rafmacalaba/data-use-mentions-extended huggingface dataset:
the network (using average daily traffic on each link). We aggregated the performance of the least-cost routes at the national level using weights that reflect the importance of each OD pair. Indeed, a simple sum of the road user cost over all the routes in the network would be misleading because some routes are used much more frequently than others.

We thus assigned weights to the different OD pairs based on the importance of the origins and destinations, which we measured in terms of traffic. We did this by:

- Summing the traffic getting in and out of each origin and destination node.

- Calculating for each route – each Origin-Destination pair – a weight _w_ based on the gravity model:

where _TO_ is the total traffic getting in and out of the origin, _TD_ is the total traffic getting in and out of the destination, and _kmOD_ is the distance between the origin and destination. We also use these weights to distribute traffic (measured at each link) between the OD pairs in the calculation of the total daily cost of the network.

The impact of a disruption at the national level is then the difference in network performance between the baseline conditions and the new network configuration with second-best routes when a link is removed. The criticality of each link is thus expressed along the three same dimensions: first, by aggregating over the network the average increase in road user cost (dollars per ton-kilometer) when the link is removed, compared with a baseline condition with no disruptions. Second, by aggregating over the network the average increase in kilometers that users have to drive when a disruption occurs. And third, by calculating the total direct economic cost linked to the disruption.

The candidate critical links are ranked according to these impacts on the network when they are disrupted: the higher these three impacts, the higher the criticality of the link.

# 2.2. Exposure of critical links

Once we have identified the critical links, we overlay the network with hazard layers to identify which ones are also exposed to natural
. Nevertheless, the richness of the new data from the Albania School Stakeholder Survey is unique, and allows researchers to investigate the relationship between school outcomes and: (1) school physical and human resources; (2) top-down accountability and transparency; (3) participatory accountability institutions and parental involvement; (4) parents‟ and community characteristics.

55. Since the data are retrospective in nature, establishing causal links is problematic due to omitted variables bias and possible endogeneity of school resources, parental characteristics and accountability measures. Fully addressing endogeneity problems is virtually impossible given the retrospective nature of the data. Indeed the Albania School Stakeholder Survey is intended first and foremost as a baseline to measure the impact of interventions the Government is still considering. That said, by employing fixed effects at the level of the Regional Educational District (RED) and clustering the error terms by district, the problems of endogeneity of school resources and top-down accountability measures can be attenuated (although not entirely eliminated).<sup>18</sup> As for the potential endogeneity of parental involvement, this is likely to be especially severe if parents have a choice in determining which school to enroll their children. In Albania this seems to be the case only in large urban areas.<sup>19</sup> Nevertheless, it is a reason for concern.<sup>20</sup>

> 18 The REDs are local education administration entities (thirteen in total) that are most directly accountable to The Ministry of Education and Science (MoES). While the MoES is directly responsible for the development of curricula, the selection of textbooks, the structure of the academic year, the workload of teachers, the allocation of resources among local education institutions, and teacher training (World Bank, 2005), the RED are responsible for the delivery of primary and secondary education in their respective districts –  37 in total.

> 19 When asked why they chose that specific school to enroll their children, 88% of parents in rural
# **Environmental Risks and Poverty: Analyzing Geo‐Spatial and Household Data from Vietnam**<sup>*****</sup>

Ulf Narloch<sup>a</sup> , Mook Bangalore<sup>b</sup> aSustainable Development Practice Group, World Bank, Washington, DC, USA bClimate Policy Team, World Bank, Washington, DC, USA

**<mark>Keywords</mark>** <mark>: Climate Change, Consumption, Environment, Households, Livelihoods, Poverty, Risks, Vulnerability</mark>

JEL: I30, I32, O10, Q50, Q54, R20

> * **Acknowledgements:** This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegatte. We are very thankful to the World Bank Vietnam team for providing the Vietnam Household Living Standards Survey (VHLSS) data and to Linh Hoang Vu and Ha Thi Ngoc Tran for helping with data questions. We greatly acknowledge data received from Vu Manh Quyet (on land degradation) and data processed by Joseph Muhlhausen (on air pollution). Very helpful suggestions were received from Diji Chandrasekharan Behr.
sectors, and the profile of green job holders in LAC. Section 4 discusses some caveats of the study and presents some robustness checks. Finally, section 5 draws some conclusions.

