rafmacalaba/datause-encoder-data huggingface dataset:
countries lead to reduction of their CO2 emissions by 11 to 43 presents in 2030. On the other hand the US$50//tCO2 carbon tax in Annex I countries causes up to 2.2 percent increase of CO2 emissions in non-Annex I countries.

**Table 2: Percentage** c **hange in total CO2 emissions at various levels of carbon tax in Annex**

**I countries in 2030 from their BAU cases**

|Country/Regions|Car<br>|bon tax rate<br>|(US$/tCO2<br>|)<br>|
|---|---|---|---|---|
||10|50|100|250|
|Australia and New Zealand|-14.9%|-36.0%|-46.2%|-58.7%|
|Canada|-9.4%|-26.7%|-36.6%|-50.8%|
|Germany|-6.4%|-21.1%|-30.5%|-43.8%|
|Spain|-4.2%|-15.2%|-23.6%|-37.5%|
|France|-2.9%|-10.8%|-17.3%|-29.4%|
|UK|-12.2%|-35.0%|-46.2%|-58.4%|
|Italy|-4.2%|-15.0%|-23.1%|-36.6%|
|Japan|-8.0%|-18.2%|-25.4%|-37.1%|
|EFTA Countries & Rest of European<br>Union|-8.3%|-25.6%|-35.9%|-50.5%|
|Russia|-16.7%|-42.8%|-55.7%|-72.2%|
|United States|-10.8%|-31.6%|-43.1%|-57.8%|
|Non Annex I Europe & Central Asia|0.5%|1.9%|3.2%|4.7%|
|Argentina|0.1%|0.3%|0.6%|1.2%|
|Brazil|0.4%|1.6%|2.7%|4.9%|
|Rest of Latin America & Caribbean|0.3%|1.2%|2.1%|4.1%|
|China|0.2%|0.8%|1.3%|2.5%|
|Indonesia|0.3%|1.2%|2.0%|3.7%|
|Rest of East Asia & Pacific|0.5%|1.8%|2.9%|5.0%|
|Malaysia|0.2%|0.8%|1.3%|2.4%|
|Thailand|0.3%|1.2%|2.1%|3.7%|
|India|0.2%|0.8%|1.2%|1.9%|
|Rest of South Asia|0.2%|0.8%|1.4%|2.7%|
|Middle East & North Africa|0.0%|0.2%|0.4%|1.0%|
|South Africa|0.7%|2.2%|2.9%|3.8%|
|Rest of Sub-Saharan Africa|0.2%|0.9%|1.7%|3.4%|

Note that at the 50% probability of maintaining the global mean temperature rise below 2°C relative to pre-industrial levels, atmospheric GHG concentrations must stabilize below 450ppm CO2 equivalence (IPCC, 2007). To achieve this target, global GHG emissions should peak by 2020 at the latest and then be more than halved by 2050 relative to 1990 (Stern 2006). Our analysis shows that the global CO2 emissions in 2030 would be only 18% lower compared to 14
# **Table 4b: Tests of the Association Between Governance Structures and a Variety Of Variables, Relations With Customers**

