rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The transition process from school to the work place, and how to increase its efficiency, is a puzzle for researchers and policymakers. Youth unemployment rates across the world are more than double those of adults, which is often interpreted as a lack of decent work opportunities for youth (ILO, 2008). Short-term informal employment and high turnover are viewed as exploitative experiences that are inherent to modern labor markets that youth face (Weller, 2007; Schkolnik, 2005).

The scarce evidence of how young workers make their transitions into the labor market suggests a different story. For example, Bosch and Maloney (2010) find that informal jobs in Latin America are used as a stepping-stone in the school to work transition providing young workers with essential skills that allow them to advance on their employment path. In a similar way, high turnover among European youth demonstrates a prolonged job search process in which young entrants search for available opportunities, revealing to themselves and their employers their strengths and preferences for jobs (Topel and Ward, 1992).

This paper maps out young people’s path from the classroom to the work place in three Latin American countries. We use panel data to follow young people’s movement between school and work and between employment sectors to better understand the dynamics of youth employment, including where youth go upon leaving school, how long they spend in each state, and where they go upon leaving various employment states. The analysis differentiates between adolescents (15-18 years old) who are at the point of deciding to leave school and enter work and may still be dependent on parents, young adults (19-24 years old) who are settling into the labor force, and prime aged adults (age 25-44), who are used as a counterfactual. We consider the behavior in Brazil, Mexico and Argentina, which offer distinct labor market characteristics that may give us further insight into the school-towork transition processes<sup>2</sup> .

> 2 Brazil has moderate
# **Annex II - Identification of Financial Instability Risks**

Many different approaches have been developed to identify and better understand potential vulnerabilities and systemic risks. Part of the literature focus on early indicator models, while other studies search for the main stylized economic patterns associated with past pre-crisis periods. Both may be useful as tools to identify fragilities and motivate preemptive action before crises.

Qualitative and quantitative intelligence can be very useful to better advice and improve policy making, but to determine timing and triggers is more difficult. In general, quantitative indicators are based on aggregate or sectors/institutions data. One set of measures commonly used are the estimates of deviations of variables from their long term trends, e.g. credit to GDP, housing prices and equity prices. Intuitively, as an economy moves away from its trend, the probability of building up imbalances increases, and consequently the chances of a crisis also rise. However, there are doubts about the capacity of these models to produce valid out-of-sample forecasts (BCBS, 2012) – among other reasons because structural changes may be the explanatory factor of the apparently deviation from trend. Another group of indicators uses micro data, e.g. balance sheet data, banks’ capital and liquidity positions, and distance to default ratios. These Indicators are constructed from medians, means, or correlations.

The IMF’s early warning exercise (EWE) is a systematic effort to combine quantitative analysis of vulnerabilities and transmission channels with feedback from financial professionals, academics and policymakers (IMF, 2010). The models and indicators are grouped in three main blocks: Sectoral and Market Vulnerabilities; Country Risk; and Systemic Implications. The first block assesses external-sector risks (e.g. cross border capital flows and external imbalances and exchange rate misalignments), fiscal risks (e.g. rollover and financing risks, market perception of sovereign risk, and sensitivity of the public sector to shocks), corporate sector risks (e.g. leverage and liquidity, and profitability)
The type of innovation introduced is also expected to matter. When “low-hanging fruits” technologies have been already adopted, the remaining innovations may be expected to yield lower returns (Gordon 2012; Gordon and Sayed 2019).

Understanding the returns to innovation is thus an empirical question. This question has received considerable attention in developed countries, where data are more likely to be available. Evidence from advanced economies, mostly in Europe, points to a positive, albeit somewhat noisy, relationship between (primarily product) innovation and (levels/growth of) productivity (see Hall, 2011 for a review). Evidence from developing countries is more limited and not as conclusive - despite some positive association documented for newly industrialized countries such as the Republic of Korea (Lee and Kang 2007), Malaysia (Hegde and Shapira 2007), and China (FisherVanden et al., 2006). Leveraging cross-country data from East Asia, we want to fill this gap in the literature and offer a consistent assessment of the interaction between productivity and innovation, exploring the complementary role played by the business environment and firms’ characteristics.

