rafmacalaba/data-use-mentions-extended huggingface dataset:
1|
|_4_|4,252|3.5|
|_Richest_|4,388|2.6|
|_All_|3,384|2.9|

_Source:_ Author’s calculations based on 2008 ILCS Another illuminating approach to examine the poverty impact of the gas price hike is to measure the extent to which the price hike is “impoverishing.” That is, if a household has total consumption expenditures with gas above the national poverty line, but their total spending without gas is below the poverty line, they could be considered to have suffered impoverishment due to gas expenses. This impoverishment due to spending on gas is illustrated graphically in 15
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2277 Determinants of Bulgarian Brady<br>Bond Prices: An Empirical<br>Assessment|Nina Budina<br>Tzvetan Mantchev|January 2000|N. Budina<br>82045|
|WPS2278 Liquidity Constraints and Investment<br>in Transition Economies: The Case<br>of Bulgaria|Nina Budina<br>Harry Garretsen|January 2000|N. Budina<br>82045|
|WPS2279 Broad Roads in a Thin Country:<br>Infrastructure Concessions in Chile|Andr6s G6mez-Lobo<br>Sergio Hinojosa|January 2000|G. Chenet-Smith<br>36370|
|WPS2280 Willingness to Pay for Air Quality<br>Improvements in Sofia, Bulgaria|Hua Wang<br>Dale Whittington|January 2000|R. Yazigi<br>37176|
|WPS2281 External Sustainability: A Stock<br>Equilibrium Perspective|C6sar Calder6n<br>Norman Loayza|January 2000|H. Vargas<br>38546|
|WPS2282 Managing Fiscal Risk in Bulgaria|Hana Polackova Brixi<br>Sergei Shatalov<br>Leila Zlaoui|January 2000|L. Zlaoui<br>33100|
|WPS2283 New Tools and New Tests in<br>Comparative Political Economy:<br>The Database of Political Institutions|Thorsten Beck<br>George Clarke<br>Alberto Groff<br>Philip Keefer<br>Patrick Walsh|February 2000|P. Sintim-Aboagye<br>38526|

AI(
)|**10.5**|8.5|13.5|10|8|8|15|9|
|Average liquid bulk handling charge<br>(US$/tonne)|**4**|5|5|3|||5|5|

* Data for the Freeport of Monrovia relate to 2008, but no data were available for the Port of Buchanan. Source: Mundy and Penfold, AICD Background Paper No. 8, 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

# **Challenges**

Port security is an issue for Liberia. The National Port Authority’s Work Plan for 2008 included obtaining ISPS certification for the port of Monrovia as one of its objectives, but as of mid-2009 this certification had not been obtained. No ports in Liberia possess ISPS certification. In 2005, the port of Monrovia was removed from the US Coast Guard restricted list after the United Nations Mission in Liberia assumed overall responsibility for it. Security remains crude at the ports, although efforts are underway to achieve ISPS compliance at the port of Monrovia.

12
census, the largest group accounts for only about 0.9 percent of the population.

Gender and ethnicity could not be determined for all individuals in all cases. Gender could not be determined if the first name was either abbreviated (i.e., if only initials were given), it did not clearly match to a single gender, or it was not included in the API datasets. Ethnicity could not be determined if the last name was not included in the ethnicity dataset. For some of the cases included in analysis, information could be extracted for plaintiffs but not defendants (and vice versa) and for gender but not ethnicity (and vice versa).

It is important to note that there is the possibility of a small amount of error resulting from the automated process of removing non-humans and determining gender and ethnicity. For example, although the list of key words for non-humans is long and we have manually scanned the data for non-humans, it is still possible that some non-humans remain. It is also possible that gender and/or ethnicity has been assigned to non-humans with certain key words included in the organization name. Similarly, if names were separated in an unusual way, it is possible that the number of defendants or plaintiffs was incorrectly counted, possibly resulting in an incorrect assignment of majority/plurality gender/ethnicity. However, having thoroughly scanned the data, we are confident that the number of such errors is insignificant.

