rafmacalaba/data-use-mentions-extended huggingface dataset:
data on smoke plumes from the National Oceanographic and Atmospheric Agency’s (NOAA) Hazard Mapping System (HMS) and data on the location and yield of corn and soybean crops from the United States Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS). Smoke plumes are divided into “low density” and “high density” categories, because we hypothesize that different smoke densities may have different signed impacts.

To identify the impact of smoke plumes on crop yields, we leverage the quasi-random variation in exposure to smoke plumes within a county from year to year. We estimate a standard two-way fixed effects model that examines how quasi-random exposure to smoke plumes in a given county and year impacts yields in that year. To isolate the impact of smoke plumes operating through the amount and type of sunlight available for photosynthesis, we control for a range of weather conditions that might influence yield including temperature, precipitation, and cloud cover.

Lastly, we estimate the impact of smoke plumes on crop yields in 2050, taking into account the current state of knowledge about future burned area and smoke exposure. Because there is large uncertainty in the future quantity and density of smoke plumes, we analyze how crop yields will be impacted under a wide range of changes in smoke exposure.

3
nightlights is equivalent to 1 percent,<sup>26</sup> instead of 6.5 percent in the first bin.<sup>27</sup> A similar result can be found in panel c, where, on average, a wider stretch of mangrove also implies a longer distance to the coast.

**Figure 5. Impact of hurricanes on nightlight by mangrove width, excluding Cuba**

<!-- Start of picture text --> 1 -3.10 1 0.15 1 5.89<br>2 -2.85 2 1.05 2 10.95<br>1 -3.66 1 0.10 1 4.92<br>2 -0.99 2 0.35 2 8.38<br>3 -4.29 3 1.33 3 11.93<br>1 -3.95 1 0.08 1 4.62<br>2 -1.60 2 0.23 2 7.43<br>3 -0.61 3 0.51 3 9.36<br>4 -5.28 4 1.54 4 12.39<br>-8 -3 2 0 2 4 0 5 10 15<br>a) Estimated impact of hurricanes b) Distribution of mangrove width (km) c) Distance to the coast (km)<br>on nightlights (NL)<br> Authors’ calculations based on data from LECZ database.<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from LECZ database.

_Notes:_ In models 1-3, from panel (a) we discretize the mangrove width variable into bins that correspond to its q-quantiles and estimate the impact of hurricanes on economic activity for each bin. Model 1 uses q=2 and is colored in green. Model 2 uses q=3 and is colored in orange
|1.340|1.809|
|Average|0.777|0.847|0.872|0.884|1.064|1.037|0.972|1.329|
|**Panel B: Poverty**|||||||||
|Malawi|0.743|0.778|0.862|0.855|0.477|0.395|0.492|0.614|
|Tanzania|0.874|0.868|0.837|0.920|0.311|0.283|0.273|0.426|
|Average|0.809|0.823|0.850|0.888|0.394|0.339|0.382|0.520|

Note: The first four columns present the coverage rate across simulations for each country. The coverage rate is defined as the proportion of confidence intervals that contain the true value, derived from the census. The last four columns present the average width of the confidence interval across simulations for each country. The results are based on 100 simulations for each country and method.
migration. Since there are both individual and province level variables in my regressions, the standard errors are adjusted for within province and urban/rural correlation (clustering). It is found that younger and more educated individuals are more likely to migrate; these results are in line with theory (e.g., Sjaastad, 1962). The result of a regression model that includes dummies to denote the levels<sup>35</sup> of education (column 2) confirms that the probability of migration increases monotonically as the education level increases (the upper secondary education is the omitted category). Females are more likely to migrate relative to males, which is in line with the trend found in the 1999 and 2009 census data (GSO/UNDP, 2001; GSO, 2011). Consistent with Nguyen-Hoang and McPeak (2010), higher unemployment at the place of origin appears to be a factor which generates out-migration.