A variety of methodologies have emerged to link productivity with innovation, despite limited data on technology adoption at the firm level. For example, some researchers resort to randomized control trials (RCTs) to generate first-hand data on technology adoption and productivity. But their data are of relatively small size and time range making generalization of their analyses difficult (Bloom et al., 2013; Atkin, Chaudhry, Chaudry, Khandelwal, and Verhoogen, 2017; Hardy and McCasland, 2016). Another approach that has gained popularity is to check the effects of innovation on the full-range firm productivity distribution. (Syverson, 2011; Juhász et al.,2020). The firm dynamics embodied in the distribution are critical for understanding differences in productivity across sectors (Hsieh and Klenow, 2009). Yet this method imposes a demanding data requirement for cross-country analyses.

Given the nature of our data, a more suitable empirical strategy is
Table 2. Determinants of civil war risk: 1946-2003 Natural resource endowments _aggravating_ the problem of weak states

|Context variable|Natural resources<br>Context variable<br>Interaction|Fearon and Laitin<br>(2003) civil war|Prio \ Uppsala civil war|
|---|---|---|---|
|Ethnic<br>fractionalization|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6912)|0.465(0.512)|1.261***(0.337)|
|Model 16|Ethnic fractionalization|0.306(0.387)|1.196***(0.292)|
||**Interaction**|**0.411(0.810)**|**-1.145**(0.567)**|
||Humphreys oilproduction(observations 5139**) **|-4.668**(2.324)|1.175(1.509)|
|Model 17|Ethnic fractionalization|0.353(0.403)|0.886***(0.303)|
||**Interaction**|**21.041***(6.677)**|**2.333(6.874)**|
||Humphreys oil reserves(observations 5171)|-1.024(0.691)|-0.081(0.174)|
|Model 18|Ethnic fractionalization|0.374(0.395)|0.869***(0.310)|
||**Interaction**|**2.698**(1.344)**|**0.741(0.943)**|
||Humphreys diamondproduction(observations 5139
|Sub-Saharan Africa|44.40|45.28|0.88|
|Tajikistan|Europe & Central Asia|33.91|34.29|0.38|
|Tunisia|Middle East & North Africa|32.88|33.33|0.45|
|Uganda|Sub-Saharan Africa|42.92|42.67|-0.26|
|Uzbekistan|Europe & Central Asia|35.46|35.38|-0.08|
|Zambia|Sub-Saharan Africa|57.38|56.77|-0.61|
|Zimbabwe|Sub-Saharan Africa|50.59|50.40|-0.19|

_Note_ : This table reports the Gini index for each country using the high frequency phone surveys for 2019 and 2020 (observed COVID distribution). The last column captures the Gini points change between 2020 and 2019. Note that the inequality of the counterfactual 2020 distribution is same as the 2019 distribution for each country.

48
Table A.20: Under-5 EFM, across states

||**Outcome: Deat**|**h before age 5**|
|---|---|---|
||Above median SRB|Below median SRB|
||(1)|(2)|
|_Firstborn girl_*_Female_|3.174***|2.050**|
||(0.794)|(0.830)|
|_Firstborn girl_*_Female_*_Post1_|-1.916*|-0.836|
||(1.047)|(1.035)|
|_Firstborn girl_*_Female_*_Post2_|-2.317**|-1.493|
||(1.021)|(0.975)|
|N|149,140|77,989|
|Mean state-level SRB|1.132|1.043|

NOTES: The table present estimates from the same specification as column (3) of Table 3. Columns (1) and (2) include states that have male-female sex ratio at birth (SRB) above and below median among all states, respectively. SRB is calculated using population counts of total number of children at age 0 in the state from the 2011 Census of India. Data: NFHS.

Table A.21: Number of births, across states

||**Outcome: Nu**|**mber of births**|
|---|---|---|
||Above median SRB|Below median SRB|
||(1)|(2)|
|_Firstborn girl_*_Female_|0.199***|0.137***|
||(0.016)|(0.019)|
|_Firstborn girl_*_Female_*_Post1_|-0.104***|-0.068**|
||
third-party agents have a higher measure of effectiveness (reflecting the k in Tables 2a and |cc 2b).<sup>67</sup> θθ<sup>�</sup>