### **Using the Binary Classification Machine Learning Model to construct the defendant_win outcome variable**

To determine the winner of each case, we created a Binary Classification Machine Learning Model using the Global Vectors for Word Representation (GloVe) algorithm (Pennington, Socher, and Manning 2014). The objective function of GloVe can be written as follows: where _Xij_ denotes the co-occurrence count between words _i_ and _j_ , and _f_ ( _·_ ) is a weighting function that serves to down-weight particularly frequent words. The objective function _J_ ( _·_ ) trains the
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 4 In practice we are constrained by the spatial resolution of available data. Census bureaus typically report this data for second-level administrative districts (e.g., municipios), although in some cases (such as Brazil) census tract level data is published. The reporting units differ widely in size.

Two consortia have independently tackled the task of assembling global population census data and using it to assign densities to finely-gridded geographic cells, so as to permit geographic analysis of the correlates of population. They have adopted complementary approaches, each with advantages and disadvantages.

Landscan, developed by the Oak Ridge National Laboratory <u>(http://www.ornl.gov/sci/gist/landscan/) uses census source data typically at the province</u> level. They then use an unpublished model to allocate population within the province level, based on road proximity, slope, landcover, and ‘nighttime lights’. The latter is a map derived from satellite images of artificial illumination, and is a useful proxy for the location of cities, towns, and major roads. (though the relation between population and illumination is of course modulated by income). Landscan’s goal is to represent population density averaged over a 24 hour day, thus blending both occupational and residential locations. The results are gridded to 0.5’ by 0.5’ grid cells<sup>2</sup> .

GPW3 (the Gridded Population of the World) is produced by CIESIN, Columbia University, and CIAT<sup>3</sup> . It employs 2.5’ x 2.5’ grid cells. It uses mostly lower-administrativelevel (higher resolution) census source data, typically at the level of the municipio or equivalent. The current version, GPW3, then assumes homogeneous distribution of population within the administrative unit. Table 6 reports, by country, the administrative level and the total number of units used. A forthcoming version
varies directly with the caseload per judge. These results suggest that the productivity of judges is endogenous; for the same caseload judges complete more cases under pressure, and complete less when new judges are appointed. Coviello et al (2019), using data from Italian courts, do however find evidence for specialization leading to faster disposition times. They estimate that when judges receive more cases of a certain type, they become faster, i.e., more likely to close cases of that type in any one of the corresponding hearings.

24
, and during the 1980s, or its first half, for the 1985 arrival. GDPpc stands for log of per capita-GDP in constant 2000 prices averaged over 1970-1975, and 1980-1985 periods, respectively, and is from the Penn World Tables (Version 6.2).<sup>31</sup> GDPpc may affect migrants’ performance through both quality and selectivity channels. The fact that people coming from high income countries are better off at arrival has been associated with the ease of skill transferability between US and equally developed countries.

> 31 Heston, Summers, Aten (2006)

17
<!-- Start of picture text --> % of Baseline % of Baseline<br>1 14<br>0 12<br>10<br>-1<br>8<br>-2<br>6<br>3<br>4<br>4 2<br>-5 0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions ——— Phaseout Schedule —— Average Emissions——— Phaseout Schedule<br><!-- End of picture text --> <!-- Start of picture text --> % of Baseline % of Baseline<br>1.4 12<br>12 10<br>1<br>8<br>8<br>6<br>6<br>4<br>4<br>22<br>00<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions Phaseout Schedule —— Average Emissions——— Phaseout Schedule<br><!-- End of picture text -->
19483.

- Bagehot, Walter (1873) “ _Lombard Street: a description of the money market_ ”, Henry S. King and Co., London.

Bloch, Marc (1961) _Feudal Society Volume 2_ Campos, Nauro F. et al, " _Whither corruption? A Quantitative Survey of Literature on Corruption and Growth”_ , (2010) Discussion Paper no: 5334, Institute for the Study of Labor.

