rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2: Mobile penetration services in Djibouti is the lowest in MENA (connection penetration), even after factoring in the multi-SIM effect (subscriber penetration)**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>UAE Qatar Bahrain Israel Kuwait Tunisia Saudi Arabia Algeria Libya Morocoo Iran Oman Egypt Lebanon Turkey Jordan Syria Mauritania Iraq Sudan Palestine Yemen Somalia Comoros Djibouti<br><!-- End of picture text --> Source: Data from GSMA, The Mobile Economy Middle East & North Africa, 2018 Unfortunately, the (under)performance of the ICT sector can have significant macro-economic effects on the economy and poverty reduction (World Bank, 2016). For example, in rural Peru, the expansion of mobile network coverage boosted household real consumption by 11 percent (Beuermann _et al._ , 2012). The ICT sector also has great capability to enable job creation and innovation in the private sector with the corresponding increase in economic opportunities for the population. Moreover, the internet is also an enabler of higher labor productivity by means of lowering information and search costs. For example, the introduction of mobile phones in the grain markets of Niger led to farmers obtaining grain price information over the phone thereby reducing search costs by 50 percent (Aker, 2010) and reduced dispersion of grain prices across markets by 10 percent (Aker and Mbiti, 2010). People’s perceptions also reveal that access to the internet has led to an increase in consumer welfare by making several products and services available digitally. From the standpoint of both the government and the private sector, the internet can bring major benefits in the provision and delivery of services. Digital identification can also improve participation of and help for the disadvantaged groups in the country to become integrated. The advantages of a well-developed ICT sector
firms not only demand academic skills, but also behavioral and socioemotional skills, with the great majority of firms reporting that these skills are in short supply. Wang (2015) tries to explicitly test for the hypothesis of degraded tertiary using Brazilian data, by comparing the college premia of younger and older cohorts, while controlling for the increase in supply. He finds evidence supporting the hypothesis since between 1995 and 2013 there was a reduction in the skill premium for younger workers.

Another possible cause put forth for the reduction in skill premium is “skill obsolescence” (Campos-Vazquez et al., 2016). Under this hypothesis, skills learned in the past may become depreciated due to rapid technological changes. This hypothesis is closely linked with the idea of age-biased technological changes (Behaghel and Greenan, 2010). The implication is symmetrically opposed to that of the degraded tertiary hypothesis, since we expected that older workers would be more harmed by the obsolescence of their skills. This hypothesis could also help account for the observed decline in experience premium. Campos-Vazquez et al. (2016) find that in Mexico, between 2000 and 2015, the wages of the oldest cohort declined, whereas the wages of the youngest cohort observed a modest gain. Messina and Silva (2017) replicate their analysis for other Latin American 10
cl_|Thailand|_nso.go.th/en_|
||_www.bcentral.cl_|UK|_www.ifs.org.uk_|
|Colombia|_www.banrep.gov.co_<br>_www.dane.gov.co_|USA|_www.bea.gov_<br>_www.dol.gov_|
|Croatia|_www.mfin.hr/en_|World Economic Outlook|_www.imf.org/weo_|
|Cyprus|_www.mof.gov.cy_|Direction of Trade|_www.imfstatistics.org/DOT_|
|Czech Rep|_www.czso.cz_|Povcal Net|_www.worldbank.org/poverty_|
|Dominican R.|_www.hacienda.gov.do_|World Development Indicators|_data.worldbank.org_|
||_www.one.gob.do_|Eurostat|_epp.eurostat.ec.europa.eu_|
|Estonia|_www.stat.ee/en_|International Finance Statistics|_www.imfstatistics.org_|
|France|_www.insee.fr/en_|OECDstats|_stats.oecd.org_|
|Latvia|_www.csb.gov.lv_|CEPALstats|_websie.eclac.cl/sisgen_|
|Lithuania|_www.stat.gov.lt/en_|LABORsta|_laborsta.ilo.org_|
|Malaysia|_www.statistics.gov.my_<br>_www.anm.gov.my_|OECD Economic Outlook|_www.sourceoecd.org_|
# **1. Introduction**

