rafmacalaba/data-use-mentions-extended huggingface dataset:
.6|0.0***|
|High school|0.1|0.1|0.0***|0.5|0.4|0.0***|
|Share classes in school with full-time day (7 or more hours)|0.0|0.0|0.0|0.01|0.0|0.0|
|Total duration of school day (hours)|4.4|4.4|-0.0|4.46|4.4|0.0*|
|N|2,098|23,276||1,976|18,037||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics in 2009 for the schools that joined _Mais Educação_ in 2010 and remained until 2011 (columns (1) and (4)) and for nonparticipating schools (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 5 annual losses (3) Constant relative flood risk -- maintain relative average annual losses (4) Risk intolerance -- keep relative average annual losses below 0.01% percent of local (5) Cost-benefit analysis -- compute the optimum protection level balancing flood damage to assets with protection costs to 2100, including capital and maintenance costs

# **3.1 The DIVA Flood Module**

The methods for assessing global coastal flood risk are taken from Hinkel et al (2014). The impact of coastal extreme events is calculated with the DIVA flooding module (Figure 1) and the key data sets that are defined in Table 3. Impacts are expressed in terms of the mathematical expectation of flood damages under a given protection level for the 12,148 coastline segments defined in the DINAS-COAST database (Vafeidis et al., 2008) which describe the world’s coasts with approximately 100 parameters per segment. Population exposure is obtained by overlaying the elevation data with population data (Table 3). Exposed population is translated into exposed assets by applying sub-national GDP per capita rates to the population data, followed by applying an assets-to-GDP ratio of 2.8. Present extreme water level distributions are assumed to be uniformly displaced upwards with relative sea-level rise, following 20th century observations (see Church et al., 2013). In addition to climate-induced sea-level rise, relative sea-level rise includes glacial isostatic adjustment and deltaic subsidence (Table 3). Current protection levels are taken from Sadoff et al. (2015) (see Table 4), who took protection levels for the biggest 136 coastal cities from Hallegatte et al. (2013) and complemented these through expert judgement for segments not associated to one of these cities. Protection level zero is assumed if the population density in the 1-in-100-years floodplain is lower than 30 people per km2.

For residual damage and population living in the 100-year flood plain, the methods of Hinkel et al (2014
4. Summary of descriptive results on the scale and type of disaster losses Firms incur a wide range of losses due to disasters, both in terms of direct damages and indirectly transmitted costs. Based on the data collected for this study, Appendix B offers a full discussion of the scale and type of firms’ disaster losses.

The survey data reveals that flood risks are high throughout most of Tanzania and confirm that firms face substantial recurring losses. Indeed, 47.7 percent of firms state that they perceive at least moderate risk of on-site flooding, and 26.8 percent perceive the risk to be high or very high. Firms also recognize potential indirect effects on their operations—for example, due to supply chain or infrastructure disruptions. Indeed, 64.2 percent of firms perceive at least moderate risk of indirect risks; 33.6 percent perceive the risk to be high or very high. The survey also confirms that floods are the predominant natural hazard to Tanzanian firms: 16.6 percent of all firms reported having experienced flooding on their premises (21.4 percent in Dar es Salaam).

Self-reported data from this survey suggest that firms in Dar es Salaam, Dodoma, and Tanga incurred at least $7.6 million in direct losses and damages due to flooding on business premises in 2018. This estimate is a lower bound estimate for several reasons: First, losses incurred by informal firms are not included, as they were not sampled for data collection. Second, firms only reported losses for the year of their worst disaster experience. For 58.6 percent of firms the worst flood experience was in 2018 and hence data for these firms are observable and included in the estimate. For the other 41.4 percent of firms’ losses in 2018 are not observable and not included in the estimate. Third, the estimate refers to a single event (e.g. the major April 2018 floods), rather than annualized losses.

For the average firm in the three study areas, direct
Table 17: Medium-term impacts of _Mais Educação_ on 2007 Portuguese test scores

|High mathematics<br>test scores in 2007|Medium<br>mathematics test<br>scores in 2007|Low mathematics<br>test scores in 2007|
|---|---|---|
|Dependent variable|||
|(1)|(2)|(3)|
|Panel A: Impacts on fifth-grade|variables||
|Dummy_Mais Educação_* year 2011<br>–2.062|–4.549*|–3.951*|
|(2.938)|(2.337)|(2.280)|
|_R_ <sup>2</sup><br>0.784|0.788|0.800|
|Observations<br>612|740|766|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|
|Panel B: Impacts on ninth-grad|e variables||
|Dummy_Mais Educação_* year 2011<br>–1.819|–2.921|–4.629*|
|(3.957)|(2.909)|(2.460)|
|_R_ <sup>2</sup><br>0.625|0.734|0.791|
|Observations<br>584|656|670|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their mathematics achievement in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1
~~�~~ /�g� � δ � where g� is average output growth and δ the depreciation rate.

