rafmacalaba/datause-encoder-data huggingface dataset:
increasing trend in publications, particularly since 2019. **Appendix Table 1** presents additional details of each study, including intervention components and evaluation design.

# **2.3.2 Intervention (contrast) level**

We also extracted information about the evaluated interventions, and **Table 4** summarizes the characteristics of the treatment arms of the included studies.<sup>8</sup> **Appendix Table 2** presents information about additional characteristics of the programs evaluated in each country.

The types of interventions used by studies in this review can be broadly grouped into cash transfers and food transfers. Cash is handed out manually or delivered as mobile money and/or cash vouchers. Similarly, food is distributed as an in-kind transfer (individually or through schools) or as a voucher that can be exchanged for food in shops or at organized fairs. We assigned humanitarian assistance interventions to _at least_ one of five intervention categories: (i) unconditional cash transfers (UCT), (ii) conditional cash transfers (CCT), (iii) vouchers, (iv) food transfers (FT), and (v) public works (PW). Each humanitarian assistance _program_ combines one or more of these five intervention categories, implemented mainly by international organizations (e.g., World Food Programme, UNICEF) and non-governmental organizations (e.g., Concern Worldwide, Mercy Corps, IRC, Oxfam). In fewer cases, these programs have also been implemented by governments.

We reviewed forty-two humanitarian assistance contrasts from twenty-one studies covering low- and middle-income countries. Regarding the intervention or contrast type, most studies focused on UCTs, followed by food transfers, vouchers, CCTs, and public works. There are, on average, 2.05 contrasts per study, ranging from two to four treatment arms (including the control group and studies without a pure control).<sup>9</sup> Since one of the focuses of this review is the relative effectiveness of social assistance modalities, it is important to understand the underlying characteristics of each of the five types of interventions in a humanitarian setting.

**_UCT interventions (20)._** Unconditional cash transfers are by far the most common intervention type included in our review. Twenty UCT contrasts/interventions correspond to

> 8 We identified several important impact evaluation design characteristics, such as intervention category, target population, intervention duration, sample size, recipient identity, transfer schedule, payment type (multiple or single), total payment value, and cost per beneficiary,.

> 9 Several studies have also pooled their interventions (e.g., CCT, Voucher, and FT) and compared them to the control group. These cases are not included in the intervention categories.

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<!-- Start of picture text --> Sample for pooled regressions<br>(measuring direct impacts)<br>Treatment 2: Treatment 1: Treatment 3: Pooled treatment | Control<br>Cash Grant with VSLA Cash grant<br>repayment<br>~ selecting 47% of<br>enrolled per locality | ~ selecting 53% of<br>“Vulnerability cater “shiglied perlocaliy Pct a a ae aia |<br>T2 ———————|_Vulnerabilityad cut-oMT enrolled~selecting per 69% locality of= | “Commonsupport” cut-off | simulatedEquivalent cut-off ||<br>Vulnerability cut-off |Pet locality |<br>T3<br><!-- End of picture text -->

<!-- Start of picture text --> Treatment 2: Treatment 1: Treatment 3:<br>Cash Grant with | VSLA Cash grant<br>repayment<br>~ selected 47% of<br>enrolled per ~ selected 53% of “<br>locality enrolled per Common Equivalent<br>Vuinerabilitycut. | locality ~ selected 69% of | support” cut-off simulated cut-<br>off T2 Vulnerability cut- enrolled per per locality off<br>off T1 locality 4<br>Vulnerability cut-<br>off T3<br>Pooled fg<br>treatment yyy<br>Sample for pooled regressions<br>(measuring local spillovers)<br><!-- End of picture text -->
islands”,Vava’u High School and Taufa’ahau I Pilolevu College (TPC) in Ha’apai.<sup>8</sup> Queen Salote and TPC are mission schools belonging to the Free Wesleyan Church, while the other three schools are Government-run. For each we take top scholars over the period 1976-2004, except for Vava’u High School, which only opened in 1985. This gives a target sample of 266 individuals, ranging in age from 18 to 50.

Websites of the schools provided some initial information on the names of these top students. Each of the schools were then visited, and school teachers and librarians helped to reconstruct the list, using school records and old school magazines. Finally, once some students were contacted, they were used to help verify the names of the other top students in their school at around the same time as them. Using this combination of approaches, we were able to identify the names of 245 of the 266 Duxes and Proxime Accesits.

