rafmacalaba/datause-encoder-data huggingface dataset:
Table 16: Impact of 3G/4G access on poverty status ($1.90 poverty line) by group (fixed effects models)

||Female|Headed|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||No|Yes|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.019|-0.031|-0.020|-0.017|-0.032|-0.022|
||(0.018)|(0.027)|(0.024)|(0.016)|(0.020)|(0.024)|
|Medium or strong 3G/4G|-0.050***|-0.003|-0.065***|-0.036**|-0.030*|-0.036|
|for one year|(0.017)|(0.024)|(0.024)|(0.016)|(0.018)|(0.025)|
|Medium or strong 3G/4G|-0.077***|-0.018|-0.117***|-0.086***|-0.051***|-0.036|
|for two years or more|(0.017)|(0.024)|(0.024)|(0.016)|(0.017)|(0.029)|
|Medium or strong 3G/4G|-0.076***|-0.003|-0.125***|-0.090***|-0.031|-0.020|
|for three years or more|(0.018)|(0.027)|(0.026)|(0.016)|(0.020)|(0.031)|
|# of observations|10,772|2,959|7,246|6,297|6,747|6,985|
||Age House|hold Head|Localit|yType|Educ|ation|
|||50 or|||Primary|Less than|
||Under 50|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.009|-0.030|-0.028|-0.011|-0.028|-0.010|
||(0.021)|(0.021)|(0.021)|(0.027)|(0.018)|(0.042)|
|Medium or strong 3G/4G|-0.058***|-0.015|-0.052**|-0.017|-0.059***|-0.010|
|<br>for one year|(0.022)|(0.019)|(0.021)|(0.021)|(0.017)|(0.039)|
|Medium or strong 3G/4G|-0.095***|-0.028|-0.080***|-0.034*|-0.087***|-0.009|
|for two years or more|(0.021)|(0.019)|(0.021)|(0.020)|(0.017)|(0.037)|
|Medium or strong 3G/4G|-0.093***|-0.027|-0.103***|0.006|-0.084***|-0.028|
|for three years or more|(0.021)|(0.021)|(0.024)|(0.022)|(0.018)|(0.040)|
|# of observations|6,738|6,574|9,442|4,290|8,645|2,076|
||Lite|racy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.026|-0.030|-0.022|-0.020|||
||(0.029)|(0.019)|(0.020)|(0.022)|||
|Medium or strong 3G/4G|<br>-0.048*|<br>-0.045**|<br>-0.025|<br>-0.053**|||
|<br>for one year|(0.028)|(0.018)|(0.017)|(0.022)|||
|<br>Medium or strong 3G/4G|<br>-0.058**|<br>-0.068***|<br>-0.032*|<br>-0.095***|||
|<br>for two years or more|(0.028)|(0.018)|(0.016)|(0.023)|||
|<br>Medium or strong 3G/4G|<br>-0.060**|<br>-0.068***|<br>-0.032*|<br>-0.093***|||
|<br>for three years or more|(0.030)|(0.018)|(0.018)|(0.023)|||
|# of observations|4,976|8,587|6,423|7,309|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

25
- S. Rao, J. Emmerling, K. Ebi, T. Hasegawa, P. Havlik, F. Humpen ̈oder, L. A. Da Silva, S. Smith, E. Stehfest, V. Bosetti, J. Eom, D. Gernaat, T. Masui, J. Rogelj, J. Strefler, L. Drouet, V. Krey, G. Luderer, M. Harmsen, K. Takahashi, L. Baumstark, J. C. Doelman, M. Kainuma, Z. Klimont, G. Marangoni, H. Lotze-Campen, M. Obersteiner, A. Tabeau, and M. Tavoni. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. _Global Environmental Change_ , 42:153–168, Jan. 2017. ISSN 09593780. doi: 10.1016/j.gloenvcha.2016. 05.009. URL `https://linkinghub.elsevier.com/retrieve/pii/S0959378016300681` .