> 16 Investment‐to‐GDP and GDP growth are averages of WDI and WEO data (1981‐2007 and 1981‐2010 respectively).

> 17 Our calibrated value is also closer to what the calculated K/Y would be based on MFMod data (2.498).

> 18 Based on data from the IMF database for K�/K in 2010, and World Bank income and region classifications for the same year. Oil‐based economies are defined as those with fuel exports at least 30% of total merchandise exports (using WDI data).

9
outcomes, it also studies the ASP’s indirect effects on nonenrolled children and investigates emotional regulation as a plausible mechanism to explain the direct effects of the intervention. To address these questions, we rely on the first stage of Dinarte’s (2018) experimental setting.<sup>16</sup> The randomization process and sample distribution of this first stage are shown in Figure 1, panel A, which shows there are two groups in this study. The first sample of “enrolled” children comprises the 1,056 students who applied to participate in the program and then were randomly assigned to treatment (T) or control (C) groups. The second sample of “nonenrolled” students consists of 1,364 children who were not interested in the ASP. Using available administrative data for both groups, we are able to compare enrolled and nonenrolled children’s characteristics. We return to this later.

Students from the “enrolled” sample were the unit of randomization and were randomly assigned to either C (25%) or T (75%) within school-by-educational-level “blocks,” as shown in Figure 1, panel B. Each education level consists of three years of schooling: the first is from first to third grades, the second from fourth to sixth grades, and the third from seventh to ninth grades.

This uneven distribution between both T and C groups addresses the need to simultaneously reduce the number of children left out of the program during 2016 in light of the high out-of-school risk these children face, but it guarantees there is enough power to capture the effects (Dinarte, 2018).

Children in T group could participate in the two sections of the ASP. Students in C group were supposed to leave school facilities after classes ended. We were able to collect their information at follow-up because we gave them an “enrollment coupon” redeemable the next year that guaranteed their participation in the traditional clubs in 2017.

Since enrolled students were assigned at the school-by-education-level block, it is important
before, the larger its perceived drop down the social ladder. This pattern may be explained by the fact that those at the top have more to lose, as those at the bottom cannot fall much in relative terms. However, it is also consistent with preliminary evidence based on household surveys (Sulla, 2011), which suggests that consumption during the crisis increased slightly in many transition countries for the poorest ten percent of households and fell most for the richer households (and that inequality fell slightly). In summary, although poor respondents

> 11 Most respondents believe that they are in the middle, regardless of their objective consumption level. The median position on the ladder is between step 4 (lower-consumption group) and step 5 (higher-consumption group).

> 12 See Bourdieu, 1979.

> 13 See Easterlin (1995). Kahneman et al (2006) argue that the correlation between satisfaction with life and income results from a "focusing illusion" because the life evaluation question draws “people's attention to their relative standing in the distribution of material well-being”.

17
)||(0.0500)|(0.0526)|(0.0943)||(0.0568)|(0.0600)|(0.107)|
|IDN exports per worker *||||0.488||||0.00255||||0.0601|
|Share of hhlds with computer||||(0.753)||||(0.354)||||(0.411)|
|Observations|165|165|165|165|165|165|165|165|165|165|165|165|
|R-squared|0.973|0.975|0.974|0.974|0.900|0.900|0.901|0.901|0.981|0.981|0.981|0.981|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

Table 25. Indonesian Exports, ICT use, and Aggregate Labor Market Indicators. Female workers. 2011-2015

|Dependent variable:|La|bor force p|articipation r|ate||Employm|ent rate|||Unemplo
, monetary compensation schemes contributed to a large extent to protect household incomes (Brewer and Tasseva 2021, Figari and Fiorio 2020, Bruckmeier et al. 2021, Kyyrä et al. 2021, Christl et al. 2022b). In the context of developing countries, the picture is more mixed. In some countries, no emergency policies were introduced to mitigate the impact of the pandemic (e.g., Mexico as shown by Huesca et al. 2021), whereas other governments implemented generous social protection programs which reduced inequalities (e.g., Brazil as shown by Lustig et al. 2021). A common trait across among many low and middleincome countries is the little effect of automatic stabilizers. In particular, due to the design of social assistance programs as proxy means-tested benefits, no automatic stabilization is provided at the bottom of the distribution in these countries. In this context, automatic stabilizers act only at the top of the distribution due to the effect of social insurance contributions and income tax (Lastunen et al. 2021, Rodriguez et al. 2022).