This study examines the contribution of human capital to economic growth in Guatemala over the past 50 years.<sup>2</sup> The interest is twofold. First, for the country itself there are very few studies that thoroughly analyze past growth patterns, and there are no studies that empirically appraise the direct impact of education on growth. In general, evidence on human capital and growth comes almost entirely from crosscountry analysis. Single-country studies, however, may be more illuminating since they overcome the heterogeneity problem and take into account the unique historical information for each country. Indeed, the original motivation of studying economic growth focuses on the time-series dynamics of macroeconomic variables. Moreover, the cross-section focus may be inadequate if returns to education or the quality of education differ substantially across countries.

Second, this study focuses on the contribution of different levels of education to growth. This is an important aspect regarding the problems associated with measuring _average_ years of schooling. Looking at education in a disaggregated way also proves more fruitful to the policy-maker since it indicates how resources should be divided between different education levels. Finally, the empirical analysis is based on an error-correction methodology, deals with endogeneity, and explores several data construction and robustness issues. All this may be relevant for future case studies as well.

This study, probably for the first time, constructs a reliable data set that accounts for the determinants of long-run growth in Guatemala. In terms of data availability, the country constitutes a most precarious case. Despite these caveats, however, satisfactory and coherent time series data were obtained. The results based on a production function augmented for human capital reveal that a better-educated labor force has a significant positive impact on long-run growth. Consistent with crosscountry evidence, primary and secondary education appear to be most important for productivity growth, followed by tertiary schooling. These findings are in agreement with the micro evidence for Guatemala. Interestingly, the results also suggest that the effect of education in both micro and macro
# **Do Refugees with Better Mental Health Better Integrate? Evidence from the** **_Building a New Life in Australia_ Longitudinal Survey**

Hai-Anh H. Dang, Trong-Anh Trinh and Paolo Verme<sup>_*_</sup>

**JEL Classification:** I15, J15, J21, J61, O15 **Key words:** refugees, mental health, labor outcomes, instrumental variable, BNLA longitudinal survey, Australia

> * Dang (hdang@worldbank.org; corresponding author) is a senior economist with the Data Production and Methods Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and International School, Vietnam National University, Hanoi; Trinh (tronganh.trinh@unimelb.edu.au) is a consultant with the Data Production and Methods Unit, Development Data Group, World Bank, and a postdoctoral research fellow at the Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Australia; Verme (pverme@worldbank.org) is a Lead Economist, Manager of the Research program on Forced Displacement and Head of Research and Impact Evaluations in the Fragility, Conflict and Violence group of the World Bank. We would like to thank Paul Anand, Jere Behrman, Andrew Foster, Owen O’Donnell, Kosali Simon, and seminar participants at the World Bank for helpful feedback on earlier versions. This work is part of the World Bank-UNHCR research program “Building the Evidence on Protracted Forced Displacement: A MultiStakeholder Partnership” and received additional funding from the Knowledge for Change (KCP) program. Both programs are funded by the UK <mark>Foreign, Commonwealth and Development Office and managed by the World Bank.</mark>
Table 12: Average Job Quality between 2007-2019, by formality

||Employees|Entrepreneurs a|nd Self-employed|
|---|---|---|---|
||(1)<br>(2)<br>Formal<br>Informal|(3)<br>Formal|(4)<br>Informal|
|2007|0.81<br>0.32|0.59|0.38|
|2008|0.77<br>0.31|0.53|0.38|
|2009|0.80<br>0.35|0.56|0.39|
|2011|0.81<br>0.32|0.57|0.43|
|2012|0.83<br>0.35|0.56|0.40|
|2013|0.84<br>0.34|0.60|0.41|
|2014|0.82<br>0.30|0.55|0.40|
|2015|0.83<br>0.34|0.56|0.40|
|2016|0.83<br>0.33|0.53|0.41|
|2017|0.82<br>0.32|0.56|0.40|
|2018|0.84<br>0.30|0.58|0.40|
|2019|0.85<br>0.29|0.56|0.41|

