rafmacalaba/data-use-mentions-extended huggingface dataset:
|**_Theme_ / Layer**|**Description**|**approximate grid**<br>**size**|**Source**|
|---|---|---|---|
|_Initial conditions_||||
|dist2cap|Distance to capital|N/A|NSO and Global|
||||Insights v6.1|
|dist2border|Distance to border|N/A|NSO|
|dist2major<br>river|Distance to major river<br>(centerline)|N/A|NSO and Nat-<br>ural Earth 3.0<br>1:10 million|
||||(2017)|
|dist2rail|Distance to railway|N/A|NSO and Global<br>Insights v6.1|
|elev30m|Elevation from SRTM|90m and 1km2|Jarvis et al.<br>(2008)|
|srtm<br>~~p~~opmask|Elevation in populated ar-|1km2|Jarvis et al.|
||eas in 2012 from Landscan||(2008); Bright<br>et al. (2013)|
|Slope|Slope (degrees of slope<br>(x100))|30-arc-seconds|Verdin et al.<br>(2007)|
|Ruggedness|Ruggedness Index|3 arc-seconds|Nunn and Puga<br>(2012)|
|rugged<br>~~p~~opmask|Ruggedness Index in popu-<br>lated areas|1km2|Nunn and Puga<br>(2012); Bright<br>et al. (2013)|
|_Agriculture_||||
|ag<br>~~h~~ybrid|Agricultural Hybrid|1km2|Fritz et al.<br>(2015)|
|lgp50<br>~~v~~6990|Length of Growing Period
0.2|10|5.7|185|
|Côte d’Ivoire (2005)|706|88|12|701|-2|
|Congo, Dem. Rep. (2011)|435|147|34|133|-69|
|Gambia, The (2015)|0.3|0.06|20|0.7|133|
|Guinea (2010)|67.3|0.21|0.3|80.7|20|
|Guinea-Bissau (2013)|0.1|0|0|0.1|0|
|Madagascar (2010)|253|210|83|87|-66|
|Mali (2013)|3.7|0.2|5|3.7|0|
|Mauritania (2019)|13|0.02|0.2|6|-54|
|Niger (2010)|106.3|0.11|0.1|26.5|-75|
|South Sudan (2014)|0.1|0|0|0.2|100|
|Eswatini (2015)|82|59|72|17|-79|

_Source: Authors’ calculations, based on USITC data._

# **3.2 Empirical Specification**

The empirical approach to identify the impact of the AGOA suspension exploits the variation in country and product eligibility and in the timing of suspension across countries. More specifically, following Frazer and Van Biesebroeck (2010) and Hakobyan (2020), we adopt a triple difference-in-differences regression model to identify the impact of suspension on exports from the suspended countries.

12
to have positive effects on children left behind in terms of more education and reduced child labor in Mexico (Alcaraz, Chiquiar, Salcedo, 2012) and in the Philippines (Yang, 2008), lower infant mortality rates and higher birth weights in Mexico (Hildebrandt and McKenzie, 2005), and better cognitive and nutrition outcomes in Nicaragua (Macours and Vakis, 2010). Other studies, on the contrary, find that parental migration has negative effects on child education in Mexico (McKenzie and Rapoport, 2011) and on child health in Tonga (Gibson et al, 2011). See Lall et al. (2006) for a detailed survey of internal migration in developing countries and Antman (2013) for a review of the literature on the impacts of migration on family members left behind.

