rafmacalaba/data-use-mentions-extended huggingface dataset:
Also, the fact that we are dealing with count data that might be correlated with previous years (hierarchy at _t_ as function of hierarchy in _t-1_ ) demands additional cautions regarding non-linearity and dynamics.<sup>29</sup> We showed that firms’ organization varies even after controlling for size (firms with the same number of workers have different number of layers).<sup>30</sup> Nonetheless, changes in hierarchy and size (measured by total number of workers) might be simultaneously determined which might result in cov( _Xit_ , _εit_ ) = 0. This might be a second source of endogeneity that may be an issue for identifying _τ_ if cov( _Xit_ , _Disrt_ ) = 0.

Regarding the count data properties of the dependent variable, we applied a logarithmic transformation of Y (number of layers) keeping the full number of layers of the firm from 1 to 5. This transformation allowed us to estimate _τ_ using a fixed-effect instrumental variable approach in order to control for timeinvariant firms’ characteristics. In order to check if the logarithmic transformation provides a reasonable approximation to deal with non-linearities, we compared the coefficients of a Poisson estimator and a standard panel fixed effects.<sup>31</sup> For the second source of endogeneity we instrumented the regressors ( _Xit_ ) that could be simultaneously determined with _Yisrt_ using their own variables with 2 lags.<sup>32</sup> The main assumption is that ( _Xit−_ 2 , _εit_ ) = 0, once we controlled for firms’ time-constant heterogeneity.

We complement the analysis by adopting a panel fixed effects procedure without instrumenting _Dit_ and using a similar procedure to deal with potential endogeneity in _Xit_ . We compared the results controlling for potential regional (state-year) and sectoral-year shocks. In this case the identification is based on a difference-in-difference (DID) approach, which assumes that there is no time-invariant unobservable that determines the access to the program and firms’ organization simultaneously. The specification is the following: Where:
|2018|106|
|---|---|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

**Table 3: Factory count by evaluation cycle (number of visits)**

|**Year**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|---|---|---|---|---|---|---|---|---|
|2010|64|1|||||||
|2011|64|52|||||||
|2012|45|50|41||||||
|2013|19|42|48|30|||||
|2014|71|17|30|42|17||||
|2015|75|45|20|25|38|16|||
|2016|41|75|32|24|30|37|14||
|2017|62|37|68|24|24|22|39|10|
|2018|17|17|12|30|3|9|4|14|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Questions are grouped into compliance points, and sub‐grouped into clusters. The eight clusters are: child labor, discrimination, forced labor, freedom of association and collective bargaining, compensation, contracts and human resources, occupational health and safety, and working time. There are 39 compliance points across the eight clusters. Table A.1 lists the different compliance points under each of the eight clusters.

Moreover, not all questions in the Compliance Synthesis
Table 1: Satellite Indicators by Resolution and Availability

|Indicator|Source|Time-frame|Coverage|Resolution|Availability|
|---|---|---|---|---|---|
|_A. Population_||||||
|Population|Census||National||public|
|Population|HIES||1421 villages||public|
|Population|Facebook||National||public|
|Population|WorldPop||National||public|
|Population|GPW||National||public|
|Population|LandScan||National||public|
|Population|GHSL||National||public|
|_B. Other sources_||||||
|Night time lights|VIIRS|2014|National|Low|public|
|Elevation|ASTER||National|Low|public|
|Slope|ASTER||National|Low|public|
|Tree cover|Hansen et al. (2013)|2000|National|Low|public|
|Tree cover gain and loss|Hansen et al. (2013)|2000 to 2014|National|Low|public|
|Built-up area|GUF, GUF+|2012, 2015|National|High|public|
|Built-up area|GHSL|2014|National|High|public|
|Built-up area|Facebook|2015|National|High|public|
|Built-up area|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Cars|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Shadows|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Roof type|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Road type|Engstrom et al. (2017)|2011
a relationship is particularly strong or rather weak. In order to address this issue, this paper uses data from an internationally comparable student achievement test which provides data for Argentina, Colombia and selected comparison countries, thereby allowing for a benchmarking of estimated parameters relative to other countries.