In order to proxy for the standard of living in the location where an individual resides, Model (3) introduces per capita expenditure as a set of dummy variables by four quartiles.<sup>36</sup> (The uppermiddle quartile is the omitted category). The coefficient estimates turn out to be consistent with the “liquidity constraint hypothesis” (Phan and Coxhead, 2010) and with the existence of a “migration hump” (Nguyen _et al._ , 2008), but are not statistically significant.<sup>37</sup> Table 4 presents the results of multinomial logit model relating four migration outcomes to a set of personal and origin characteristics. For the purpose of identification, non-migrants constitute the base category. The coefficients are reported in terms of the impacts of the variables

> 35 Five levels of education - college (and above), upper secondary (grade 12 completed), lower-secondary (grade 9- 11 completed), primary (grade 5-8 completed) and no primary (less than grade 5 completed) - are considered.

>
Zeitlin, “Information and Collective Action in Community-Based Monitoring of Schools: Field and Lab Experimental Evidence from Uganda” Working Paper (2012).

> 96 Priyanka Pandey, Sangeeta Goyal, and Venkatesh Sundararaman, “Public Participation, Teacher Accountability, and School Outcomes: Findings from Baseline Surveys in Three Indian States,” World Bank Policy Research Working Paper Series (November 1, 2008).

> 97 Esther Duflo, Pascaline Dupas, and Michael Kremer, “School Governance, Teacher Incentives, and Pupil -Teacher Ratios: Experimental Evidence from Kenyan Primary Schools,” National Bureau of Economic Research Working Paper 17939, Cambridge, MA (2012).

98 M. Bjorkman, D. de Walque, and J. Svensson, “Information Is Power: Experimental Evidence on the Long-Run Impact of Community-Based Monitoring,” Policy Research Working Paper 7015, World Bank (2014).

99 V. Misra, “Pilot Study 1, Andhra Pradesh, India: Improving Health Services through Community Score Cards: Learning Notes,” Social Accountability Series, Washington, DC, World Bank (2007).

100 A. Ravindra, “An Assessment of the Impact of Bangalore Citizen Report Cards on the Performance of Public Agencies,” Evaluation Capacity Development Working Paper 12, Washington, DC, World Bank, Operations Evaluation Department (2004). 101 B. Goldfrank, “Lessons from Latin American Experience in Participatory Budgeting,” Presentation at the Latin American Studies Association Meeting, San Juan, Puerto Rico (March 2006).

102 Chad Kendall, Tommaso Nannicini, and Francesco Trebbi, “How Do Voters Respond to Information? Evidence from a Randomized Campaign.” _American Economic Review_ 105 (1) (2015) p. 322–53.

15
more comprehensive than just skills with green words.

This reliability test is carried for two countries: the U.S.—a high-income country with rich data—and Indonesia—a middle-income country with some data. For the U.S., we use the BG skills database for 2017; for Indonesia we also use a database retrieved by BG for 2020. While both databases follow the same protocols, there are important differences related to the size of the online job intermediation market and the localization of the algorithm, mainly the exclusion of postings in Bahasa Indonesian.<sup>56</sup> One problem common to both databases is that they do not cover all occupations. For the U.S., 773 out of 840 SOC titles (version 2010) are observed in the BG database, and for Indonesia, 272 out of 465 occupations appear in the BG database. Given the number of vacancies per occupation in each database, the reliability test includes all occupations for the U.S. and only high-skilled occupations (159) for Indonesia.

Applying the (narrow) green dictionary to skills databases resulted in 343 green skills out of 10,798 unique skills for the U.S. and 193 green skills out of 6,263 unique skills for Indonesia. As we did for tasks, a green skills intensity (GSI) index can be defined as the percentage of green skills in an occupation o , as described in the following formula G =<sup># geee ttkkiilttoo</sup> oo Two things can be concluded from the comparison between the GTI and GSI indexes. First, the applied GG oo # ttkkiil tt Two things can be concluded from the comparison between the GTI and GSI indexes. First, the applied text analysis methodology is appropriate to capture green tasks, and therefore green jobs, since both

> 56 For more details on the data collection as well as potential biases see Granata, Posadas, and Testaverde (2021).

25
into World Bank operations and technical assistance.

Investment Climate Assessments have been completed for 18 countries and more are underway, including Chile, Mongolia, Oman, South Africa, Syria, Thailand, and Turkey.

