rafmacalaba/data-use-mentions-extended huggingface dataset:
at lease once since they took their first job. The formality status is therefore generally stable and equivalent to the formality of their last job. Moreover, since hardly any Syrians have formal jobs, this is equivalent to comparing each group of informal workers to formal Jordanian workers. Nonwage workers are included as a separate category in the regression, but we do not show their results given their limited sample size.

S (2) iitt ii tt 1tt ii tt 2tt ii tt iitt yy = αα + γγ + ββ ∗HH + ββ JJSS ∗HH + εε S : _dummy for Syrian whose last job was private informal, 0 otherwise._ ii _: dummy for Jordanian whose last job was private informal, 0 otherwise._ ii JJSS In the third, fourth and fifth models we limit the sample to Syrians. We use an in-camp indicator to test the effect of living in camp among Syrians (3), a permit indicator for examining the effect of holding a work permit (4), we then account for both variables in the same model (5). (3) iitt ii tt 1tt ii tt iitt yy = αα + γγ + ββ CC ∗HH + εε : takes 1 if Syrian living in camp, omitted category is Syrian not living in camp ii CC (4) iitt ii tt 1tt ii tt iitt yy = αα + γγ + ββ PP ∗HH + εε : takes 1 if Syrian holding work permit, omitted category is Syrian not holding permit ii PP (5) iitt ii tt 1tt ii tt 2tt ii tt iitt yy = αα + γγ + ββ CC ∗HH + ββ PP ∗HH + εε Finally, using the full sample we include two dummy variables for being a Syrian who lives in camp or being a Syrian who does not live in camp to have a closer look at these categories in comparison to Jordanians. In this model we also control for formality of the last job. (6) iitt ii tt 1tt ii tt 2tt ii tt
(Bloom and Van Reenen 2007) advanced the literature significantly by asking explicit questions on ownership/management structure variables across a broader spectrum of firms, but overall sample size within and across countries remains modest.<sup>6</sup> as regional characteristics that covers over 119,000 firms from 134 regions across 11 European countries. We draw on the Amadeus (Bureau Van Dyke) database that offers consistent data on firm performance as well as the full names of owners and managers which we then match to systematically measure management modes. To validate our measures, we show they are highly correlated with the explicit measures of family

> 3Indeed, Bloom et. al. (2013) argue that Indian textile firms do not hire professional managers because if they steal from the plant or receive kickbacks in exchange for inflated materials prices, and even if discovered, the owner’s ability to successfully prosecute them and recover the assets is likely minimal because of the inefficiency of Indian courts.

> 4Bertrand and Schoar (2006) make the point that family-controlled businesses are still common in countries like the US and Sweden with excellent investor protection. Claessens, Djankov, Fan, and Lang, (2002) find that family firms underperform relative to nonfamily firms even in Southeast Asian countries with low investor protection. Both moments suggest that weak institutions cannot fully explain family control, and that is not what we want to put forward in this paper. Indeed, the higher fixed costs of professional management in our model (one interpretation of which is the disamenity from not employing family members as managers) predicts that family-managed firms will exist even with perfectly enforced contracts. Instead, we use changes in trust across regions within countries to show that firms with the same observable characteristics are relatively more likely to be family-managed in regions within countries characterized by low trust.

> 5Others have also worked with similar sized datasets. Claessens, Djankov and Lang (2000) look at 2,980 firms in 9 east Asian countries Lins (2003) covers 1,
manual. The methodological annex further equips practitioners with a toolkit for effective implementation.

Policy makers focusing on skills policy are urged to adopt a narrow definition of green jobs, emphasizing specific tasks aimed at reducing environmental impact. The paper recommends relying on the task approach and the proposed methodology to avoid assumptions inherent in classifications from other countries. Moreover, the utilization of online vacancy data with rich skills information is encouraged for profiling green occupations as well as understanding which skills may become in-demand in non-green occupations as the greening of the economy advances, offering a more dynamic and real-time understanding.