Specifically, we use data from the most recent international achievement study, the Progress in International Reading Literacy Study (PIRLS), which tested the reading performance of fourth-grade students in 2001, with data released in 2003. A uniform data generation process for representative samples of students in all participating countries ensures comparability across countries. Latin America’s poor educational performance is well documented in PIRLS: Argentina and Colombia ranked 31<sup>st</sup> and 30<sup>th</sup> , respectively, out of a total of 35 participating countries,<sup>2</sup> while at the same time featuring relatively large inequality among their students.<sup>3</sup> The PIRLS study not only provides comparable information on the reading performance of students, but also extensive information on student and school background to an extent not generally available in student achievement studies. This allows for an analysis of the determinants of learning both in the realm of the family and of the school. The available information stems from background questionnaires answered, respectively, by students, teachers, school principals, and – as a particular advantage over other recent international achievement tests – a home background questionnaire completed by the parents of tested students. The latter questionnaire provides particularly relevant information which primaryschool children themselves could not have provided. The available student background features include information on the students’ pre-school reading performance, their kindergarten attendance, school starting age, language spoken at home, immigration status, parental education, occupation and income and rural versus urban location, among several others. The school features include class size, instruction time, proxies for the material endowment of the classroom, teacher characteristics including gender, education, recent training participation and experience
’ _Bulletin of the World Health Organization_ 55(4), 489–498

- WFP (2001) _Identifying Poor Areas in Cambodia: Combining Census and Socio-Economic Survey Data to Trace the Spatial Dimensions of Poverty_ (Phnom Penh: World Food Programme)

- WHO Working Group (1986) ‘Use and interpretation of anthropometric indicators of nutritional status.’ _Bulletin of the World Health Organization_ 64(6), 929–941

- (1995) ‘An evaluation of infant growth: the use and interpretation of anthropometry in infants.’ _Bulletin of the World Health Organization_ 73(2), 165–174

- Wilkinson, R.G. (1996) _Unhealthy Societies: The Afflictions of Inequality_ (Routledge)

- World Health Organization (2002) _World Health Report 2002: Reducing risks and promoting healthy life_ (Geneva: World Health Organization)

- Yitzhaki, S., and J. Slemrod (1991) ‘Welfare dominance: An application to commodity taxation.’ _American Economic Review_ 81(3), 480–496

- Zeini, L.O., and J.B. Casterline (2002) ‘Clustering of malnutrition among Egyptian children.’ mimeo, Cairo University and Population Council

42
by particular causes vary by country, and the same is true for classification of COVID-19 deaths. The extent to which deaths are attributed to COVID-19 varies depending on the overall effectiveness of the country’s vital statistics system in generating cause of death information, the country’s guidelines for attributing deaths to COVID19, and the extent to which accurate COVID-19 diagnosis through testing and other means is available. In virtually all countries with a substantial number of COVID-19 deaths, it is likely that 13
The BOOST program was launched in 2010 as a platform for improving, standardizing and curating data collection associated with the World Bank’s Public Expenditure Reviews in countries. It tracks wellclassified and highly disaggregated budget data derived from engagements with individual countries in a country-specific manner. The BOOST database covers the period 2006 - 2018. The sample is quite unbalanced, with many earlier year observations missing. Furthermore, the available sample shrinks at increasingly fine levels of disaggregation. While aggregate public expenditure is available for over 70 countries, the number shrinks to 61 countries when looking at disaggregated expenditure for roads, and 46 countries for disaggregated expenditure on electricity. Coverage is also uneven depending on the subcategory of data considered, with greater availability of data on investments than on recurrent expenditure, and even less on subsidies. Combining BOOST data with other sources to build a wider picture of on- and off-budget expenditure further reduces the sample due to limited overlap in countries for which multiple data sources are available.

