rafmacalaba/data-use-mentions-extended huggingface dataset:
# Panel B. Mean regression

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>ALB BGR BRA COL CRI ECU IDN MEX MKD PER PHL ROU TUR ZAF<br>Bias using 2006-2009 Bias using 2010-2018<br><!-- End of picture text --> <mark>Notes: Actual 2020 employments levels come from National Statistical Offices. The exact data of actual 2020</mark> employment data is shown in Table A1 in the Appendix.

# **<mark>3. Microsimulations</mark>**

This section turns to the microsimulations themselves. We use actual employment changes taken from the ILO as an input into the microsimulation models. We use the elasticity-based estimates as a robustness check. Whenever possible, these models should use actual available employment data, but when that data is not yet available, analysts would have to use employment projections. The macro-micro simulation model applies country-specific macroeconomic projections to a behavioral model built on household survey microdata. The microsimulations are based on a household income generation model (Bourguignon and Ferreira 2005), while the macro data come from a variety of macroeconomic projections in combination with observed data for 2020 obtained from different data sources for each country. The macro-micro model allows for three main channels of transmission of the 2020 shock: through job losses, labor income changes, and nonlabor (remittance) income changes. Having estimated (or used actual) labor and remittances income changes, we predict the household per capita income or consumption value for 2020 and we calculate the shares of poor, vulnerable, and middle class in each country.

<mark>We apply this model to five countries in five regions: Brazil, the Philippines, South Africa, Sri Lanka, and Türkiye
exports might be an imperative channel either through the lens of IndiaChina, South-South Trade, Advanced-Emerging trade clusters or possible new trade networks.

As outlined above, to have a better understanding of how service sophistication affects growth, we need a better and more disaggregated data on service exports, treating them at par with data on goods exports. Considering that service exports sophistication is positively associated with economic growth, our paper set out the future research agenda. What are the determinants of sophistication of service exports and how do they differ from the determinants of good exports sophistication? What are the dynamics in the product space of different service activities? What structural, institutional and economic policies in country-specific context create value added within service exports, foster service productivity growth and export performance, i.e., what are the channels through which service export sophistication affects growth? What is the relation between local trade restrictions and global trade agreements and export of sophisticated services? What are countries doing to prevent sophistication in service exports? Is this nature of service growth inclusive?

There are many examples, but the Indian experience has shown that even when approximately 1 percent of its population is engaged in high productivity service exports, it has contributed to overall high growth. Various factors may drive competitiveness amongst countries exporting services, driving specialization and sophistication in service exports. Countries should continue to build on specialization, but also extend this idea to sophistication in service activities as a potential route to economic growth.

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they could generate an upward bias in the elasticity of earnings with respect to work city size (when observed working in big cities with average or below average wages). Taken together, these two insights explain our finding of a larger elasticity of wages with respect to college than work city.

This finding highlights the importance of our pre-labor market locational data. Administrative datasets usually follow an individual from the beginning of her work life and use repeated observations per individual to handle the inherent self-selection into the work city, with identification of (work city) agglomeration effects being driven by migrants (Combes, Duranton, and Gobillon, 2008, D’Costa and Overman, 2014, De la Roca and Puga, 2017). In contrast, our pre-labor market locational data enable us to examine the spatial trajectory leading individuals to their work city, thereby underscoring at what point of this trajectory a large city pays off the most.

# **4. Summary and conclusions**

Our study examines the dynamic sorting that leads to differences in skills across cities of different sizes, and the consequences of this pre-labor market sorting on estimated agglomeration effects. We find an elasticity of wages with respect to city population of 0.052, in line with the previous literature. In addition, we find a substantial effect on wages for college city size; this effect is much larger than that of high school or work city size. We also document substantial returns to mobility, particularly with moves to attend college in big cities.