The overarching recommendation for policy makers involves the development of a robust labor market information system. This includes investments in detailed occupational classifications or job title taxonomies, updated more frequently than traditional classifications, and informed by ISCO-08, online job vacancy data, ONET-type data, and qualitative insights from labor market stakeholders. Additionally, the collection of skills and tasks data for narrowly defined occupations is emphasized, drawing lessons from established frameworks like ONET, ESCO, PIIAC, and STEP. Regular and comprehensive surveys are proposed to monitor changes in the labor market, inform skills policies, and enhance the understanding of green jobs and the skills they require.

It is crucial to recognize that green skills policy is just one facet of broader skills policies promoting the transition to a green economy. The paper underscores the importance of comprehensive policy instruments, such as advanced labor market information systems, to keep the population informed about in-demand jobs across various sectors. The integration of identified green jobs into competency standards, qualification frameworks, curricula, and certification programs is highlighted as an essential step for countries with well-established systems and a critical focus for those in the process of modernizing their educational, TVET, and employment systems. Accelerating this modernization is deemed vital to capitalize on the opportunities presented by the green transition.

# **6. References**

- Alatas, H.; Granata, J.; and Posadas, J. (2020) Indonesia’s Occupational Tasks and Skills:
, -71%]|[-82%, -72%]|[-76%, -58%]|[-116%, -41%]|
|Tail-areaprobability|0.001|0.001|0.001|0.001|

_Notes:_ The table shows the effect of the Covid-19 lockdowns on traffic congestion in Bogotá, New Delhi, New York City, and Paris. For each city, we report the BSTS model using: (A) traffic conditions in Hong Kong and weather in treated cities as the synthetic control series; and (B) traffic condition in Hong Kong, Cairo, Sofia, Stockholm, and Tokyo as the synthetic control series.

social distancing, mandatory face mask-wearing measures, or state of emergency. Using a sample of cities for which we have the required data, we augment our initial list of synthetic control series—i.e. weather conditions in treated cities and congestion in Hong Kong SAR—with traffic congestion data from Cairo, Sofia, Stockholm, and Tokyo, all of which enforced some form of relatively flexible and less constraining lockdown policies. Like in the case of Hong Kong SAR, we do not expect traffic restrictions in the treated cities to directly influence traffic conditions in these cities. However, unlike Hong Kong SAR, people in these cities might have responded to the softer lockdown measures by changing their travel behavior in the midst of the Covid-19 outbreak. But this is unlikely to occur exactly the day when the strict lockdown measures where enforced in the treated cities.

We show in Figure 4 the posterior inclusion probability of the synthetic control series in the regression sub-model of the Bayesian Structural Time Series model. For Bogotá, the covariates with a probability of inclusion exceeding 0.05 are traffic conditions in Stockholm (with an inclusion probability of 1), Cairo (0.79), Hong Kong SAR (0.46), 23
standard deviations larger than the mean share of that type of OOP healthcare expenditures in total household consumption with the mean share of those expenditures in household consumption times the respondent’s household’s consumption. That is, for respondent _i_ in household _h_ and OOP type _j_ , we replace _OOPihj_ with the sample mean value of ( _OOPihj_ / _Ch_ ) times _Ch_ , where _Ch_ is household consumption. In all, we change 112 healthcare expenditure records (0.2 percent of the sample) with these procedures.

An important problem with this estimate of OOP healthcare expenditures is that it applies only to expenditures incurred in the past two weeks. But healthcare expenditures in Ghana are not regular or frequent, so if we want to know how much is spent in a year, simply multiplying the reported amounts by 26 will be inaccurate. Survey respondents who happened to have incurred healthcare expenditures in the past two weeks are not the only people who will incur healthcare expenditures through the year, nor will their annual healthcare expenditures be anywhere near 26 times what they spent in the past two weeks.

