rafmacalaba/datause-encoder-data huggingface dataset:
even with a comprehensive skills measurement component, a large share of our respondents’ earnings reflects other considerations. Another way to see this is to note that the extent to which the coefficient on years of school will decline once we include other skills depends on the covariance of the measured skill with labor earnings and with years of schooling. Using the coefficients for the mean regressions from Table A9, a year of schooling is associated with a $5.2 increase in labor earnings for men. We also know that a year of schooling is associated with a 0.17sd increase in cognitive and a 0.03sd increase in SEM skills. Multiplying these gains with the associations between earnings and skills suggests that the increase in skills associated with one more year of schooling will increase earnings by $1.51. This still leaves two-thirds of the correlation between labor earnings and years of schooling as an unexplained residual. These results continue to raise important questions regarding the types of skills that schools impart to their students that are then rewarded in the labor market.

**IMPLICATION 3: MIGRATION AND SKILLS** . Our final point considers how labor markets reflect skills and relates to a lively debate on migration, labor earnings, and skills. This literature is concerned with the returns to migration. The main challenge is that people who migrate may have systematically different skills so that earnings for migrants reflect both the causal impact of migration and the differences in skills. Although studies have designed clever natural and randomized experiments to get around this problem, we are unaware of studies that directly assess the skills of migrants and non-migrants to understand how these are rewarded in the labor market at different locations. Combining our sampling strategy and skills measurement allows us to present correlational evidence on this question—with several noteworthy findings.

First, we find those with higher cognitive and SEM skills are more likely to migrate (at least within the country, if not outside). Second, accounting for cognitive and SEM skills lowers the difference in labor earnings of migrants versus non-migrants. However, a remarkable—and novel—finding in this sample, which we corroborate with data from Cambodia, is that the association of earnings with SEM skills is positive (and large) _only_ for non-migrants, and the correlation with years of schooling is higher for migrants. This result suggests that the returns to different skills vary by the migration status of the respondent. One possible explanation raised by Carranza et al. (2022) in South Africa is that this pattern reflects a lack of information among employers who rely on observable signals instead (years of schooling). They provide job seekers and employers with assessment scores for “non-specialist skills such as communication, numeracy, and grit” and find that both employers and job seekers react 30
main pillar of the public health system in South Africa—has risen steadily from 44.5 percent in 2004 to 59.6 percent in 2012.<sup>54</sup> The public sector, which spent just over 4 percent of GDP in health expenditure in 2010/11, serves roughly 83 percent (41.3 million) of the South African population.<sup>55</sup> About 17 percent (8.3 million) of the population have private health insurance (termed ‘medical aid’ in South Africa) and mostly use private facilities, with total private sector health related spending amounting to about 4.3 percent of GDP.<sup>56</sup> <!-- Start of picture text --> Figure 15. Progressivity of health spending:<br>Concentration curves and Lorenz curve for market income<br>Lorenz for Market<br>100%<br>Income<br>Direct Transfers<br>90%<br>In-kind Transfers Total<br>80%<br>In-kind Education<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 1 2 3 4 5 6 7 8 9 10<br>Source: Own estimates based on IES 2010/2011.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 16. Health spending: incidence and beneficiary households by income group<br>A. South Africa. Incidence of health spending B. Health share of individuals in beneficiary<br>(Share of market income deciles) households by income group<br>1000% 100%<br>900% 90%<br>80%<br>800%<br>70%<br>700%<br>60%<br>600%<br>50%<br>500%<br>40%<br>400% 30%<br>300% 20%<br>200% 10%<br>0%<br>100%<br>0%<br>y<1.25 1.25<y<2.5 2.5<y<4 4<y<10 10<y<50 y > 50<br>Source: Armenia (Younger and Khachatryan, forthcoming), Bolivia (Paz et al, 2014), Brazil (Higgins and Pereira, 2014), Ethiopia<br>(Woldehanna et al, forthcoming); and Indonesia (Jellema et al, forthcoming). For South Africa, own estimates based on IES<br>2010/2011.<br>Share of market income Share of Target Population<br><!-- End of picture text -->

# **RDP Housing**

As mentioned earlier, due to lack of detailed administrative data on housing values, we have not included this category of spending in the analysis. However, the IES does have information on whether any member of the

> 54 Presidency (2014).