Cioli, Alessio (2017) “ _Rethinking Early Antitrust History: From the Sherman Act to Theodore_ _Roosevelt”, Dissertation for the Advanced Master of American Studies_ , University of Gent. Dixit, A. “ _Governance Institutions and Economic Activity”_ , American Economic Review (2009) Gander, James P., _“Microeconomics of Corruption among Developing Countries”_ Economics, Management & Financial Markets, Sep 2011, Vol. 6 Issue 3, p11-22.

- Geddes, Barbara, “A game theoretic model of reform in Latin American democracies”, American Political Science Review, June 1991, Vol. 85, No 2.

Geddes, Barbara, “Politician’s Dilemma: Building State Capacity in Latin America”, Berkeley, University of California Press, 1994.

Page **23** of **25**
sures of well-being. First, we develop a simple conceptual framework of whether an individual is satisfied with their consumption where we incorporate the salience of nonconsumption factors. The framework incorporates the well-known issues affecting subjective indicators of well-being- the possibility that scales used to answer such questions are individual-specific and can be impacted by frame-of-reference effects. However, this framework adds a new way in which subjective welfare metrics might be difficult to interpret- the possibility that the respondent-specific scales on which subjective welfare questions depend might not be stable over time. Specifically, we demonstrate how crises might cause the scales used by individuals to determine satisfaction to change at the same time that consumption might be changing, and it becomes an empirical issue as to whether subjective welfare metrics move in the same direction as consumption.

Second, we empirically investigate the ability of subjective measures of welfare to capture changes in traditional measures of deprivation in a crisis using the mostly non-violent capture of the Republic of Yemen’s capital. The capital was captured by Houthi forces in September 2014 while a nationally representative household survey was being conducted. The survey included both traditional consumption modules and a full subjective welfare module. Importantly, previous research has demonstrated that the capture led to immediate and large increases in consumption-based and employment-based measures of deprivations, and there is little evidence that the sampling of the survey was affected by the capture (e.g., Tandon 2019).

However, the change in subjective welfare metrics did not match the increase in deprivations captured by more traditional welfare metrics. The survey asked the household head to rate their satisfaction with a number of welfare dimensions individually (e.g., satisfaction with food consumption, income, etc.). Despite the large and immediate increase in traditional measures of deprivation following the capture, the subjective metrics of welfare moved in the _opposite_ direction. There was an increase of between 11 and 15 percentage points of household heads that reported to be satisfied with food consumption, income, and work, all of which were dimensions in which
Table 3: Tests of _β_ -convergence conditional on public spending and CCT data, 2000–14

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
|||**a. All mun**|**icipalities**||
||_−_0_._025***|_−_0_._059***|_−_0_._026***|_−_0_._015***|
|_ln yit−τ_|(0.002)|(0.004)|(0.003)|(0.004)|
||0.003**|0.006**|_−_0_._010***|0.025***|
|Public spending|(0.001)|(0.003)|(0.003)|(0.005)|
||0.035***|0.067***|_−_0_._023|_−_0_._055**|
|Annual growth in CCT coverage|(0.010)|(0.014)|(0.026)|(0.024)|
|Obs.|1,957|1,957|2,106|2,035|
|R<sup>2</sup>|0.367|0.348|0.182|0.065|
|||**b. Urban m**|**unicipalities**||
||_−_0_._025***|_
months. This suggests little “fade-out” and potentially larger effects as the model scales up (Figure S4 and Table S14 in the Supplemental Material).