|||__________|_ FDR_____|_________|___________|_FWER_____|__________|
|---|---|---|---|---|---|---|---|
|Description<br>|p-value|1%|5%|10%|1%|5%|10%|
|Country|0.000***|0.0004***|0.0020**|0.0040*|0.0004***|0.0020**|0.0040*|
|_Attitudes towards courts_||||||||
|"the court system is fair..."|0.280|0.0076|0.0380|0.0760|0.0014|0.0071|0.0143|
|Courts as an obstacle|0.000***|0.0008***|0.0040**|0.0080*|0.0004***|0.0021**|0.0042*|
|_Relations with business associations_||||||||
|Belong to a business association?|0.041**|0.0032|0.0160|0.0320|0.0006|0.0028|0.0056|
|Regularly interact with a business association?|0.024**|0.0020|0.0100|0.0200|0.0005|0.0024|0.0048|
|_Sector of operation_||||||||
|Manufacturing, retail, or other services|0.000***|0.0012***|0.0060**|0.0120*|0.0004***|0.0022**|0.0044*|
|Disaggregated sector|0.034**|0.0028|0.0140|0.0280|0.0005|0.0026|0.0053|
|_Management practices_||||||||
|Management practices (higher means better)|0.031**|0.0024|0.0120|0.0240|0.0005|0.0025|0.0050|
|Top manager’s experience working in sector|0.056*|0.0036|0.0180|0.0360|0.0006|0.0029|0.0059|
|_Firm characteristics_||||||||
|Size|0.160|0.0044|0.0220|0.0440|0.0007|0.0033|0.0067|
|Age|0.180|0.0056|0.0280|0.0560|0.0008|0.0042|0.0083|
|Proportion domestic private ownership|0.200|0.0064|0.0320|0.0640|0.0010|0.0050|0.0100|
|At least 10% foreign owned|0.019**|0.0016|0.0080|0.0160|0.0005|0.0023|0.0046|
|Exporting directly at least 10% of sales|0.180|0.0060|0.0300|0.0600|0.0009|0.0046|0.0091|
|Proportion owned by females|0.240|0.0068|0.0340|0.0680|0.0011|0.0056|0.0111|
|Female top manager|0.720|0.0092|0.0460|0.0920|0.0033|0.0167|0.0333|
|Main Market – local, national, international|0.620|0.0088|0.0440|0.0880|0.0025|0.0125|0.0250|
|Multi-establishment|0.910|0.0100|0.0500|0.1000|0.0100|0.0500|0.1000|
|Legal form|0.240|0.0072|0.0360|0.0720|0.0013|0.0063|0.0125|
|Sole Proprietorship|0.170|0.0048|0.0240|0.0480|0.0007|0.0036|0.0071|
|Shareholding company|0.450|0.0080|0.0400|0.0800|0.0017|0.0083|0.0167|
|Share of transactions with customers that were||||||||
|fulfilled smoothly|0.180|0.0052|0.0260|0.0520|0.0008|0.0039|0.0077|
|_Corruption and security_||||||||
|Bribery depth|0.900|0.0096|0.0480|0.0960|0.0050|0.0250|0.0500|
|Bribery incidence|0.530|0.0084|0.0420|0.0840|0.0020|0.0100|0.0200|
|Payingforsecurity|0.110|0.0040|0.0200|0.0400|0.0006|0.0031|0.0063|

34
# **<u>SMEs, Age, and Jobs: a Review of the Literature, Metrics, and Evidence</u>**<sup>**1**</sup>

|Gemechu Aga<sup>a</sup>|gayanaaga@worldbank.org|
|---|---|
|David C. Francis<sup>a,b</sup>|dfrancis@worldbank.org|
|Jorge Rodriguez Meza<sup>a</sup>|jrodriguezmeza@worldbank.org|

a Development Economics, Enterprise Analysis Unit, World Bank Group b Corresponding author JEL codes: E24, L25, L26 Key words: Jobs, employment, SMEs, firm dynamics

> 1 The authors are thankful to Alvaro Gonzalez, Reyes Aterido and members of the World Bank Group’s Jobs CrossCutting Solutions Area for helpful comments.
- It;6-

1983-1988 period is 22%.18 One possible explanation for the larger Korean ratio is the use of compensating balances to increase the effective bank lending rate. However, if banks were able to attain the market clearing rate via compensating balances, there would be no subsidies in domestic loans and hence no credit rationing. The implication of no credit rationing clearly contradicts the chronic shortage of bank credits in Korea. Furthermore, if the effective interest rates were at the market clearing level, firms would not use domestic loans to make speculative investments, because the equilibrium risk adjusted expected returns on speculative assets are equal to the risk adjusted cost of non-subsidized loans. Thus, if this equivalency held, there would be no systematic relation between a frm's access to domestic loans and its holdings of speculative assets. Thus, m estimating the switching regression model in equation (1), we are testing the joint hypotheses that subsidized loans lead to increased speculative holdings and that compensating balances have not completely circumvented the interest rate ceilings. To the extent that compensating balances reduce the interest subsidies in domestic loans, however, our test contains a bias against detec,ing a positive relation between subsidized loans and speculative asset holdings.