To address this, we divide the sample into deciles based on consumption per adult equivalent and into NHIS members and nonmembers; 20 subgroups in all.<sup>17</sup> For each respondent, we then draw and sum 26 random values from the distribution of the share of OOP healthcare expenditures in total household consumption for her/his subgroup whether or not she or he reports healthcare expenditures in the past two weeks. We then multiply the sum of the 26 random draws by total household consumption to obtain our estimate of the individual’s annual OOP healthcare expenditure. This assumes that every member of a subgroup has an equal probability of incurring a healthcare expenditure, though the probability can vary across subgroups. In doing so, we ignore the possibility of serial correlation of OOP healthcare expenditures, that is, the possibility that those who report OOP healthcare expenditures in the past two weeks are more likely to have OOP healthcare expenditures in the
-7.5|
|1985-1998|39.2|71.4|12.5|21.7|0.9|-0.7|1.6|-2.0|6.2|-9.4|
|1999-2007|38.1|62.6|9.7|12.2|0.3|-0.4|0.9|-1.4|2.7|-4.3|
|2008-2019|36.1|125.2|8.1|11.3|0.3|-0.4|0.6|-1.6|2.1|-4.0|
|2020-2024<br>(May)|27.4|120.2|10.5|10.2|0.4|-0.4|1.6|-1.4|4.2|-3.4|
|Change or<br>percent<br>change<br>from 1970-<br>84<br>period<br>to 2008-19<br>period|||||||||||
|Change<br>|-18.7|77.0|-8.6|-2.4|-1.1|0.6|-1.6|1.2|-7.5|3.5|
|%<br>Change|-0.3|1.6|-0.5|-0.2|-0.8|-0.6|-0.7|-0.4|-0.8|-0.5|

Sources: Authors’ calculations using the turning points for rate cycles listed in Appendix Table 1 and data ending on May 31, 2024. Notes: This table shows averages across all tightening or
# **Table 8. Stability of Rankings Based on Various Program-Level Outcomes and Value-Added Contributions**

## _Panel A. Correlation of Rankings_

||Grad|uation|Formal E|mployment|
|---|---|---|---|---|
||Outcome|Value Added|Outcome|Value Added|
||(1)|(2)|(3)|(4)|
|Formal employment|-0.00|0.08|||
|Wages (log)|-0.05|0.05|0.54<sup>***</sup>|0.50<sup>***</sup>|

_Panel B. Percent of Programs that Move 3 or More Deciles Across Rankings_

||Grad|uation|Formal E|mployment|
|---|---|---|---|---|
||Outcome|Value Added|Outcome|Value Added|
||(1)|(2)|(3)|(4)|
|Formal employment|57|55|||
|Wages (log)|51|51|34|34|

_Source_ : Authors’ calculations using administrative data.

_Notes_ : This table shows the stability of program rankings based on program-level average outcomes or value added (6 rankings in total). Panel A shows the correlation among rankings based on a given outcome or value added. For instance, the correlation of the rankings based on (log) wages and formal employment is 0.54, whereas the rankings based on (log) wage value added and formal employment value added have a correlation of 0.50. Panel B shows the percent of programs whose decile position differs by at least 3 deciles across rankings. Since the number of programs for which we estimate value added differs across outcomes, we compute only these correlations for the
US Dollars 6. Data collected from almost 20 developing countries indicate that most countries made only slight retail price adjustments in response to the -60% drop in international oil prices between November, 1985 and July, 1986.3' In fact, a few countries actually raised nominal retail prices during this period. Figures 1 and 2 illustrate the magnitude of diesel and fuel oil retail price in the appendix.

Retail petroleum product price changes from late 1985 to mid 1986 in fourteen developing countries are tabulated

**3/**
<!-- Start of picture text --> MHHH-FHHH gaps in use of chemical fertilizer vs. overall use<br>4BGD<br>=<br>oD<br>a<br>fH<br>= APAK<br>=<br>=<br>Lab}<br>=o a =-<br>oo ao<br>oa _ =—<br>rab _-7<br>3 Leot A ALE<br>£ ao<br>= aan «GTM<br>a -_—<br>Ea ae<br>a eo ANGA<br>2 ~ “PAN<br>2 aGHA “BGR<br>=Lak] ANPL aMWI<br>= ANIC<br>So<br>0 2 4 6 8<br>country level of use (% of households)<br>Source: RIGA data.<br><!-- End of picture text -->
the impact evaluation in footnote 6.

