rafmacalaba/data-use-mentions-extended huggingface dataset:
suppliers (Antras et al. 2012). Appendix C discusses the derivation of these indices and the procedure for calculating them. Appendix figure A4 (panel b) shows the change in sectoral input downstreamness between 2004 and 2013. The change in the downstreamness for mining is higher in the south (18 percentage points) than in the north (15 percentage points). Overall, regional variation in the change in downstreamness and the intermediate input use share point to mining’s stronger role as a downstream industry in the south than in the north. The relevance of mining in the supply chain has gradually increased over time. While the large-scale production of oil and gas in Ghana since 2010 has directly contributed to it, it is undeniable that a stronger intersectoral linkage between “other industry” and mining has also played a major role in growing importance of mining in the supply chain.

# **Final Remarks on Intersectoral Linkages and Structural Transformation**

As a final step, we estimate sectoral TFP using the I-O tables. This technique is based on Jorgenson et al. (1987), a standard reference in the literature for the estimation of TFPs. Since we only have I-O tables for two points in time, 2004 and 2013, we can only calculate TFP for one period (2004 to 2013). As reported in appendix table A2, the TFP ratio between mining and agriculture is much larger in the south (12.088) than in the north (–9.594). The south experienced a much larger productivity shock in mining than in the north—the TFP ratio between mining and “other industry” is larger in the south (0.532) than in the north (0.204). On the other hand, the TFP ratio between mining and WRT services in the south (–1.785) is much smaller than that in the north (21.434).

Table 3 summarizes the main findings on regional differences in intersectoral linkages between 2004 and 2013 using the
municipality. In addition, the opening investigation of a specific regulatory barrier in the initial municipality was often prompted by the complaint of a single firm. In other words, the complaint of a single firm in a random municipality, once confirmed, triggered the elimination of similar regulatory barriers across different municipalities across the country. The dependence on such precedents thus led to a ‘roll-out’ of eliminated regulatory barriers across municipalities which can be regarded as exogenous to other contemporaneous changes in municipalities’ local business environment (such as infrastructure investments) that may have promoted higher productivity growth of establishments operating in these municipalities. Moreover, the eliminated barriers were often sector-specific, providing additional exogenous variation in the data.<sup>16</sup> For example, the initial complaint by a second mobile phone operator that had been denied a municipal permit to install antennas in one of Lima’s 42 municipalities enabled INDECOPI to enforce the issuance of permits to install antennas in 3 other Lima municipalities in 2013 and 9 other municipalities in other parts of the country in 2014. Similarly, the refusal of granting an operating license for a passenger transport firm that did not possess the required minimum number of fleet vehicles in one municipality in 2013, led to the elimination of the de facto market entry barrier of minimum fleet size requirements across numerous municipalities in Peru. The province municipality of Chanchamayo, for instance, had to eliminate the requirement to possess at least 10 vehicles to be eligible to obtain an operating license while the district municipality of Puente Piedra had to eliminate the minimum requirement of 30 vehicles.

# **4. Data**

## **4.1 Establishment census data**

The analysis is based on establishment-level data from the Annual Economic Survey (EEA) collected by Peru’s National Institute of Statistics (INEI) for the 2008-15 period. The survey is representative of formal establishments in agriculture, manufacture, commerce, construction, transport & communication and services sectors in all regions. The sample frame comes from SUNAT, the
<u><mark>https://openknowledge.worldbank.org/bitstream/handle/10986/28907/WPS8246.pdf?sequence=1&isAl lowed=y</mark></u> ECLAC (2023), Non-contributory Social Protection Programmes Database Latin America and the Caribbean. Pensiones Sociales Brasil [Data set] https://dds.cepal.org/bdptc/archivos/44/BRA_PS_PR_v12.xlsx Enami, A., Lustig, N., & Aranda, R. (2018). Analytic foundations: Measuring the redistributive impact of taxes and transfers. In Lustig, N. (Ed.). (2018). Commitment to equity handbook: Estimating the impact of fiscal policy on inequality and poverty. Brookings Institution Press.