2
# **2.3 Student Attitudes**

PISA applications in each test round have a focus on one of the subjects and in PISA 2012 the focus subject was mathematics. Mathematics happens to be the subject where the mean score difference is highest between Vietnam and Dev7 countries. The PISA questionnaire for students includes a very interesting series of questions regarding students’ perceptions of their abilities, their effort and their reported practices. The details of these questions can be found in the PISA technical report [OECD, 2014a]. Typically, each question includes a set of Likert scaled items to which the student provides a discrete response on a four point agree-disagree scale. These responses are then combined under specified algorithms to provide an index value. For instance, MATWKETH, is meant to measure a student’s “mathematics work ethic”. Students either agree or disagree with a set of 9 items on a 4 point likert scale - strongly disagree, disagree, agree and strongly disagree. The items include items such as “I work hard on my mathematics homework”, and “I listen in mathematics class”, “I keep my mathematics work well organized”. In the case of MATWKETH, when a student agrees/strongly agrees with a positive statement, or disagrees/strongly disagrees with a negative statement, he or she would tend to be deemed to have a stronger work ethic towards mathematics. The raw data from the Likert scale is converted into an index using IRT scaling procedures, so that the mean for OECD countries is 0 and the standard deviation is 1. Table 3 indicates a most interesting finding regarding a range of such indices from the PISA database.

**Table 3: Student self-perception regarding mathematical ability and student effort**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks.

> 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays. Of the 25 countries analyzed by the paper, none presently reports real-time monthly official food price data; half had not reported food price data going back 12 months. Annual statistics are similarly far from complete. Of all countries in the World Bank’s WDI, only 60% (40% of the analyzed countries) had an inflation figure for the most recent 2019–2020 period. The IFS data reported similarly on only half of the countries. This was last checked on August 31, 2021; WDI data identifier FP.CPI.TOTL.ZG, and IFS data identifier PCPI ~~P~~ C ~~P~~ P ~~P~~ T.
in a simple “formal/informal” dichotomy, as indicated by the variable “registration of enterprise” - the relationship is more complex, with variations across and within countries. Though from a research point of view a national register of all HEs would be helpful, it does not seem necessary (or even feasible) to try to develop such a program given the vast number of HEs and their lack of integration into the national economy. A focus on local governance for HEs is justified also by 28
. Thus, it is imperative to empirically assess who suffers from limited job accessibility and whether choosing to live in informal settlements offers relatively good job accessibility to low-income populations in exchange for giving up decent living conditions. In a worse case, informal settlements provide neither good access to jobs nor a healthy environment, instead creating poverty traps (Marx, Stoker, and Suri 2013; Turok and Borel-Saladin 2018).

Unfortunately, empirical evidence on how job accessibility and poverty are linked in African cities is scarce. Such analysis requires the data on (a) the spatial distribution of jobs, (b) transport networks, and (c) housing rents and household consumption. Spatial distribution of jobs is hard to obtain for African cities, given the prevalence of informal jobs. Even more difficult is information about minibus networks, which is a common transport mode in many African cities. Although housing rents and household consumption are usually available in Living Standards Measurement Survey (LSMS) type household surveys, they are rarely representative at both the informal and formal residential areas. By addressing all these data challenges, this paper examines the link between job accessibility and urban poverty in an African city by assessing the spatial distributions of job accessibility, housing rents, and poverty.

Our approach is threefold. First, we assess the overall level of job accessibility for a city. Our study focuses on Nairobi, Kenya. A relatively monocentric city (Antos, Lall, and Lozano-Gracia 2016; Henderson et al. 2016), Nairobi accommodates more than 1 million residents in informal settlements, most of whom are tenants (KNBS 2012). The severity of living conditions in Nairobi’s informal settlements (particularly Kibera) has been widely documented (for example, Bird, Montebruno, and Regan 2017). We measure job accessibility based on the number of jobs accessible within a certain range of travel time from a residence or neighborhood. Second, we investigate who has better or worse job accessibility in Nairobi, by focusing on poorer people 2 In the Unites States
the Hicks-neutral measure of plant’s efficiency, commonly used in the literature to capture the residual output variation after controlling for tangible inputs of production. This strips out agglomeration effects arising from availability of capital or other inputs and thereby focuses on externalities arising from matching and spillovers.<sup>7</sup> Because it is based on nominal revenue (sales), this measure incorporates prices and thereby potentially confounds higher costs and plant’s market power, with efficiency. Our data permit constructing plant-level price indices which, using the method in Foster et al. (2008), then allows to work in physical units of output and building a measure of technical efficiency:

> 6 Even when price data (e.g. local housing costs) are available, it is not clear whether real wages capture agglomeration effects (Chauvin et al., 2017) because the elasticity of wages with respect to housing costs, for example in a sample of US cities examined in Beaudry et al. (2014) is nearly 1 such that “people follow jobs” rather than wages (nominal or real).