> 55 National Planning Commission (2011).

> 56 Ibid (2011).

26
formal employment.<sup>6</sup> In fact, among the five MENA countries with available data on labor market outcomes during the pandemic, Jordan exhibited the greatest stability in employment over time (Krafft et al., 2022). Moreover, Jordan undertook strict closure policies especially for camps whereby entering and exiting refugee camps was prohibited, including for humanitarian workers providing assistance to refugees.

# **Data and descriptive statistics**

To analyze the trends of employment across Jordanians and Syrians before and during the COVID19 pandemic we would ideally need panel data that covers this period. Such data does not exist. The Survey of Young People in Jordan (SYPJ) 2020 (Assaad et al., 2021a; OAMDI, 2022) offers a suitable alternative. Collected between August 2020 and October 2020<sup>7</sup> from a nationally representative sample of Jordanian and Syrian youth between 16 and 30 years old, the survey captures the labor market experience of this population before and during the pandemic through retrospective questions about their job history since they entered the labor market. We use data on the characteristics of the individual’s current job as well as data on all their previous jobs since they entered the labor market. Individuals can report as many jobs as they took with no upper limit. The maximum number of jobs reported in our sample was seven jobs.

The other source of micro data for Jordan during the pandemic is the COVID-19 MENA Monitor (CMM) data (OAMDI, 2021). This data was collected over three waves in February, June and August 2021 through a phone survey. It also has retrospective data on individuals’ employment

> 6 Defence order number 6 stipulated, early April, that institutions subject to the labor law must allow their employees who were dismissed or whose services were terminated since the beginning of the pandemic to return to their work < https://www.jordantimes.com/news/local/pm-issues-defence-order-no-6-stipulating-labour-rightsunder-defence-law>.

> 7 Additional data was collected between February and March 2021 from refugee camps.

9
evidence that the financing patterns of large firms in financially developing countries are consistent with the pecking order theory of capital structure.

When we examine data from the latest round of Enterprise Surveys, conducted from 2006-2010, we find that debt finance and bank finance in particular, is the most common type of external finance for firms of all sizes across countries. Although bank financing is the most prevalent type of external finance, a large proportion of firms, especially small and medium firms, do not have any bank loans. The firm survey results suggest that this reflects both firms being refused bank loans as well as a lack of demand for bank loans either because of other financing sources or lack of good projects to finance. Some of the common reasons why firms are excluded from bank finance include high interest rates, collateral requirements, corruption in banking, and cumbersome paperwork.

# **_5.3 Cash Holdings and Liquidity Management_**

Most of the cross country studies of capital structure focus on debt levels and there have been relatively few studies of cash holdings and overall liquidity management of firms in developing countries. From the studies of US firms, we know that under capital market imperfections, there are two views on the optimal amount of cash holdings: According to the precautionary motive for liquidity, firms hold cash so as to not have to raise funds when external capital is expensive (Myers and Majluf, 1984; Almeida, Campello, and Weisbach, 2004; Acharya, Almeida, and Campello, 2007; and Gamba and Triantis, 2008); by contrast, according to the agency view, management and controlling shareholders may not use cash in the best interests of the firm, such as on excessive spending and value destroying acquisitions (Jensen (1986), Blanchard, Lopezde-Silanes, and Shleifer (1994), Harford (1999), Opler et al. (1999), Dittmar, Mahrt-Smith, and Servaes (2003), Mikkelson and Partch (2003), Pinkowitz, Stulz, and Williamson (2006), Dittmar and Mahrt-Smith (2007), Kalcheva and Lins (2007), and Harford, Mansi, and Maxwell (2008). The tradeoff view of capital structure then predicts that firms trade off the precautionary motives with the agency motives in arriving at optimal cash balances. More recently, Bates, Kahle, and Stulz (2009) find that the average cash-to assets ratio for US industrial firms more than doubled over the period 1980 to 2006 and that this increase can be explained by the precautionary motive 41
‘ _d_ ’ stands for domestic, ‘ _i_ ’ for imports, ‘ _x_ ’ for exports, and ‘ _o’_ for output. Indices ‘ _r_ ’ and ‘ _s_ ’ denote source and destination regions, respectively; ‘ _k_ ’ denotes sectors.

