rafmacalaba/data-use-mentions-extended huggingface dataset:
shifts out of non‐tradables. This can create unemployment, and even if new jobs are created in tradable sectors, relative wages will likely change. Asset prices, including real estate prices, will likely drop sharply. Due to the overall reduction and shift of final demand, relative prices of consumption goods will also change: prices of non‐tradables (for example of services) will contract more severely than prices of tradables. Finally, a fiscal channel may also be at work as transfers from the government, which is facing shrinking oil revenues, may be under pressure.

In the CGE model, the terms of trade shock is implemented as a 50 percent reduction of international oil prices<sup>9</sup> and, as shown in Table 1, this causes a reduction of consumption per capita of about 7 percent. This is a one‐off large welfare reduction that materializes even if production and employment do not contract. In fact, the model simulates the impact of the oil shock as moving to a new long term equilibrium with full employment of labor and physical capital. Gross domestic production marginally contracts because of a reduction in the supply of natural resource in the oil sector, but employment does not go down. It may be possible that factor markets do not adjust to this new equilibrium in the short run and unemployment can rise with additional negative impacts for GDP. However, this exercise aims at modeling the impact of the shock as if it were permanent and after the economy has fully adjusted to the new relative prices.<sup>10</sup>

> 9 The 50% reduction was the one estimated at the end of 2014. During 2015 the price of oil dropped even more, reaching 36 USD per barrel in December 2015 (World Bank Commodity price data).

> 10 Further below the assumption of full employment is (partially) relaxed and the distributional consequences of an increase in unemployment are assessed.

11
# _<u>Maersk Sealand</u>_

Online business services offer: Schedules by location or vessel, Rates of exchange applicable to shipments, port to port freight rates and surcharges, cutoff times for ports within a country/region, and standard transport statements and certificates.

Data on maritime transport cost can be downloaded per origin and destination from the query rates. The rates vary depending upon the requested origin and destination and are constructed from a combination of ocean, truck, and rail charges. Rate quote may not include local destination charges such as, but not limited to, Cleaning, Port fees, Overweight charges, Documentation fees, or Customs inspections.

# _<u>US Department of Transportation (USDOT)</u>_

The monthly U.S. foreign waterborne transportation statistics are based on the U.S. Bureau of the Census trade data matched to the U.S. Customs vessel entrances and clearances. The cargo summary contains value and weight information by type of service on U.S. waterborne imports and exports.

Maritime transport costs, referring only to US and its trade partner countries are available through the “US Import Waterborne Databank" CDs. This includes a detailed breakdown on liner transport charges, import values, the percentage of containerized cargo, total imports carried by liners, and the market share of tramp services. The containerization variable is 28 __________________________________________________________________________________________________________
uses selected examples to make the discussion concrete. The second component proposes a methodology for systematic and realistic benchmarking. Among the various potential methodologies in the received literature, we select the stochastic frontier (SF) approach. This allows us to measure the inefficiency or room for improvement for a given development indicator while considering the structural endowments or predetermined conditions that can limit the indicator ́s potential.

The paper illustrates the use of the SF benchmarking methodology by constructing and using a large crosscountry panel data set, consisting of 142 countries, yearly data for 2005-14, and 10 development indicators. Despite its large scope, this exercise should be mainly considered as an illustration because it takes specific choices from the array of possibilities presented in the conceptual framework and assumes homogeneity of structural endowments across all indicators. Further work can strengthen the benchmarking exercise, focusing on a smaller set of development indicators and using more appropriate assumptions for their structural endowments and distribution functions.

The paper is organized as follows. After this introductory section, we discuss a conceptual framework for international benchmarking. Next, we present a selected review of the literature, where we consider the relative advantages of the stochastic frontier approach. We then present in detail the SF methodology and the cross-country panel data used for its application. Finally, we discuss the results on a set of development indicators and conclude by summarizing the paper ́s contributions and proposing extensions for further research.

2
sections that allow richer analysis of poverty dynamics (Dang _et al._ , 2019; Dang and Lanjouw, 2023). But since there are few applications of these imputation methods for studying the impacts of climate change (see, e.g., Nakamura _et al._ , 2023), more studies in different contexts would be useful.