To illustrate the usefulness of our methodology in generating new findings, consider Bolivia in these figures. Governance structures including more than just bilateralism are relatively more prevalent in Bolivia than in all others. This is surprising. Bolivia is the least developed of the six countries and scores lowest on standard measures of the quality of the legal system (e.g., the World Governance Indicator's rule of law index). Given the low levels of personal trust in Bolivia <mark>(Latin American Public Opinion Project, 2016/7),</mark> it is tempting to think that this might reflect comparative, rather than absolute, advantage in the legal realm. However, this cannot be a complete explanation, since LCA's estimation of a firm's governance class does not use data on a firm's country. Therefore, the greater effectiveness indicated for legal institutions in Bolivia than in, say, Uruguay, is inconsistent with the latter country's much higher ratings on indexes of legal institutions.<sup>68</sup> This is a puzzle, and one that is most clearly raised in an exercise like the current one.

# **V.2 Cross-Regional Variations in Governance Structures**

We next look at within-country, regional variation in governance structures. We apply the third step of the 3-step method to each country separately, using regional dummy variables as covariates. Table 4 reports the _p_ -values of within-country Wald tests of the null hypothesis that there is no variation across a country's regions in the patterns of governance structures. On the

> 67 Thus the ordering of classes is different from that in Tables 2a and 2b, which followed LCA's somewhat arbitrary ordering. 68 Bolivia has the highest estimated shadow-economy presence among the six countries studied in this
POLICY RESEARCH WORKING PAPER 2017

# Summary findings

East Asia's financial crisis has been attributed in part to the weak performance and risky financial structures of Asian corporations. In the period before Asia's financial crisis, however, analysts were not suggesting that the financial structures of many East Asian corporations would be unable to withstand the combined shocks of increased interest rates, depreciated currencies, and large drops in domestic demand.

To document the basic record of corporate performance and financing structures for East Asian corporations, Claessens, Djankov, and Lang analyze data for _5,550_ firms in nine countries for the period 1988-96. They find large differences in performance and financial structure across countries.

- Profitability as measured by real return on assets - (ROA) in local currency was relatively low in Hong Kong, Japan, the Republic of Korea, and Singapore in the decade before the crisis. Corporations in Indonesia, the Philippines, and Thailand averaged high returns - roughly double those in Germany and the United States for the same period.

In 1994-96, measured performance declined somewhat in several East Asian countries, especially Japan and Korea. Those differences in performance were not fully reflected in sales growth, as investment rates were high and continued to drive output growth in all countries.

These stylized facts suggest that the East Asian miracle was indeed based on a vibrant corporate sector.

But the combination of high investment and relatively low profitability in some countries meant that much external financing was needed. Outside equity was used sparingly -in part because stock markets were depressed (Japan) or because insiders preferred to retain control - so borrowing was heavy in most East Asian countries, and leverage increased in the years before 1996 in Korea, Malaysia, and Thailand.

Risk increased as short-term (foreign exchange) borrowing became increasingly important in the 1990s, especially in Malaysia, Taiwan (China), and Thailand.

In other words, it is now apparent that some of the vulnerabilities in corporate financial structures that were to become an important factor in East Asia'
(12) i i i iiggrrccrr ii where 11(LLeeL eeLLTTL nnssiittiiccnn)i is a dummy variable that equals one if a change in leader occurred = PP + ββgg + μμ + ee ii in year 11(LLeeL _t_ in country eeLLTTL _c_ nnssiittiiccnn) and i is the contemporaneous growth residual. Estimation with country fixed effect ( ), yields an estimate of gg = −0.5 ( _t-stat=-6_ ), verifying the endogeneity of leader ii transitions: a 1ppt lower growth rate increases the probability of leader transition by 0.5ppts.μμ ββ̂<sup>26</sup> To test the effect of this endogeneity on our methodology, Panel C of Table 1 re-produces all of the least-squares Monte Carlo estimates when leaders change endogenously with the growth rate, as in Equation 12 with = −0.5.<sup>27</sup> Despite being very statistically significant, the potential endogeneity of leader tenure has almost no effect on any of the least squares Monte Carlo ββ̂ estimates. Why? This form of endogeneity leads to an upward bias in average growth rates at long tenures due to sample selection bias (leaders with higher growth rates are more likely to survive). But its quantitative size is small, and so selection bias is miniscule at the average tenure of about 6 years. Moreover, it seems that in terms of RMSE, sample selection bias is a second-order issue relative to shrinking iid error noise.<sup>28</sup>