# 2. Methodology and Data

## Green occupations

There are different definitions of green jobs. This paper uses the Occupational Information Network (O*NET)<sup>7</sup> classification of occupations developed by Dierdorff et al. (2009, 2011) to define green and nongreen jobs.<sup>8</sup> Dierdorff et al. (2009) argue that the first step to classifying jobs as green or non-green is to define the green economy, which they consider involving economic activities related to reducing the use of fossil fuels, decreasing pollution and GHGE, improving energy efficiency, increasing recycling and adopting renewable forms of energy. Considering this, the “greening” of occupations takes place when the growth of the green economy _increases the demand for existing occupations, shapes the work and worker requirements needed for occupational performance, or generates unique work and worker requirements_ .

The classification is based on the US 2010 Standard Occupational Classification (SOC) System. It provides a list of 1,110 occupations, 204 of which are identified as green and divided into three mutually exclusive categories: Green Increased Demand (64 occupations), Green Enhanced Skills (62 occupations), and Green New and Emerging (78 occupations). Table 1 shows the definition of each group of occupations with examples of jobs falling into each category. The rest of the listed occupations (906) are considered nongreen and represent jobs that are not affected directly by the greening of the economy.

> 7 See also the Appendix for more detailed information on the data sources used in this paper.

> 8 In contrast, ILO (2018) defines green jobs based on whether “they reduce the consumption of energy and raw materials, limit GHGE, minimize waste and pollution, protect, and restore ecosystems and enable enterprises and communities to adapt to climate
Policy Research Working Paper 9128

# **Abstract**

This paper assesses the relationship between the length of recall and nonrandom error in agricultural survey data. Using data from the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture in Malawi and Tanzania, the paper shows that key input and output variables are systematically related to the length of the recall period, indicating the presence of nonrandom measurement error. With longer recall periods, farmers report greater quantities of harvest, labor, and fertilizer inputs. Farmers list fewer plots as the recall period increases. The paper argues that it is plausible that farmers overestimate plot-level outcomes, or they forget some of their more marginal plots due to longer recall periods. The analysis also finds evidence of measurement error related to the length of recall in common measures of agricultural productivity. The size of the recall effect typically varies between 2 and 5 percent per additional month of recall length, which is economically significant. With data reliability affecting policy effectiveness, improving agricultural survey data quality remains an important concern. Mainstreaming objective measures where possible and reducing the risk of recall error through shorter recall periods appear to be promising avenues to improve the quality of key variables in agricultural surveys.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of
These results thus provide detailed measures of welfare that in turn can be disaggregated for each district in Central Asia. The results highlight the heterogeneity of progress in the region. Poverty estimates range from 0 to nearly 100 percent by both the LMIC poverty line and the UMIC line. The results are robust to many separate validation approaches.

The maps created with these data can be used to identify areas where progress is being achieved most rapidly, as well as those areas that are lagging. With better and more detailed information at the ready, policies and interventions can be better targeted to support people in need where they live.

31
member and records their data in ProGres. The PA is also the person who receives assistance from UNHCR although he/she may not be the only recipient of humanitarian assistance.<sup>8</sup> Although income data are available, we use expenditure data from JD-HV. The existing literature leans toward computing poverty estimates based on expenditure rather than income because (i) income does not capture the possibility that there are people who can live off their savings or have assets to meet their immediate needs; (ii) consumption is smoother and less variable than income, especially in agricultural economies; and (iii) income reporting appears to be more misleading, especially for those with more resources who tend to underreport income more (Carletto et al, 2021; Cutler & Katz, 1991; Deaton & Zaidi, 2002; Meyer and Sullivan, 2003, 2011, 2012).

Aggregations of expenditure categories were used to compute total expenditure; however, the main challenge was constructing comparable poverty estimates across waves given a change in the number of spending categories included in the questionnaire. In the 2013 wave, there were six expenditure categories, namely, rent, utilities, food, health treatment, education, and others. In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits.

To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation
In Figure 20, we plot the gap between back casted poverty projections and the actual poverty rate for 2004 across studies. Estimates closer to the horizontal axis show that the predicted poverty rates were close to the actual rate observed in 2004. The graph shows that approach 1 of our study predicts 2004 poverty rate to be 3.4 percentage points lower than the actual headcount across India and 3.2 percentage points lower rate for urban samples.<sup>27</sup> In comparison, estimates from Newhouse and Vyas (2019) are 2.2 percentage point apart from the actual national rate but the differences for urban samples are 9.2 percentage points higher. Deviations from the actual poverty rate in Edochie, et al. (2022) are in the same direction as our estimates but the magnitude is considerably higher in their study across all samples. Overall, these out-of-sample predictions for NSS-2004 suggest that our approach yield estimates that are closer to the actual headcount rate across rural, urban and all-India samples.