An important issue is differences between countries in the institutional coverage of the budget data provided. While 36 of the countries provide General Government data, which includes expenditure by subnational authorities, the remaining 41 countries provide only Central Government data. Examination of the General Government data shows that for most countries subnational expenditure represents no more than 6 percent of the General Government total, except in federal states. This suggests that pooling General Government and Central Government data is a defensible approximation, particularly for relatively small and unitary states as long as large federations supplying only Central Government data are excluded.<sup>4</sup> To build up a comprehensive picture of on-budget and off-budget expenditure, it is essential to combine BOOST data with other sources of information that capture development aid as well as domestic spending through off-budget vehicles. When aggregating these different sources of data some possibility of doublecounting is introduced, across some though not all of the categories. Wherever possible this issue is examined and any double-counting directly addressed through subtraction. However, due to data limitations this is
In the 2006 World Values Survey 65% and 42% of men and women agreed with the above statement respectively.

> 14As reported in the World Bank data bank, see https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS?locations=ID/.

> 15Of the wives of male respondents who worked, 58% were wage workers and 62% were employed in the formal sector. Cameron et al. (2023)) find that women who were employed in the formal sector prior to having their first child were 20 percentage points less likely to be working than other women in the year following the birth.

7
Liechtenstei|n Dec 81|<br>26 F|UNPVSR<br>7/86|||
|Luxeatourg|Mar 81|365J|UNPVSR<br>7/86 (official<br>est.)|Eurostat 1987|Eurostat 1987|
|Macao|Mar 81|242J|UNPVSR<br>7/86 (official<br>est.)|Based on USBOC<br>1985|Based on USBOC<br>1985|
|Madagascar|74-75|7604 F|Bank projection<br>from census|Bank assessment of|Bank assessment of|
||||and 1980 budget survey|1980 survey|1980 and 1984 surveys|
|malaii|Sep 87|7983 F|Bank projection from census,<br>after excluding<br>refugees|Based on 1977 census<br>Analytical<br>Report|U.N. 1988 revision (pretim.)|
|laysia|Jun 80|13183 F|Official<br>est.|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Idives|Mar 85|<br>181 F|U.N. 1988 revision<br>(prelim.)|Bank est.|Bank est.|
|Ii|Dec 76|6395 J|Bank projection from census|Bank assessment of 1960-61<br>survey and age data from<br>1976 census|Bank assessment of 1960-61<br>survey and age datafrom<br>1976 census|
|tta|Nov 67|316 F|Bank projection from<br>official<br>1982 est.|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|rtinique|Mar 82|<br>327 J|UNPVSR<br>7/86 (official<br>est.)|1980-85.<br>U.N.
persons use an “improved” toilet<sup>1</sup> at all times. A community-led total sanitation (CLTS) approach was adopted by the DWSS to motivate communities to attain ODF status. The central pillar of this approach is a behavior change communication (BCC) campaign that focuses on raising awareness and demand for health, hygiene, and sanitation; mobilizing collective action toward behavioral change; and generating peer pressure through the creation of community-based committees that monitor neighborhoods and encourage sanitary practices. The project also supported the construction of toilets in households across the state. As an incentive, Rs. 15,000 was offered to eligible beneficiaries to cover the full cost of constructing twin leach pit latrines.

It is expected that project interventions will have positive effects on people’s awareness and practice of household hygiene and sanitation, resulting in improved health and nutritional outcomes. The project promotes handwashing with soap and safe disposal of child feces, and encourages households to construct and use toilets. The use of toilets will lead to a reduction in the fecal contamination of soil and groundwater, thus improving water quality at the point of extraction and reducing intestinal worm infections. While project impacts on hygienic and sanitary practices, including toilet construction and use, are expected to be observable in the short run, positive health and nutritional outcomes will take longer to materialize.

