rafmacalaba/data-use-mentions-extended huggingface dataset:
(self assessment)<br>Student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

The system of examination, ranking, selection, and promotion has often been viewed as meritocratic. However, the limitation of this system serving as a mechanism for social mobility is well recognized. Children from disadvantaged background tend to already lag when they reach Grade 9. The CEPS data set also reveals another layer of hidden cost related to the selective system for achieving social equity. According to the survey, students from a more disadvantaged background appear to sacrifice more to go through the highly competitive and selective process of participating in senior secondary education. Not only do they have higher pressure and spend more time on homework and sleep less, they are also less likely to be exposed to other activities— enrichment or entertainment—such as visiting museums and exhibitions, going to movies, or participating in sports (Figure 3.20). This layer of cost can be manifested by the situation that even when they are able to continue to the higher levels of education, children from disadvantaged background can have difficulties adjusting and adapting to a new environment where a lot more outside academic studies matter for a fulfilling life.

42
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- World Bank. 2011c. ―Getting Electricity-Database.‖ www.doingbusiness.org/data/exploretopics/gettingelectricity.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gökgür, Nilgün, and Leroy Jones. 2006. ―Impact of Privatization in Africa: Air Senegal International.‖ Boston Institute for Developing Economies (BIDE) and Investment Climate Department and Africa Private Sector and Infrastructure Department, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

Port Autonomme de Dakar. 2010. www.portdakar.sn/spip.php?article8.

- World Bank. 2007b. _Project Appraisal Document on a Proposed Credit in the Amount of SDR59.5 Million ($90 Million Equivalent) to the Republic of Mali for a Second Transport Sector Project._ Washington, DC: World Bank.
Figure 15: Asset returns around major financial crises Panel A: New York Panic of 1907: An Episode of Financial Contagion <!-- Start of picture text --> 150<br>125<br>100<br>75<br>50<br>-3 -2 -1 t 1 2 3<br>Years around financial crisis<br>US equities US Treasuries (10 year)<br>External sovereign bonds<br>Cumulative total return index<br><!-- End of picture text --> Panel B: Great Depression: A Global Crisis <!-- Start of picture text --> 150<br>125<br>100<br>75<br>50<br>25<br>0<br>-3 -2 -1 t 1 2 3<br>Years around financial crisis<br>US equities US Treasuries (10 year)<br>External sovereign bonds<br>150<br>125<br>100<br>75<br>50<br>-3 -2 -1 t 1 2 3<br>Years around financial crisis<br>US equities US Treasuries (10 year)<br>External sovereign bonds<br>Cumulative total return index<br>Cumulative total return index<br><!-- End of picture text --> Panel C: Global Financial Crisis of 2008: An Advanced Economy Crisis _Notes:_ This figure shows a cumulative real return index around three financial crisis events: the New York Panic 1907, starting in October, the Great Depression (dated on Black Tuesday in October 1929) and the Global Financial Crisis (culminating in September 2008). The series are indexed at 100 in the starting month of the crisis. The series on external sovereign bonds is based on the sub-sample of serial defaulters (see Section 4 for details).

42
lower in the lean season across the consumption distribution. Looking at the probability density functions for the log of consumption in the lean season and non-lean season in Burkina Faso, Niger, and Senegal demonstrates that it is only around the 90<sup>th</sup> percentile that the two distributions begin to converge (Figure 6). Thus, poorer households – including

> 21 Including the consumption data that are available for Mali would, if anything, strengthen the results. Estimating Equation 1 with the data for Burkina Faso, Mali, Niger, and Senegal, consumption was 11.5 percent lower in the lean season than in the non-lean season in the specification without controls and 10.2 percent lower in the specification with full household and location controls.

> 22 Here we are reading the coefficient in the log-linear regression directly as a percentage change. Exponentiating this coefficient does not dramatically alter the results. It should also be noted that these results differ slightly from those presented in the previous policy brief using the EHCVM data. There, the difference in means were calculated using a linear model rather than a log-linear model.

12
by five sub-dimensions (income generated by work, occupational status, tenure, social security affiliation, and excessive working hours). Consistent with the findings of Brummund et al. (2018), they observe that Chile, Uruguay, and Brazil outperform their regional peers. Furthermore, they identify a gender gap in employment quality across all countries in their study, particularly in the dimensions of income and tenure. Similarly, Apablaza et al. (2021) employ a similar method to evaluate employment quality across regions in Chile between 1996 and 2017. Their results highlight the heterogeneity of job quality across the country but also indicate a process of convergence driven by minimum wage regulations and the statutory working week.