# _Papua New Guinea_

In strong contrast to Tonga and New Zealand, PNG is very far from universal education. Net primary school enrolments are less than 60%, and net secondary school enrolments less than 20%. Historically there was a major winnowing of students first at grade 6, and then at grade 10. Grades 11 and 12 were only taught at four National High Schools, where students were given the same funding as tertiary students. As an example of this winnowing process, only 7 percent of the grade 6 cohort in 1995 went on to complete grade 12 in 2001. Education reforms in the mid-1990s introduced a fifth National High School and also allowed many provincial and religious schools to teach Grades 11 and 12. PNG has a reasonably comprehensive tertiary sector with six universities. The University of Papua New Guinea was established in 1965 and offers degree programs, including a medical school and a law school. The PNG Institute of Technology (or Unitech) was established at the same time and offers degree programs in technological and applied sciences. There is also the University of Goroka which was formerly a teacher’s college, a University of Natural Resources and the Environment also established in 1965, the Pacific Adventist University established in 1984, and the Divine Word University established in 1980.

> 8 The Vava’u and Ha’apai island groups contain most of the remaining one-third of the Tongan population and Vava’u also has the second international airport.

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deprivation-LIM makes no inclusion error and does not make the exclusion errors that the achievement-LIM makes on the areas Aex and A<sup>′</sup> ex<sup>.Hence,</sup> the deprivation-LIM identifies the poor better than the achievement-LIM.

<!-- Start of picture text --> (a) (b)<br>x2 U x2 U<br>Aex<br>z ′<br>Intex<br>z z<br>e2 e2<br>Int ′ ex A ′ ex<br>x1 x1<br>e1 e1<br><!-- End of picture text -->

**Figure 7:** The intersection-LIM identifies worse than the achievementLIM (a), which in turn identifies worse than the deprivation-LIM (b). The intersection-LIM and the achievement-LIM are based on z while the deprivation-LIM is based on z<sup>′</sup> . The identification contour of the achievement-LIM is dashed in (b).

As graphically illustrated in Figure 7, we have successively improved on the intersection-LIM by considering alternative LIMs whose identification contours are better approximations of the indifference curve U . The concave identification contour associated to an intersection-LIM does not approximate well a convex indifference curve. The linear identification contour associated to an achievement-LIM provides a better approximation, but not as good as that of the more flexible identification contour associated to a deprivation-LIM.

# **3.3 Status-LIMs versus Achievements-LIMs**

Ravallion (2011) criticizes the identification inherent to status-LIMs. He argues that status-LIMs used in practice aggregate dimensions in a way that is ad-hoc and unrelated to well-being. In particular, he observes that their weights w<sup>S</sup> are typically unrelated to the marginal rates of substitution at z. He suggests using instead achievement-LIMs with weights w<sup>A</sup> that correspond to these marginal rates of substitution. In this section, we use our theory in order to compare status-LIMs and achievement-LIMs. Our results show that Ravallion makes a valid point, but that his critique is not necessarily devastating for status-LIMs.

Under two dimensions, status-LIMs are either intersection-LIMs or unionLIMs. Proposition 1 shows that achievement-LIMs are indeed superior to intersection-LIMs. As the proof reveals, the only information that is necessary to construct an achievement-LIM that identifies the poor better than

> (z1<sup>′, z</sup> 2<sup>′) = (x1, x′</sup> 2<sup>).Theremaining threeparameters w</sup> 1<sup>D,w</sup> 2<sup>DandkDsolve thefollowing</sup> system of three equations: k<sup>D</sup> = w1<sup>D∗(x1−x′</sup> 1<sup>)/x1,kD=w</sup> 2<sup>D∗(x′</sup> 2<sup>−x2)/x′</sup> 2<sup>and</sup> w1<sup>D+ w</sup> 2<sup>D= 1.</sup>

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example, the risk of sudden fatality from an accident would be different from the risk of reduced future life span from long‐term pollutant exposure.

Unfortunately, VSL values are all but non‐existent for developing countries, so it is necessary to make rough approximations based on VSL estimates for developed countries, in particular the USA. In this analysis a VSL of Taka 15.5 million (approximately $0.2 million) has been used. This estimate for Bangladesh has been computed by updating the central estimate of VSL of USA., $7.4 million ($2006) available from the EPA with price adjustment between 2006 and 2008 and GDP differential between USA and Bangladesh<sup>29</sup> . Multiplying this figure by the expected value of the increased number of lives at‐risk from a 10 year return period cyclone under climate change, an increased economic damage of $1.03 billion from greater fatality risk has been estimated.