- S. C. Sherwood, M. J. Webb, J. D. Annan, K. C. Armour, P. M. Forster, J. C. Hargreaves, G. Hegerl, S. A. Klein, K. D. Marvel, E. J. Rohling, M. Watanabe, T. Andrews, P. Braconnot, C. S. Bretherton, G. L. Foster, Z. Hausfather, A. S. Heydt, R. Knutti, T. Mauritsen, J. R. Norris, C. Proistosescu, M. Rugenstein, G. A. Schmidt, K. B. Tokarska, and M. D. Zelinka. An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence. _Reviews of Geophysics_ , 58(4), Dec. 2020. ISSN 87551209, 1944-9208. doi: 10.1029/2019RG000678. URL `https://onlinelibrary.wiley.com/doi/10. 1029/2019RG000678` .

- C. J. Smith, P. M. Forster, M. Allen, N. Leach, R. J. Millar, G. A. Passerello, and L. A. Regayre. FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. _Geoscientific Model Development_ , 11(6):2273–2297, June 2018. ISSN 1991-9603. doi: 10.5194/gmd-11-2273-2018. URL `https://gmd.copernicus.org/articles/11/2273/2018/` .

- United Nations Framework Convention on Climate Change. _Adoption of the Paris Agreement. I: Proposal by the President_ . United Nations Office, Geneva, 2015.

- A. Vogt-Schilb, G. Meunier, and S. Hallegatte. When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment. _Journal of Environmental Economics and Management_ , 88:210–233, Mar. 2018. ISSN 00950696. doi: 10.1016/j.jeem. 2017.12.001. URL `https://linkinghub.elsevier.com/retrieve/pii/S0095069617308392` .

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approximately one and a half city blocks and there were no roads and few other reference points illustrated. Nevertheless, over 30 percent of the re-engineered locations fell within 25 meters of the actual residence where a death occurred.<sup>19</sup> In several cases both the re-engineered location and the field verified residence where death occurred were for the same house. The authors scatter a series of random coordinates throughout the study area to show that chance alone would not give the same level of discovery. They conclude that: “[t]he fact that many of the reengineered coordinates could be used to identify an actual address, or an address within the immediate vicinity, should sound a note of caution for academics publishing maps displaying human cases as points” (p.53).

Typical approaches for maintaining confidentiality with GPS data are to limit access to approved researchers who promise not to identify respondents, to convert point data to either surfaces or distances so that individual locations are not revealed, to aggregate and report data only for larger areas, and to use some geographical masking procedure. These masking procedures add either stochastic or deterministic noise to the geographic coordinates for sampled households and communities. Many surveys use a combination of these four approaches.

Stricter approval procedures for obtaining geo-referenced data than for ordinary household survey data are common. For example, researchers have to provide additional justifications and commitments before they can obtain (masked) GPS data on community locations in DHS surveys. An even more stringent approach is to use a data enclave, which is typically located within a survey organization and accredited researchers come to the enclave to run their analysis. All output is checked for disclosure risk before release and even in this monitored environment there are typically restrictions on the linking of datasets and on the identity or location of individual respondents. Researchers often have to pay entry fees to these data enclaves and the limited number of enclave locations may act as a barrier to research. Remote (or virtual) data enclaves are being explored in some countries to overcome these problems. With a remote enclave a researcher can access the data server where the confidential data are stored, carry out the desired analysis and obtain aggregated results (e.g. choropleth maps or regression coefficients). Various rules on minimum cell sizes or the size of spatial units to be mapped can be imposed so that no individual-identifiable details are provided to the researcher.

> 19 The validation for the actual location where deaths occurred came from the search and rescue markings on dwellings, which were recorded during a field survey.

- 23 -
WHO-II (moderately hazardous) and 258 metric tons of WHO-III (slightly hazardous) active ingredients in the storage sites.<sup>7,8</sup> The currently-available information in Tunisia is sufficient to compute the index in (13) for the population and per-capita risk factors. A further avenue for future work could incorporate several of the other parameters such as per-capita income for governorates and poverty weights, as well as unit cleanup costs for pesticides with different hazard ratings and protective container conditions.

Population information was downloaded from the 2004 Demographic Census of Tunisia9 and two vulnerable population classes were constructed - children under the age of 5 and women of childbearing age 20-49. Land area was computed at the Delegation level from the GIS databases of the ANGed.