Dollar, Hallward-Driemeier and Mengistae (2003) provide various infrastructure indicators for 10 countries based on firm-level surveys in Bangladesh, Brazil, China, Honduras, India, Nicaragua, Pakistan, and Peru. The standardized surveys highlight the monetary costs of bottlenecks and delays in infrastructure services such as power, telecom as well as customs administration (See <u>Annex C).</u> In addition, work is underway on _country level supply chain/ logistics studies_ that are diagnostic assessments of logistics costs for selected strategic products using a supply chain approach in several countries. The methodology is being built on information used for _Forging Subregional Links in Transport and Trade Facilitation (2001)_ . Nigeria, Kenya, Lesotho, Ghana, Honduras, Nicaragua are some of the countries where these studies will be carried out.

8 __________________________________________________________________________________________________________
40

|Collection|Title in full|Type of collection|Institution|Geographic<br>focus|Type of survey(s)|Further information on data and access|
|---|---|---|---|---|---|---|
|SHES|World Bank<br>standardized<br>household<br>expenditure surveys|Ex post<br>harmonized|World Bank|Global|HBS, HIES,<br>multipurpose surveys|Surveys produced by World Bank’s Data<br>Group as part of the International<br>Comparison program. Accessible only to<br>World Bank staff|
|SHIP|World Bank Sub-<br>Saharan Africa<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>Sub-Saharan<br>Africa region|HBS, HIES,<br>multipurpose surveys|Some publicly accessible via the World Bank<br>Microdata Catalog. Rest accessible only to<br>World Bank staff via World Bank Microdata<br>Library.|
|WHS|WHO World Health<br>Survey|Multi-country<br>survey initiative<br>– highly<br>standardized|World Health<br>Organization|Global|Health survey,<br>including out-of-<br>pocket expenses and<br>some data on<br>household<br>consumption|Details of the WHS survey can be found at the<br>WHS webpage, and access is via the WHO<br>Central Data Catalog.<sup>45</sup>|

45 The WHS webpage is http://www.who.int/healthinfo/survey/en/. The WHO Central Data Catalog is at http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog
as measured by test scores at a point in time (Todd and Wolpin, 2003). Let _Ait_ be student _i_ ’s academic achievement (or test scores), and **S** _it_ and **X** _it_ denote vectors of school-supplied inputs (e.g. student _i_ ’s class size, school infrastructure, quality of teachers, etc) and family-supplied inputs at time _t_ , respectively. Allowing for the idiosyncratic error, which includes all omitted inputs (observed and unobserved) and measurement error, _εit_ , and inherited ability, _ai_ 0, the general achievement production function is given as: In this framework, a student’s performance at the university level is influenced not only by her current individual and household characteristics, but also by current and previous school/universitysupplied inputs. Because skill begets skill, the influence of past school-supplied inputs is partly reflected in the child’s performance in JC and COSC/LGCSE examinations.

# **4 Data and Descriptive Statistics**

This paper uses the National University of Lesotho’s students’ administrative records data for years 2010 up to 2014. It makes use of first-year and second-year students’ data. As the largest and oldest undergraduate degree-awarding university in Lesotho, the NUL admits the largest share of all students who achieve the minimum score for university entry in the senior high school exit examinations. It therefore admits the largest proportion of students who have gone through the FPE system. Given that the first cohort that has gone through the FPE program first entered university level in 2012, the second-year students are a control group, while the first years are a treatment group. I discuss this group categorization in detail in Section 5.

The data contains information on each student’s gender, date of birth, academic year, the senior high school exit (or Cambridge Overseas School Certificate (COSC)) examination pass grade, the NUL Admission Point System (APS) score<sup>7</sup> for each student,
**Alatas, V., A. Banerjee, A.G. Chandrasekhar, R. Hanna, and B.A. Olken** , “Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia,” NBER Working Paper 18351, National Bureau of Economic Research, Cambridge, MA 2012.

- **Alcaraz, C., D. Chiquiar, and A. Salcedo** , “Remittances, schooling, and child labor in Mexico,” _Journal of Development Economics_ , 2012, _97_ (1), 156–165.

- **Alderman, H., P.A. Chiappori, L. Haddad, J. Hoddinott, and R. Kanbur** , “Unitary versus collective models of the household: is it time to shift the burden of proof?,” _World Bank Research Observer_ , 1995, _10_ (1), 1–19.