21In Amaral et al. (2021), we use participants’ responses about the use and knowledge of different stressmanagement and positive-parenting techniques and concepts to assess their assimilation of the content provided in the parenting intervention. In addition to this midline survey, we conducted three other surveys with different samples of individuals. Each survey asked about techniques taught in the previous two to three weeks.

> 22Following information protection protocols, data collected from all the other surveys was stored on a private server of the survey firm. Only the project staff and researchers had access to this.

23To collect baseline data, we sent a text message to caregivers inviting them to self-complete the baseline survey by clicking on a link included in the message.

> 24See Table A8 for further details on the description and data used to estimate the main outcomes of this survey-mode experiment.

11
# A Firm of One’s Own: Experimental Evidence on Credit Constraints and Occupational Choice

Andrew Brudevold-Newman, Maddalena Honorati, Pamela Jakiela, and Owen Ozier<sup>_∗_</sup>

**Keywords:** youth unemployment, microenterprises, entrepreneurship, credit constraints, cash grants, training, Africa, gender

**JEL Codes:** J24, M53, O12

> _∗_ Brudevold-Newman: University of Maryland, abn@umd.edu; Honorati: Social Protection and Labor Global Practice, World Bank, mhonorati@worldbank.org; Jakiela: University of Maryland and IZA, pjakiela@umd.edu; Ozier: Development Research Group, World Bank, oozier@worldbank.org. We are grateful to Maya Eden, David Evans, Deon Filmer, Jessica Goldberg, Markus Goldstein, David McKenzie, Patrick Premand, seminar participants at Duke, USC, and the University of Oklahoma, and numerous conference attendees for helpful comments. Rohit Chhabra, Emily Cook-Lundgren, Gerald Ipapa, and Laura Kincaide provided excellent research assistance. This research was funded by the IZA/DFID Growth and Labour Markets in Low Income Countries Programme, the CEPR/DFID Private Enterprise Development in Low-Income Countries Research Initiative, the ILO’s Youth Employment Network, the National Science Foundation (award number 1357332), and the World Bank (SRP, RSB, i2i, Gender Innovation Lab). The study was registered at the AEA RCT registry under ID number AEARCTR-0000459. We are indebted to staff at the International Rescue Committee (the implementing organization) and Innovations for Poverty Action for their help and support.
to the DM).|
|Economic risk rating|**PRS (ICRG)**|A means of assessing a country's current economic strengths and weaknesses. Risk<br>points are assessed for each of the component factors of GDP per head of population,<br>real annual GDP growth, annual inflation rate, budget balance as a percentage of GDP,<br>and current account balance as a percentage of GDP.|
|**Transition variables**|**EBRD**<br>**transition**<br>**database**|The transition indicator scores reflect the judgment of the EBRD’s Office of the Chief<br>Economist about country-specific progress in transition.More information available<br>on the EBRD website.|
|Privatization||Index of the level of Privatization|
|Price liberalization||Index of the level of Price liberalization|
|Competition policy||Index of the level of Competition policy|

Source: authors’ elaboration 34
traditional line-up method happens to do well when projecting forward into 2011-12 because growth in mean reported consumption in the survey was unusually high between 2009-10 and 2011-12 and nearly matched growth in HFCE. This is a sharp break from the pattern observed during the previous period, and in most other contexts. It remains to be seen whether this correspondence between HFCE and survey mean growth will be sustained, but there are at least two reasons to speculate it may not be. The first is that survey-based consumption may be more responsive than national account data to weather shocks. India experienced significant droughts during 2004 and 2009, followed by above-average rainfall in the 2011 season. To the extent that survey-based consumption is more sensitive than national accounts to agricultural income, favorable rainfall in 2011 would cause a larger jump in survey 5
|<br>OECD, Haver Analytics|
|Industrial Production|The industrial production index covers the volume of<br>production in sectors such as mining, manufacturing,<br>electricity, gas, steam, and air conditioning. The<br>reference year is 2015 (OECD) unless specified<br>otherwise. Growth rates of production are calculated on<br>a month-over-month basis or relative to the past 12<br>months based on the analysis.|OECD, Haver Analytics|
|Unemployment rate|Unemployment rate (in percent):<br>unemployment/labor market participation|OECD|
|Employment|Number of persons employed with age 15 and over (in<br>thousands)|OECD, IFS, ILO, FRED,|
|Oil price|Nominal oil prices (average of Dubai, WTI, and Brent<br>oil prices). Oil price growth rates are calculated on a<br>month-over-month basis.|World Bank (Pink sheet<br>database)|

Notes: Sample period is from 1970m1 to 2024m3, except policy rate data is through 2024m5. Economies included are Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland, France, the United Kingdom, Greece, Ireland, Israel, Italy, Japan, the Republic of Korea, Netherlands, Norway, New Zealand, Portugal, Sweden, the United States, and the euro area.