_Notes:_ Based on data from EH 2007-2019. We define formality for employees with some written contract and employers and self-employed with a Tax Identification Number (TIN). The 2020 and 2021 values are not included as the National Household Survey for those years did not capture information on the Tax Identification Number.
||**Table**|**7: US/EU/EF**|**TA IPAs (Bil**|**ateral Trade,**|**58 IPAs)**||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.352***<br>(0.083)||||||||
|Sector × log(GDPi)||0.485***|0.144*|0.309***|0.267**|0.504***|0.424***|0.330***|
|||(0.098)|(0.085)|(0.092)|(0.106)|(0.084)|(0.119)|(0.081)|
|Sector × LI × IPAi|–0.072|0.316|1.661*|0.016|0.010|1.171*|0.452|0.432|
||(0.318)|(0.555)|(0.969)|(0.620)|(0.745)|(0.639)|(0.765)|(0.471)|
|Sector × LMI ×
**Non-Linearities**

In the previous section we assumed that the relationship between unobserved governance and observed indicator scores was linear. This assumption places strong restrictions on the units in which governance is measured in the various indicators in our sample. For example, consider an indicator such as Gallup which asks respondents how many cases of corruption there are among public officials, and offers the choice of four broad categories: 'none", "a _few",_ "many" and "a lot". Our observed data consists of numerical scores on a scale of one to four corresponding to these categories. The assumption of a linear mapping from unobserved graft into observed scores implies that the difference in graft between a country with a score of 4 and one with a score of 3 (i.e. the difference between "a lot" and umany" cases of corruption) rs the same as the difference between two countries with scores of 3 and 2 (i.e. the difference between "many" and 'a few"). Given the somewhat vague response categories, it is not at all clear that the assumption of linearity is warranted. Moreover, even if these categories were equally-spaced according to some appropriate metric, the fact that the observed data are discrete while our unobserved govemance indicator is continuous violates the assumption of a linear relationship between the two.<sup>13</sup> Finally, the mere fact that indicators are non-representative may also contribute to a non-linear relationship between governance and observed scores. A number of the indicators in our sample cover primarily developed countries together with a few developing countries. It is possible that the developing countries in these indicators suffer from a "curse of inclusion" in the sense that they receive worse scores than they might otherwise have received simply because they are implicitly being compared with countries in which various aspects of govemance are likely to be much better.14 For example, representative surveys such as DRI or PRS assign moderate scores of 55/100

> 13
treatment status for a concentration was jointly identified using two primary sources: [1] the Kankor exam selection sheet produced by the administering authority as an example, is shown in Figure A1.2, and [2] the Kankor matriculation dataset, which explicitly denoted whether an applicant was admitted into the treated units. If there were any discrepancies between the two sources, the author contacted a specific university and the MoHE to identify the treatment status for these concentrations. For example, four religious’ studies units were admitting applicants based on gender before 2016 in the matriculation dataset. Nonetheless, these units historically enrolled students in a gender-specific manner for cultural reasons, and were not subject to the gender quota. Hence, these concentrations were not considered treated units because of the gender quota policy in the analytical sample.

The yearly descriptive statistics of Kankor participants—the share of female students and that of female students admitted into 4-year public IHEs are reported in Table B1.1. The number of concentrations increase over time, as shown in Table B1.1, which does not threaten the identification strategy, as I controlled for the concentration fixed effects. This increase was driven by the splitting of enrollment levels from general admission into several concentrations in some fields of study, the enlargement of academic programs, and the establishment of universities for further details (see Appendix A.4).