Table 1 provides distribution statistics for all the relevant variables in our model. It is clear that the distributions of all variables are highly skewed. Population and area follow their standard skewed (rank-size) distribution, while the pesticide variables display even more skewness. The two riskiest pesticide classes (WHO-Ia and Ib) are not stockpiled in 75% of the delegations, and large volumes only appear in delegations above the 90<sup>th</sup> percentile. Similarly-skewed patterns are evident for the other pesticide classes and for the three classes of container security, the latter indicating that containers are intact at most stockpile sites.

[Insert Table 1]

> 7 The remaining 26 metric tons were classified under WHO-Table 5 - unlikely to pose a health hazard; 1 metric ton under WHO - other, and 196 metric tons under WHO - not classified, respectively. 8 Although detailed data for each storage site was used for computation in this paper, the information is sensitive and classified by the government, hence could not be included in this paper. 9  Institut National de la Statistique.

9
# **5. Results**

## **5.1 Summary Statistics and Balance**

We start by providing an overview of the study sample and examining balance across the treatment and control groups in table 3. Panel A of Table 3 focuses on demographic information on jobseekers from the FEI database. Overall, in line with the objectives of the program, nearly 80 percent of beneficiaries were women, more than half were above the age of 30, more than 70 percent had a secondary school education or less, and 43 percent had been unemployed for over a year. As can be seen from the last column of this table, there are no differences in the average characteristics across control and treatment groups.

Panel B examines jobseeker characteristics collected through the baseline survey. Roughly half the sample is married, and the average household size is 3.36. About 60 percent of beneficiaries live in households with monthly income below 900 KM. For comparison, the average household monthly consumption in the country in 2015/16 was 1,419 KM and in 2021/22 was 1,778 showing that program beneficiaries came from relatively poor households. Of the beneficiaries 29 percent had some vocational training. Once again, there are no significant differences between the control and treatment groups for any of these characteristics. Finally, the baseline survey included a standard battery of questions to characterize each of the jobseeker’s “Big 5” personality traits - openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. The table reports standardized averages of each of these traits and again the treatment and control groups are (on average) identical. Panel C shows the distribution of the job counselor’s assessment of applicant employability. Of the jobseekers, 78 percent are categorized as “ _employable_ ”, whereas 11 percent each are categorized as “ _highly employable_ ” and “ _less employable_ ”. Again, these distributions are identical across the treatment and control arms.

## **5.2 Differences in Services Provided to Treatment and Control Groups**

Next, we establish that the experimental design was adhered to by looking at differences in services provided across the treatment and control groups. By design, all jobseekers should have had access to similar counseling services, and as can be seen in Table 4 this was indeed the case with their being no differences between the treatment and control groups **.** Profiling was completed and IAP’s developed for more than 96 percent of our sample (columns 1 and 2). Column 3 shows that there were no differences in counseling services received across the two groups.

12
Focusing on residential properties only, in 2018, assessed values spanned from 1,800 AR$ to _≈_ 4 million AR$ in 2018 based on the municipality estimates. The median assessed value falls at _≈_ 759,000 AR$, while the mean residential property assessed value is 979,000 AR$, as shown in Figure A.2. When examining residential property ownership patterns across genders, as illustrated in Figure 2, similarities emerge in the ownership distribution up to the 25th percentile of property values, with women, men, and jointly-owned properties encompassing a range of 30%-40%. However, as property values ascend beyond the 50th percentile, a noticeable trend unfolds: women’s total ownership percentage begins to diminish, dwindling to around 20% of total properties at the highest decile, and further declining to 15% at the 99th percentile. In contrast, men maintain a relatively stable ownership share, hovering between 30% and 35% of total properties at the upper levels of property value distribution. Concurrently, jointly-owned properties experience a surge from the 50th percentile onwards, representing approximately 55% of all properties at the top of assessed values.