Recent studies have utilized administrative data, which typically offers better census-type coverage and longer-run panel data, especially in wealthier countries where such data is readily available. For instance, Duque _et al._ (2018) use administrative data from Colombia to examine how early-life exposure to adverse weather conditions and subsequent human capital investments via conditional cash transfers (CCTs) affect children’s long-term outcomes. Focusing on school performance, Park (2022) exploits student-level administrative data from a public school district in the U.S. to examine how hot temperatures impact high-stakes exam performance and subsequent educational attainment. Similarly, Heyes and Saberian (2019) analyze U.S. immigration judges’ decisions, demonstrating a link between outdoor temperature and immigration adjudications. Yet, while administrative data offer multiple advantages over household survey data such as accuracy, representativeness, and longer panel data, it is still rarely used in poorer countries due to weaker statistical capacity (Dang _et al._ , 2023b), which could range from fewer resources to less established data infrastructure. This explains the limited analysis of administrative data for poorer countries, particularly in the context of poverty and inequality.

 Do recent studies use big data (e.g., satellite data) more often than previous studies? The rise of big data, together with progress in geographic information system (GIS) technology, has greatly expanded the range of data sources available for studies on climate change, particularly natural disasters. An alternative to household surveys is to use global disaster databases such as the Emergency Events Database (EM-DAT), which provides comprehensive 14
crime and violence in El Salvador, might endanger both children and instructors.

Instead, following Chandler et al. (2011), I estimate a predictive model of violence and crime from existing data using a Two-Sample Least Squares strategy. First, using an existing anonymized database of youths’ violence and crime from El Salvador (FUSADES, 2015),<sup>10</sup> I estimate the likelihood of having committed a violent act _Vf_ as a function of a wide range of covariates: where _Df_ is a vector of violence determinants of student _f_ in the FUSADES dataset. This vector includes variables that indicate individuals’ vulnerability to violence, such as student characteristics (e.g., age, gender, time spent alone at home, and education level); children’s household variables (e.g., residence area, mother’s education, and household composition); and school-level controls (e.g., school location and commuting time to school).<sup>11</sup> Descriptive statistics and comparison of means ( _p_ -values) between the FUSADES sample and the one from this study are in Appendix Table A1. Estimations indicate that both samples are similar in most of the determinants except for some variables such as student’s age and their report of being without adult supervision after school hours.

All estimated coefficients _α_ ˆ1 have the expected sign according to the literature of violence determinants, as shown in Appendix Table A2. For instance, boys are more likely to be violent than girls, adolescents behave worse than children (Rodriguez-Planas, 2012), and lack of parental supervision increases the probability of committing a violent act (Gottfredson et al., 2004). Statis-

> 10This database was created using the _El Salvador Youth Survey_ ’s instrument. It consists of a sample of 8,640 students in sixth and ninth grades who are enrolled in public schools in El Salvador. This database includes a large number of variables that measure crime and violence
years in which a DHS survey is available. The lines connect the dots that belong to the same cohorts as they age. For easier interpretation, we group cohorts from different decades by color.

The typical inverted ‘U’ life cycle profile for employment, with the middle part of the life cycle with the highest participation, is clearly shown in Figure 5. Zooming in on the cohort of women who were born in 1970-74 (middle dark blue line in the figure), it appears that about 70 percent of these women were working at age 20-24, then this percentage increases to 80 percent when they are 25–35-year-old, and finally it declines to below 70 percent when they reach 45 years.<sup>14</sup> Figure 5 also highlights the cohort effect, namely that lines of different cohorts, when compared at the same age, display different levels of participation in the labor market. The darker shaded lines plotting labor participation for more recent cohorts lie mostly below the lighter shaded lines of their predecessors, confirming the declining trend in participation found in Figure 2 (left panel) even when the age composition is controlled for. The vertical distances between the lines of Figure 5 can be explicitly plotted as the cohort effect, and this is done in Figure 6 for Nepal and other countries in the region.<sup>15</sup> In this figure, the lines connect different cohorts observed at the same age.

In the case of Nepal, the left panel of Figure 6 shows cohorts born more recently have lower participation in the labor market at all ages. In the cases of India and Pakistan, the recent decreasing participation is confirmed by the cohort plots shown in the right panel. The exception of Bangladesh with an increase in participation for the recent cohorts is confirmed.<sup>16</sup> Going beyond this graphical examination, we applied the formal econometric Age-Period-Cohort (APC) decompositions discussed in the methods section for two countries where we have a sufficient number of evenly
Obs.|2,130|2,130|2,015|
|Annual tuition cost (in COP)|-28,285.2|80,234.1|87,502.3|
|Obs.|2,130|2,130|2,015|
|Total enrollment in 2017|66.7|-65.3|-88.9|
|Obs.|2,130|2,130|2,015|
|HEI is SENA|0.003|-0.014|-0.019|
|Obs.|2,130|2,130|2,015|
|Program has high quality||||
|accreditation|-0.006|-0.003|-0.002|
|Obs.|2,130|2,130|2,015|
|**_Panel C. Dominican Republic_**||||
|<br>HEI is a university||-0.376**|-0.038|
|Obs.||209|93|
|HEI is public||0.073|-0.039|
|1.57|1.23|1.93|-0.69|0.00***|
|Has Electric Sewing Machine|0.43|0.40|0.45|-0.05|0.35|
|# Electric Sewing Machines|0.57|0.48|0.68|-0.20|0.04***|
|Has Any Electric Machine|0.82|0.75|0.88|-0.13|0.00***|
|# Any Electric Machine|1.74|1.40|2.10|-0.70|0.00***|
|Has Generator|0.03|0.01|0.04|-0.03|0.13|
|Number of Firms|342|184|158|||