Another set of studies has developed job quality indices using alternative methodologies. For example, Del Carpio et al. (2017) examine the dynamics of job quality in Turkey between 2009 and 2016. The authors define various components related to job quality and employ the principal component analysis (PCA) method to aggregate them into a composite index. Their findings reveal an improvement in the quality of wage employment at the aggregate level over the specified period. These improvements are largely attributed to a shift towards more formal wage jobs. However, the authors also observe a wide distribution of employment quality across Turkey.

Closely related is a study conducted by Ya ̃nez (2018), which examines the evolution of job quality in Bolivia from 2006 to 2015. The author employs the National Household Surveys as the primary dataset and constructs a job quality index using the fuzzy sets theory developed by Zadeh (1965).<sup>5</sup> The index incorporates variables such as labor income, weekly hours worked, unemployment, number of jobs, contract availability and tenure.<sup>6</sup> The findings indicate that the index witnessed a modest increase of approximately 0.7 percent over the decade, which is lower than anticipated given the commodity boom period. Consistent with other research, the study highlights the presence of
<!-- Start of picture text --> Figure 14: Reform Indicators on Price Formation and Full-Cost Recovery (C3)<br>a) Cost Recovery and Regulation Index b) Change in Cost Recovery and Regulation Index<br>90% 90%<br>R2 = 0.042 R2 = 0.0016<br>80% Philippines 80% Philippines*<br>70% 70%<br>Peru<br>60% Pakistan Peru 60%Dominican Pakistan India -Odisha<br>Dominican Republic India -Odisha Ukraine Republic India - Ukraine<br>50% 50% India -Andhra Rajasthan<br>Pradesh<br>40% Vietnam 40% Vietnam<br>Egypt, Arab Kenya Colombia Kenya Colombia<br>30% Rep. Uganda 30% Uganda<br>Egypt, Arab<br>20% 20% Rep.<br>10%0% Tanzania Tajikistan Senegal Morocco 10%0% Morocco Tanzania Senegal Tajikistan<br>50% 70% 90% 110% -50% 0% 50% 100%<br>Full-Cost Recovery Change in Full-Cost Recovery<br>c) Cost Recovery and d) Change in Cost Recovery and<br>De Facto Regulatory Autonomy Index De Facto Regulatory Autonomy Index<br>120% 120%<br>R2 = 0.0078 R2 = 0.0004<br>100% Peru 100% Peru<br>80% Egypt, Arab Senegal 80% Dominican Senegal India -Odisha<br>Rep. Uganda India -AP Uganda<br>Dominican Republic Pakistan India -OdishaUkraine Republic Pakistan India - Egypt, Arab Rep. Ukraine<br>60% Tanzania Colombia 60% Tanzania Rajasthan Colombia<br>Kenya Philippines Kenya<br>Philippines*<br>40% 40%<br>Tajikistan Tajikistan<br>20% Vietnam 20% Vietnam<br>0%<br>0%<br>50% 70% 90
practices at the school, as reported by a range of actors. In addition, the samples are usually large (often census-based) and contain school identifiers, thereby enabling researchers to explore heterogeneity and answer a wide range of policy-relevant questions. We illustrate how our approach can be applied widely to other national surveys using the example of _Prova Brasil_ . This national survey plays a significant role in Brazil’s education policy because its test results, along with promotion, dropout, and retention rates, are the main inputs to the _Índice de Desenvolvimento da Educação Básica_ (IDEB), a national index representing educational quality at the school, municipality, and state levels.

We followed the same steps to create a Prova Brasil-based management index: we classified 33 questions (14 from the principal questionnaire and 19 from the teacher questionnaire) into 8 WMS topics, coded responses following the same rubric, and used the Anderson [2008] method to build a school management index.

**Potential concerns.** One of the key differences between the WMS survey and PISA or school census surveys is that the WMS is administered and analyzed by an independent interviewer, while the latter surveys are self-reported. There are a number of issues with self-reported data: for example, problems with translation and interpretation, and/or measurement equivalence. To address measurement error of cross-cultural understandings and norms on answering questions in our PISA index, we standardize our PISA-based management index _within countries_ . This has an important implication: since all 65 countries have a mean score of zero, our index cannot be used to construct cross-country rankings of school management. Instead, the value of our PISA-based index lies in enabling academics and practitioners to study the (within-country) correlation between management and other variables for a far wider set of countries than was previously possible.<sup>11</sup> This is not a concern for country-specific national surveys.

Another concern with self-reported data is that it is difficult to assess whether respondents are being accurate and truthful. In the WMS
- Demirgüç-Kunt, A., & Klapper, L. (2012). Measuring Financial Inclusion: The Global Findex Database. World Bank Policy Research Working Paper 6025.

- Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, D.C.: The World Bank.

- <mark>Demirguc-Kunt, A., Klapper, L., Singer, D., & Van Oudheusden, P. (2015). The Global Findex Database 2014: Measuring Financial Inclusion around the World. Policy Research Working Paper No. 7255.</mark> Washington, D.C.: The World Bank.

- Dercon, S. and Porter, C. 2014. Live aid revisited: long-term impacts of the 1984 Ethiopian famine on children. _Journal of the European Economic Association_ 12 (4): 1542-4766

- Dercon, Stefan. 2004. “Growth and Shocks: Evidence from Rural Ethiopia.” Journal of Development Economics 74 (2): 309–29. https://doi.org/10.1016/j.jdeveco.2004.01.001.

- Dimitrova, A. (2021). Seasonal droughts and the risk of childhood undernutrition in Ethiopia. _World Development_ , _141_ , 105417. https://doi.org/10.1016/j.worlddev.2021.105417

- <mark>Dobermann, T. (2023). How building up the human capital of the world's poor can help lessen the climate crisis.</mark> _<mark>LSE Business Review</mark>_ <mark>.</mark>

- <mark>Dube, A. (2019). Minimum Wages and the Distribution of Family Incomes</mark> _<mark>. American Economic Journal: Applied Economics</mark>
amount _ci_<sup>_′_</sup> ( _xi_ ( _t_ )) at time _t_ is equivalent to paying the ow _r · ci_<sup>_′_</sup> ( _xi_ ( _t_ )) from _t_ to _∞_ . Multiplying all terms in equation (16) by _r_ , one gets: Annualized MICs are expressed in $/tCO 2 so they can be compared easily to the marginal abatement costs derived from the model without inertia (Fig. 3). Even if they can be expressed in the same metrics, annualized MICs dier from the MACs à la Nordhaus (1992): as explained in sections 2 and 3, MICs depend on the decarbonizing pace in each sector, while MAC only depend on the current abatement (also in each sector).

## 4.3 Results

The optimal mitigation strategy by the two models are compared sector by sector in Fig. 3, and the aggregated pathways in terms of abatement and nancial eort are presented in Fig. 4. The pathways suggested by these models are not optimal pathways to mitigate climate change: they only consider a target in the 2007-2030 window and are based on crude available data.

The two models give the same result in the long run: abatement in each sector eventually reaches its maximum potential. By construction, they also achieve aggregated abatement target at the same discounted cost. But the optimal strategies according to the two models dier radically in terms of aggregated abatement and nancial costs, and, more importantly, at the sector level.

The optimal abatement pathway in the model without economic inertia includes signicant abatements as soon as the climate policy is implemented, while the model with inertia has a more realistic abatement pathway that starts at zero and increases progressively over time.

In terms of nancial eort, the model with inertia provides an optimal pathway that starts higher and decreases over time toward zero this is the decreasing side of the bell-shaped trajectory discussed in the
40<br>7,276|7,556.22<br>7,276|7,721.43<br>7,276|+2.72|+2.63|+4.87|

Notes: All values based on observed – not predicted – per capita household expenditure. Poverty calculations made using 2000 World Bank poverty line of 4,319 quetzals/ person/ year. All values are weighted. In 2000, 1 Guatemalan quetzal = US $0.128.

Source: Calculated from Guatemala 2000 ENCOVI Survey
Online Appendix depicts the three-point estimates of my preferred specification for the pool of all workers, low-skilled workers, and high-skilled workers, respectively. Overall, the results imply that locations close to the new subway line experienced a decrease in workers’ informality rates. In particular, the ratio of formal to informal individuals increased between 3.0% and 6.9% after the shock. These results are robust to different specifications, for example, to the use of different definitions of the treatment variable or to the use of different sets of fixed effects or controls. In addition, in panels C and D, I control for the change in workers’ composition in terms of skills and report the results only for low-skilled workers. The estimates are very similar to the ones found for the entire pool of workers. For instance, the ratio between formal and informal low-skilled workers increased on average between 4.0% and 7.1%. Moreover, in panels E and F, I report the results restricting the sample to the areas not located in the CBD of Mexico City. The effects should be larger in these locations since more informal workers live in these areas. Overall, I find larger effects for this specification; the ratio between formal and informal workers increased by almost 10% in these areas.

> 20For the municipality fixed effects specifications, I classify locations in the State of Mexico into four different groups: northwestern, northeastern, west central, and east central for a total of 20 municipalities.

> 21Equation 3.2 corresponds to a structural relationship that I will derive from the model in section 5.