# V. EMPIRICAL RESULTS

We report below our estimates of the proportion of speculative assets held by Korean corporations that are attributable to the availability of loan subsidies. Since our theoretical analysis shows that the sample separation point depends on the optimal level of productive invesunents, we first estimate investment equation (11) utilizing ordinary least squares (OLS) regression methods and pooled time-series cross section data. Our regression model includes a set of yearly and industry dummies. Inclusion of the yearly dummies is designed to capture the effect of macro economic shocks on corporate investrnent behavior; the industry dummies adjust for industry differences.

> 18 See Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations (1990). The definition of liquid assets for U.S. firms is identical to that used for Korean fims.
the SCI as a benchmark to validate their new statistical indexes (Sanga _et al._ , 2011), or analyzed the SCI for various purposes such as investigating the relationship between good governance and statistical capacity, or the impacts of statistical capacity on reducing procyclical fiscal policy, or identifying countries where investments in statistical capacity can be most fruitful (Beegle _et al._ , 2016; Tapsoba _et al._ , 2017; Hoogeveen and Nguyen, 2019).

Table 1 compares the SCI with the statistical capacity measurement indexes used by other organizations, including PARIS21, the Food and Agriculture Organization of the United Nations (FAO), the United Nations Economic Commission for Europe (UNECE), the United Nations Economic Commission for Africa (UNECA), and the U.S. Census Bureau. Table 1 highlights several main advantages of the SCI over other indexes. It is the only tool that provides comparable data across different countries over time. Furthermore, the SCI also covers the largest number of countries (i.e., 146 countries) over the longest period (i.e., starting from 2004 to date).<sup>4</sup> Yet, there are several areas in which the existing SCI can be improved. First, the various aspects of the capacity of an NSS that the SCI measures have fast become outdated. Since its launch in 2004, the SCI’s methodology and coverage have remained the same, while the global data landscape has changed significantly. Technological advances with computing and data storage capacity have enabled NSSs to make significant advancements with data collection methods and better dissemination practices. The international community’s adoption of the SDGs also raised the bar for NSSs regarding their capacity to produce higher-quality and more data. For example, the SCI includes no indicators of some important surveys such as the labor force surveys

> 4 There is also a key methodological difference between the SCI and other assessment tools. The latter methods collect data directly from national statistical offices’ staff or local experts. While this procedure may provide more in-depth analysis and uncover finer details in the organization of an NSS, it incurs high costs and is (far) more time-consuming. Furthermore, even the best evaluator can bring personal biases, non-uniform conceptions of capacity, or other subjective elements. Direct interviews of government officials might bias responses and complicate comparability across countries. On the other hand, since the SCI is based on publicly available data, it can provide an assessment of a country’s statistical capacity in an internationally comparable and cost-effective manner.