Source: Author calculations from The Hohoe Garment Makers Project Data 16
# **5. Poverty Correlates**

After analyzing the poverty profile, this section turns to the factors associated with poverty in rural Mexico (areas with less than 15,000 inhabitants). Many individual characteristics such as labor market association and human capital are important correlates of poverty and the dynamics thereof. This section investigates the marginal impact of each individual attribute on the likelihood that a household falls below the poverty line. The analysis is undertaken applying probit regression techniques.

Other researchers have addressed the determinates of poverty, for example, Ferreira, Lanjouw and Neri (1999) for Brazil, and these authors perform the study for one year, which gives a good but static picture of the situation in a country. Here the analysis is more dynamic in nature as it is based on three ENIGH data from the years 1992, 1996, and 2002. This allows for an evaluation of the evolution of poverty over time and the most important variables determining poverty. Hence, three poverty analyses are performed for rural Mexico as a whole using data from localities with less than 15,000 inhabitants. This analysis reveals: (i) conditional correlation between poverty and characteristics of household heads; (ii) information about the volatility of the impact of the attributes on the likelihood that a household experiences poverty during the beginning and mid-1990s and beginning of the 2000s; and (iii) information about groups that are particularly vulnerable and changes thereof over the past decade.

The status of the household—poor or nonpoor—is regressed on relevant individual and household characteristics. The dependent variable takes the value of 1 if the average percapita income is below the SEDESOL capacity poverty line and 0 otherwise. The dependent binary variable takes the value of one when income is below the indigence line and zero otherwise. The vector of five sets of independent variables includes: (1) attributes of household head: gender, education, experience and labor market connection, whether the household head works, the type of relation with the labor market, sector of
9%|0.4%|0.4%|0.2%|0.2%|0.2%|0.1%|
|Cost of withdrawal<br>(other bank)|2,918|1.6%|0.8%|0.8%|0.4%|0.3%|0.3%|0.1%|

_Source: Superintendencia Bancaria; Encuesta de Calidad de Vida 2003_ 15. Interestingly, credit cards are available in Colombia even for people without bank accounts, as outstanding balances can be settled by cash or by check. However, in the practice, only high income banked clients use credit cards. Processing and monthly fees for credit cards are relatively high, corresponding to 4-5% of a minimum monthly salary. In addition, eligibility criteria de facto preclude low income clients from access to credit cards. For example, banks such as Bancolombia require a minimum salary of at least 2.5 times the regulatory minimum salary for credit card issuance.

**Table 2. 4  Costs of domestic credit card services by income quintiles**

|||% half<br>min. wage|% min.<br>wage|% Quintile<br>1|% Quintile<br>2|% Quintile<br>3|% Quintile<br>4|% Quintile<br>5|
|---|---|---|---|---|---|---|---|---|
|Average monthly<br>income (in COP)||179,000|358,000|379,241|727,087|846,080|917,584|2,321,841|
|**Domestic credit**<br>**cards**|||||||||
|Processing costs|
form of rainwater is its higher pressure (for uses such as house cleaning, individual showers and so forth) as well as convenience; i.e., not having to collect, filter, and store rainwater or maintain a water pump, in the case of groundwater. The amenity value of high pressure and convenience would not carry over to water stored or shared between households.<sup>11</sup> That said, our data allow a test for the potential effects of arbitrage, both inter and intrahousehold. With respect to the former, data on water sharing obtained in the quarterly monitoring surveys rules out significant inter-household trade. Just under 2 percent of the experimental sample (about 28 households per quarter on average) shared in piped water from their neighbors over the course of the price experiment, whereas less than 2 percent (about 25 households per quarter) shared out their piped water to a neighboring household that itself had piped water. Moreover, we find no evidence that household water consumption or its response to price discounts were affected by water sharing (in or out) during a quarter in which experimental price differentials prevailed (see Appendix C).<sup>12</sup> With respect to intra-household arbitrage, we use information on the volume of the concrete rain water tank, to which households could potentially divert piped water to store for future use. A larger tank implies greater scope for storage; median tank volume is 5 cubic meters (interquartile range 2-9; 4 percent of households have no tank) against median monthly piped water consumption of 6 cubic meters. If such arbitrage were important, we would expect households with larger tanks to have a greater response to discounts. However, as shown in Appendix Table D.1, row (6), we fail to reject equality of price responses across households with varying water tank sizes ( _p_ -value=0.920).