**Impacts on non-incentivized outcomes** : One concern with regulations on specific inputs is that they can reduce quality along non-incentivized dimensions (Blau, 2003, 2007). We were particularly concerned about this possibility given the results presented by Contreras-Loya et al. (2021), who find that structural improvements are accompanied by declines in the quality of clinical processes in private facilities. We therefore estimated the impact of the regulation on multiple process and structural measures of quality that were not part of the JHIC instrument. These include: (a) compliance with infection prevention and control practices across 19,178 observations of clinical interactions; (b) quality indicators reported by patients in 11,098 exit surveys and; (c) healthcare staff composition and remuneration for 7,663 staff.<sup>27</sup>

Fortunately, we do not find significant negative changes along any of these dimensions, with small typically positive effect sizes and statistically insignificant after correcting for multiple hypotheses. To the extent that we can interpret the individually significant estimates, in public facilities, we find an _increase_ in consultation length, which has shown to be positively correlated with clinical accuracy, as well as an increase in the ratio of healthcare workers to total staff and total

> 27In the 13% of health facilities with more than 15 staff, we chose a random sample stratified by cadre.

28
job durations and the estimation of job search models. Studies using these approaches have found considerably higher WTP values for non-wage job attributes. For instance, Gronberg and Reed (1994) found that white male workers in the United States are willing to pay 28 percent of their average salaries for jobs without extreme work conditions.<sup>iii</sup> Similarly, using Dutch longitudinal data, Van Ommeren et al. (2000) found that the average WTP for one hour of commuting is estimated to be equal to almost half of the hourly wage rate. Dey and Flinn (2008) found that, in the United States, households would be willing to have the husband (wife) take a 41 percent (45 percent) pay cut to get a job with health insurance coverage. Using data from eight European countries, Bonhomme and Jolivet (2009) found that the marginal WTP for job attributes are usually between 20 percent and 40 percent of earnings, depending on the job attribute and the country in question.<sup>iv</sup> Similarly, the recent choice experiment literature has found substantial WTP for various job attributes. For example, Maestas et al. (2018) estimated that the full bundle of amenities included in their experiment is valued at 56 percent of wages. Mas and Pallais (2017) found that workers are willing to give up 20 percent of wages to avoid employer discretion in work scheduling. This study complements, and contributes to, this strand of literature in several ways. Most workers in Bangladesh, as in many developing countries, are informally employed or self-employed, and thus there are few data on their current job attributes, on which to base revealed preference estimates. Even among formally employed workers, longitudinal data are scarce, limiting the extent to which more sophisticated revealed preference methods such as search models can be used. Our study demonstrates how a choice experiment can convincingly be used to estimate WTP in such a setting.

7
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
- <mark>Letta, Marco, Pierluigi Montalbano, and Richard SJ Tol. (2018). "Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Tanzania."</mark> _<mark>World Development</mark>_ <mark>, 112: 1332.</mark>

- Mathiassen, Astrid. (2009). “A Model Based Approach for Predicting Annual Poverty Rates without Expenditure Data”. _Journal of Economic Inequality_ , 7:117–135.

- Mathiassen, Astrid, and Bjørn K. Getz Wold. (2021). "Predicting poverty trends by survey-tosurvey imputation: the challenge of comparability." _Oxford Economic Papers_ 73, no. 3: 1153-1174.

- <mark>Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(2), 87-106.</mark>

- <mark>Naudé, W. and  Vinuesa, R. (2020). Data, global development, and COVID-19.</mark> _<mark>W</mark> IDER Working Paper 2020/109_ .

- Ravallion, Martin. (2016). _The economics of poverty: History, measurement, and policy_ , New York: Oxford University Press.

- Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew Dabalen. (2015). "Data deprivation: another deprivation to end." _World Bank Policy Research Paper no. 7252_ , World Bank, Washington, DC.

- <mark>Sheahan, Megan, and Christopher B. Barrett. (2017). "Ten striking facts about agricultural input use in Sub-Saharan Africa."</mark> _<mark>Food Policy</mark>_ <mark>,
early marriage in 2015, with 34.2 percent of ever-married women aged 20-24 married before age 18. In contrast, the Riau Islands had a rate of 11.7 percent. East Java, South Kalimantan and Central Java have the greatest number of high prevalence districts (female adolescent marriage higher than 25 percent). Child marriage is particularly an issue for girls at the senior secondary level, though in some areas child marriage sometimes occurs as early as primary school, and principals noted that girls often must leave formal school if this happens.

