunities for the population means ensuring that the quality of life of its inhabitants—particularly the least fortunate—is improved; that the city invests in human capital formation; and that all inhabitants are participating and included in political and cultural life.<sup>12</sup> In order for cities to provide opportunities for their citizens it is helpful to understand the primary forces that dictate existing levels of opportunities experienced by urban populations. Bourguignon, Ferreira, and Menéndez (2007), using Brazilian household data, find that individuals’ place of residence, parents education, occupation, and race are the most significant variables linked to opportunity. Their analysis additionally reveals a sizable inequality of opportunities in Brazil and shows that urban residents have potentially more opportunities than rural residents.

As cities plan for an uncertain future, current practices illustrate the positive outcomes that can result from the empowerment of citizens during the planning and financing of urban renewal projects. The Asian Coalition for Community Action (ACCA), for example, uses small grants (less than US$58,000) funded by the Bill and Melinda Gates Foundation both to leverage government and private capital, and to promote community involvement in planning urban projects. The program has enabled the construction of street lights, community latrines, transport infrastructure and other public goods in cities of all sizes in Cambodia, Vietnam, Indonesia,

> 12 On opportunities and empowerment, see Stern, Dethier and Rogers (2005).

15
# (c) Job Contraction Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|49.9|50.1|70.9|29.1|57.2|42.8|75.1|24.9|14.0|18.3|67.7|

Table 5 shows these growth rates by age-size categories. Again, confirming previous results from ADM young SMEs show the largest rate of net employment growth across all age groups regardless of the size thresholds used to define SMEs and the method used to classify establishments. The table furthermore shows the same trend in the gross job creation rates. Regarding job contraction rates, in Table 5, mature large firms exhibit greater contraction rates than SMEs when the cutoff threshold is 100 employees.

**Table 5: Job Growth Rates by Establishment Size-Age**

<mark>(a) Net Employment Growth Rate</mark>

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|2.6|0.3|2.8|0.1|2.4|0.5|2.7|0.1|
|Mid-age|0.9|0.2|1.0|0.1|0.7|0.4|0.9|0.2|
|Mature|1.2|0.1|1.3|0.1|0.8|0.3|1.0|0.1|

## (b) Gross Job Creation Rate

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|2.9|0.5|3.2|0.2|2.7|0.6|3.1|0.2|
|Mid-age|1.3|0.4|1.5|0.2|1.1|0.6|1.4|0.3|
|Mature|2.2|1.2|2.7|0.7|1.9|1.5|2.6|0.8|

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|0.3|0.2|0.4|0.1|0.3|0.1|0.4|0.1|
|Mid-age|0.4|0.2|0.5|0.1|0.5|0.1|0.6|0.1|
|Mature|1.0|1.3|1.5|0.8|1.1|1.2|1.5|0.7|

Table 6 shows the same rates as shares, broken down by establishment size-age categories. Here again, young SMEs constitute the largest share of both net employment growth and gross job creation. This is the result of a combination of both their relatively high gross job creation share and also a small relative share of job contraction. For larger-mature establishments, by contrast, gross job creation shares are also notably high, consistently higher than mid-age and young, large establishments. However, mature, large establishments also
labor market rewards simple signals of teamwork for the average applicant. Teamwork also plays an important role in the context of labor market discrimination. While signaling leadership experience has no effect, signaling a teamwork skill reduces the discrimination gap by 40%. Competition also matters. Companies facing competition in the labor market, measured by the number of competitors advertising similar positions, are 56 to 66% less likely to discriminate. On the supply side, discrimination increases with the relative quality of the pool of applicants. These results provide novel evidence that soft skills and labor market competition both play an important role in understanding hiring discrimination. This underlines a potential pathway to overcome labor market discrimination and improve job matching.