# **5. Green Sectors**

<mark>Despite great interest of policy makers across countries in creating “green jobs”, there is no commonly accepted definition of “green jobs” that exists globally. Various organizations have suggested different definitions and approaches of “green jobs”, some of which focus on the purpose of output or work, i.e., whether the produced goods and services can be used to “green” the economy. While others focus on environmental impacts of output and work, i.e., whether the production or consumption damages the environment or is unsustainable.</mark> <mark>ILO/UNEP (2008)</mark><sup>28</sup> <mark>study broadly defines a green job as any decent job that contributes to preserving or restoring the quality of the environment whether it is in agriculture, industry, services or administration. This includes jobs that help to protect ecosystems and biodiversity, reduce energy, materials, and water consumption through high efficiency strategies, de-carbonize the economy, and minimize or altogether avoid generation of all forms of waste and pollution.</mark> <mark>While the definition adopted by Eurostat (2009)</mark><sup>29</sup> <mark>focuses on the environmental goods and services industry emphasizing on green jobs firmly within green industries explicitly concerned with environmental improvement. A green job comprises “activities which produce goods and services to measure, prevent, limit, minimize or correct environmental damage to water, air and soil, as well as problems related to waste, noise and eco-systems.” This includes technologies, products and services that reduce environmental risk and minimize pollution and resources.</mark> <mark>The US Department of Commerce (2010)</mark><sup>30</sup> <mark>defines the green economy for only the private sector in a broad sense, as “clean and energy efficient.” The jobs that are created and supported in businesses that produce green products and services are green jobs, without consideration of the environmental impact of their production or social context.</mark> <mark>The US Workforce Information Council (2009)</mark><sup>31</sup> <mark>describes green jobs to be comprising the jobs whose work is essential to green economic activity. These jobs refer to work that supports environmental sustainability with specific focus on “economic activity” and not by their particular skills or skill-level, wages or other compensation, or even the tasks being</mark>

> 28 ILO/UNEP (2008). Green Jobs: Towards Decent Work in a Sustainable Low Carbon World, United Nations Environment Program.

> 29 Eurostat (2009). The Environmental Goods and Services Sector: A Data Collection Handbook, European Commission.

> 30 U.S. Department of Commerce (2010). Measuring the Green Economy, U.S. Department of Commerce, Economics and Statistics Administration.

> 31 Workforce Information Council (2009). Measurement and Analysis of Employment in the Green Economy, http://www.workforceinfocouncil.org.

28
beginning until the end of the period, it cannot capture the fast rise in _TFP_ in the rst decade of the acceleration nor it can it explain the decline thereafter. As said, the smooth impulse implied by the calibrated reform, leads to a more protracted growth in aggregate productivity and cannot generate contractions.

Figure C.3: Development Dynamics: Chile's Growth Acceleration 1984-2011 <!-- Start of picture text --> TFP Investment Rate<br>Model<br>Data<br>1985 1995 2005 1985 1995 2005<br>Year Year<br>Employment Share Innovation Av Size<br>0 5 10 15 20 25 30 1985 1995 2005<br>Year Year<br>1.4 0.15<br>0.10<br>1.2<br>0.05<br>Relative to SS 1998 1.0 Diff from SS 1998 0.00<br>0.8 −0.05<br>0.07 2.0<br>1.8<br>0.06<br>1.6<br>0.05 1.4<br>Diff from SS 1998 0.04 Rel. to SS 1998 1.2<br>1.0<br>0.03<br><!-- End of picture text --> Note: The data for _TFP_ is constructed from the Penn World Tables Database (Zeileis 2021). We construct _TFP_ using _rgdpna_ as the measure of real _GDP,_ the product of the population and the human capital index ( _pop∗hc_ ) as the measure of the labor input, and _rkna_ as the measure of the capital stock. We xed the labor share at the value reported by the data for the year 2011, _lsh_ (2011) . Once the series of _TFP_ is construct it, we linearly de-trend it assuming an annual productivity growth in the U.S. of 0.85%. The investment rate is drawn directly from the Penn World Tables. We construct the average rm size from the ENIA (Encuesta Nacional Industrial Anual), extracted from the replication material for Chen and Irarrazabal (2015). The average rm size is dened as the ratio between total employment and the total number of rms.