<mark>Fiala, N., Rose, J., Aryemo, F., & Peters, J. (2022).</mark> _<mark>The (very) long-run impacts of cash grants during a crisis</mark>_ <mark>(No. 961). Working paper. Available at: https://www.rwi-</mark> <u><mark>essen.de/fileadmin/user_upload/RWI/Publikationen/Ruhr_Economic_Papers/REP_22_961.pdf</mark></u> <mark>Gentilini, U. (2022a). Cash Transfers in Pandemic Times: evidence, practices, and implications from the largest scale-up in history. Available at:</mark> <u><mark>https://documents1.worldbank.org/curated/en/099800007112236655/pdf/P17658505ca3820930a2540 18e229a30bf8.pdf</mark></u> <mark>Gentilini, U et al. (2022b). Social protection and jobs responses to COVID-19: a real-time review of country measures. Available at:</mark> <u><mark>https://sites.fordschool.umich
**7 Tables**

Table 1: Mental health outcomes and category at each survey round.

|**Outcome**|**Defnition**|**Rapid**<br>**Resurvey**|**Midline**|**Endline**|
|---|---|---|---|---|
|= 1 if Minimal Depression|PHQ-8_≤_4|Primary|Primary|Primary|
|= 1 if does not sufer from<br>psychological distress|GHQ-12_<_3|Primary|Primary|Primary|
|Score on PHQ-8|Continuous score (0-24)|Secondary|Secondary|Secondary|
|Score on GHQ-12|Continuous score (0-12)|Secondary|Secondary|Secondary|
|Self-Esteem|Score on Rosenberg Self-Esteem Scale<br>(0_−_30)|Secondary|Secondary<sup>_∗_</sup>|Secondary|
|Resilience|Score on the Child and Youth Re-<br>silience Measure-Revised (0-34)|Secondary|Secondary<sup>_∗_</sup>|Secondary|
|Locus of control|On a scale of 1_−_10, how much control<br>do they feel they have over their lives|Secondary|Secondary<sup>_∗_</sup>|Secondary|

Notes: _∗_ these outcomes were not measured during the midline survey because it was implemented over the phone instead of face-to-face given restrictions due to COVID-19, and thus had to be significantly shortened.

31
||(3.821)|(3.639)|(8.866)|(2.390)|(2.461)|(7.698)|
|Number of observations|1,242|1,242|326|1,242|1,242|326|
|R2|0.387|0.367|0.492|0.429|0.409|0.499|
|Adjusted R2|0.378|0.359|0.472|0.421|0.400|0.478|
|_Number of Countries_|_126_|_126_|_56_|_126_|_126_|_56_|

Source: Authors’ calculations based on the World Bank’s January _Global Economic Prospects_ , the International Monetary Fund’s January _World Economic Outlook_ , January Consensus Forecasts, January Focus Economics Forecasts, the World Bank’s _Statistical Capacity Indicator,_ the World Bank’s _World Development Indicators_ , the Export Commodity Price Shocks data from Gruss and Kebhaji (2019), and the UCDP-PRIO Dataset for Conflict.

Note: Statistical significance level 0.01 - ***; 0.05 - **; 0.1 - *. Robust standard errors clustered at the country level. All regressions include year fixed effects, and the reference year is 2015. Columns (1) and (4) samples refer to same-year World Bank GEP January Forecasts sample from 2010 to 2020, covering 126 countries. This common sample is used to define the columns (2) and (5) samples, for same-year IMF WEO January Forecasts from 2010 to 2020. Columns (3) and (6) samples refer to same-year Consensus January Forecasts and the MENA Region Focus Economics January Forecasts, from 2015 to 2020, covering 56 countries, and are not forced to be a common sample with WB-GEP and IMF-WEO. Log of SCI (lagged) is the one-year lagged log of Statistical Capacity Indicator. Growth Volatility (lagged
and Ki-Hong Choi (1997) ‘Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method.’ _The Energy Journal_ pp. 59–73