# **3 Data and methodology**

The analysis makes us of EUROMOD I4.0, the most recent version of the European tax-benefit microsimulation model, to evaluate the effectiveness of the current system in providing support as well as to simulate counterfactual scenarios excluding COVID-19-specific policies.

## **3.1 EUROMOD and the underlying micro-data**

EUROMOD combines detailed country-specific policy rules with representative household survey data to simulate direct taxes, social insurance contributions and cash benefits for the household 5
relationship between family and school, and child health. We were mostly interested in symptoms directly or indirectly related to poor handwashing habits. We asked about diarrhea and ALRI prevalence in the last 48 hours and in the last week. Both illnesses are mainly transmitted through the hands—that is, the hands constitute disease vectors carrying respiratory microorganisms and fecal material.<sup>27</sup> We also performed anthropometric measurements of children following standard international procedures, because the synergistic relationship between malnutrition and infection is usually exacerbated in diarrheal episodes given that children tend to eat less during episodes and their ability to absorb nutrients is reduced.<sup>28</sup> These measurements helped us detect the children’s nutritional situation.

A more detailed assessment of the presence of intestinal parasitic infestations in children was achieved by the laboratory analysis of children’s stools. Stool samples<sup>29</sup> were also collected during the interviews in duplicates and sent to Lima for parasitical analysis, using quantitative eggs per gram estimates of soil transmitted helminthes, quantified using Kato-Katz microscopy tests. We registered the prevalence of parasites and the parasite count in stool samples of children under five years of age.

Parasitic infestations pose serious threats to young children’s health because the associated diarrhea and micronutrient malnutrition often leads to iron-deficiency anemia.<sup>30</sup> We therefore also measured anemia in children between six months and five years of age. Health specialists analyzed hemoglobin concentrations from the children’s capillary blood using the HemoCue Hb 201 photometer, a portable device that allows for immediate and reliable quantitative results.

> 27 Hendley et al. 1973; WHO 2003.

> 28 WHO 2003.

> 29 Stool samples were collected only for children under five years of age in the treatment and control groups of the school treatment.

> 30 See, for example, Hesham et al. 2004.

15
Table 6: Parental Stimulation & Home Play

||(1)<br>Parental<br>Stimulation<br>(index)|(2)<br>Home Play<br>(index)|
|---|---|---|
|Treatment|0.171<sup>_∗∗∗_</sup>|0.077<sup>_∗_</sup>|
||(0.054)|(0.045)|
||[0.006]|[0.096]|
|Observations|3760|3763|
|Control mean|0.000|0.000|

_Notes:_ *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.10. OLS estimates at the target child-level are presented using outcomes reported by the caregiver at endline. Standard errors clustered at the community level are shown in parentheses. All regressions include district fixed effects. The parental stimulation index in column (1) was constructed using 12 questions that asked parents about activities they did with the target child, including reading books, singing songs, telling stories, and playing games. The home play index in column (2) was constructed using 7 questions about items that the target child plays with at home, including homemade or store-bought toys and musical instruments. These indices were constructed following Kling et al. (2007). Westfall and Young (1993) _p_ -values are shown in square brackets, where 10,000 replications were used. Estimates are weighted to give each community an equal weight in the analysis. Covariates are not included in the regressions.

# **6.4 Parental Time Investments**

We explore the impacts of the DICIPE program on other outcomes related to parental time investments, motivated by the large bodies of work that highlight the role of parental investments in complementing early childhood interventions (Cunha and
The previous three studies were focused on the e¤ects of private pensions on personal saving but none of them considered the e¤ects of private pensions on aggregate private saving. Feldstein (1978) remarked that the net e¤ect of private pensions on private saving depended on three key issues: how do employees covered by private pensions alter their ordinary saving, the amount of anticipated pension bene...ts that ...rms e¤ectively fund and the response of shareholders to unfunded liabilities of their ...rms.

Ideally one would like to measure these three responses separately. However, data limitations only allow an estimate of the aggregate response. Using aggregate annual time series data for the U.S. and a sample with almost 40 observations, he obtained the following results (standard errors in parenthesis): where SPRIV denotes per capita real private saving, Y D is per capita real disposable income, W is the per capita real value of wealth, SSW is social security wealth, RE is retained earnings of the ...rms and SPEN is pension saving.<sup>15</sup> Given that the coe¢ cient of the pension saving variable is smaller than the standard error, the hypothesis that private saving is not a¤ected cannot be rejected. One possible rationalization of this result is that employees

> 15Pension saving is measured as the increase in the book value of pension fund reserves during the calendar year. These pension values are also adjusted to real dollars with consumer price index and converted to per capita amounts.