# **2.3 Results of price experiment**

**Overview** The
the proposed method for targeting purposes. In particular, we compare the targeting performance of our method with the targeting method currently used in Chad to administer cash assistance to refugees as well as with the global experience.</mark> <mark>The estimation results indicate that the limited set of variables available in the UNHCR registration data predict household consumption (welfare) reasonably well. Estimates from the three sets of data available for the analysis produce similar welfare figures. Th</mark> e <mark>current targeting strategy in Chad, which is used jointly by the N</mark> ational Commission on the Welcoming and Resettlement of Refugees <mark>(CNARR), UNHCR, and World Food Programme (WFP), is accurate in predicting household welfare. However, our results suggest that this targeting strategy could be further improved by reducing the inclusion and exclusion errors. If these encouraging results are replicated</mark> 3
of $0.5, for example, increases FAI into H by $3.5 but leaves measured FDI unaffected. On the other hand, any changes in the amount of retained earnings in the host affiliate that is not invested in real assets would change FDI, but not FAI: with uninvested retained earnings in H of 0.8, for instance, FAI in H remains at 3 _._ 5, while FDI into H increases to 3 _._ 8 and global FDI to 4 _._ 8.

# **3.2 The FAI Dataset**

The FAI dataset used here has been constructed by combining information on inward real investment from the _Foreign Affiliate Statistics_ (FAS) provided by Statistics Canada, the 14
Predicted probabilities of stunting during infancy (6-23 months of age) for select conditions <!-- Start of picture text --> 23<br>Predicted baseline risk —_o—<br>29.9<br>Pre-natal environment: Inadequate —_o—<br>18.6<br>Pre-natal environment: Adequate —_o—<br>Food security/diversitytas . environment:. . 24.1O<br>Inadequate<br>Food security/diversity environment: Ad<br>Adequate<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2b (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
15-24 are more likely to be enrolled in an educational institution or training course or looking for a first job, while respondents 55 and over may have stopped working for pay for a variety of reasons, including voluntary retirement and ill health. To address estimation issues associated with outliers, we estimate our models using 1%-trimmed samples.

> 40 The assessment includes sets of questions taken from the OECD’s International Program for Assessing Adult Competencies (PIAAC), the International Adult Literacy Survey, and the Adult Literacy and Life Skills to produce Reading Literacy Assessment Scores and derived “plausible values” of literacy proficiency (see Pierre et al., 2014).

22
<mark>(2014)</mark><sup>29</sup> <mark>. Second, it incorporates two additional dimensions: education and health</mark><sup>30</sup> <mark>, similar to Alkire and Apablaza (2016) and Weziak-Bialowolska and Dijkstra (2014). An individual is educationally deprived if at least one adult (18+) in the household has only attained primary education (or less).</mark><sup>31, 32</sup> <mark>An individual is deprived in the health dimension if at least one adult (16+) in the household has unmet needs for medical examination or treatment during the last twelve months.</mark><sup>33</sup>

**For the Roma-specific analysis, AROPE indicators are mostly unavailable among ethnic population subgroups (with the exception of Bulgaria) when using the EU-SILC survey instrument.** While other countries may have available ethnic identifiers in other official surveys (such as Household Budget Surveys), the indicators that conform with the AROPE cannot be constructed with other survey instruments.

# **To understand factors associated with multidimensional poverty, a series of Probit models**

**with different specifications were selected** . These were chosen as a suitable statistical approach for modeling dependent variables characterized by dichotomous outcomes, such as the presence or absence of AROPE (at risk of poverty or social exclusion), low work intensity, and severe material and social deprivation. The model includes household and individual-level characteristics. This choice is grounded in its ability to estimate the probability of an event occurring, making it particularly well-suited for binary outcomes. The models were run with different sets of covariates and 2021 and 2022 data to capture the stability of the relationship between a set of explanatory variables and the likelihood of experiencing these specific forms of deprivation and identify any emerging trends or shifts in socio-economic dynamics and the robustness of the results to the specification