- Ang, BW, XY Xu, and Bin Su (2015) ‘Multi-country Comparisons of Energy Performance: The Index Decomposition Analysis Approach.’ _Energy Economics_ 47, 68–76

- Atkeson, Andrew, and Patrick J. Kehoe (1999) ‘Models of Energy Use: Putty-Putty versus Putty-Clay.’ _American Economic Review_ 89(4), 1028–1043

- Bjørner, Thomas Bue, and Henrik Holm Jensen (2002) ‘Interfuel Substitution within Industrial Companies: An Analysis Based on Panel Data at Company Level.’ _The Energy Journal_ 23(2), 27–50

- Br ̈annlund, Runar, and Tommy Lundgren (2004) ‘A Dynamic Analysis of Interfuel Substitution for Swedish Heating Plants.’ _Energy Economics_ 26(6), 961–976

19
(ILO 2018). Compared to its closest aspirational peers, Senegal significantly lags in terms of female informality (Indonesia, 87.1 percent and Guatemala, 80.8 percent). However, Senegal outperforms its WAEMU peers having the lowest female informality rate across the region.

Labor market rigidities are commonly attributed to crowding out formal employment into the informal sector (World Bank 2019). Recent evidence shows that Senegal has among the most rigid labor markets in the world, resulting in bottlenecks for hiring and

> 2 Per the ILO, informal employment is not subject to national labor legislation, income taxation, social protection or entitlement to certain employment benefits (advance notice of dismissal, severance pay, paid annual or sick leave, etc.). Informality is determined or based on firm operational criteria such as social security contributions by the employer (on behalf of the employee), and entitlement to paid sick leave and paid annual leave. For more information, please refer to ILO 2018.

> 3 West Africa is composed of Benin, Burkina Faso, Cabo Verde, Côte d'Ivoire, The Gambia, Ghana, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo.

> 4 This estimate differs from the informality rate per the EHCVM, which stood at 93.7 percent in 2018-19. 5 Aspirational peers were chosen for their similarity regarding the proportion of the economy engaged in international trade, the value of natural resource extraction, the size of the agriculture sector, the state of human capital, and size. However, these countries achieved greater GDP per capita growth than Senegal. Based on this approach, Indonesia, Armenia, and Guatemala are categorized as Senegal’s aspirational peers. Please refer to World Bank 2018 for more information.

5
business class, which represents only about 8.5 percent of global passenger kilometers, the WBG share is more than 8 times as high.

# **5.3 Total Footprint Calculation for HQ-Booked WBG Air Travel in CY-09**

Our final step is to derive the overall carbon footprint of HQ-based WBG air travel for CY-09. Note that ICAO‘s method for calculating the footprint relies on (a) obtaining fuel consumption figures of the aircraft involved, (b) making adjustments for the overall distance traveled in the flight, (c) multiplying the fuel burnt by a factor of 3.157 (which is the number of tons of CO2 released when burning one ton of jet fuel), (d) applying the appropriate load factors (see table 5 above), and (e) assigning the seat portion of this footprint. Our calculated fuel consumption is based on 48 known and specified aircraft types. Other aircraft types, that have similar consumption patterns to specific known aircraft types, are assigned a related “equivalency” code, yielding a total set of 196 aircraft types to be included.<sup>26</sup> This means, for example, that a 737-300 or 737-500 will be treated identically to a 737-400. Trips are also placed in 16 distance classes ranging from 125 nautical miles to over 6,000 nautical miles, with (as noted above) consumption per distance reduced by trip length up to a limit, and increased beyond that limit.

The formula for computing the average carbon footprint related to air travel is then: CO2 per pax = 3.157 * (total fuel*pax-to-freight factor)/(number of y-seats*pax load factor).