Figure A.2: Assessed values for residential properties, 2018 <!-- Start of picture text --> p25 p50 p75<br>0 1,000,000 2,000,000 3,000,000 4,000,00<br>Assessed value, 2018<br>10,000<br>8,000<br>6,000<br>Frequency<br>4,000<br>2,000<br>0<br><!-- End of picture text --> _Note:_ This figure presents the histogram with the assessed value of residential properties for 2018 in _Tres de Febrero_ . These values are provided by the provincial tax authority (ARBA). The vertical red lines show the 25th, 50th, and 75th percentiles, respectively. In the Y-axis we report the number of properties, while X-axis corresponds to Argentinian Pesos (AR$). The distribution was trimmed at the 99th percentile for visualization purposes.

A-2
Increased pressure on government budgets in recent years has led to greater interest in issues of cost and effectiveness of government expenditure, and this has been true in the social sectors as in other areas. There has been a relatively large amount of work already done on the cost and effectiveness of education, health and social security expenditures, but relatively less attenticn has been paid to government expenditures on nutrition programs as distinct from health. This paper tries to sumrnarize what is known in the existing literature on unit costs, cost structire, cost-effectiveness and financing of nutrition programs. The paper tries to cover 8 different types of nutrition interventions (MCH feeding programs, school feeding programs, nutrition education, breastfeeding promotion, targeted food subsidies, micronutrient supplementation, micronutrient fortification and growth monitoring). Insufficient information was obtained to assess several other inter. entions (comprehensive early childhood interventions, home gardens and food safety and quality control).

Since a large number of interventions are covered, the paper relies on existing surveys of the costs of individual types of interventions. However in view of the paucity of available literature, there is some new analysis of project level data, either obtained from existing compilations, or directly from project documents (in particular World Food Program - WFP - project proposals). Suggestions are also made as to where further cost data might be obtained, particularly for those types of interventions where existing studies are most scarce. As regards financing, there are eoually very few studies. Previous studies containing data on government expenditure on nutrition interventions were found only for Brazil and India (in general, expenditures on nutrition are not presented separately from those on health). The present study therefore presents results from three country case studies, on Chile, Philippines and Malawi, to give some idea of the range of financing levels and sources in three countries of different levels of per capita income.

Section I of the paper discusses some of the methodological issues on costs, section II presents unit cost figures, section III deals with cost structure (in particular the share of food costs in total
pronounced than for final products. We find no evidence that firms re-shored or nearshored production and increased import diversification to mitigate risk. They also did not switch to other top suppliers. Rather firms replaced suppliers from Japan with suppliers from developing countries and larger countries where scale economies could be realized. We also find that in the years following the shock, production relocation decisions were largely driven by fundamentals rather than policy.

While COVID-19 is a global shock unlike any other in recent times,<sup>19</sup> the evidence on the long-term consequences of the 2011 earthquake may help understand if and how firms may reset their supply chains after a large shock. Our results—in line with intuition by Antràs (2021) and Baldwin (2020)—suggest that the reshaping of GVCs in the post-COVID-19 world may be less pronounced than expected by many observers. A major difference between the current shock and the one studied here is that physical capital has not been destroyed. Capital in place coupled with established relationships will tend to reduce the extent of reconfiguration as compared to the Japan shock. Some producers dependent on risky suppliers may reassess their location decisions in the aftermath of the pandemic--but this is likely to be gradual, given capital in place. Sectors and stages of production where search frictions are lower may continue to relocate where economic fundamentals are more attractive, creating opportunities for developing countries. But broader trends towards reshoring, nearshoring or diversification are unlikely to result from firms’ decisions to reconsider the balance between efficiency and resilience in light of COVID-19, unless supported by pronounced government intervention.

> 19 See Espitia et al. (2021)  for an analysis of the trade impact of COVID-19 during the first six months of the pandemic.

20
its citations from journals and books that specialize more on development and also tend to be more policy oriented. The RePEc h-index at the institutional level is author-based: an h-index of 20 on RePEc’s institutional ranking means that 20 individuals in the institution have an h-index of at least 20, based on citations of the individual’s RePEc-processed documents by other documents processed by RePEc. The Bank ranks 18<sup>th</sup> out of 242 on this metric, with a score of 12 (Table 9). The RePEc/IDEAS database also provides rankings on the number of abstract views and the number of document downloads via RePEc. The Bank ranks first on both indicators.