Turning to the determinants of pre-labor market sorting, particularly with respect to the choice to move for college, we find that ability (as proxied through Saber 11, a national high school exit exam) is not a key determinant for mobility in Colombia. Instead, we find that access to college opportunities and household income are major drivers of pre-labor market sorting. Individuals from small cities also perceive a value in moving for college that goes beyond mere access to college, as those from small cities are disproportionately more likely to
technology indicator variable equal to one from January 1998 and zero otherwise. A similar regulation of emission standards for new vehicles went into effect in 2009; hence, a specification adds a technology indicator variable equal to one from January 2009 and zero otherwise.<sup>24</sup> Because roadwork was intensified during the drastic phase, a regression adding a variable with the annual total roadwork investments is estimated. The sensitivity of the estimates to the time trend and weather polynomial order is also evaluated. Time trend polynomials of sixth and seventh orders and weather quartics (as in Davis 2008) are tested. Due to the concern of collinearity among meteorological variables, which may make the estimates less precise, the model is also estimated replacing the weather covariates with their corresponding principal components. All models in this section address serial correlation.

The PYP coefficients of the moderate and drastic phases for all time slots are shown in tables 3 and 4, respectively. The results for both phases across all specifications remain largely unchanged. There is no evidence of a reduction in CO in any model or phase. As for the moderate phase, none of the coefficients is statistically significant at conventional levels. Regarding the drastic phase, four of the estimations show a positive PYP impact on CO, though the estimates are less precise, as in the main RD results. Finally, to provide a broader evaluation of PYP, equation (1) is also estimated using other pollutants (NOx, PM10, and SO2) as outcome variables. Given that these pollutants, unlike CO, are emitted from more varied sources, the estimations are conducted using only data from stations mainly influenced by traffic emissions (table S.2 in the supplemental appendix). All models show that PYP did not reduce any of those pollutants.

> 24. These technology dummies are not included in the original model due to uncertainties regarding whether these regulations were enforced.

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the unique school identifiers. There were no issues in the linking process: when a school in a given QAT was expected to be also found in another QAT, it was. Program schools in the QAT 4 database were however only identified by school name (at times augmented by location markers inserted into the school name field). Thus, schools in QAT 4 were linked to the schools in QAT 5 by visually matching on school names. There were 674 program schools that took QAT 4; 658 of them were expected to be found in QAT 5. Matching on school names yielded 625 linked schools (a success rate of 95%).

Table 3 presents the number of phase-3 and phase-4 program schools that took each of the QATs. It also presents the number of phase-3 and phase-4 schools that qualified for entry and 14
Policy Research Working Paper 0

# **Abstract**

This paper develops a framework for the quantitative analysis of individual income dynamics, mobility and welfare. Individual income is assumed to follow a stochastic process with two (unobserved) components, component representing measurement error or transitory income shocks and an Autoregressive (AR(1)) component representing persistent changes in income. The analysis uses a tractable consumption-saving model with labor income risk and incomplete markets to relate income dynamics to consumption and welfare, and derive analytical expressions for income mobility and welfare as a function of the various parameters of the underlying income process. The empirical application of the framework using data on individual incomes from Mexico provides striking results. Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 Wmaloney@worldbank.org .

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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
# **Tweet Classification - Identifying relevant crowdsourced reports**

We first developed an algorithm to identify whether a tweet is crash related or not, using the truth data set to train the algorithm. We extract features from tweets by extracting n-grams from tweets. We employ a grid search, tuning the models by testing all combinations of multiple parameters. The three main parameters we test are: (1) extracting 1-grams, 1-2 grams or 1-3 grams, (2) not removing any features or removing features that occur in less than/more than 0.01%/99.9%, 1%/99% or 5%/95% of tweets, (3) defining features as the number of occurrences of the n-gram in the tweet or using the Term Frequency - Inverse Density Frequency (TF-IDF) of the n-gram<sup>2</sup> For the Support Vector Machine, we also vary the regularization parameter–which controls how the algorithm weighs misclassification versus simplicity–using 0.5, 1, 2, 10, 100 and 1000.