In ICAO’s emissions calculator, a multiplier of 2 is added at the end of the calculation for passengers in premium classes. In our calculations, the proportion of a class’s contribution to the footprint was established earlier, and is also adjusted proportionally to the overall (and in Phase III, class-specific)
WTO, 2021; World Bank, 2021). Sector characteristics related to the inputs used in production (e.g., whether inputs are imported and in particular from China and the intensity of unskilled labor use) are shown to drive the negative effect of the COVID-19 shock on exports (Bas et al., 2022). Country characteristics pertaining to the severity of the domestic shock (see Almunia et al. (2021) for Spain) and the extent of credit constraints (see Paravisini et al. (2015) for Peru) also seem to have mattered for firms’ trade adjustments during the GFC. A novel contribution of our paper is that it uncovers the effect of the pandemic on GVC firms relative to other globally engaged firms.

A growing set of studies using trade data at the aggregate and product level emphasize the benefits of global connectedness and trade in promoting resilience during the COVID-19 crisis (Miroudot, 2020; Espitia et al., 2022). Emerging firm-level evidence from the current pandemic also suggests that exporters and firms in GVCs are more resilient than domestic firms (Hyun et al., 2020; Brucal et al., 2021). Hyun et al. (2020) examine the limited set of 7,832 publicly listed firms in 71 countries focusing on their stock market performance in response to the COVID-19 shock, while Brucal et al. (2021) use the first round of the BPS data for about 50 countries covering more than 100,000 firms but only emphasize the responses of firms in South Asia. The study closest to ours is Borino et al. (2021) who use cross-sectional data for a small sample of 4,433 firms in 133 countries to illustrate that globally engaged firms were more strongly affected by the early stages of the pandemic, experiencing more difficulties accessing inputs and logistic services (to manage supply chains) and selling outputs but have taken more resilient actions than domestically-oriented firms, such as working remotely, sourcing from new suppliers, developing new products, or temporarily
R<sup>2</sup>|0.34|0.51|0.89|0.89|0.76|0.73|0.55|0.41|
|N1(base survey)|711|711|711|711|711|711|711|711|
|N2(target survey)|698|698|698|698|698|695|698|698|

**True poverty rate in TA 1**

21.0 <u>(2.2)</u>

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in TA 1. Food and non-food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 3 use the estimated parameters based on the TA 1. 1000 simulations are implemented. True poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

37
such training data for Ukraine, _in-situ_ data collection along main roads was undertaken every year during the 2016-18 period along different routes as displayed in figure 1. Experts covered these routes two times each year to capture images of winter as well as summer crops to serve as training data following standard guidelines. Appendix table 1 provides information on the distribution of fields by crop type.

To generate crop cover estimates for 2016, 2017, and 2018, all optical data from Sentinel-2 and SAR data from Sentinel-1 during the vegetation period were used to run a convoluted neural network on the Amazon Web Services cloud computing platform.<sup>8</sup> For classifier training, half of the data was randomly assigned to training and independent validation samples and accuracies obtained based on independent test sets for each of the crops received are in appendix table 1. Resulting maps for the four crops of interest are displayed in figure 2 with differences across years visible even at this scale.

> 7 Recently passed legislation would partly address this constraint through a phased opening of agricultural land purchase markets starting on July 1, 2021 when agricultural land purchases will be allowed only by individuals with an ownership limit of 100 ha per person.

> 8 See Kussul _et. al_ (2018 and 2019) for a detailed description of methodology.

7
geographical coverage, individuals were not found in the address given to surveyors or they did not respond. In addition, a sub-sample of 1,994 individuals outside the relevant age groups was mistakenly surveyed.<sup>10</sup> In our analysis we use all the individuals sampled.

Table 3 gives further detail about the different possibilities of sample attrition. In this table we use a logit model to relate household baseline characteristics with the probability of attrition. Even columns add the interaction of the baseline characteristics with the treatment dummy to further explore the possibility of selective attrition. In columns (1) to (4) we present evidence about the attrition of the original households, which are the foundation of the sampling design. Columns (1) and (2) give evidence of the locality selection (similarly as in Table 1 Panel A) while columns (3) and (4) give evidence of the possibility to find respondents in the selected 334 localities. As these columns show, older head members of smaller households are more likely to be missing. This is consistent with the evidence presented in Table 1, where we found no evidence of selective attrition. Finally, columns (5) and (6) include newly formed household attrition. Given that these households did not exist in 1997, its attrition is based on the possibility to find members in the relevant ages that moved out of the original household. In this sense, the attrition results from the 4,207 members who were searched for and not found. Section VI.A.3 does a robustness exercise where inverse probability weights are employed to adjust for attrition.

