rafmacalaba/datause-encoder-data huggingface dataset:
financial inputs into children’s education was collected by identifying and interviewing each child’s main caregiver.<sup>12</sup> Financial expenditures include payments and other “gifts” to schoolteachers, tutoring expenditures, and transportation expenditures that we will use as different dependent variables in the analysis.<sup>13</sup> Non-financial inputs include how often the main caregiver helps the child with homework and other school activities in the month prior to the survey interview on a six-point scale ranging from “never” to “every day.” In addition to the household survey, community questionnaires were filled out by local officials, typically in the mayoral office. Finally, we match data from the World Bank’s open budget initiative (BOOST) to provide school-level data on public education expenses in the respective communities and schools (see appendix S.1 for more details). Our baseline sample consists of 2,148 children from 1,448 households.

# _Descriptive Statistics_

A migrant household is defined by the existence of at least one adult who, in the 12 months prior to the survey, has spent a minimum of three months living abroad. In our sample, 29% of children live in a migrant household (table 1).<sup>14</sup> The average student from migrant households is 12.6 years old, five months more than her non-migrant peer. Before accounting for selection into migration, the average grade (GPA) is 0.06 points higher for children in migrant households. Migrant families are slightly larger on average and more likely to come from rural areas. Despite this, their average total income and average per capita income are significantly higher than those of non-migrants.<sup>15</sup> Figure 1 also reflects the underlying effect of migration, showing no difference in assets in 1999 but significantly higher assets for migrant families in 2011.<sup>16</sup>

> 12. The main caregiver is the person responsible for nutrition, health, and schooling of a child at the time of the survey.

> 13. In addition, there is a residual category of “other expenditure” for which we find statistically insignificant effects.

> 14. Our dataset does not allow us to compare the differences between migrant households with and without children. Comparisons to other representative data (details on request) reveal that, in households with children, female migration is on average less common. The education level and gender composition do not differ markedly. 15. In reality, the difference could be even wider since migrant households systematically underreport their received remittances and other sources of income (Akee and Kapur <u>2012).</u>

> 16. The asset indexes were constructed by a weighted-sum of the following items: (i.) number of cars, motorcycles, bicycles, washing machines, refrigerators, radios, TVs, computers, and cell phones; (ii.) existence of working phone landline and internet access; and (iii.) number of rooms in the house. For 1999, the last three items were excluded due to a large number of missing values. The weights for the index were obtained from a principal

8
|P070899|2004|2006|12/27/2005|4 Closed|Philippines|
|---|---|---|---|---|---|
|P070899|2004|2006|6/15/2006|5 Closed|Philippines|
|P070899|2004|2007|12/14/2006|6 Closed|Philippines|
|P070899|2004|2007|3/15/2007|7 Closed|Philippines|
|P070899|2004|2008|9/18/2007|8 Closed|Philippines|
|P070899|2004|2008|6/17/2008|9 Closed|Philippines|
|P070899|2004|2009|12/23/2008|10 Closed|Philippines|
|P070899|2004|2009|6/19/2009|11 Closed|Philippines|
|P070899|2004|2010|12/2/2009|12 Closed|Philippines|
|P070899|2004|2010|6/23/2010|13 Closed|Philippines|
|P070899|2004|2011|2/8/2011|14 Closed|Philippines|
|P070899|2004|2012|9/19/2011|15 Closed|Philippines|
|P070899|2004|2012|4/1/2012|16 Closed|Philippines|
|P070899|2004|2013|11/11/2012|17 Closed|Philippines|
|P070899|2004|2013|6/5/2013|18 Closed|Philippines|
|P070899|2004|2014|12/26/2013|19 Closed|Philippines|
|P070899|2004|2014|4/28/2014|20 Closed|Philippines|
|P070917|2001|2001||0 Closed|Bosnia and Herzegovin|
|P070917|2001|2002|6/24/2002|2 Closed|Bosnia and Herzegovin|
|P070917|2001|2003|6/17/2003|4 Closed|Bosnia and Herzegovin|
|P070917|2001|2004|6/15/2004|6 Closed|Bosnia and Herzegovin|
|P070917|2001|2005|4/21/2005<br>|8 Closed|Bosnia and Herzegovin|
|P070917|2001|2006|6/27/2006|9 Closed|Bosnia and Herzegovin|
|P070917|2001|2007|6/27/2006|9 Closed|Bosnia and Herzegovin|
|P070920|2001|2001|6/29/2001|2 Closed|Kenya|
|P070920|2001|2002|4/30/2002|4 Closed|Kenya|
|P070920|2001|2003|5/20/2003|6 Closed|Kenya|
|P070920|2001|2004|5/28/2004|8 Closed|Kenya|
|P070920|2001|2005|5/6/2005|11 Closed|Kenya|
|P070920|2001|2006|12/27/2005|12 Closed|Kenya|
|P070920|2001|2019|5/9/2019|13 Closed|Kenya|
|P070937|2002|2002||0 Closed|Uruguay|
|P070937|2002|2003|6/25/2003|3 Closed|Uruguay|
|P070937|2002|2004|6/3/2004|5 Closed|Uruguay|
|P070937|2002|2005|4/24/2005|7 Closed|Uruguay|
|P070937|2002|2006|10/6/2005|8 Closed|Uruguay|
|P070937|2002|2006|11/17/2005|9 Closed|Uruguay|
|P070937|2002|2006|5/24/2006|10 Closed|Uruguay|
|P070937|2002|2006|6/14/2006|11 Closed|Uruguay|
|P070937|2002|2007|12/27/2006|12 Closed|Uruguay|
|P070937|2002|2007|3/11/2007|13 Closed|Uruguay|
|P070937|2002|2008|11/5/2007|14 Closed|Uruguay|
|P070937|2002|2008|5/1/2008|15 Closed|Uruguay|
|P070937|2002|2008|6/27/2008|16 Closed|Uruguay|
|P070937|2002|2009|11/29/2008|17 Closed|Uruguay|
|P070937|2002|2009|5/30/2009|18 Closed|Uruguay|
|P070937|2002|2010|12/4/2009|19 Closed|Uruguay|
reliable modern forms of energy services for cooking. The SE4ALL and Global Alliance for Clean Cookstoves are a few examples in this direction.