To calculate the economic damages from increased injury risk, a crude lower‐bound estimate based on the WHO figure for cost per outpatient visit at a secondary hospital visit in Bangladesh, $4.86 (Cropper and Sahin, 2009) has been adopted. This yields a total economic damage from increased injury risk of $0.352 million. This estimate, however, does not include any value of lost production and income from injury, or the more subjective losses of well‐being resulting from being injured or incapacitated.

The balance of this section focuses on sectors experiencing high monetary damage and loss during Sidr, 2007 – namely housing, education, agriculture, non agriculture productive sector and infrastructure: roads, power, coastal protection. (See Box 1).

# _<u>Potential damage in housing</u>_ <u>:</u>

The housing sector experienced maximum damage and loss during the Sidr cyclone in 2007 and accounting by housing/ dwelling type further indicates that “ _semi‐pucka_ ”<sup>30</sup> houses, “ _kacha_ ”<sup>31</sup> houses and “ _jhupris_ ”<sup>32</sup> were the predominant categories of damage.

> 29 GDP Deflator 2008/ GDP Deflator 2006= 1.1; Per Capita USGDP ($ 2008, PPP) = 46,716 and Per Capita Bangladesh GDP ($ 2008, PPP) = 1,334 have been used in this computation.

> 30 Housing with foundation made of earthen plinth or brick and concrete, walls made of  bamboo mats, CI sheet  and roof made of CI sheet with timber framing

> 31 Housing with foundation made of earthen plinth with bamboo, walls made of organic materials and roof thatched made of straw, split bamboo etc.
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a complex endeavor in a region that lacks long-term longitudinal data, as is the case for Latin America. Nonetheless, we use one of the few nationally representative household surveys in the region, Mexico’s ENOE (the _Encuesta Nacional de Ocupacion y Empleo_ ), which follows the same households for five quarters. We use these data to estimate multinomial models of youths’ decisions to test the predictions of the theoretical model developed by Behrman et al. (2014). Our results contribute to the improved design of policies dealing with the complex topic of at risk youth in Latin America, and will complement previous research on youth unemployment (see Cunningham et al. 2008, for example) and school drop-out (see Bassi, Busso, and Muñoz, 2013).<sup>7</sup> In Section 2, we briefly describe the ENOE data and do some basic analysis of Nini rates in Mexico. Section 3 outlines the theoretical model by Behrman et al. (2014), the framework that guides our empirical endeavor, as well as our econometric strategy. Our results are discussed in Section 4. Conclusions and suggestions for future research are in Section 5.

# **2. Nini Incidence in Mexico**

# **2.1 Data**

We use household data from the Mexican National Survey of Occupation and Employment (ENOE) for our analysis. The survey, conducted quarterly since January 2005, is administered by the National Institute of Statistics and Geography (INEGI), and is publicly accessible on the institute’s website. The total ENOE sample for a single quarter consists of approximately 120,000 households, and is representative at the national and state levels, as well as by the size of locality.

The advantage of ENOE data for our purposes is the rotating panel nature of the survey, which allows us to track education and employment status over time for the same individuals. Each

> 7 Our research builds on previous work by Cunningham and Bustos-Salvagno (2011) who study youth employment transitions in Argentina, Brazil, and Mexico. They use Mexican data for the period from 1987 to 2003 to estimate and decompose transition matrices, which allow them to characterize where young people were before getting jobs and where they go upon leaving them. They find that youth transition from school to informal sector jobs, then to formal sector jobs with longer durations of employment, and then to self-employment. Although the analysis by Cunningham and Bustos-Salvagno (2011) does include a category for Ninis (called _“out of the labor force”_ in their paper), the focus of their analysis is on the formal or informal status of youth employment. In a different paper that uses the same data, Cunningham (2009) provides a full discussion of the transitions into and out of unemployment for the same set of countries.

6
We also show how the restructuring of utilities‟ tariffs has great potential benefits for both equity and efficiency of public spending.

The tool-kit has been designed to be user friendly. All estimation commands have been programmed in Stata. This is the most popular statistical package in the World Bank and also the most popular for distributional analysis worldwide. Users can run Stata modules interactively with windows or by embedding commands in programming codes. This should allow different types of users to take full advantage of the tool-kit.

This is an initial product that is expected to be tested, improved and expanded during the next fiscal year. At present, we are expecting to conduct tests in Morocco, Tunisia and Egypt where governments are currently asking to the World Bank for support in subsidies reforms. To expand on the possible users of the tool-kit, we are planning to create an add-on package for Stata and a new module for ADePT. We will also explore how to extend the work in various directions such as the use of savings to simulate alternative household responses to price changes or the simulations of alternative reforms that include the reform of social assistance benefits. In essence, this is the first stage of a work in progress and we very much hope to receive comments and feedback from readers and users. Please send your views and comments to pverme@worldbank.org and Araar.Abdelkrim@ecn.ulaval.ca.