# **3. Data and estimates of variance components**

**Data sources.** Data on growth are taken from Penn World Tables (PWT) version 9 over 19512014 (the latest version at the time of writing, Feenstra et al. (2015), and data on leaders are taken from the Archigos 4.1 data set (described in Goemans et al. 2009).<sup>29</sup> We categorize countries as

> 26 Another potential interpretation is
<!-- Start of picture text --> 8g<br>8¢Zo¢ZoZo<br>3saa a 1//<br>Zo / \ ' r¢<br>ae ¢// \ ,HH<br>> i L*<br>5 i \<br>odevv I ‘y.NaNa ®JJ<br>NCR I<br>arn !<br>vo!<br>¢<br>8<br>0 1 4 6 8 9 10 11 «12 «130«14«15~«16—=O 18 19 20 21 22 23<br>Hour of the Day (Kampala time)<br>—®— Avg PM2.5 Static —-®-—- Avg PM2.5 Mobile<br><!-- End of picture text --> <!-- Start of picture text --> g<br>8 o,<br>oqq ra row7»7»<br>So , 4 \<br>ao== /ii \yy I11 e »<br>s 1 /<br>3 1<br>g |ii \\\ }$$<br>! \<br>| & /<br>e *® ! Ne *<br>8 Ng i e ! N /<br>NI>> P~gnanannanan ae aed<br>0 6 8 9 10 11 «12 «13 «14 «15 «16 =«1 18 419 20 21 22 23<br>Hour of the the Day (Kampala time)<br>——®—_ Median PM2.5 Static ——-@®-—- Median PM2.5 Mobile<br><!-- End of picture text -->

<!-- Start of picture text --> 8g<br>8¢Zo¢ZoZo 8
_For labor or for divorce?_

# _4.2.Data and working samples_

The main source of data is repeated cross sections of nationally representative household and individual surveys for the period 1987-2017 from the Demographic and Health Surveys (DHS). Additionally, two cross-sectional data sets from the Household Consumption Expenditure Survey by the High Commission for Planning (HCP) are used in the case of Morocco, where the DHS survey was not conducted (ie: for 2007 and 2014, the latest available waves of the survey).

**Table 1 - Data Source used per country**

|**Data Sources**|**Before reform**|**After reform**|
|---|---|---|
|Morocco|DHS 1992 and 2003|HCP Household survey 2007 and 2014|
|Egypt, Arab Rep.|DHS 1988, 1992, 1996|DHS 2003, 2005, 2008, 2014|
|Jordan|DHS 1990, 1997|DHS 2002, 2007, 2009, 2012, 2017|

_Source: Authors._ In analyzing women’s labor outcomes, we collect respondents’ answers to the question ‘are you currently working’. This question does not just measure labor force participation but, rather, also current occupation in a ‘paid labor’ position at the time of the survey. This means that we avoid the bias of having high unemployment rates in the estimations and limit our results to the effective employment outcome of ‘treated’ mothers for a given year.

In the case of Morocco, the DHS survey is completed by equally nationally representative data for the years 2007 and 2014 from the High Commission for Planning (HCP). To harmonize the DHS data and HCP Household Consumption Expenditure Survey, as in the latter there is no information on the number of births by women, we construct variables on children based on the age of the youngest member of the household. As with DHS data, only married, widowed or divorced women
# Fossil Fuel Prices and Air Pollution: Evidence from a Panel of 133 Countries

## **Kentaro Mayr**<sup>**1,2**</sup> **Jun Rentschler**<sup>**2**</sup>

1 Energy Institute, University College London, UK 2 Office of the Chief Economist for Sustainable Development, The World Bank, US

**Keywords:** Air pollution, PM2.5, Fuel prices **JEL codes:** Q53, Q41, C22, C23, I18

**Topics:** Energy Subsidies, Fuel Subsidies, Air Pollution, Economic Statistics

**Acknowledgments:** This study is a technical background paper for the World Bank flagship report _Detox Development: Repurposing Environmentally Harmful Subsidies_ . The authors are grateful for valuable feedback and inputs by Simon Black, Richard Damania, Ebad Ebadi, Jason Russ, Stephen Stretton, and Nate Vernon.
.