Our review of existing literature finds that wide range of factors, including socioeconomic, health, behavioral, cultural, local environment, technologies, policies and access to infrastructure, affect household's cooking fuel choice and adoption of ICS. Although households with higher income and education are more likely to use modern fuels, their decision for cooking fuel choice and adoption of ICS are quite complex and multi-dimensional; deep understanding of the interaction of these factors is necessary for designing government plans, policies and strategies to improve access to modern cooking fuels and adoption of ICS.

Several studies provide evidence of significant negative health impacts caused by indoor air pollution from biomass burning for cooking in developing countries, mainly among women and young children. Existing studies also find that biomass combustion for cooking is a key source of black carbon emissions that has an adverse influence on the climate system.

In low income household decision making, costs associated with cookstoves and the opportunity cost of time spent for collecting biomass, in general, outweighs perceived health benefits by adopting ICS and financial benefits from fuel savings. This suggests that a program or policy to deploy ICS or increasing access to modern fuels, especially in the rural areas, would be successful if it also helps income generation. The study also finds significant limitations in methodologies used for estimating the social costs and benefits of adoption of ICS and fuel choice and notes a need for further research to better understand the adoption of ICS over time.

29
# **<u>Table C.1 First-stage regressions, Arab Republic of Egypt</u>**

||**2012-2**|**015**|**2015-2**|**017**|**2017-2**|**020**|
|---|---|---|---|---|---|---|
||**2012**|**2015**|**2015**|**2017**|**2017**|**2020**|
|Head`s age|0.006***|0.012***|0.010***|0.011***|0.009***|0.012***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Head is female|0.141***|0.141***|0.136***|0.234***|0.226***|0.198***|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Highest education level is primary|0.142***|0.126***|0.123***|0.097***|0.099***|0.091***|
||(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|
|Highest education level is secondary|0.199***|0.215***|0.209***|0.179***|0.173***|0.194***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Highest education level is tertiary|0.461***|0.482***|0.481***|0.395***|0.394***|0.486***|
||(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|
|Urban|0.241***|0.210***|0.215***|0.126***|0.126***|0.162***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|_cons|7.995***|8.091***|8.196***|8.555***|8.604***|8.632***|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|adjusted R2|0.23|0.25|0.24|0.17|0.16|0.21|
|N|5102|8338|7836|8301|7799|7286|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

79
# **6 Conclusion**

This paper uses the employer-employee census in Brazil and data from ORBIS to better measure the state footprint in Brazil. BOS in Brazil are, on average, larger, older, and more innovative than the average private sector firm. This is especially the case for private firms with public investments, which are almost double the size of public firms and much more innovative when proxied by the share of technical occupations. This may suggest that the government’s investment decisions are strategically focused on large, more productive, and innovative companies. In addition, SOEs are more present in sectors with high sunk costs and natural monopolies, but participated firms are more present in some manufacturing sectors, such as spacecraft or motor vehicles. Another interesting result is that there are heterogeneities across BOS types - public, mixed-capital, and private firms with direct or indirect state participation. In the three dimensions explored, participated firms outperformed public and mixed firms, which suggests inherent differences between this group of firms and other BOS.