3
|Tail 5%<br>1978-1998<br>(8)<br>1.890***|(0.000)<br>1.488**<br>(0.010)<br>-2.973***<br>(0.000)<br>-1.247*<br>(0.077)<br>0.091**<br>(0.016)<br>-0.010***<br>(0.001)<br>52<br>0.571||
|---|---|---|
|Trimming<br>1952-1998<br>(7)<br>0.741**|(0.012)<br>0.472*<br>(0.074)<br>-1.975***<br>(0.000)<br>-0.337<br>(0.116)<br>0.047**<br>(0.049)<br>-0.003**<br>(0.019)<br>53<br>0.459|ical signifcance.|
|(6)<br>1.929***|(0.002)<br>1.517**<br>(0.018)<br>-4.150***<br>(0.000)<br>-1.248<br>(0.112)<br>0.149***<br>(0.003)<br>-0.009***<br>(0.009)<br>57<br>0.668|% and 1% statist|
|1978-1998<br>(5)<br>1.981***|(0.004)<br>1.652**<br>(0.026)<br>-4.278***<br>(0.000)<br>0.358<br>(0.671)<br>57<br>0.515|indicate 10%, 5|
|Sample<br>(4)<br>2.537***|(0.000)<br>3.420***<br>(0.000)<br>57<br>0.266|tes.<br>* respectively|
|Full<br><br>(3)<br>0.857***|(0.004)<br>0.524**<br>(0.048)<br>-2.096***<br>(0.000)<br>-0.576**<br>(0.045)<br>0.055**<br>(0.010)<br>-0.004**<br>(0.014)<br>57<br>0.598|al Growth Ra<br>ported. *, **, **|
|1952-1998<br>(2)<br>0.879***|(0.007)<br>0.642**<br>(0.035)<br>-1.860***<br>(0.000)<br>-0.001<br>(0.997)<br>57<br>0.430|illa on Annu<br>cepts are not re|
|(1)<br>0.867**|(0.015)<br>0.818**<br>(0.036)<br>57<br>0.110|and Guerr<br>thesis. Inter|
|_Power Structure_<br>YRD|Guerrilla<br>_Initial Conditions_<br>Ln_GVOPC_52<br>Ln_GVOPC_78<br>Ln_pop_52<br>Ln_pop_78<br>_Geography_<br>Share of Plain (%)<br>Distance to Xiamen<br>(Km)<br>Obs.<br>_R_2|Table 7: Separate Efects of YRD<br>Note: White Standard Errors are in paren|

29
- DUDBC, 2023. Specifications of Building Construction (Civil) Works. DUDBC, Ministry of Urban Development, Government of Nepal, Kathmandu.

- EIA, 2022. Coal Explained: Types of Coal. Energy Information Administration (EIA), Washington, DC.

- Eil, A., Li, J., Baral, P., Saikawa, E., 2020. Dirty Stacks, High Stakes: An Overview of Brick Sector in South Asia. The World Bank, Washington, DC.

- EPIC INDIA, 2018. Using Emissions Markets to Manage the Social Costs of Fossil Fuels: A Handbook. Energy Policy Institute at the University of Chicago (EPIC India), New Delhi.

- FNCCI, 2017. Factsheet: Energy Efficiency for Brick Industry. Federation of Nepalese Chambers of Commerce and Industry (FNCCI), Kathmandu.

- Fordham, M., 2021. Build Up Nepal - Carbon Evaluation of CSEB Blocks. Carbon Evaluation of Compressed Stabilised Earth Blocks (CSEB) in Nepal. Max Fordham LLP, London.

- Geres, R., Lausen, J., Weigert, S., 2021. Roadmap for a greenhouse gas neutrality Brick industry in Germany: A path to climate neutrality for the industry by 2050 (in German, machine translated to English by google). FutureCamp Climate GmbH, Munich.

- Heierli, U., Maithel, S., 2008. Brick by Brick: The Herculean Task of Cleaning UP the Asian Brick Industry. Swiss Agency for Development and Cooperation (SDC), New Delhi.

- ICIMOD, 2019a. Brick Sector in Nepal: Fact Sheet. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu.

ICIMOD, 2019b. Burnt Clay Brick Sector in India. ICIMOD, Kathmandu.

ICIMOD, 2019c. Gauging the Bankability of the Brick Sector in Nepal. ICIMOD, Kathmandu.

IEA, 2022. Coal 2022: Analysis and Forecast to 2025. International Energy Agency (IEA), Paris.

IEA, 2023a. Coal 2023: Analysis and forecast to 2026. IEA, Paris.

- IEA, 2023b. Driving Down Coal Mine Methan Emissions: A Regulatory Roadmap and Toolkit. IEA, Paris.