Moreover, I obtained monthly nightlight data as an average of the Day/Night Band radiance values in nanoWatts/cm2/sr units from Stray Light Corrected VIIRS composited at the district level for 2014– 2015<sup>18</sup> . I excluded pixels with zero values across all images to increase the accuracy of the zonal statistics computations at district level. The use of nightlight as a proxy for economic development and measure of economic activities (Basihos, 2016; Beyer et al., 2018; Galdo et al., 2020; McCord & Rodriguez-Heredia, 2022; S ̈anger et al., 2022) is becoming popular.
framework approximated by the current CPIA, which assesses recent changes in policies and institutions and, by design, does not correlate with recent growth;<sup>16</sup>

- initial conditions (per capita GDP in 1990 and CPIA index in 1996); and

- controls (specific development indicators around 1990).<sup>17</sup>

The probability function across the different states of MDG performance is estimated using the multinomial logit model. Estimations are performed for each of the nine development targets under consideration using ―far from the target‖ as the reference group or base category. The

> 15 It is important to point out that these simple graphical patterns can be driven by more fundamental development factors, such as growth and institutions. The next section tackles some of these issues.

> 16 A study (IDA/DECVP 2007) found the correlation between contemporaneous CPIA and growth to be weak and the correlation between CPIA and future growth to be strong. The CPIA measures the level of policies, not the change; and it focuses on actual implementation, not just introduction or announcement. It is therefore backward looking. The inclusion of the 1996 CPIA is an attempt to capture the policy achievements close to the reference year in 1990, and the 2009 index will include the more recent record.

> 17 See annex table A8 for sources and definitions.

16
we are also interested in another 6,480 changes for the same indicators over the 1995-2005 and 2005-2015 time periods.

To convey as much of this rich information as possible, we report information in three different ways. First, we make available a data file that contains the complete information for all countries, partners, industries, and years for each of the metrics we consider. Second, in the Appendix, we report for each metric one table with the results for all countries and partner-regions but limited to a single industry (manufacturing) and a single year (2015).<sup>14</sup> Third, we discuss below some patterns that we believe are of interest at a more aggregate level, for example the average change over all sub-Saharan countries.

Based on the collapsed country-specific IO tables, we calculate the following four dimensions of GVC participation:

1. Share of imported intermediate inputs: by dividing the sector- and region-specific flows of intermediates in the different rows of the IO table with the total output of each

> 13 The national IO structure in the 5 regions (4-8) and the bilateral interactions between these regions is identical in each of the 45 tables and we only need to calculate that information once.

> 14 For the fourth metric that we consider, the domestic value added in gross exports, there is no partner-country dimension, hence we report results for all 3 years and the 2 decadal changes.

21
# 1. Introduction and overview

This paper explores the sovereign ‐ bank nexus in emerging markets and developing economies (EMDEs), which reflects the interconnectedness between the health of the sovereign and the banking system, whereby stress in one sector may create and amplify stress in the other. We identify two _direct_ channels (bank’s direct exposures to the sovereign and government support to failing banks) and two _indirect_ channels (fiscal and bank interactions with the real sector).

Our analysis of data from 140 EMDEs seem to suggest that the sovereign‐bank nexus is rising, but deeper country analysis is required given country idiosyncrasies, including the structure of sovereign debt and the composition of the investor base and data lags and opacities. First, banks have increased their exposures to their sovereign in the last decade, both relative to their balance sheets and country GDP – this tightens the exposure channel. The median banking system has seen its total exposures to government and non‐financial public entities more than double since 2007 (current median: 8% of GDP). Regressions for 36 EMDEs suggest this factor is also positively associated with the probability of default of financial institutions. Further, banks’ sovereign securities holdings as a fraction of banking system assets have doubled as well (current median: 10% of assets). Second, government debt has grown, and fiscal positions have deteriorated, raising the specter of sovereign stress, particularly in an environment of tightening global financial conditions ‐‐ this tightens all direct and indirect channels and may activate the nexus. Third, banking systems assets and bank credit to the private sector have steadily increased which may restrict the sovereign’s capacity to contain a banking crisis – this tightens the government support channel. However, bank buffers have improved during this period as well. Yet, this may be the result of larger exposures to the sovereign which do not carry a risk weight. Capital buffers may thus be overstated. Fourth, empirical evidence documents the existence of the nexus in EMDEs and suggests that it
Comparing weather data from 1986 to 2002 with their historic means (from 1951 to 1985), there appears to be an increase in the number of _temperature_ shocks (both negative and positive), but no similar increase in _rainfall_ shocks (Table 3) in Mexico.<sup>19</sup> The survey date is used to match each household to the weather information. Each household is assigned the wet season and dry season prior to the survey. That is, if a household was surveyed in dry season of year _t_ , the weather shocks would based on the weather in the dry season _t-1_ and the wet season _t-1_ . However, if the household was surveyed in the wet season of year _t_ , the weather shocks would be based on weather in dry season _t_ and wet season _t-1_ . As an illustration, for the households in the 2002 wave of the MxFLS, the weather variables of interest are rainfall and GDD from the 2001 wet season and the 2002 dry season (Figure 2). The harvest from the 2002 wet season would not have been harvested prior to the surveys and thus the households‘ income and production would be based on the 2001 wet season and the 2002 dry season harvests.