83
and over time.

Our findings suggest that disparities in income and consumption measures between surveys and national accounts are much larger than found by both Ravallion (2003) and Deaton (2005). On average, across all countries, we find that per capita consumption means are about 22 percent lower in surveys compared with national accounts (i.e., HFCE), and per capita income means from surveys are about 52 percent lower than per capita GDP. This indicates considerably larger discrepancies than what was found in Deaton’s and Ravallion’s assessments. Ravallion 3
the mobility level in Kinshasa reduces the chance of workers experiencing income decrease by 5.8 percent and 11.6 percent, respectively. Panel A in Figure 8 visually illustrates the result.

As a robustness check, we estimate the same set of specifications by restricting the sample only from the first to the fourth rounds, the period in which the mobility level continuously grew (Panel B in Figure 5). Restricting the sample for the first four rounds does not substantially change the results (columns 5 to 8 in Table 4).

We then analyze the additional heterogeneity of the income-mobility elasticities by worker characteristics, such as sex, skills, self-employment, and poverty status. The estimation results of the fixed-effects models with additional interactions with those characteristics are summarized in Table 5. We find that the labor incomes of female and/or low-skilled workers in neighborhoods that are located farther from the CBD are more elastic to mobility changes. As shown in Panel C (female workers) and Panel D (low-skilled workers) in Figure 8, the probability of their experiencing income reductions changes to a greater degree corresponding to the mobility changes.

Another analysis of Kinshasa involves the neighborhood characteristics based on their precariousness and building density. Table A4 in Appendix A summarizes the estimation results of regressions that interact the mobility index with the indicator about neighborhood characteristics. The positive coefficient estimates for the neighborhood characteristics dummies—with the precarious and high-density neighborhoods as the reference category—imply that labor incomes are more elastic to mobility changes in precarious and high-density neighborhoods. The results are, however, not clear enough to distinguish density across precarious neighborhoods due to wide standard errors in the estimates (see Figure A6 in Appendix).

**Table 4. Regression results with distance from CBD: Kinshasa**

||Whethe<br>|r the househ<br>Rounds<br>|old head di<br>1to 6<br>|d not work or<br>|decrease lab<br>|or income d<
The International Trade Index (ITI) may also be considered.

> 35 For related discussions See Stiglitz, Sen, and Fitoussi 2010, p. 9; Coyle 2015, pp. 35, 130, 145; and Mwabu 2016. 36 Coyle 2015, p. viii.

> 37 UNCTAD 2013.

> 38 <mark>See Mwabu 2016.</mark>
# _IV.bSelected country cases_

**Figure 7:** Selected HICs (global) overperformers

- a) Singapore b) Northern Europe (Denmark, Finland, Norway)

<!-- Start of picture text --> 0.25 0.25<br>0.20 0.20<br>0 .15 0 .15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Other Non-ov. (i) HICs Other Non-ov. (i) HICs<br>Other Non-ov. (ii) ECA-NAC HICs Other Non-ov. (ii) ECA-NAC HICs<br>Overperformer (SGP) Overperformers (Northern Eur.)<br>Digital Infr. Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperformer HICs in Europe, Central Asia, or North America, while dotted red shows the scores for all other non-overperformer HICs. The “spider” package is taken from Naqvi (2024a).

Singapore’s AI readiness strategy strongly emphasizes all the AIPI pillars detailed above, and particularly regulation and ethics, as well as the development of robust digital infrastructure and solid human capital and labor market policies to enable responsible and efficient AI adoption (Figure 7a).<