The IDEAS project reports many other variants of the columns in Table 9, and also presents an overall ranking using a harmonic mean of the ranks on 31 indicators. (Citations, for example, are included unadjusted but also adjusted for age, the “quality” of the citing journal, etc.) Table 10 gives the top 20 institutions in all fields of economics; the Bank comes in at number 3, with only the Departments of Economics in Harvard and Chicago ahead of the Bank. Note that the Bank also appears under “Economic Research, World Bank” in ninth place; this is a subset of those authors affiliated to the Bank who identified themselves this way; roughly speaking, these are current and former members of the Bank’s research department. Similarly other institutions appear more than once. The IDEAS project has also produced a consolidated ranking, at institutional level rather than departmental level; this allows for the fact that economists are found in more than one place in many universities (including some of the top ones). By this method the World Bank comes in at the seventh slot, with only six universities ahead of the Bank (Harvard, Chicago, Berkeley, Stanford, Columbia and NYU).

Again, it should be recalled that the Bank’s research is highly concentrated in terms of its fields. If we focus instead on research outputs mapped to the field “development” then the World Bank ranks number one, ahead of all universities. Table 11 gives the rankings corresponding to Table 10 for the field “development” in RePEc. On top of the fact that the Bank’s ranking rises, given its specialization, there are a number of other differences with Table 10. Brown University, Cambridge University, Zurich University and the International Food Policy Research Institute join the top 20; the University of California at Berkeley, Columbia, Boston and Toulouse Universities drop out when we focus of the development field in the RePEc database. A number of the top universities in economics (Harvard, MIT, Chicago, Oxford, LSE) are equally prominent in development (and probably other subfields) as they are in economics as a whole.

24
Another 95 un-cited chapters come from four collections: “An Opportunity for a Different Peru: Prosperous, Equitable, and Governable”, “Rural Development, Natural Resources and the Environment: Lessons of Experience in Eastern Europe and Central Asia”, “Proceedings of a Conference on Currency Substitution and Currency Boards”, and “Global Issues for Global Citizens: An Introduction to Key Development Challenges.” None of the 24 chapters in the rural development volume has ever been cited in the documents surveyed by Google Scholar.

# **_Comparisons with other institutions_**

Tables 7-9 compare the Bank on citation data to the same 14 universities and seven development agencies in Tables 1-4. In Table 7 we take the same sets of SSCI-indexed articles in Tables 1 and 2, and report citations of these articles by articles included in the SSCI database. In Table 8 we report two sets of citation data for the 27,613 economics articles in the SCOPUS database: first we report citations of these articles by SCOPUS-indexed articles; and then we report citations of the same 27,613 articles by documents included in Google Scholar.

We saw in Table 1 that the Bank ranks second among the universities and first among the development agencies in the number of economics articles it publishes. How does the Bank fare in terms of average citations? On the SSCI data (Table 7), the Bank ranks 11<sup>th</sup> among the universities and second among the development agencies. On the SCOPUS data (Table 8), the Bank ranks 10<sup>th</sup> among the universities and first among the development agencies. The Bank’s position improves when we take the same set of SCOPUS articles and count citations by the larger set of publications included in Google Scholar: the Bank ranks first among the development agencies and seventh among the universities.

Table 8 also reports the percentages of articles that received any citations (100 _minus_ the % un-cited). All institutions have an “undistinguished tail” of un-cited articles. These tails are longer for the development agencies than the universities: typically around 35% of articles published by multilaterals are never cited by articles indexed in SCOPUS; the figure for FAO is over 50%. At 20%, the Bank stands apart from the rest of the multilaterals. In fact, the Bank compares favorably with the universities on the SCOPUS citation data, ranking fifth. When we use the Google Scholar citation data, non-citation rates fall for all institutions, but the switch to the broader citation base improves the Bank’s relative position. On the Google Scholar data, only 11.7% of the Bank’s articles indexed in SCOPUS have never been cited; the Bank has the second-shortest “undistinguished tail” among the universities, ranking just behind Brown.

20