**Available information.** Both for _original_ and _new_ households, dwelling’s characteristics and information on durable assets were gathered at the household level, we use such information to explore PROSPERA’s long-term impacts on welfare. Individuals in the critical age groups described were asked several additional questions on income and work-related topics. An important limitation of the ENCEL 2017 data is
indicate that there is a significant difference between tertiary educated and other migrants. The first group seems to overcome initial gaps in placement while the latter do not.<sup>23</sup> The issue of convergence has also been explored by the literature (measuring migrants’ performance by earnings), sometimes with conflicting results.<sup>24</sup> To further test the strength of convergence detected by examining our charts, we regress the change in hypothetical individuals’ occupational placement during a decade on their occupational placement at the beginning of the decade. In accordance with the predictions of classical β-convergence models (Barro,1991, Barro,1997) the _θ_ coefficients conditional on human capital endowments are expected to be negative and significant. For each pair of Census years t=1980, 1990 and 2000, where _c_ represents the country of origin.<sup>25,26</sup> Table 5 presents the estimation results, which provide evidence of strong convergence as suggested by the negative and significant coefficients for the predicted log of OP in 1980 and 1990 respectively. This result holds for all the alternative specifications of the dependent variable.

# **_3.3. EXAMINATION OF MAJOR SOURCES OF BIAS_**

The findings of the previous section may reflect a distorted picture of the real trends in immigrants’ occupational placement if there are factors that systematically impact the composition of cohorts across censuses. This section aims at quantifying the effect of two important sources of bias: out-migration and usage of repeated cross-section datasets.

> 23 The details of this exercise are also available upon request.

> 24 Duleep and Regets (1997) detect significant negative correlation between the initial earnings and earnings’ growth when controlling for the education level of migrants. Conditional on similar initial human capital endowments, Borjas (2000) finds similar results, but they are not robust to simple attempts to control for the measurement error bias.  In addition, when human capital variables are not accounted for, Borjas obtains a positive, but weak
IBRD threshold will also decline.

As a third indicator of institutional development, we include a transition economy dummy variable for the countries of Central and Eastern Europe and the former Soviet Union. For given income levels, their economic and social institutions for sustained market-based development were relatively backward in the early years of transition. Engagement with the IFIs and other donors – particularly in the early transition years – focused on reform of legal, tax and other systems designed to produce a convergence with other European countries in economic and social institutions. Based on this reasoning, we would expect transition countries to graduate later than others with similar incomes. On the other hand, the desire to be like other European countries – including by joining the EU, OECD and NATO – could cause them to transition from borrower to donor status before other countries at similar income levels. The net effect of these two potential influences of transition status on the likelihood of graduating could be positive or negative.

Graduation is also supposed to depend on the country’s ability to access foreign credit markets. Actual borrowing or bond issuance would not be an ideal measure of access, however, because it depends not only on the country’s ability to borrow but also on its need or desire to do so. Instead, we measure the willingness for foreign markets to lend by Institutional Investor’s Creditworthiness ratings, based on a survey of economists’ and risk analysts’ perceptions of a country’s creditworthiness. The ratings are on a 100-point scale, with larger values representing greater creditworthiness.<sup>5</sup> This indicator measures the risk of default on sovereign debt, which should be a good proxy for access “on reasonable terms” to private capital markets. The graduation

> 5 Ratings are provided twice a year, in March and September.  We took the average of these.  We did not use sovereign bond ratings from rating agencies like Fitch and Moody’s because of the incompleteness of their coverage.  Early in our sample period, in