We believe that the inability to model changes in household asset ownership overtime could have led the earlier papers to overestimate poverty reduction in 2015 and 2017 and produce incompatible back casted estimates of poverty for 2004 (asset indicators were unavailable in the surveys used in the two papers). Our analysis in Section 6 using PLFS shows that asset indicators are important predictors of household consumption; failing to capture these indicators leads to divergent poverty estimates even within the same survey.

# **6 Corroborative evidence**

Our estimates of poverty are at odds with findings from the leaked NSS-2017 survey which shows a rise in poverty between 2011 and 2017. Both sources point to a moderation of inequality since 2011, but the magnitude of changes to inequality are significantly higher in the NSS-2017 relative to our estimates. In this section, we corroborate our main findings using a range of independent data sources.

## **6.1 Headcount poverty has declined after 2011 with larger reductions in rural areas**

**Estimated consumption
Longe, O. M., N. Rao, F. Omowole, A. S. Oluwalami, and O. T. Oni. 2017. “A Case Study on Off‐Grid Microgrid for Universal Electricity Access in the Eastern Cape of South Africa.” _International Journal of Energy Engineering_ 7 (2): 55–63.

- Mensah, Justice Tei. 2018. “Jobs ! Electricity Shortages and Unemployment in Africa.” WPS8415. The World Bank. http://documents.worldbank.org/curated/en/659751524142624281/Jobs‐ electricity‐shortages‐and‐unemployment‐in‐Africa.

- Ministère du Plan et Suivi de la, and Ministere de la santé publique. 2014. “DEUXIÈME ENQUÊTE DÉMOGRAPHIQUE ET DE SANTÉ (EDS‐RDC II 2013‐2014).”

- NREL, 2018 “Annual Technology Baseline and Standard Scenarios | Energy Analysis | NREL.” n.d. Accessed December 21, 2018. https://www.nrel.gov/analysis/data‐tech‐baseline.html.

- O’Neill, Brian C., Elmar Kriegler, Kristie L. Ebi, Eric Kemp‐Benedict, Keywan Riahi, Dale S. Rothman, Bas J. van Ruijven, Detlef P. van Vuuren, Joern Birkmann, and Kasper Kok. 2017. “The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21st Century.” _Global Environmental Change_ 42: 169–180.

- Pachauri, Shonali, Bas J. van Ruijven, Yu Nagai, Keywan Riahi, Detlef P. van Vuuren, Abeeku Brew‐ Hammond, and Nebojsa Nakicenovic. 2013. “Pathways to Achieve Universal Household Access to Modern Energy by 2030.” _Environmental Research Letters_ 8 (2): 024015. https://doi.org/10.1088/1748‐9326/8/2/024015.

- Samad, Hussain A., and Fan Zhang. 2016. “Benefits of Electrification and the Role
# <u>Concerning areas that did not receive a shock:</u>

1. Correct exclusion, non-poor district is defined as a dummy equal to 1 when aid distribution rates are zero, and the 2010 district poverty rate is less than or equal to 50 percent.

2. Correct exclusion, poor district is defined as a dummy equal to 1 when aid distribution rates are zero, and the district poverty rate is less than or equal to 50 percent.

3. Incorrect inclusion, poor district is defined as a dummy equal to 1 when aid distribution rates are greater than zero, and the 2010 district poverty rate is greater than 50 percent.

4. Incorrect inclusion, non-poor district is defined as a dummy equal to 1 when aid distribution rates are greater than zero, and the 2010 district poverty rate is less than or equal to 50 percent.

We expect that households in non-poor districts that neither received weather shocks nor any aid distribution would be least likely to receive aid. Thus, the expectation is that households in the other three categories would be more likely to receive aid, particularly in poor districts.