**Figure 6. Proportion of women aged 20-24 married before age 18**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Percent<br><!-- End of picture text --> Source: UNICEF Global database (countries’ data from 2014-16).

> 29 - UNICEF (2016). Child Marriage in Indonesia: Progress on Pause. <u>https://www.girlsnotbrides.org/wp content/uploads/2016/11/UNICEF-Indonesia-Child-Marriage-Research-Brief-1.pdf</u>

15
data from the final follow-up survey in 2010 (Round 3). We focus on the program effect on the treated households (ATE) and estimate the following regression:

# (1)

where _yihv_ is an educational outcome for child _i_ in household _h_ and village _v_ in Round 3 _, CCThv_ is the treatment indicator that takes the value one if a child lives in a household that was randomly selected to receive conditional cash transfers and zero otherwise, _UCThv_ is the treatment indicator that takes the value one if a child lives in a household that was randomly selected to receive unconditional cash transfers and zero otherwise, _Xihv_ is a vector of child characteristics (gender and age) included to reduce residual variation across arms after randomization, and ε _ihv_ is a random, idiosyncratic error term.

Since our data collection included baseline and follow-up surveys, we can control for differences across villages in the baseline values of the variables. To do so, we use the following difference-in-differences model: (2) where _yihvt_ is an educational outcome for child _i_ in household _h_ in village _v_ and year _t, T2_ and _T3_ are round indicators for the first and second follow-up surveys (Rounds 2 and 3, respectively), _Dj_ is the treatment indicator that takes the value one if a child lives in a household that was randomly selected to receive treatment _j_ (CCT or UCT) and zero otherwise, _Xihvt_ is a vector of child characteristics (gender and age), _Lν_ is a village fixed effect, and ε _ihvt_ is a random, idiosyncratic error term.<sup>22</sup> The impact of transfer scheme _j_ ( _j_ =1, 2) in period _t_ (t=2, 3) is given by _α t j_ , the coefficient of the interaction between the treatment status and the round dummy. Since

> 22 Correlation among the error terms of children living in a village and experiencing similar shocks in the baseline or follow-up rounds, combined with the design effect of our village-level before and after treatment,
larger, than the estimates for total trade. The EFTA, the Agadir Agreement, and with Türkiye each imply increases of more than 1,000 percent after entry into force.

**Figure 3.3 PPMLHDFE Gravity estimates of Morocco’s Trade Agreements and Apparel Trade**

<!-- Start of picture text --> Agadir Agreement<br>EFTA-Morocco<br>EU-Morocco<br>PAFTA<br>Turkey-Morocco<br>United States-Morocco<br>GSTP<br>-1 0 1 2 3<br><!-- End of picture text -->

**Source:** Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO). Plotted estimates are incidental-biascorrected coefficients and standard errors following Correia et al. (2019).  The solid vertical line indicates the estimated value of the “average” agreement as described in equation 1 when the dependent variable is bilateral apparel trade (HS61 and HS62). The estimates of the “average” agreement are basically identical across most agreements because Morocco’s individual agreements have little influence on the average estimate of the other 262 agreements (Morocco is not one of the largest trading countries).

We also compare furniture, which is a labor-intensive yet less politically-sensitive sector. Figure 3.4 shows the coefficients for the model considering trade in the furniture sector. The differences between Figure 3.4 and previous figures shows that results vary significantly by sector, which helps explain the variation in the effects of trade agreements generally. The coefficients of this model show mixed results: only the trade agreements with the European Union and the Pan-Arab Free Trade Area show consistently greater than average effects on trade flows. Others, such as the agreement with the U.S. and the Agadir Agreement, have decreased trade in the furniture sector.

15 | P a g e