5
propensities, all else being equal. The implied percent increases in the propensities of firms to undertake innovation related activities are prominent given their moderate starting points in the countries studied. Furthermore, the relationship between city size and innovation propensities is found to be robust and pervasive across developing Asia. Urban agglomeration effects are heterogenous across different subsamples defined in terms of development level or subregion, but the differences seem to be secondary.

This paper’s findings also suggest that firms in cities with top universities are more likely to carry out innovation-related activities. In addition, across academic disciplines, the presence of top ranked engineering majors is especially closely linked with firm innovation.

These results have some clear policy implications. First, in light of the importance of innovation to economic growth, policymakers in developing economies should be more welcoming of the growth of larger urban agglomerations than they often are. In particular, calls for regionally balanced development and a belief that large cities have become too large can lead to policies that inadvertently undermine the potential benefits from larger urban agglomerations. Second, policies to improve the quality of universities and promote interactions between universities and local firms are likely to have high payoffs. Further, the results on the importance of high-quality engineering majors in enhancing the propensity to undertake innovation related activities is consistent with the findings of developed country studies emphasizing the role of STEM education in bolstering innovation. Overall, setting up new universities or improving the quality of existing ones may be prioritized for those larger cities without a university (or a highquality one).

The results also suggest several issues for future research. First, an effort to establish causality would be clearly worthwhile, though it would most probably require adopting a country-specific approach given the need to find natural experiments that yield plausible instrumental variables. Second, it would be useful to explore how other city characteristics, such as the quality of transport connectivity within and between cities, might affect the spatial distribution of process and product innovations. Third, there are some differences across the results for product and product innovations. Not only do they have unequal baseline probabilities, they also respond to agglomeration differently across low and lower-middle income countries and upper-middle income countries. Similarly, the analysis on the quality of different academic disciplines suggests clear differences regarding their effect on propensities to innovate. These differences could be explored. Finally, gaining in-depth understanding of the channels through which the co-location of universities and firms influences innovation in a developing country setting would be very worthwhile.

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schools, the greater the effect. In order to guarantee greater resources, the nearby public schools need to match the performance of the concession schools.

To estimate the indirect impact of the program, I use a two-step process. I first identify the baseline. Given the identification of nearby schools as discussed above, I match them based on their common characteristics with similar schools outside the influence area in 1999. As stated before, concession schools started in 1999 were fully operational by 2000. These control schools are determined using propensity scores. Second, using data for 2003, I estimate the indirect impact using those students who attended schools close to a concession school as treated individuals, and those students who attended the matched schools found in the previous step as the control group.

The baseline data can be used to control for differences in initial characteristics. However, another problem that may persist is the difference in pre-existing trends between the treated schools and the matched schools. Unfortunately, data prior to 1999 does not exist to test for this possibility. However, a priori, there is no theoretical ground to believe that there exist systematic differences in trends between schools under the influence of concessions and schools outside the area of influence of concessions.

In short, the estimation that I present below is based on matching estimators using propensity scores to determine the treatment and control groups. The estimation will present evidence of “direct impact” (dropout rates and test scores for concession versus matched non-concession public schools outside the area of influence) and “indirect impact” (dropout rates for schools near the concession against matched schools outside the area of influence of concessions).

# **4. Data and Results**

# **a. Description of the data**

The paper uses data from two sources the Ministry of Education (surveys C600 and C100) and Institute for the Development of Higher Education (Instituto Colombiano para el Fomento de la 12
# **How Do Governments Respond after**

# **Catastrophes? Natural-Disaster Shocks and the Fiscal Stance**<sup></sup>

Martin Melecky World Bank Claudio Raddatz World Bank

**Keywords:** Natural Disasters, Fiscal Sustainability, Budget Deficit, High- and Middle Income Countries, Fiscal **Keywords:** Natural Disasters, Fiscal Sustainability, Budget Deficit, High- and Middle Insurance. **JEL classification:** Income Countries, Fiscal Insurance. Q54, H50, H60, E32

>  We thank the Catastrophe Insurance project conducted by ECA FPD for financial support and to Javier Ayala for able research assistance. We also thank Eduardo Ley, John Pollner and Luis Serven for their comments.