Correspondence studies have been conducted in different contexts, empirically testing the existence of discrimination in race, gender, ethnicity, and age, among others (e.g., Bertrand and Mullainathan (2004), Bosch et al. (2010), Bartoˇs et al. (2016), Deming et al. (2016), Edelman et al. (2017), Kline et al. (2022)). The two known theoretical models of discrimination in the literature are “taste-based discrimination” (Becker, 1971), and “statistical discrimination” (Phelps (1972),Arrow et al. (1973)). Taste-based discrimination occurs when employers derive utility from hiring a candidate based on their ethnicity or gender. In the model of statistical discrimination, employers hire based on their belief that workers of a less favored group have deficiency in some skills (Lang and Lehmann, 2012). In other words, employers assume an average productivity based on demographics. The existing evidence shows that statistical discrimination does not always explain employers’ discriminatory behavior. For instance, Bertrand and Mullainathan (2004) do not find differences in callback rates between good quality and bad quality resumes for Black candidates, while for White candidates the difference is 30 percent.<sup>1</sup> We make three contributions. First, we are among the first to explore how signals of leadership and teamwork soft skills influence hiring decisions at the application stage, particularly in a context of hiring discrimination. A growing literature documents the relevance of soft skills in the labor market. Evidence suggests that employers look for teamwork skills (Weidmann and Deming, 2021) and decision-making skills (Deming, 2021). High-paying jobs require social skills (Deming, 2021) and employers’ ratings of employees positively correlate with communication skills and measures

> 1Good quality resumes are defined as having more labor market experience and fewer gaps in unemployment.

3
While in the first pilot the positive relationship between external and introjected motivations is also sizable and significant - with even larger correlation coefficients - the negative association between autonomous motivation and both external and introjected motivation is not observed. Indeed, the only significantly negative association observed is between female autonomous and external motivation within the buying/raising livestock domain (results available upon request).

**Table 1.  Matrix of correlation between motivation subscales, Uganda vignettes pilot**

|||**Women**||||**Men**||
|---|---|---|---|---|---|---|---|
|**Growing crops**||||||||
||External|Introjected|Obs.||External|Introjected|Obs.|
|Introjected|0.277***|||Introjected|0.428***|||
|Autonomous|-0.379***|-0.029|187|Autonomous|-0.140*|-0.157 **|145|
|**Taking crops to market**||||||||
||External|Introjected|Obs.||External|Introjected|Obs.|
|Introjected|0.512***|||Introjected|0.487***|||
|Autonomous|0.094|0.119*|185|Autonomous|-0.031|0.068|140|
|**Buying/raising livestock**||||||||
||External|Introjected|Obs.||External|Introjected|Obs.|
|Introjected|0.452***|||Introjected|0.506***|||
|Autonomous|-0.152**|-0.013|176|Autonomous|-0.078|-0.168**|133|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 In addition, we find a significant positive correlation in RAI scores across domains, reflected in Table 2 below. For women and men, the highest correlation emerges between growing crops and raising livestock (about 0.60). In the case of women, the next highest correlation is between growing and taking crops to market (0.54), whereas this is only 0.45 for men. Given that all domains included in the vignettes related to agricultural activities, the fact that correlation coefficients are not higher suggests that motivational autonomy is action-specific, and that generalizing results across actions or activities should be done with caution, especially when they span more varied domains (such as deciding on asset purchases and health care decisions).

11
The other main explanatory variables are country income and political characteristics. Data on income per capita ( _ln gdppc_ ) are taken from World Bank (2009). Democracy is measured by the _polity2_ variable taken from the Polity project (Marshall, Jaggers and Gurr 2006). Despite being coded on a 21-point scale from -10 to 10, _polity2_ is not truly a continuous variable and observations are heavily clustered toward the lower and upper end of the scale. In line with much of the political science literature, we therefore dichotomize the democracy variable and classify a country as a democracy if it has a _polity2_ score of 5 or above, which holds true for roughly 40 per cent of country years. The age of democracy is measured by the number of years since a country has become a democracy, defined as 5 or above on the Polity scale. The institutionalization of the ruling party in non-democracies is calculated as the age of the largest government party less the years in office of the current leader, with both variables taken from the Database of Political Institutions (Beck, et al. 2001), which starts in 1975, thus restricting the relevant sample to the 1975 to 2005 period. The last variable that captures the political incentives of governments is corruption. Corruption measures are subjective and available only for recent periods. We use the source that provides the earliest time coverage, the corruption index of the International Country Risk Guide from Political Risk Services <u>(www.prsgroup.com). They report corruption data</u> from 1982 onwards and the estimation model which includes corruption as an explanatory variable is therefore restricted to the period 1982 to 2005. As a further control variable we use the log of population size ( _ln pop_ ) from World Bank (2009) since more populous countries will face larger mortality, all other things equal.