> 22One caveat of this specification is that I cannot test for parallel trends due to data constraints because I cannot observe the location of informal/formal residents before the 2000 Census.

12
# **1. Introduction**

The ability of different countries around the world to integrate into global value chains (GVC) has become an important policy topic. Unfortunately, there is little systematic evidence to compare the relative success of different countries. The available evidence focuses mostly on the very macro level, comparing overall GVC engagement at the country level, or on the very micro level, studying case studies of specific industries in one country or an even smaller geographic area. Our objective is to provide some systematic cross-country evidence of GVC engagement at intermediate levels of detail using two widely-used data sources.

A large literature studies GVCs of specific industries, often even limited to narrow geographic areas, based on detailed case studies.<sup>1</sup> Such an approach is particularly valuable to understand the key drivers that explain the different vertical organization of industries. Case studies can also provide detailed insights into how the nature of buyer-supplier interactions are shaped by industry-specific requirements for coordination and collaboration along the value chain. For example, Gereffi, Humphrey and Sturgeon (2005) draw heavily on such case studies to develop their model of optimal governance of GVCs.

An almost entirely disconnected literature studies interactions between sectors of different countries using global input-output databases. Such a data source collects the aggregate interactions between the economies of different countries through buyer-supplier relationships at the sectoral level. For example, a global input-output table will indicate what fraction of inputs used by the transportation equipment industry of Germany is imported from the basic metal industry of Japan. In turn, it will also indicate what fraction of these German-made vehicles are sold as inputs to other industries or consumed as final demand, both domestically and for all potential export destinations in the world.<sup>2</sup> An important summary statistic on GVC engagement that can be constructed using this type of information is the value added to gross output ratio (Johnson and Noguera, 2012).

A drawback from the perspective of learning about the GVC integration of firms or industries in
(1959), Kudrov (1969), West (1971), Smith, Hitchens and Davies (1982), Smith (1985)1, and Davies and Caves (1987). Table 1.3 shows their coverage so far as we could determine. Another incicator of the adequacy of their sample is the number of items matched (first column of table 1.3). On the latter criterion, our study is among&s the most comprehensive.

Some of the studies cited in table 1.3 used a mixed methodology, in the sense that they combined independently determined PPPs by industry of origin with some proxy PPPs derived from expenditure studies. This was true in particular of Paige and Bombach (1959), to a smaller extent of Smith, Hitchens and Davies (1982) and Davies and Caves (1987). In our study we stuck strictly to the industry of origin approach, without using proxy PPPs. The different studies vary in the way they summarize their results for manufacturing as a whole. In most cases, the sample results themselves are presented as representative. Paige and Bombach, the Czech-French study, West and our study are the only ones to adjust the sample in order to present a blown-up estimate for manufacturing as a whole (see end of chapter III for deb;ails).

- **1** In fact Smith (1985) is derived from the same data set as Davies and Caves (1987).
10 as a follow up of the Uganda National Household Survey 2005/6, its primary aim is to inform policy makers in budgetary decisions and policy interventions and to monitor major national policies and programs. Implemented on an annual basis, the UNPS also provides representative information on income dynamics at the household level and consumption expenditure estimates to monitor poverty during intervals between other national survey efforts. Moreover, the UNPS collects high-quality agricultural data integrated within a multi-topic framework, which allows for understanding the linkages between agriculture, welfare, and other sociodemographic characteristics.

Since its launch in 2009, the UNPS conducted 8 waves of data collection on a sample of approximately 3,000 households. The UNPS 2009/10 was followed by additional rounds in 2010/11, 2011/12, 2013/14, 2015/16, 2017/18, 2018/19, and 2019/20. Its sample is representative at the national, urban-rural, and regional levels. This study uses data from the UNPS 2009/10 (wave 1), the UNPS 2013/14 (wave 4), and the UNPS 2015/16 (wave 5). The UNPS 2009/10 comprises a sample of 2,975 households, the UNPS 2013/14 counts 3,118 households, and the UNPS 2015/16 interviewed 3,304 households. After data cleaning and data preparation for the analysis, our final dataset comprises 18,313 individuals from wave 1, 17,377 individuals from wave 4, and 15,905 individuals from wave 5.

Figure 6.1 shows the change in status of the surveyed individuals across the 3 panel waves of data collection used in this work. The 2009/10 level represents the 18,313 individuals interviewed in the first wave. Of these, 45% (8,211 individuals) were found and reinterviewed in 2013/14, 15% were not found, and 40% were rotated out. The sample refresher of 2013/14 brought in 5,387 new individuals, or