> 7 Further, as Combes and Gobillon (2015) note, wages may differ from true gains to the degree that local firms have monopsony power and TFPR avoids making any assumption about the relationship between the local monopsony power and agglomeration.  By construction, however, a limitation of this measure is the assumption that all employees within a firm earn the same average wage. Thus, it masks any underlying agglomeration mechanisms arising from heterogeneity in skills and occupations. For a more granular analysis with administrative worker-level data, see De la Roca and Puga (2017) and Quintero and Roberts (2018).

6
0.6, and average inter-item covariance from 0.5 to 0.12. The overall PSM index, however, shows higher Cronbach’s alpha of 0.76.

The Integrity index, comprised of eight items, has an alpha of 0.61 and average interitem covariance of 0.14.

Each of the Big Five components show rather low alphas, between 0.2 and 0.4. These results, however, are consistent with those found by Pierre et al. (2014) using the same instrument in different low-income countries. It should be noted that we use a simplified Big Five Instrument, with only 15 questions instead of the usual 44-question instrument. The overall Big Five Index presents moderate reliability (alpha = 0.57). Finally, the overall Non-Cognitive Index shows higher reliability with alpha = 0.68.

Table A1: Reliability of Selection Traits

||Number of items|Cronbach’s alpha|Avginteritem covar.|
|---|---|---|---|
|Attraction to Policy Making|4|0.198|0.056|
|Commitment to Public Interest|7|0.426|0.057|
|Social Justice|5|0.319|0.049|
|Civic Duty|7|0.549|0.080|
|Compassion<br>|7|0.441|0.089|
|Self Sacrifce|6|0.613|0.115|
|PSM Index|36|0.765|0.056|
|Integrity Index|8|0.608|0.143|
|Extraversion|3|0.257|0.060|
|Conscientiousness|3|0.278|0.044|
|Openness|3|0.246|0.053|
|Emotional Stability|3|0.197|0.036|
|Agreeableness|3|0.437|0.
nations that accepted large numbers of immigrants. Jordanians voted in the 1989 elections and expected future elections, but their impact on policy through voting was limited. Third, Jordan did not have a welfare state as large as those in the developed world (Brynen 1992).

# **4. Data**

This study uses Gwartney, Lawson, and Hall (2017) Economic Freedom of the World Annual Report (EFW) to measure changes in Jordan’s economic institutions. The EFW index is a reasonable proxy for economic institutions, incorporating 42 variables collected from numerous sources (including the World Bank and IMF) across five categories including the size of government, legal structure and property rights, access to sound money, freedom to trade internationally, and regulation of credit, labor, and business (Powell, Clark, and Nowrasteh 2017; Feldmann 2017). The EFW index ranges from 0 to 10, in which a higher score denotes a greater level of economic freedom. Polity IV is used as a measure of democratic political institutions (Center for Systemic Peace 2018). The Polity IV index measures how democratic a country’s political institutions are on a scale of –10 to +10, in which a higher score denotes a more democratic government.

Political liberalization in Jordan in 1989 was not a permanent transition toward democracy and it occurred before the refugees arrived (Sütalan 2006). In contrast, the economic institutions did change permanently and rapidly after the surge of Kuwaiti-Palestinians. The study relies on the EFW index as the outcome of interest as it represents economic reforms (Sütalan 2006). EFW scores were originally calculated in five-year intervals with most countries’ series of scores beginning in 1975, and on an annual basis following 2000. The uneven spacing of EFW time series and relatively short time series of observed EFW scores prior the Jordanian refugee surge in 1990 are two potential problems. The study therefore conducts multiple specification checks that probe the sensitivity of the results to avoid overstating the precision of our findings.
Tamil Nadu, Karnataka and Delhi, labor laws do not seem to draw significant complaint in the states of Uttar Pradesh and Punjab.