Furthermore, we have explored the employment outcomes associated with working on a BOS in more detail. Consistent with the findings in other countries, we find a positive and significant wage premium associated with working on a BOS. Workers in BOS enjoy a wage premium ranging from 15.6% to 18.5%, partially due to their skill composition. However, when controlling for worker fixed effects, the premium decreases in magnitude to 4.5%. Although the average wage of BOS relative to private firms is highest for participated firms, this difference disappears once we account for occupation and worker characteristics essential to the wage-setting mechanism. Hence, in our preferred specification, we observe a wage premium only with public and mixed firms.

As an additional exercise, we use some episodes of privatization of public and mixed firms, especially in 2018, to explore what happens to wages and employment when firms are privatized. Consistent with the episodes in the 1990s (Arnold, 2022), we find that workers experience a wage decrease of around 10% in t+2 and 14% in t+3, and around 10% when restricting only to workers that stay in the privatized company. This suggests that some of the wage premium that workers in BOS experience is likely to be related to wage-setting mechanisms rather than productivity premiums, which are corrected once the firm is privatized. Interestingly, privatized firms managed to keep employment levels.

Finally, we provide some initial evidence on the potential effects of BOS presence over business dynamism by analyzing the correlation between the employment share of BOS and measures of market dynamics at the industry level. Using two different levels of data aggregation, at 2- or 3-digit sector levels, we find that a high concentration of BOS is 45
**Figure 10: Rates of Increase per Year of Age in COVID-19 Mortality vs. Vital Statistics Performance Index by Country**

<!-- Start of picture text --> CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>ESPSWE<br>ITA KORDEU<br>PRT NOR HUN<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>MYS COL<br>ZAF<br>PER<br>PAK<br>MEX<br>0 20 40 60 80 100<br>VItal Statistics Performance Index<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis. LMICs are shown in red and HMICs in blue. The Vital Statistics Performance Index (Phillips, Lozano et al. 2014) is based on analysis of the 2013 Global Burden of Disease data.

The relative rate of infection of older people may vary across countries. Intergenerational households are much more common in low- and middle-income countries, which may mean that older people are more exposed to infection risk, particularly given the often crowded dwellings. Roughly 20 percent of households in LMICs have at least one household member under age 20 and one over age 60, as compared to just 5 percent in high-income countries (Winskill, Whittaker et al. 2020). In 54 LMICs with recent Demographic and Health Surveys, just half of the households have no more than two people per sleeping room (Brown, Ravallion et al. 2020). On the other hand, in HICs a greater share of older people are long-term care facility residents, who appear to be particularly at risk. Approximately a quarter of all COVID-19 deaths in the United Kingdom have been residents of care facilities and by one estimate 43 percent of deaths in the United States are care facility residents or staff (New York Times 2020, Public Health England 2020). Older people in developing countries, who are less likely to live in care facilities with a concentrated number of people vulnerable to the disease, may have thus faced lower risk of infection, even living in multigenerational households.

15
- Freund, Caroline and Emanuel Ornelas. 2010. “Regional trade agreements.” Annual Review of Economics 2(1): 139-166.

- Fudenberg, Drew, David Levine and Eric Maskin. 1994. “The folk theorem with imperfect public information.” Econometrica 62(5): 997-1039.

- Furusawa, Taiji. 1999. “The Negotiation of Sustainable Tari¤s.”Journal of International Economics 48(2): 321-45.

- Furusawa, Taiji and Hideo Konishi. 2007. “Free trade networks.” Journal of International Economics 72(2): 310-35.

- Geng, Difei and Kamal Saggi. 2014. “The case for non-discrimination in the international protection of intellectual property.”Mimeogr., Vanderbilt.

- Goyal, Sanjeev and Sumit Joshi. 2006. “Bilateralism and free trade.” International Economic Review 47(3): 749-78.

- Grossman, Gene M. 1981. “The theory of domestic content protection and content preference.” Quarterly Journal of Economics 96(4): 583-603.

- Grossman, Gene M. and Elhanan Helpman. 1994. “Protection for sale.”American Economic Review 84(4): 833-50.