Tables 4a and 4b show the distribution of rainfall and GDD shocks for the rural municipalities in the final samples from MxFLS and ENN, respectively. Although the number of municipalities from which the household surveys are drawn is relatively small, we do still have some variability in the weather variables. There are municipalities that experienced positive and negative rainfall as well as GDD events. As Table 4 shows, there are more GDD shocks than rainfall shocks in the sample.

The original MxFLS localities, those chosen for the 2002 survey, come from 16 different Mexican states and from all the different regions of the country. Although these states vary in the percentage of land cultivated under rainfed technologies, in most at least 75% of the land is rainfed (Table 5). Also, in most at least 50%
|All|13.64%|12.51%|
|Age: upto 29|14.96%|14.52%|
|Age: 30 – 39|13.66%|12.73%|
|Age: 40 – 49|13.99%|12.37%|
|Age: 50 – 59|12.31%|11.65%|
|Age: 60+|14.42%|11.56%|

_Source: Own estimates based on administrative tax data. This is estimated as the share of men or women of each cohort earning between the old and the new (simulated) minimum wage._

**The minimum wage increase can significantly impact the accommodation and food services sector, affecting almost half of employees. In contrast, fewer than 1% of employees in the electricity, gas, steam, and air conditioning supply sectors are potentially affected.** Figure 9 illustrates the potential longrun impacts of the minimum wage increase across various economic sectors. Significant impacts are observed in the accommodation and food services sector, wherein almost half (50%) of the employees experience the

> 29 Workers in the construction sector have been exempt from social health insurance since 2019.

42
Given concerns on multiple hypotheses testing, I follow Kling et al. 2007 and construct a business index averaging the z-scores of business survival and performance outcomes. The index exhibits an increase of 0.13 standard deviations (p-value=0.02) as a result of the intervention. As table A.5 shows, the results are qualitatively similar when using inverse hyperbolic sine transformations, but effects on weekly revenues become insignificant at conventional levels (p-value=0.16). Additionally, table A.7 reveals that most of these effects are coming from business survival rates: conditional on business survival, the treatment is associated with positive but statistically insignificant increases in business revenues and profits.

Higher business survival for the treatment group is consistent with recent evidence suggesting that female microentrepreneurs are particularly likely to face market access constraints, which are often underestimated in standard enterprise surveys (Hardy et al. 2022). Results are also promising, especially when compared to existing evidence on growth-targeting policies among microenterprises. For example, an experiment in Sri Lanka randomizing (male-owned) enterprises’ access to wage subsidies for a worker over a 6-month period found significant effects on business survival, but not on sales and profits during the subsidy period or after it (De Mel et al. 2019).

Next, I look at whether the intervention impacted client type. I start by looking at the effects at the sample level in columns (7)-(12) of table 3. Broadly, we see an increase in the likelihood of selling to strangers, the number of online clients, and the likelihood of offering delivery services. This composition effect, however, might be coming mechanically from business survival, since closed businesses have no clients at all. Similarly, we see a decrease in informal practices usually adopted with customers within business owners’ social networks, such as selling on store credit. These effects might also be mechanical, since closed businesses have no sales.

In table A.7, I reports on these outcomes among the