- Tibrewal, K., Venkataraman, C., Phuleria, H., Joshi, V., Maithel, S., Damle, A., Gupta, A., Lokhande, P., Rabha, S., Saikia, B.K., Roy, S., Habib, G., Rathi, S., Goel, A., Ahlawat, S., Mandal, T.K., Azharuddin Hashmi, M., Qureshi, A., Dhandapani, A., Iqbal, J., Devaliya, S., Raman, R.S., Lian, Y., Pandithurai, G., Kuppili, S.K., Shiva Nagendra, M., Mukherjee, S., Chatterjee, A., Najar, T.A., Jehangir, A., Singh, J., Sinha, B., 2023. Reconciliation of energy use disparities in brick production in India. Nature Sustainability 6, 1248-1257.

- Timilsina, G., Malla, S., 2023. Brick Production in Nepal: A Rapid Survey. Unpublished Document. World Bank, Washington, DC.

- US EPA, 1995. Compilation of Air Pollutant Emission Factors, Fifth Edition. US Environmental Protection Agency, Washington, DC.

- Valdes, H., Vilches, J., Felmer, G., Hurtado, M., Figueroa, J., 2020. Artisan Brick Kilns: State-ofthe-Art and Future Trends. Sustainability 12, 7724.

- Vasić, M.V., Goel, G., Vasić, M., Radojević, Z., 2021. Recycling of waste coal dust for the energyefficient fabrication of bricks: A laboratory to industrial-scale study. Environ. Technol. Innov. 21, 101350.

- WECS, 2022. Energy Sector Synopsis Report 2021/2022. Report No. WECS 03/077/078. Water and Energy Commission Secretariat (WECS), Government of Nepal (GON),
br>São Tomé and Príncipe_2013 6.8% São Tomé and Príncipe_2013 192.1%<br>São Tomé and Príncipe_2014 6.1% São Tomé and Príncipe_2014 174.1%<br>Madagascar_2011 1.9% Madagascar_2011 121.0%<br>Madagascar_2013 2.3% H Madagascar_2013 140.3%<br>Madagascar_2014 2.2% Madagascar_2014 145.8%<br>Gambia, The_2012 5.1% Gambia, The_2012 93.1%<br>Gambia, The_2013 4.6% Gambia, The_2013 84.2%<br>Gambia, The_2014 5.8% Gambia, The_2014 115.2%<br>HIGH QFD AND WORSENING HIGH QFD AND WORSENING<br>Botswana_2010 1.8% Botswana_2010 93.9%<br>Botswana_2011 1.7% Botswana_2011 87.7%<br>Botswana_2012 3.7% Botswana_2012 200.9%<br>Botswana_2013 3.4% Botswana_2013 174.0%<br>Guinea_2011 1.4% H Guinea_2011 181.6%<br>Guinea_2013 2.1% Guinea_2013 247.5%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

98
# **5. Conclusion**

In this paper we show that exports do not respond symmetrically to appreciations and depreciations of the RER in Pakistan, and that the extent of the asymmetry is substantial, limiting the role that competitive RERs have had in boosting export growth elsewhere. We find this asymmetric pattern to be robust to the periodicity of the data, as well as to the level of disaggregation – asymmetric export responses are also found when using product-destination-level data for exports, and BRERs. The asymmetric export response to appreciations and depreciations only vanishes when exchange rate changes are very large – in the top 5 percent of the exchange rate change distribution, suggesting that it is very large depreciations that allow exporters to overcome information costs, supply constraints, or PTM behavior from global buyers.

We exploit sectoral variation in the extent of product differentiation – proxying the variation in costs associated with gathering information on the buyers when ramping up exports; and variation in access to finance, financial dependence, and factor intensity – proxying variation in supply constraints.

We find, first, that differentiated products – more intensive in information at the time of increasing shipments or building new export relationships than homogeneous products – show a distinct export response to appreciations and depreciations, being more responsive to the former than to the latter, while exports of homogenous products react symmetrically to upward and downward changes in the RER. Second, we confirm well-established conventional wisdom – a part of the failure of exports to grow after RER depreciations lies in the inability of sectors to create ‘exportable surplus’ – or, in other words, to increase supply when conditions improve. Exports from sectors with higher access to finance schemes, lower finance dependence, and higher labor intensity react more to depreciations as they face lower supply constraints. They can obtain 42