- IHME, 2020. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019. Dataset. Institute for Health Metrics and Evaluation (IHME), Seattle, Washington.

- ILO, MOLESS, 2022. National Occupational Safety and Health Profile for Nepal 2022. International Labour Organization (ILO) and Ministry of Labour, Employment and Social Security (MOLESS) Kathmandu.

- ILO, UNCEF, CBS, 2020. Report on Employment Relationships in the Brick Industry in Nepal. ILO; United Nations Children's Fund (UNICEF); Central Bureau of Statistics (CBS), Kathmandu.

37
In our survey, we asked managers directly about their firm’s use of these other policies in the recent past. In this paper we use factual questions about what types of margins firms have used. Concretely, we identified the following main strategies to cut labour costs (other than wages) reported by the majority of national surveys (see question 18 Appendix 4) by asking: _Has any of the following strategies ever been used in your firm to reduce labour costs?_ Firms were allowed to choose as many options as they wished from the following list:

- Reduce or eliminate bonus payments;

- Reduce or eliminate non-pay benefits;

- Change shift assignments or shift premia;

- Slow or freeze the rate at which promotions are filled;

- Recruit new employees at a lower wage level than those who left voluntarily;

- Encourage early retirement to replace high wage employees by entrants with lower wages;

- Use other strategies.

Obviously, these various margins are likely to be used to respond to different shocks. As an example, changing the workforce composition could be used following a permanent shock to the firm, while changing shift assignments or shift premia might be a more common reaction to a temporary shock. This is beyond the scope of the factual survey questions on which this paper is based. However, these factual questions have the great advantage of being more likely to solicit precise information. Using hypothetical questions from the same survey, Bertola et al. (2009) look into the reaction of firms to different types of shocks, distinguishing the adjustment of wages, prices, margins, output and employment.

Summary statistics of the percentage of firms (weighted by employment) that reported use of at least one of the first six strategies listed above are presented in Table 1. It clearly indicates that firms make extensive use of different cost cutting strategies in Europe, albeit there is substantial variability across countries. While in Lithuania all workers have seen how at least one of the strategies has affected their labour relations, in Portugal the percentage of affected workers falls to 40%. On average, 63% of the workers in our sample have been affected, and differences in the incidence of these adjustment mechanisms between Euro-area and non-Euro area countries do not seem to be particularly relevant.

Perhaps the first and most striking feature of Table 1 is that the prevalence of individual strategies varies quite substantially across countries. The reduction of bonus payments is the most common method used by firms outside the Euro-area: in the Czech Republic (32%), Estonia (40%), Lithuania (41%) and Poland (24%). The western European countries appear 8
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**Table 2: Mobility, COVID Cases and NPIs, Select Countries**

|<br>|(1)<br>Brazil|(2)<br>Italy|(3)<br> Japan|(4)<br>S. Africa|(5)<br>Sweden|(6)<br>UK|
|---|---|---|---|---|---|---|
|Close N.E. business|2.996|-28.781***|3.054|-5.871**||-20.337***|
||(2.375)|(0.836)|(2.190)|(2.166)||(0.322)|
|K-12 closure|-2.135|||-13.583***||-12.670***|
||(1.680)|||(2.275)||(0.462)|
|Cancel public events|-1.697|||10.798***|-7.837***||
||(1.842)|||(2.150)|(2.039)||
|Close public transport.<br>||<br>|<br>|4.102*<br>(1.782)||<br>|
|Public info. camp.<br>||<br>|<br>|46.285***<br>(7.338)|12.420***<br>(1.794)|<br>|
|Restr. on internal mov.<br>|<br>|<br>|<br>|-37.443***<br>(0.924)||<br>|
|Log cases|-1.413**|-2.775***|0.166|-1.294|-4.499**|0.719|
||(0.595)|(0.865)|(0.561)|(1.982)|(1.796)|(0.517)|
|Log national cases|-3.544***|-3.157**|-3.229***|-4.371**|-2.601|-6.994***|
||(0.464)|(1.134)|(0.553)|(1.711)|(2.290)|(0.566)|
|Constant|9.550***|22.787***|3.909*|25.710***|18.885*|39.349***|
||(1.982)|(6.500)|(1.976)|(5.624)|(9.309)|(2.783)|
|Time FE|Yes|No|No|No|No|No|
|Day of the week FE|Yes|Yes|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|# of States|27|20|46|7|21|95|
|Obs.|762|865|2361|169|758|2566|
|R-squared<br>|0.811|0.945|0.484|0.956|0.637|0.956|