# **(b) Business environment indicators: power shortages**

Disparities in power supply and transport are often cited as sources of regional gaps in economic performance in India. Regional differences in road and rail transport networks cannot be identified separately from other components of market access and should be treated accordingly as influences on productivity. When we turn to power supply the basic problem in India at the moment is one of shortage, high costs and unreliability, all reportedly stemming more from problems of transmission and distribution than of generation. There are several indicators in the World Bank Investment Climate Survey in terms of which regional differences in the gravity of the problem could be described. One of these is the frequency of outages reported by managers. According to the survey, power outrages occur almost every other day for the average business in India, as compared to once every two weeks in China, and once a week in Brazil. A more accurate indicator from the point of industrial productivity seems to be, however, the output that managers estimate to lose on account of the outages. This is the indicator we use in estimating the productivity equation in the next section. According to World Bank surveys, the average manufacturer in India reports losing 8.4 percent a year in sales as opposed to under 2 percent for the average manufacturer in China or Brazil. Outages could lead to loss of sales by forcing downtime (or idle capacity) on managers. They could also cause wastage of material that would have been in-process at the point of the outage and could not be used when production subsequently resumes. Then there could be the additional cost of equipment maintenance costs directly attributable to physical damages done by outages. Regional variation in reported losses due to outages within India are shown in the first column of Table 2, losses ranging from under 4 percent in the state of Gujarat to more than 15 per cent
|(0.0377)|(0.0281)|
|Land size 2 to 5 ha|0.288***|0.799***|
||(0.0443)|(0.0348)|
|Land size >5 ha|0.604***|1.055***|
||(0.0468)|(0.0371)|
|Share of Adults >50%|0.0476*|0.0206|
||(0.0254)|(0.0251)|
|Improved Irrigation System|0.130***|0.177***|
||(0.0340)|(0.0249)|
|Owns land|-0.218***|0.0647**|
||(0.0336)|(0.0252)|
|Sierra|-0.151***|0.00957|
||(0.0336)|(0.0326)|
|Selva|0.0414|-0.0303|
||(0.0404)|(0.0422)|
|Rural|0.0757**|0.0935***|
||(0.0302)|(0.0303)|
|Constant|4.249***|4.182***|
||(0.0784)|(0.0845)|
|Observations|6,870|7,117|
|R-squared|0.123|0.238|
|Sigma|0.922|0.894|
|Standard errors in parentheses. *** p<0.01,|** p<0.05, * p|<0.1|
|Source: Own estimates based on Peru's EN|AHO 2004 -20|10.|

58
While numerous studies have explored the roles of personality traits and gender role attitudes on labor market outcomes, most of these studies have focused on Western societies, and a few on Asia. However, there is notable absence of such studies in the Ghanaian context, rendering this paper crucial for Ghana and, by extension, Africa. This study explores the respective contributions of gender role attitudes and personality traits to labor market outcomes and examines their interactions within the context of a developing country.

Ghana is an interesting country to study for a number of reasons. Firstly, Ghanaian society is predominantly patriarchal, charactarized by strong cultural influences that govern daily life. Secondly, there are substantial disparities in labor force participation between men and women, with men’s participation being approximately 8 percentage points higher than that of women, largely attributed to cultural and gender role attitudes (DTDA, 2020). Thirdly, women in Ghana are disproportionately engaged in family and own-account work, with 31% of women involved in own-account work compared to 11% of men (GSS, 2014). They often opt for low-productivity jobs that provide flexibility for domestic chores (Owoo & LambonQuayefio, 2022). Lastly, the availability of household-level data, including genderdisaggregated information on labor market indicators, as well as personality traits and gender role attitudes, provides a vital context for this study. This dataset covers two independent regions in Ghana with diverse cultural background and gender perspectives.