20
comparison LGAs, drawing on three sources of information:

1. Propensity score matching exercise. The matching process involved a "5-Nearest Neighbors" matching exercise based on population size in 2011, total revenues in 2011, revenue growth between 2007 and 2011, area in sq km in 2012, and population density. The results of this exercise are reported below in Table A1 and Figure A1, which report the coefficients of the probit regression used to estimate the propensity scores and the distribution of the estimated propensity score, respectively. As shown in Figure A1, common support (i.e., finding untreated LGAs with values of the propensity score similar to that of treated LGAs) is very thin for higher values of the propensity score and some treated LGAs are off support (i.e., the estimation cannot identify a comparison LGA that is close enough, in terms of values of propensity score, to the treated LGAs).

2. Survey of treatment LGAs. A short survey was sent to representatives of local offices of PO-RALG in all 18 treatment LGAs. The survey asked representatives to identify the most similar LGA in their own region/zone (compared to their own), the most similar LGA outside of their region, and then to rank the 3 most similar LGAs within own region/zone.

3. Suggestions of potential comparison LGAs from the PO-RALG central team based on their knowledge Based on the information above, we prepared 2 lists for each program LGA:

1. Survey list: The three most similar LGAs as reported in the survey: (a) most similar within region, (b) most similar by rank beyond the LGA listed in (a), and (c) the most similar LGA outside of the region. If a similar LGA outside of the region was not provided, we replaced it with the next most similar within the region. If any of these listed LGAs was actually in the program, then we moved down the list to the next most similar not in the program
Policy Research Working Paper 6354

# **Abstract**

This study investigates the impact of key business environment indicators on productivity, innovation, and growth in Sri Lanka through a cluster-level productivity analysis, a firm-level total factor productivity analysis, and a firm-level innovation analysis. For the clusterlevel productivity analysis (as measured by output and value added per worker), it combines two established data sources in a novel way by importing average ‘industry-size-location’ cluster-level business environment variables from the World Bank Enterprise Survey to the comprehensive Sri Lanka Census of Industry productivity data available for similar clusters of enterprises. For the firm-level total factor productivity analysis, it compares data from the 2011 World Bank Enterprise Survey with those from 2004. For the firm-level innovation analysis, it compares findings from the 2011 World Bank Enterprise Survey with a representative sample of enterprises collected as part of the Sri Lanka Longitudinal Survey of Enterprises. The empirical findings highlight the importance—for cluster-level productivity, firmlevel total factor productivity, and innovation—of connectivity to global knowledge (reflected by one or more of export participation, directly imported inputs, foreign ownership, and use of the internet), availability of skills, access to finance, and competition. The paper also presents evidence, under the assumption that the samples are statistically representative, that both allocative and average technical efficiency have improved, with allocative efficiency increasing roughly four-fold between 2003 and 2010, and accounting for the overwhelming share of the aggregate increase in total factor productivity over this time period. Most of the improvement in allocative efficiency has occurred among larger firms, and in large rather than small cities.

This paper is a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network. 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:// econ.worldbank.org. The author may be contacted at mdutz@worldbank.
14 developing countries reduces the adverse effect on net farm income of own-country agricultural reform (and conversely for non-agricultural liberalization). This illustrates yet again the interdependencies of policies of different countries in our globalized world, and underscores the importance of addressing these distortions in a multilateral forum as provided for by the WTO.

The tariffs reported in Table 1 show import protection in South Asia and SubSaharan Africa to be half as large again in agriculture and food processing as in other manufacturing. Yet careful studies that compare domestic and border prices for some of those countries have found very little actual protection delivered to farmers from import barriers (e.g., Mullen, Orden and Gulati 2005; Martin and Wang 2004), suggesting there may be ‘water’ in the agricultural tariffs that are included in the GTAP database. What would be the impact of trade reform on net farm incomes if the delivered protection rates to agriculture were in fact zero?

To see how much difference that could make to the results, we re-calibrated the model assuming agricultural tariff protection in 2001 was actually zero in Bangladesh, India, and Sub-Saharan Africa other than South Africa, and then re-ran the full global liberalization simulation. The difference this makes to the net farm income results for those countries, shown in the lower part of Table 6, suggests that the earlier small gain to Sub-Saharan African farmers from global liberalization would nearly quadruple, and the small losses to farmers in Bangladesh and India would switch to a gain of around 4 percent. These simulations underscore the point that the results depend heavily on owncountry agricultural protection rates, which in the current GTAP database are assumed to be fully reflected in the applied import tariff rates and a few producer subsidies in the