Our hypotheses postulate that income and political incentives condition the effect of earthquake propensity to pursue policies that reduce earthquake mortality. Such conditional effects are typically estimated in a model in which two or more variables are interacted. Unfortunately, the interpretation of interacted terms of two or more continuous variables is problematic in non-linear models. As Ai and Norton (2003: 129) have shown, in nonlinear 23
# **1 Introduction**

Labor markets exhibit high levels of ethnic and racial segregation. Evidence from multiple countries consistently shows that minority workers are segregated from majority workers (Aslund<sup> ̊</sup> and Skans, 2010; Forth _et al._ , 2023; Glitz, 2014; Hellerstein and Neumark, 2008). In Brazil, the focus of our study, about 80% of white workers’ colleagues are also white, while for non-white workers the share of white peers is substantially lower, at about 50% (Gerard _et al._ , 2021). Leading explanations for labor market segregation include differences in residence, education, and job choices across racial groups (Hellerstein and Neumark, 2008), as well as differential hiring policies due to discriminatory behavior of managers or co-racial referral hiring (Giuliano _et al._ , 2009, 2011; Hsu Rocha and Dias, 2021; Miller and Schmutte, 2023). The role of post-hiring dynamics in explaining racial segregation has received less scrutiny, although the composition of a firm’s workforce depends not only on who enters the firm but also on who stays.

In this paper, we examine one critical factor that can influence the retention of workers at a firm: the presence of same-race peers. A long tradition of theories in the social sciences, based on similarity attraction, social identity, social categorization, and discrimination (Byrne, 1971; McPerson _et al._ , 2001; Tajfel, 1981; Turner _et al._ , 1987), suggests that individuals exhibit homophilic preferences: People “love those who are like themselves”, as already noted by Aristotle (1835) in his _Nichomachean Ethics_ . If homophilic preferences based on race prevail in the workplace, a reduction in exposure to same-race peers may lead workers to move to a different job. The same prediction arises in related theories of organizational demography (Pfeffer, 1983). Reduced similarity along demographic characteristics can entail lower levels of cooperation and communication, hinder social integration within a group, and weaken mentoring relationships and informal support networks among coworkers (Hoffman, 1985; Ragins, 1997; Tsui and O’Reilly III, 1989). Thereby, a decrease in the presence of same-race peers may lower job satisfaction and, ultimately, reduce retention at the firm (Jackson _et al._ , 1991; O’Reilly III _et al._ , 1989; Riordan and Shore, 1997).

We study the effects of a sudden change in the racial makeup of coworker peer groups on worker retention. Using matched employer-employee data from Brazil, we exploit exogenous shocks to the composition of coworkers that arise from unexpected deaths of workers from different racial groups. We compare the retention of incumbent workers in peer groups that encounter the death of a non-white worker with those confronted with the death of a white worker. Causal identification thus relies on the race of the deceased worker being independent of all other factors that drive incumbent workers’ retention. We check that, after we control for the number of non-white and white workers before the death, incumbent characteristics do not 1
# **Appendix A ‐ Stylized summary of risk management practices in four countries**

|**Process/**<br>**country**|**Colombia**|**Sweden**|**Turkey**|**Indonesia**<sup>**51**</sup>|
|---|---|---|---|---|
|**Context and**<br>**defining**<br>**characteristics**| USD 700 m. exposure<br>managed by DGCPTN<br> SOEs and sub-nationals<br>potential beneficiaries<br> 3 staff in credit risk team| USD 4 bn. exposure<br>managed by Guarantee<br>and Lending unit at SNDO<br> Beneficiaries mostly<br>SOEs, no sub-nationals<br> 11 staff| USD 11.2 bn. exposure<br>managed by credit risk<br>team at Treasury<br> Beneficiaries SOEs, banks,<br>municipalities<br> 5 staff| USD 16.7 bn. exposure<br>managed by CL team at<br>DGFRM<br> Beneficiaries SOEs and<br>infrastructure fund<br> 8 staff|
|**Credit risk**<br>**analysis**| No internal risk<br>assessment; public credit<br>rating used| Credit rating models<br>favored, simulation<br>models, and structural<br>models;<br> Industry-specific score<br>cards using rating<br>agencies’ methodologies| Statistical regression<br>model for each type of<br>beneficiary<br> Model calibrated using<br>historic data of<br>beneficiaries performance<br>vis-à-vis Treasury| Industry-specific internal<br>credit rating methodology<br>developed for electric<br>utilities and water systems<br> Score cards based on<br>rating agencies’<br>methodologies|
|**Credit risk**<br>**quantification**| Expected and unexpected<br>losses<br> PDs based on transition<br>matrices and solvency<br>curve<br> LGDs at 75 percent,<br>adjusted for collateral| Expected losses and<br>market values<br> PDs based on rating<br>agency databases<br> LGDs based on rating<br>agency databases<br> Market values based on<br>spreads in borrowing costs| Expected losses as result<br>of regression model<br> PDs estimated using<br>financial ratios of<br>beneficiaries<br> LGDs based on historic<br>performance vis-à-vis<br>Treasury| Expected losses and<br>market values<br> PDs based on rating<br>agency databases<br> LGDs based on. standards<br>and historic performance<br> Market values based on<br>spreads in borrowing costs|
|**Application in**<br>**risk**<br>**management**| Guarantee approval,<br>collateral, guarantee fees,<br>actual contingency reserve<br>account, monitoring and<br>reporting<br> Fees equalexpectedlosses| Guarantee fees, partial<br>guarantee coverage,<br>notional reserve account,<br>monitoring and reporting<br> Fees based on expected<br>costs or market values| Decision to grant<br>guarantees, guarantee fee,<br>partial guarantee coverage,<br>limit setting, actual reserve<br>account, monitoring| Potential tools include<br>guarantee approval,<br>guarantee fees, reserve<br>account, limit setting,<br>monitoring and reporting|