We employ both descriptive and econometric approaches to achieve the study’s objectives. To understand the drivers of labor market participation decision, we use the multinomial logit (MNL) model with the dependent variable comprising three mutually exclusive categories: not in the labor force, employed, and unemployed. The choice between wage and self-employment is examined using the linear probability model (LPM).

The empirical results suggest that both gender role attitudes and personality traits significantly influence individuals’ decisions regarding labor market participation and employment choices in Ghana. In most cases, gender role attitudes tend to mask the effect of
While this approach was designed primarily to be applied to individual NMHSs for assessing either whole-of-service or specific products, we apply it on a global scale. This results in a less detailed SEB analysis that should be considered a rough estimate of the global potential of GPCs providing forecasting products to LICs.

The baseline of this study is the status quo of weather and climate forecasting quality in LICs. Here, it is assumed that in the status quo, LICs lack full access to global forecasting products from GPCs. The relevant change is the gaining of full access to forecasting products available from GPCs.

# 3.1 Conservative approach

A critical challenge in assessing costs and especially benefits in this field is that it is difficult to attain comprehensive, robust and accurate figures. Firstly, it is somewhat ambiguous as to what exactly should be included under costs and socioeconomic benefits. Secondly, assuming the first issue is solved, it is not possible to obtain data on every aspect. This issue is difficult to solve, but its consequences can be controlled. In consideration of these limitations, we assess ranges of benefit and cost estimates, rather than singular values. Furthermore, the trend of potential biases can be managed; when confronted with data gaps, socioeconomic benefits are intentionally systematically understated, while the more robust cost data are considered more comprehensive.

Under such a conservative approach, the resulting net benefits likely underestimate the true net benefits. Assessment biases therefore affect only the magnitude and not the overall trends of the results, increasing confidence in the conclusions.

# 3.2 Costs

Costs are defined as the total amount of sacrifices made to accomplish a task or produce a product or service (WMO et al 2015). In the case of weather forecasting, costs occur at different stages:

- Service production and delivery (GPCs, RSMCs, NMHSs, commercial weather services, etc.) including staff, forecasting infrastructure investment and maintenance as well as the costs of buildings, utilities, etc. Taking a wider perspective, the use of public goods
FDI and inflows are volatile (see sections 3 and 4.1.1). Less FDI in Kenya may stall growth plans, plans that would upgrade much need infrastructure. Because China’s domestic consumption will shift toward final consumer goods, demand for natural resources and commodities will fall, and Kenya’s recently discovered oil will face lower global prices.

# **2.6.1 China’s rebalancing will help reduce poverty by 2030**

Lakatos _et al_ (2015) use a Global Income Distribution Dynamics (GIDD) model that incorporates micro and macro data for simulation of the effects of economy-wide changes in Sub-Saharan Africa. Changes in demographics, sectoral employment, per capita consumption growth, relative wages across sectors, and relative food to non-food prices are recorded by the model. It also uses 17
12|58%|8|62%|
|VEN|58|33%|91|40%|3|33%|10|80%|12|42%|
|Total|2,396|53%|3,341|48%|54|27%|192|59%|127|35%|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the number of active regulations in 2017 by country and NTM type.

In addition to the number of active regulations and the relative importance of each NTM-type, another way to measure the incidence of NTMs is through their product coverage. Table 8 reports the number and share of products affected by at least one active NTM in 2017. Cuba and Argentina exhibit the highest product coverage: 98% of products that could be produced and imported at the HS 6-digit level are affected by at least one technical measure (SPS, TBT or PSI) while non-technical measures, such as price and quantity restrictions, affect at least 96% of imported products. At the other extreme, Paraguay has the lowest product coverage with 9% of products affected by at least one technical measure and less than 14
the business has

-

- A target set for sales over the next year

- A budget of the likely costs your business will have to face over the next year

- An annual profit and loss statement

- An annual statement of cash flow

- An annual balance sheet

- An annual income/expenditure sheet

# _Wage worker and SME surveys_

We also make use of data from surveys of wage workers and larger firms. The wage worker survey was conducted in all urban areas in Sri Lanka at annual intervals from 2008-2011. The initial 51