The atypical behavior of the investment rate during Chile's acceleration cannot be accounted for by the model either. While we could have improved the model's t by adjusting the average idiosyncratic distortion to attain a higher level of the capital to output ratio at the end of the acceleration period, the model would not have been able to capture the cyclical behavior of the investment rate. The model does capture, however, the qualitative property of an increasing pattern of the investment rate, 49
agreements signed: for example, in Chad nine peace agreements were reached in the period under study. Other conflicts, such as the one in Congo-Brazzaville, have only seen one peace agreement during the same period.

# **_Statistical Technique and Structure of the Datasets_**

The empirical evaluations are conducted using duration analysis. In this case the interest lies in the duration of peace after the signing of a peace agreement, up to the point where the warring parties may engage in post-settlement armed conflict (i.e. experience an event), if at all. I use a Cox proportional hazards model which has the advantage of not assuming a specific parametric form for its distribution (Cox, 1972).

In an effort to evaluate the issue of inclusiveness as thoroughly as possible, two different datasets have been constructed, one focusing on the settlement level, whereas the other shifts the focus to the dyadic interactions between the government and each rebel group. When analyzing the _settlement_ level, the unit of analysis is the post-agreement-year, whereas the analysis at the _dyadic_ level concerns the government and each rebel group, and the unit of analysis is the post-agreement-dyadyear. In the dyadic dataset there may be several dyads in a conflict, where the government remains the same in each dyad.

In both datasets the peace agreements and dyads, respectively, are at risk of experiencing an event – that is, armed conflict involving a government and a rebel group that results in at least 25 battle-related deaths in a year – from the year after the signing of an agreement, until the observation period ends on the 31<sup>st</sup> of December 2004.<sup>8</sup> The first dataset consists of 82 peace agreements, and in the dyadic dataset there are in total 84 dyads, which may or may not have signed one or more settlements. Since some of the variables vary over time, models with time-varying covariates are used for both sets of data, which makes it possible to study changes over time. This means that there are multiple observations for each of the settlements and dyads, respectively.

In the dyadic dataset, the dyads run the risk of experiencing post-settlement armed conflict after each of the agreements (if there are several signed in the

> 8 The primary focus is on the parties that were active prior to the signing of the agreement, and therefore new parties that later emerge are not taken into account in the main analysis. However, in the analysis at the settlement level a robustness check is conducted using an alternative dependent variable that captures the conflict behavior of new parties too.

9
technologies (Ruiz Larraguivel, 2004; Brading, 1971; Marichal, 1997). Even in Zacatecas, San Luis de Potos ́ı, and Guanajuato, long centers of mining, engineering density was at low levels compared to the newcomers in the US West. Around 1900, abandoned, underexploited and newly discovered mines fell to foreign hands that could bring new global technologies to bear. As an example, the Guggenheim interests opened smelters in Monterrey (1892) and Aguascalientes (1894) purchased the largest Mexican Smelting and Refining company in 1906, introduced modern methods of extracting and refining silver ores and in addition, started the production of lead and zinc mining (Bernstein, 1964; O’Brien, 1989). By the early 20th century, the Americans absolutely dominated the industry.<sup>25</sup> In Upper Peru (now Bolivia), the decline of silver production in some of the most famous mines, like those at Potos ́ı, arose from the “failure to apply new mining techniques, heavy mortality among Indian laborers and the exhausting of previous rich veins” (Scobie, 1964, p.59). In Ecuador, Hurtado (2007) argues that the discovery of new mineral deposits was hindered by a resistance to methods.<sup>26</sup> The US eventual dominance of the Chilean copper and Mexican mining industries strikingly illustrates the road that could have been taken with the same homogenous product. Not only does Wright (1999) argue that US in the 19th century “parlayed its [natural] resource-based industrial prosperity into a well-educated labor force, an increasingly sophisticated science-based technology, and world leadership in scientific research itself” (Wright, 1987, p. 665), but he uses precisely the US copper industry as an example of national learning and of innovation as a network phenomenon. Wright stresses that in

> 25See Maloney (2015) for data on nationality of owners.