A multiple-arm, cluster-randomized impact evaluation was designed to measure the impact of the SBMG in Punjab. This reports the results from a midline survey, focusing on the project’s short-term impacts on hygiene awareness and related practices and toilet construction and use. An end-line survey, planned for October 2020, will capture the project’s impacts on children’s health and nutritional outcomes.

This study provides evidence of the project’s success in triggering behavior change and promoting toilet construction and use. Specifically, the impact evaluation sought answers to the following research questions:

- i) Did the various project interventions influence households’ decisions to construct toilets?

- ii) Did the
��� and E��z���� , are observable, and not the entire distribution of possible future outcomes which would be required to calculate E��e<sup>�����</sup> � and E��e<sup>�����</sup> � , the last term serves as a lower bound on expected future human capital.  Naturally, given the convexity of the human capital function, a higher variance of education and health across individuals, and a higher covariance between the two, increases the gap between the lower bound and the expectation.   To keep notation simple, s�� and z�� denote the likely future values E��s���� and E��z���� that represent the expected education and health of the next generation of workers.

4 Data on under‐5 mortality are produced by the UN Child Mortality Estimates. Most of the cross‐country variation in mortality under 5 is due to cross‐country variation in under‐1 mortality rates.

22
historical distribution of drought, to quantify vulnerability, defined as high probability of falling into poverty in future periods. The analysis herein then attempts to develop the model of drought and consumption further, in particular to extend the heterogeneous impacts on different household types. The validity/generalizability of the derived functions are then assessed, to infer applicability of the derived relationships within a CAT risk modelling framework.

For the applied regression analysis representative cross‐section data for Rural Ethiopia in 2005 and 2011 is used, as also used by Hill and Porter (2014) for vulnerability analysis (Household Income Consumption and Expenditure Survey, HICES, and Welfare Monitoring Survey, WMS). The data comprise household characteristics, consumption outcomes (our variable of interest for poverty analysis) and measures of realized shocks such as household member illness, food prices, crop damage, unemployment. The analysis also incorporates the drought data as compiled by the world food program (WFP, LEAP data). This drought measure shows the expected crop losses at community level based on water adequacy specific to the crops grown in that community. A regression model is fitted to the pooled data, and several different specifications are compared. The main goal is to assess the stability of the drought impact across years, and also across “types” of household (analogous to catastrophe risk modelling e.g. of hurricane impacts on different building structures). The results show significantly different impact of the drought for households with assets, access to the PSNP, and financial access: these characteristics are seen to be mitigating the impact of the drought. However, households that suffered other crop damage experienced a heavier impact of the drought.

An attempt is then undertaken to “stress‐test” the model by using statistical methods of resampling. This involves randomly splitting the data into “training” and “testing” datasets. The training data is used to fit the model, and then the goodness of fit is tested on the holdout sample or testing 2
must state the name of their husbands when applying to register to vote. Unmarried women should indicate the names of their mothers and fathers when registering to vote. This is not required for married or unmarried men. This provision is established in India’s 1960 Registration of Electors Rules.

# **3.2.3. Voter registration procedures and documents required**

In Chile and Vietnam, the voters list is generated automatically based on a pre-existing civil registry. From a public administration point of view, this type of voter registration system enables the production of more reliable and up-to-date voters lists with higher coverage since they account for all citizens that are already registered in a civil registry database. However, such a register can be very costly to establish and maintain. Automatic registration cuts back on most procedures that a person would have to go through to register to vote. It also cuts the time needed to perform data entry and to compile voter lists (ACE 2013).

When voter registration is directly drawn from a pre-existing civil registry, citizens only need to go through one procedure when they are registered in the public civil registry for the first time. In Chile, this happens when an individual is registered at birth and obtains his NID. Once registered at birth, the individual is

> 12 https://www.idea.int/data-tools/data/voter-turnout/compulsory-voting.