Table S2: Example Tweet and Augmented Tweet accident <u>past garden</u> city near thika rd and kamiti rd <u>junction</u> accident <u>past #landmark-name#</u> near <u>#road-name#</u> and <u>#road-name# junction</u> An additional parameter we test is using the original tweet text and, following [6], replacing landmark names and road networks with generalized names (just indicating the presence of a landmark or road). Generalizing landmark and road names helps to reduce the dimensionality of the feature space. Table S2 demonstrates how a particular tweet is transformed into one with general landmark and road names. Table S3 shows examples of the features extracted in regular and augmented tweets where landmarks and roads have been replaced. This augmentation assumes that the occurrence of a road or landmark name contributes equally to the probability of
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The analysis presented in this paper is meant to draw attention to a potentially important relationship, rather than to establish a definitive connection. First, it is hard to infer a causal relationship between policy uncertainty and trade because policy itself responds to economic circumstances and is likely to be forward looking. Second, when studying GVCs, it is hard at this stage to disentangle the contrasting forces that link global value chains and uncertainty. This does not allow us to properly test whether the consequences of uncertainty are more serious for GVC trade. Finally, we would want to separate different types of economic policy uncertainty and their effect on trade and global value chains. However, the available data do not allow for a precise test. Addressing these data and conceptual issues should be the focus of future research in this area.

# **References:**

Ahir, H., N. Bloom and D. Furceri. 2018. “World Uncertainty Index. Stanford, mimeo.

Ahir, H., N. Bloom and D. Furceri. 2019. “Caution: Trade uncertainty is rising and can harm the global economy”, VoxEU.org, July 4.

Al-Haschimi, A., M. Gächter, D. Lodge, W. Steingress. 2016. “The Great Normalization of Global Trade”, VoxEU.org, October 14.

Constantinescu, Mattoo and Ruta. 2018. “The Global Trade Slowdown: Cyclical or Structural?” World Bank Economic Review. Published electronically May 23. https://doi.org/10.1093/wber/lhx027.

Baker, S. R., N. Bloom, and S. J. Davis (2016). “Measuring Economic Policy Uncertainty.”The Quarterly Journal of Economics, 131 (4), 1593-1636.

Bernanke, Ben S. (1983), “Irreversibility, Uncertainty and Cyclical Investment,” Quarterly Journal of Economics, 97, 85-106.

Crowley, M, H Song, and N Meng (2018), "
shown that military expenditure is the main source of volatility in U.S. government expenditure, this is not the case in all developing countries. In fact, for some developing countries, military expenditure is a negligible portion of budgetary expenditure. And in those developing countries whose expenditures are driven by large swings in military spending, it is hard to argue that these war buildups have little direct impact on GDP. Moreover, there is a growing literature arguing that either business cycle conditions or swings in natural resource prices are major drivers of public expenditure in developing countries’. This makes it difficult to find a general econometric instrument that drives public spending in developing countries, but which has no direct effect on economic activity. On the tax side, there is to date no comprehensive documentation of tax changes as in Romer and Romer (2010) for countries other than the U.S. and the U.K. Second, the SVAR approach may be more effective at identifying unanticipated fiscal shocks in developing countries than in high-income countries. while Ramey (2010) has presented evidence that SVAR shocks were forecasted by the private sector, this is less likely to be the case in developing countries. As IMV discuss, government expenditure is very volatile on a quarter-to-quarter basis in developing countries. Fiscal planning is poor and not credible. Quarterly fiscal data is subject to large revisions even a year or two after the fact. It is therefore unlikely that the public will have foreseen many of the fiscal shocks identified by an SVAR, and even less likely that their exact timing at a quarterly frequency will have been known in advance. Moreover, if the volatility of public expenditures is driven largely by political pressures to increase expenditures when resources are abundant, a VAR should be able to pick up on this relationship, as long as it is relatively sys- <u>~ 58ee for example KaminskReinharty,</u> and Végh (2004), Izetzki and Végh (2008) for stylized facts; Talvi and Végh (2005)
All other parameters were determined either by using shares for the base period, by dwelling on the literature on Ethiopia, or (when country-specific data were not available) by using plausible values for low-income developing countries in general. The elasticities of substitution on the production side were kept at relatively low values (see Appendix C). For instance, the elasticity of substitution between T and KP, σJ, was set to 0.3; the elasticity of substitution between LE and Kghea/POP<sup>θH</sup> , σT, was set to 0.3; and the elasticity of substitution between J and KGinf, σY, was set to 0.5. Measures of congestion effects were difficult to estimate, given the lack of information for developing countries in general. We used relatively low values to avoid putting undue weight on these parameters. Specifically, for the parameter capturing congestion effects in the education system, θE, we chose a value of 0.3; for the parameter determining the strength of congestion effects in the provision of health services, θH, we chose a value of 0.1; and for the parameter capturing congestion effects in infrastructure capital, θI, we chose a value of 0.2. Relatively small values (in the range of 2 to 4 percent) were also chosen for the depreciation rates of the various capital stocks, in line with available estimates. The long-run elasticity of transformation in domestic production was set at 0.3, whereas the long-run elasticity of substitution between domestic and imported goods was set at 0.4. We also assumed that the allocation of private expenditure between these goods (equation (A14)) occurs gradually, with an adjustment parameter that captures a low propensity to substitute between domestic and imported goods in the short run. Similarly, the allocation of domestic output between domestic sales and exports (equation (A4)) was also assumed to be subject to a partial adjustment process.