_Source_ : Sub-directorate of Risk at the Directorate General Public Credit and National Treasury of Colombia, Swedish National Debt Office, Turkish Undersecretariat of the Treasury, Directorate General of Financing and Risk Management in the Indonesian Ministry of Finance, author

> 51 Credit risk management practices are currently being developed.

42 | P a g e
# TABLE OF CONTENTS

|I.|Introduction ...................................................|1|
|---|---|---|
|H.|Properties of the Inequality Data ......................................|2|
|IE.|Regression Results ..............................................|_9_|
|IV.|Sensitivity Analysis .............................................|19|
|V.|Conclusions ..................................................|23|
|App|endix I: Data<br>........................<br>.........|.................<br>25|
|Ref|erences<br>..........................<br>...............|.............<br>27|
Substituting _z_ = 0 _._ 818 into λ 95 = 2 _ze_<sup>_B_</sup> 90<sup>λ65+1 yieldsλ65 = 2</sup><sup>_._696</sup><sup>_,_andα= 3419 then follows</sup> from λ 65 = α _L_ 65 λ 65 _._ especially as the available data impose a calibration process anchored to 1965. As pointed out in the introduction, however, the AIDS prevalence rate rose from about one per cent in 1990 to just over 20 per cent a decade later. This is a strong argument for choosing 1990 as the date of the outbreak of the epidemic in South Africa, and hence 1960 as the starting point in the chosen 30-year framework. The above interpolation from Table 2 yields λ 95 = 1 _._ 189 λ 65 = 3 _._ 205, which implies that λ grew at an annual rate of 0.58 per cent. This yields, in turn, λ 60 = λ 65 _/_ (1 _._ 0058)<sup>5</sup> = 2 _._ 620.

Some comments on these estimates are now in order. First, the parameter α has the dimension of 1995 US dollars per efficiency unit of labor per year. According to these estimates, therefore, a two-parent household in 1960 with two economically active adults and all the children attending school full-time would have had a family income of αλ 60 _,_ or $17,915. In the event of a complete collapse that left the entire population uneducated, the family’s income would be just $6840 in the absence of child labor.

Second, a related aspect of this part of the calibration is the ratio of the labor force to the total population. According to the World Bank’s Development Indicators (2002), this ratio stayed constant, at about 0.37, until 1980, and then began to inch up, reaching 0.39 in 1995. It will now be argued that this squares reasonably well with the model’s structure under pooling, in which a representative two-parent family cares for ( _nt/_ κ _t_ ) children. In 1960, the total fertility rate was about four (WDI, 2002) and κ _t_ was about 0.80 (see below), so that ( _nt/_ κ _t_ ) was about five. The corresponding figures for 1990 were about three, 0.86, and 3.5, respectively. Hence, if adults were to work throughout life and children not at all, as in the model, the ratio of the labor force to the total population would be 2/(2 + 5) = 0.286 in 1960 and 2/(2 + 3.5) = 0.36 in 1990. Allowing for the model’s unit time period of 30 years, the fact that many individuals started to work at fifteen or so and were recorded as in the labor force, 66