- Grossman, Gene M. and Elhanan Helpman. 1995a. “Trade wars and trade talks.” Journal of Political Economy 103(4): 675-708.

- Grossman, Gene M. and Elhanan Helpman. 1995b. “The politics of free-trade agreements.” American Economic Review 85(4): 667-90.

- Grossman, Gene M., Henrik Horn and Petros C. Mavroidis. 2013. “National treatment.” In Legal and Economic Principles of World Trade Law, edited by Henrik Horn and Petros C. Mavroidis, 205-345. New York: Cambridge University Press for the American Law Institute.

- Grossman, Gene M. and Edwin Lai. 2004. “International protection of intellectual property.” American Economic Review 94(5): 1635-53.

- Goldberg, Pinelopi Koujianou and Michael M. Knetter. 1997. “Goods Prices and Exchange Rates: What Have We Learned?” Journal of Economic Literature 35(3): 1243-1272.

- Gulotty, Robert. 2014 “Governing Trade Beyond Tari¤s: The Politics of Multinational Production and Its Implications for International Cooperation.”Mimeogr., Stanford University.

- Handley, Kyle. 2014. “Exporting under Trade Policy Uncertainty: Theory and Evidence,” Journal of International Economics 94(1): 50–66.

109
<!-- Start of picture text --> 2014<br>Number of IDP HHs<br>>= 3,000 & < 5,000<br>>= 1,000 & < 3,000<br>>= 500 & < 1000<br>< 500<br>! $= 5,000 & < 10,000<br>Number ofconflicts SaRL<br>OrsQ \ilgel{ad.<s fF Z|<br>— saestA<br>ager Sex<br>AS) NaS). RETSRY|<br>Arce)<br>ere<br><!-- End of picture text -->

<!-- Start of picture text --> 2015<br>>= 5,000 & < 10,000<br>>= 3,000 & < 5,000<br>>= 1,000 & < 3,000<br>>= 500 & < 1000<br>Number of IDP HHs<br>Number ofconflicts SaRL<br>5: aed2 ot? Ve) 7<br>O* saest AN<br>— Lager (Set Fa<br>AS) NAS. Te EELS<br>WArsGeal<br>ergs<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Numberse>== 3,0005,0001,000 of & IDP&& £<< 5,000 3,000 10,000 HHs<br>500lone or 000 =ls<br>Number <S i}<br>of conflicts Pry]<br>i at Y f} / |<br>AR© Bi<br>So) SR, RETIRE TALS<br>Uys gas<br><!-- End of picture text -->

<!-- Start of picture text --> 2017<br>Number of IDP HHs<br>>= 5,000 & < 20,000<br>or25= 500 4000&oe $9,000<>?<br>None or NA OYls<br>Number of conflicts KS i}<br>Q5 (>ae<br>20 Ne aos? 3 A) |<br>AD gor @ CO)<br>AS) ES, EEABOE |<br>yecen tga<br><!-- End of picture text -->

<!-- Start of picture text --> 2018<br>Number of IDP HHs<br>=F 3 G00 &s< oy00"<br>>= 1,000 & < 3,000<br>>= 500 & < 1000<br>i 500 NA<br>Numberlone or <S |“I}<br>O 25 of conflicts Crm<br>O: 7s5.0 Sagesdaed a JJ |<br>SyLeeOL ie<br>ee PatsA<br><!-- End of picture text -->

<!-- Start of picture text --> 2019<br>Number of IDP HHs<br>32= 5,000T'000 & & << 10,000 3,600<br>>= 500 & < 1000<br>Reaad NA<br>jone or<br>Number of conflicts <S lS (}<br>Os0 Crem<br>O8: »15 yetaetad |<br>DaeLS gy oS<br>iaCIE Eeeaad<br><!-- End of picture text -->
Figure 5: Projections of Secondary Enrollment <!-- Start of picture text --> 7000000 120.0%<br>6000000<br>100.0%<br>5000000<br>80.0%<br>4000000<br>60.0%<br>3000000<br>40.0%<br>2000000<br>20.0%<br>1000000<br>0 0.0%<br>2017 2018 2019 2020 2121 2022 2023 2024 2025<br>Lower secondary enrollment, GoT Lower secondary enrollment, WB<br>Lower secondary GER, GoT Lower secondary GER, WB<br><!-- End of picture text --> Source: 2017 Government of Tanzania and World Bank secondary enrollment projections.