Notes: Regression of Google measure of work-related mobility on NPIs, the log of cases, the log of national cases. Mobility, Cases and National Cases at subnational level. NPIs at national level with the exception of Brazil for which all data is at the subnational level. Robust clustered errors are in parenthesis. *** p<0.01, ** p<0.05, *<0.1

# _Global Sample_

Figure 3a-c groups the global sample of countries which have national data on mobility and NPI. Figure 3a divides the sample into those with and without restrictive orders. As in the individual case, there appears to be evidence for both the impact of restrictions and of the relationship with cases incidence. Figure 3b breaks the data apart into four income categories, Low Income Countries (LIC), Lower Middle Income (LMIC), Upper Middle Income (UMIC) and High Income (HIC) which include primarily the wealthier OECD countries (see Annex III for categorization). Figure 3c is the same, but only for country/periods when official restrictions on non-essential businesses are in place. In both cases, the downward slope appears across all income categories.

Table 3 largely confirms previous findings. Each specification is presented with and without time fixed effects which, in some categories, consume substantial degrees of freedom. Preliminary explorations suggest that world COVID case incidence does not enter and we drop that term. This makes sense if we think that citizens of a country may pay attention to national trends, as was the case in the United States, but maybe less cases 9
# **1. Introduction**

Even though Brazil has reached almost universal enrollment in primary education, grade repetition and school drop-out and push-out are common. As a result, completion rates are substantially lower than enrollment, and many children abandon school with relatively low levels of completed education. The problem is particularly acute in poorer neighborhoods. Lower chances of finding a good job and poverty are some of the consequences of low educational attainment. As illustrated by Perlman “in the late 1960s, parents in the _favela_ [shantytown] would often tell their children that if they did not stay in school they would end up as garbage collectors. In July 2003, the city [Rio de Janeiro] opened a competition for 400 garbage collector jobs, and 12,000 people applied. A high school diploma was a prerequisite for application” (Perlman 2003: 13).

This paper aims at identifying the major drop-out and push-out factors that lead to school abandonment in neighborhoods in Fortaleza, Northeast Brazil. It relies on an extensive survey that specifically addressed possible risk factors faced by the population in these neighborhoods, in particular the youth. The major advantages of the survey are its wide coverage, of both inschool and out-of-school youth, of both genders, and the wide set of issues addressed, namely: socio-economic background; education; health and sexuality; social capital and violence; and employment and economic activity.

In this paper we are particularly interested in evaluating the role of early parenthood, child labor and poverty pushing children out of school. However, those variables are potentially endogenous, first, because there may be determinants of the schooling decision on which we have no information and are therefore omitted from the set of explanatory variables we consider, and second, because child labor and early motherhood may be determined simultaneously with school drop-out. We therefore estimate an economic model of school drop-out and push-out using an instrumental variables method, taking advantage of the rich set of variables in the dataset. From the question “What age do you think is the ideal age to start having sexual relationships?” we derive an identifying instrument for early motherhood, since the belief about this ideal age is likely to induce variation in the age at which parenthood first takes place, but it should have no additional direct impact on the probability of leaving school. Similarly, we use the question “What is the minimum monthly salary that you would accept to work?” to instrument the variable child and youth labor. Again, the reasoning is that the reservation wage is likely to be correlated 1