- **Altonji, J.G. and A. Siow** , “Testing the response of consumption to income changes with (noisy) panel data,” _Quarterly Journal of Economics_ , 1987, _102_ (2), 293–328.

- **Amuedo-Dorantes, C., A. Georges, and S. Pozo** , “Migration, Remittances, and Childrens Schooling in Haiti,” _ANNALS of the American Academy of Political and Social Science_ , 2010, _630_ (1), 224–244.

26
2 depth surveys in particular fields such as kindergartens,<sup>3</sup> housing,<sup>4</sup> and asset divestiture in problem industries and regions.<sup>5</sup> We shall draw on this literature as appropriate in our own study' of Belarus, and attempt to augment it in areas which, in our opinion, have not received as much attention, such as the implications for local govermment and the interactions of enterprise provision of these functions with the tax 6 system.

# **Analysis of Enterprise Social Spending**

5. Tables 1-3 use enterprise statistics in Belarus to examine characteristics of spending on employee remuneration and "social development" and "social benefits" by type of benefit, size of firm, sector of the economy and region. In addition, but not showNn in the tables, the relative size of enterprise spending on these fringe benefits was compared for the years 1994 and 1995. It was also reported to us that there are systematic differences in fringe benefits available to different occupations" some occupations such as teaching allegedly receivc fewer fringe benefits. Our enterprise data did not allow us to examine such occupational differences, however.

6. There are measurement difficulties. Most importantly, data on some types of user cost recovery are incomplete, such as, for instancc. where fees arc collected for use of facilities such as kindergartens. This imparts a downward bias to comparisons over timc, since user cost recovery has been increasing for housing and utilities as a result of government policy and indeed is required as a condition of loans to Belarus by the IMF. A furthcr factor impeding comparisons over time is that data on the important category of housing maintenance undertaken by ente-prises was not collected in 1994.

7. With these qualifications in mind, four components of employee remuneration and enterprise spending on social functions are distinguished in Tables 1-3: wages; other monetarv payments to employees (such as bonuses and retirement allowances). enterprise spending on "social development
unirrigated regions. While high transaction costs in land markets also act as a disincentive to voluntary consolidation (i.e. the voluntary exchange of plots), the importance of holding land as a liquid asset, in the absence of other durable assets or steady income-streams, is a critical factor in explaining the widespread resistance to land consolidation in practice.

# _4.2.6 Policy issues_

Land consolidation, where it may successfully be implemented, is more likely to contribute positively to agricultural productivity than to improving access to land for the poor. In theory, consolidation ought not change net land distribution, and so should not leave the poor worse off. Our findings in Orissa suggest that land consolidation, like land survey and settlement operations, actually tends to worsen land distribution at the bottom end, to the extent that it has brought about any real change on the ground. While land consolidation may contribute positively to agricultural productivity under certain conditions, it will not help increase access to land for the rural poor unless attention is also paid to other aspects of a land reform agenda. For instance, insecure tenants and share-croppers whose names do not appear in land records may lose their rights in land, if consolidation is pushed through without first securing their rights.

48
Table 9 reports the LATE estimates for the pooled model. The results indicate that ENTRE reduces dropout by 3.1 percentage points for students in complier schools (column 3 of Table 9), roughly doubling the ITT estimates. As expected, the LATE estimations are significantly more imprecise, with the standard errors three times larger than those corresponding to the ITT. Table 10 shows a similar pattern of heterogeneous effects by dropout probability, school size, and gender. The results show that participating in the program reduces dropout by about 6 percentage points for the students with the highest risk, 4.5 percentage points for students in large schools, and approximately 4 percentage points for boys.

Table 9. Impact of the program on the probability of student dropout: LATE estimates.

|||LATE||
|---|---|---|---|
||(1)|(2)|(3)|
|_ENTRE j_|-0.025|-0.030**|-0.031**|
||(0.040)|(0.013)|(0.013)|
|Observations|130,762|124,626|124,626|
|R-squared|0.000|0.294|0.325|
|Controls at student level|NO|YES|YES|
|School fixed effects|NO|NO|YES|

Notes: (1) Local Average Treatment effects were estimated by instrumenting treatment (proxied as the effective reception of the ENTRE guide according to self-reported data in follow-up 3) with treatment assignment. (2) Standard errors clustered at schoolyear level in parenthesis. (3) *** p<0.01, ** p<0.05, * p<0.1. (4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year _t_ and year _t
WGI data available online, we also report the 90 percent confidence interval in percentile rank terms, i.e. we report the percentile rank (among all country estimates of governance) of the upper and lower bounds of the 90 percent confidence interval for each country. For illustrative purposes, we have labeled a selection of countries equally spaced throughout the distribution of governance scores for both indicators.