> 26More generally, Di Tella (1985) argues that Argentina proved unable to move beyond a state of exploiting the pure rents of a frontier or extraction of mineral riches, and beyond the “collusive rents” offered by statesanctioned or otherwise imposed monopolies to tap the “unlimited source of growth” found in exploiting the quasi-rents of innovation, as the US, Canada and Australia were able to do. They remained in an adoption or, perhaps, stagnation equilibrium. The same phenomenon is seen in agriculture where the absence of innovative infrastructure was recognized by contemporary Argentines as key to explaining the lagging performance of the wheat industry compared to that in Canada and Australia. Fogarty et al. (1985) attributes to weak innovative capacity the outcome of a quasi-experiment whereby the Spanish Merino sheep were introduced into New South Wales, Australia, and Argentina’s River Plate region in the same year and had the same access to European capital, but by 1885 showed yields of only half the wool per sheep in Argentina.

28
Table 3: Overlap of Monetary Poverty (USD 1.90$ a day) and Deprivations a) Wave 3

|Monetary|Individual Lev<br>Health D|el Child Depriva<br>eprived<sup>26</sup>|tions<br>Food D|eprived|Education|Deprived|Not in Scho|ol Deprived|
|---|---|---|---|---|---|---|---|---|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|Deprived|
|non-poor|||42%|7%|49%|2%|46%|5%|
|Poor|||35%|16%|36%|13%|34%|15%|
|Monetary|Household Le<br>Sanitation|vel Child Depriv<br>Deprived|ations<br>Shelter D|eprived|Water D|eprived|Informatio|n Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|Deprived|
|non-poor|41%|9%|41%|9%|38%|12%|49%|2%|
|Poor|35%|15%|23%|27%|29%|21%|44%|5%|
|b) Wa|ve 2<br>Individual Le|vel Child Depriv|ations||||||
|Monetary|Health|Deprived|Food D|eprived|Education|Deprived|Not in Scho|ol Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|Deprived|
|non-poor|44%|8%|45%|5%|47%|4%|45%|6%|
|Poor|38%|11%|44%|6%|35%|14%|33%|16%|
||Household L<br>|evel Child Depri<br>|vations<br>||||||
|Monetary|Sanitatio|n Deprived|Shelter|Deprived|Water D|eprived|Informatio|n Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|41%|11%|43%|9%|37%|14%|49%|3%|
|Poor|31%|17%|23%|25%|29%|19%|40%|9%|
|c) Wa|ve 1||||||||
||Individual Leve|l Child Depriva|tions||||||
|Monetary|Health D|eprived|Food De|prived|Education|Deprived|Not in Scho|ol Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|49%|9%|47%|11%|49%|6%|47%|8%|
|Poor|29%|13%|31%|12%|33%|12%|31%|15%|
||Household Lev|el Child Depriv|ations<br>||||||
|Monetary|Sanitation|Deprived|Shelter D|eprived|Water D|eprived|Informatio|n Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|44%|11%|42%|12%|37%|18%|44%|11%|
|Poor|32%|13%|23%|23%|25%|20%|29%|16%|

Source: Authors’ estimates based on GHS Wave 1-3.

One hypothesis could be that the imperfect overlap between monetary and deprivation-based poverty is purely driven by those children with consumption levels close to the poverty line, but it does not apply to children at the top of this income distribution. Table 4 however proves this proposition wrong. Unlike one may expect, there are even among the children living in households with the top 1% income, up until wave 3 about half the children facing at least one deprivation. While this share remains constant until the top 10 percent consumption level for wave 3, the share of monetary non-poor children facing at least one deprivation is comparatively rising though when expanding the sample to children with top 5 percent and top 10 percent consumption levels in the earlier waves. This is consistent with findings presented in World Bank Group ‘s (2017) estimates of the top income quintile, not having complete access to sanitation and water, based on earlier other data sources. Among severe sanitation, shelter and water deprivation, Table

> 26 Health Deprivation did not exist in wave 3.

51