15 | P a g e
# The Lost Human Capital

Teacher Knowledge and Student Achievement in Africa<sup>*</sup> Tessa Bold<sup>a</sup> , Deon Filmer<sup>b</sup> , Ezequiel Molina<sup>c</sup> , Jakob Svensson<sup>d</sup> JEL Classification: C31; H52; I21; I24; I28; J45; O15 Keywords: Education; Student Learning; Teacher Effects; Teacher Performance; Education Policy and Planning; Public Service Delivery

> * We are grateful to the members of the SDI team and the many researchers, survey experts and enumerators who have worked on the country surveys that made this paper possible. We would like to especially thank Gayle Martin, Christophe Rockmore, Brian Stacy, and Waly Wane whose collaboration on earlier analysis of these data is gratefully acknowledged. We would like to thank Peter Fredriksson, David Strömberg and Jishnu Das for extensive discussions and suggestions. We appreciate comments from seminar participants at Stockholm School of Economics, NHH, Oxford University, UPF, University of Warwick, and the World Bank. We are grateful to the World Bank, and in particular the Service Delivery Indicators Trust Fund (funded in large part by the Hewlett Foundation) for supporting the research. We also thank the Riksbankens Jubileumsfond (RJ) and Handelsbankens forskningsstiftelser for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

aIIES, Stockholm University and CEPR, tessa.bold@iies.su.se; bThe World Bank, dfilmer@worldbank.org; cThe World Bank, molina@worldbank.org;<sup>d</sup> IIES, Stockholm University and CEPR, jakob.svensson@iies.su.se.
# **Table 2**

Investor-Only Momentum at the Fund Level

||**AllSample**|**Non-Crisis**|**Crisis**|
|---|---|---|---|
|**Investor-Only:FundLee**||||
|**LOM**|**1.99*****|**0.97*****|**3.78*****|
|T-statistic|_5.49_|**3.55**|**4.30**|
|Observations|**127**|**81**|46|
|LIM|0,54***|0.49*|**0.63****|
|T-statistic|**2.83**|**1.79**|**2.09**|
|Observations|**_115_**|**72**|43|

LOM is the point estimate for the mean lag-0 momentum trading measure. LIM is the point estimate for **the** mean lag-I momentum trading measure (measured from return over the previous month). Investor-Only: Fund Level reflects only investor redemption effects at the fund level as in equation (4). **All** t-statistics are corrected for heteroskedasticity across funds. Full sample: quarterly data from April **1993** to January **1999.** The crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the sample. The total of roughly **127** observations is **13** funds times an average of about **10** quarters of available data per
that sustains informal risk-sharing arrangements may well be more common in village-based societies than urban areas with more mobile populations.<sup>19</sup> Comparing how our tests for relative deprivation differ between urban and rural areas also offers a clue as to how the importance of relative poverty might change over time, as the economy becomes more urbanized.

In the light of the above observations, we take two approaches to testing. In both cases, our dependent variable is provided by the answers to a ‘satisfaction with life’ ( _SWL_ ) question. (The question does not refer to any specific aspect of ‘satisfaction with life’. We give the precise question used in section 3.2.) This has probably been the most widely used question for assessing overall well-being in psychology and economics.<sup>20</sup> We shall do one test for RD that is similar to the methods used in past research, except that we allow for interaction effects. This method uses a standard objective measure of economic welfare based on consumption, including that of the geographic area in which the respondent lives. However, our data and methods allow us to take a different approach that avoids the need to pre-judge the comparison group and the precise welfare metric. For this purpose, questions were added to the Malawi survey on the respondent’s assessments of the economic welfare of the family’s ‘friends’ and ‘neighbors.’ This has the advantage that it is tailor-made to the perceptions of the respondent. However, as we also discuss below, that advantage comes with the drawback that there may be latent variables that jointly influence both the respondent’s overall satisfaction with life and how she perceives the welfare of friends and neighbors.

While friends and neighbors are obvious groups for both social comparisons and as generators of positive externalities such as through risk-sharing, there may be other groups that matter. Finding that people do not feel RD with respect to their friends and neighbors does not

> 19 For supportive evidence