The length of the confidence intervals varies across countries, for two reasons: (a) the intervals are smaller for countries where the estimate of governance is based on more data sources, and (b) the intervals are smaller if the data sources for the country are more precise, in the sense of having a smaller estimated variance of measurement error, σ<sup>2</sup> (k) . For many countries, these confidence intervals are substantial. This means that in many cases, pairwise comparisons of governance estimates between

> 15 The units of the estimates of governance are those of a standard normal distribution.  This means that roughly 99 percent of estimates will fall within the range -2.5 to 2.5.  It is however possible to see more extreme values in rare cases.  Looking across all six aggregate indicators for all 25 years covered in the WGI, just 77 out of 30,974, or 0.2 percent of observations, fall outside this range.

16
welfare. Second is that any analysis that uses primary employment at the individual level, looking for Lewisian transformation, misses livelihood transformations. This transformation of the traditional household sector is rather subtle and will not be immediately mirrored in large changes in the structure of primary economic activities for the labor force. Household‘s livelihoods in Uganda are best understood by looking beyond the main employment activities and taking the approach that household incomes come from a portfolio of several economic activities. Tracking the role of the HE sector in transforming the economy and in poverty reduction requires collecting and analyzing economic and employment data through the livelihood lens, not the lens of a labor market where one person sells labor and another buys the labor services. This is a different approach than orthodox labor economics. It requires analyzing the production decisions of households over the year, as social and economic units, and how these are transformed in response to opportunities and challenges presented.

# **References**

Acemoglu, Daron, and James Robinson (2005). _Economic Origins of Dictatorship and Democracy_ . Cambridge, U.K.: Cambridge University Press.

Bakeine, Amos (2010). ―Uganda Country Study Report: Raising Productivity and Reducing the Risk of Household Enterprises‖. World Bank, Washington DC Chenery, Hollis B. and Lance Taylor. 1968. ―Development Patterns Among Countries and Over Time,‖ _Review of Economics and Statistics_ , Vol. 50, no. 3, pp. 391-416.

Chenery, H.B. and M. Syrquin. 1975. _Patterns of Development_ . London: Oxford UniversityPress.

31
4 30% and, by late 1997, a number of municipalities across the country have acquired initial experience with targeting mechanisms designed to provide housing support for the most needy segments of society.

Since early 1997, housing reform has increasingly moved into the ceniter of the GOR's reform efforts. Present policy calls for a reduction in stages of housing subsidies so that cost-recovery from all but the neediest tenants will be 100% by 2000. But this more than three-fold increase in the household contribution to financing the costs of housing over the next five years will almost certainly not be matched by a commensurate increase in household incomes. Thus, the share of housing costs in the average household budget will steadily increase. It remains to be seen whether this policy will survive the political pressures to reschedule cost-recovery increases that will undoubtedly be brought to bear on it from various quarters.

# Towards a **More** Efficient Allocation of the Russian Housing Stock

The allocation of the housing stock that was part of the Soviet legacy to the Russian Federation was determined over decades by essentially non-market forces and was characterized by a high degree of economic inefficiency. Thus, while increased recovery of housing costs has exacerbated for many the pain of transition, to the extent that this tendency represents the removal of gross distortions in the housing sector, it has played a positive role in helping to stimulate the development of the housing market and the reallocation of the existing housing stock to a more efficient state.

An indication of just such a move towards a more efficient allocation is found in the analysis of "mover households", that is, those who moved at some point in the years under study. As is detailed in Section 2 below, survey data revealed that a certain part of the mover households moved from their original apartments to apartments that were in some respect inferior (smaller, farther from the center, in lower quality buildings, etc.) in reaction to the economic forces affecting them. They benefit by selling their superor apartments to a buyer