Table 3 gives the percentage of households falling into each of these categories for the IHPS and FIAS samples in 2016, for the subset of households falling into each category. Looking at the IHPS sample for when a shock occurred, the highest percentage of households were located in “correctly” targeted poor districts that received a shock (35 percent), but 19 percent of households facing a shock in poor districts only received limited aid, capturing potential geographic errors of exclusion. Looking at areas where shocks did not occur, we note that most households were located in districts with positive distribution rates despite no shock occurring, and in fact 16 percent were located in non-poor districts with positive distribution rates, capturing geographic errors of inclusion. Looking at the FIAS sample, results suggest somewhat lower errors of exclusion (11 percent) but higher errors of inclusion (19 percent).

24
out firms that are (a) relatively small and sometimes informal and (b) likely to have different accounting standards.

For the analysis on country heterogeneity in Section 5, we use country-level data on economic, bank, and capital market development from the World Development Indicators database and the Financial Structure and Development database. Economic development is measured using gross domestic product (GDP) per capita in constant 2011 U.S. dollars. Bank development is the ratio of private credit by deposit money banks to GDP. Capital market development is measured as the sum of equity and corporate bond market capitalization to GDP. For the analysis on growth opportunities in Section 6, we use industry-level and country-level data. Appendix Table 2 lists the different variables and sources used for Sections 5 and 6.

The results reported throughout this paper are robust to a number of alternative sampling approaches. First, the results hold: (a) when excluding China or the United States, which together account for about 40 percent of world GDP; or (b) when simultaneously excluding Canada, Japan, and the United Kingdom, which together account for the largest number of issuers—23 percent of the total number of capital market issuers in the sample. Second, our findings are robust to (a) excluding utility companies (SIC codes 4900-4999) or (b) examining only manufacturing firms. Third, our findings also hold when excluding initial public offerings (IPOs), indicating that firms going public do not drive the results. Fourth, we observe a statistically significant increase in firm growth and investments in the year of issuance after excluding the years in which firms were acquirers in merger and acquisition (M&A) deals. This suggests that firms’ expansion comes from their own internal growth, in addition to any growth generated by the M&A activity. To conduct this last test and identify acquirer firms, we use the variable _Net Assets from Acquisitions_ from Worldscope. This variable represents assets acquired through pooling of interests or mergers. About 52
using bootstrapping.<sup>39</sup> The results are reported in Table 8. This table also gives the parameter estimates of the variables 21 and 22 that are used to perform a Hausman (1978) test of endogeneity of Provision and Protection.<sup>40</sup> The hypotheses that the variables and pp� pp� are not endogenous in the MENA and OECD datasets are both rejected, which justifies the need PP1 PP2 to instrument them as we did.

The results reported in Table 8 confirm those from the simple fractional Probit as to our main hypothesis, i.e., Provision ( ) and Participation ( ) are negatively and significantly related in MENA, implying that governments in the region seem to bargain more Provision PP2 PP3

> 37  As to the controls, we see that rurality is associated negatively to Participation in OECD countries and that human development is associated positively to it in MENA countries. As we will see later, because we have not yet accounted for potential endogeneity problems, these results should not be taken as final.

> 38 For an overview of the control function approach, see Wooldridge (2015).

> 39 Following Papke and Wooldridge (2008), we consider 500 bootstrap replications.

> 40 For a contribution on the application of the Hausman approach to testing for exogeneity in the Probit modeling framework, see Rivers and Vuong (1988).

23
**6.1.7 Differences by trade unions**

**Table 8 Trade Unions**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>ItJ|Coef|Std. Err.|<br>t|P>Itt|
|dunion|0.141|<br>0.063|2.222|0.026|-0.172|0.031|-5.585|0.000|

Does unionization make a difference to wages? The answer is yes, as the union premium in the Zimbabwean manufacturing sector is statistically significant and negative. Union members earn less than non-union members do when controlling for firm and individual characteristics in the wage regression. The estimated wage differential associated with being a union member is approxirnately -17 percent. This provides evidence that trade unions reduce wage differentials, and, therefore, positively affect the income distribution. This finding is surprising because findings from wage analyses in developed countries usually show that union members earn more than their non-union member colleagues. However, Rama (1997) finds also that union members are paid less in CFA countries applying a cross-section dataset of African countries. Additionally, union members are statistically significantly more productive than non-members; the productivity gap is 14 percent. The test of the hypothesis that the wage and productivity gap is of equal size is highly rejected (F=23.48, Prob > F = 0.00).

# **Enterprise characteristics**

Are employer or enterprise characteristics associated with individual wages and productivity. Findings not presented in this version indicate that omitting enterprise-level control variables from the analysis would bias the coefficient estimates in an individual23