These increases in enrollment have exceeded the rate at which the government is hiring teachers and building classrooms, leading to larger class sizes. In 2018, a primary school classroom was used by an average of 81 students - double the standard of 40 students per classroom (World Bank 2019b). In 2016/17, this was met with a non-commensurate 9 percent increase in the number of primary classrooms. This led to a higher primary stream/classroom ratio, which rose from 1.8 streams per classroom to 1.9.

Data from the 2016 Sauti za Wananchi survey corroborate these shortfalls - households with children that participated in this survey reported shortages of teachers (34 percent), lack of desks (30 percent) and lack of classrooms (13 percent) as the main challenges facing public schools in Tanzania (Sauti za Wananchi 2016). Across the system, estimates of teacher requirements show that there are only half of the needed secondary school mathematics teachers and only 75 percent of the required physics teachers (Figure 8, World Bank 2018b).

To maintain an average of 602 students per school, World Bank projections estimate that the system needs 7,000 new schools - an increase of 40 percent. In addition to infrastructure, the rapid expansion of the system calls for increased provision of teaching and learning materials and significant increases in teacher numbers. If current input levels are maintained, the annual cost for lower secondary education is projected to rise threefold and the cost of upper secondary is projected to rise fivefold from 2018/19 to 2025/26 (Tanzania Public Expenditure Review, 2019).

8
importing inputs for producing items to export to another link, another participant, in another region or another country. But this system of “importing to export” involves more than trade transactions among participating firms, and includes the sharing of blueprints and management practices, through which new ideas and know-how are continuously transferred across national and international borders and regions.

# 2.2 Data

The core of the GVC analysis relates to trends in indicators of linkages to a manufacturing GVC along two dimensions, namely, upstream (or backward) linkages and downstream (or forward) linkages. The indicator of upstream linkages to a GVC is the share of import content or Foreign Value Added (FVA) in the country’s gross exports. What is left after deducting from the gross exports the FVA component constitutes the country’s domestic value-added exports (DVA, for short). The indicator of the country’s downstream (or forward) linkages to a GVC is the share in the total value-added exports of that country that is used as intermediate input to other countries’ exports to third parties (DVX). A country’s overall rate of linkages to a GVC is the sum of the share of FVA in its gross exports and the share of DVX in the same.

In practice, lack of firm-level data has meant that the measurement and analysis of GVC participation generally and for most African and developing economies particularly is mostly based on the use of projected inter-industry trade data to extract the value-added component of official data on country imports and exports.

To compute the GVC indicators, we draw on the latest release of the EORA MIRIO database of the University of Sydney.<sup>2</sup> The database is a set of input-output tables covering 189 countries for the period 1990-2015, providing the largest coverage of countries in Sub-Saharan Africa that we are aware of. It also provides information for 26 sectors distributed across five broad sectors of production, namely, agriculture, mining, manufacturing, construction, and tradable services. The series includes observations on the following manufacturing industries

> 2 The database can be accessed at <u>https://worldmrio.com.</u>

5
|Exp. 4: Increase from|Min to Max RoL|All|47.87%|21.60%|13.93%|computes the|in TFP from|TFP from a 1SD|aw index from the||
|---|---|---|---|---|---|---|---|---|---|---|
|Exp. 3: 1 SD Increase|in RoL|All|16.36%|7.87%|5.18%|d Switzerland. Experiment 1|ment 2 computes the change|t 3 computes the change in|from increasing the rule of l||
|om Min to|l Trust|High<br>RoL|10.13%|4.94%|3.27%|nds, Finland an|of 0.357. Experi|0.673. Experimen|change in TFP||
|Increase fr|x Regiona|Low<br>RoL|31.44%|14.65%|9.54%|ny, Netherla|all average|maximum|omputes the||
|Exp.2:|Ma|All|23.23%|11.01%|7.21%|tries Germa|om it’s over|the regional|eriment 4 c|sample.|
|ease in|st|High<br>RoL|2.47%|1.23%|0.82%|le of law coun|1 SD (0.134) fr|mum 0.153 to|countries. Exp|orway) in our|
|1: 1 SD Incr|Regional Tru|Low<br>RoL|7.16%|3.52%|2.33%|y for high ru|asing trust by|regional mini|index across|maximum (N|
|Exp.||All|5.42%|2.68%|1.78%|ol is a dumm|P from incre|st from the|e rule of law|ussia) to the|
|||untries|2|4|6|te: HighR|nge in TF|reasing tru|rease in th|nimum (R|
|||Co|_θ_ =|_θ_ =|_θ_ =|No|cha|inc|inc|mi|

33