We calibrated the model for 2002, the most recent
may want to carry out the calculation in Panel B of Table 7 for multiple parameter vectors in order to get a sense of the distribution of gains from adaptivity. The most consistent approach would be to draw many values from the prior distribution of the model parameters, but this can give a misleading picture of the gains from adaptivity when uninformative priors are used (not to mention that the computational cost is high). As an example, in the IVR experiment, the flat priors combined with the logit transformation in the model mean that treatment arm averages based on random draws from the distributions of the _β_<sup>_E_</sup> are almost always close to 0 or 1. In Appendix C.3, we therefore show results from a modified exercise in which we independently and randomly draw the _θ_<sup>_k_</sup> from the uniform distribution on [0 _,_ 1]. As it turns out, this exercise is not meaningful either: in many cases, the drawn parameters are so far apart that, given our large sample of students, even equal assignment shares lead to a very high probability of picking the correct arm. A more meaningful approach might be to assume correlated prior distributions for the _βk_<sup>_E_,orusetheprior</sup> same distribution for each _θ_<sup>_k_</sup> but with a mean obtained from pilot data. An alternative to drawing from a prior distribution is to examine learning gains for a few well-chosen parameter vectors. Again, this is similar to the approach taken in typical power calculations for experiments, see (e.g. Duflo et al., 2007).

_Remark: When is Adaptive Sampling Most Valuable?_ As the simulations show, the efficiency gains from adaptive sampling vary significantly across different problem instances. For best-arm identification, closely clustered treatment effect averages make the problem “hard,” as it is difficult to distinguish these arms. From a welfare-maximization (regret minimization) perspective, however, two
***p < .01, **p < .05, *p < .1. Investment has been trimmed at the 99th percentile. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm’s sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the followup survey. The detailed regression results are provided in Table A.3 in the appendix.

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# **1. Introduction**

Poverty is an indicator of paramount importance for gauging the socioeconomic well-being of a population. Especially during or after a shock, poverty estimates are invaluable for understanding the situation, as well as for assessing the severity of the impact and for identifying which parts of the population were most affected. Especially in the developing world, consumption-based monetary poverty measures are used, defining the poor as those households with consumption levels that fall below a set poverty line (Angus Deaton and Zaidi 2002). The poverty line is usually set at a consumption level adequate for sustaining the minimum level of welfare required for healthy living (Ravallion 1998). Consumptionbased poverty measures are widely used in development contexts and play a critical role in policy decisions (e.g. Beegle et al. 2016).

The measurement of consumption, however, has traditionally been very time consuming. A typical household consumption questionnaire contains a series of questions about the price and quantity consumed for each item, and whether it has been purchased, self-produced, or bartered. Usually encompassing more than 200 food and nonfood items, the time required to administer such a questionnaire can often substantially exceed two hours. In addition to high administration costs due to long interview times, measurement errors may become significant towards the end of the questionnaire as enumerators and respondents become fatigued. Respondents might also cancel the interview before it is completed, thus contributing to a higher non-response rate.

Enumerator and respondent fatigue are well documented in the literature (Tourangeau, Rips, and Rasinski 2000; Krosnick 1991) and become more pronounced for longer questionnaires (Rolstad, Adler, and Rydén 2011; Diehr et al. 2005; Snyder et al. 2007). Enumerator fatigue increases measurement errors often over the course of a day as well as over the time the survey progresses (Baird, Hamory, and Miguel 2008). Especially in consumption surveys, a long list of items can lead to enumerators cutting corners and fabricating data (Fiedler and Mwangi 2016; Finn and