rafmacalaba/data-use-mentions-extended huggingface dataset:
looks at districts within two countries, Ghana and Cˆote d’Ivoire, and argues that local production of cash crops, specifically cocoa, spurred urbanization outside of the few largest cities. In his setup, consistent with mine, these secondary towns form primarily as “consumption cities” where farmers sell their products and buy services and imported goods, as opposed to manufacturing centers as is often assumed in models of urbanization and city formation. Unlike all these papers, the present outcome of interest is a proxy for economic activity (lights) that is available for individual cities on an annual basis, as opposed to population, which is typically only available for censuses carried out at most every ten years. This allows me to observe short-run (annual) changes and to control for all potentially confounding country-level variation with country*year In stressing the role played by the largest city in each country, this work also has implications for the study of urban primacy (Ades and Glaeser, 1995; Henderson, 2002) and decentralization. I find that primate cities are growing slower than others on average net of the transport cost effect. Finally, in focusing on the importance of coastal cities, this work relates to the literature on geographic determinants of growth, including Gallup, Sachs and Mellinger (1999) and Collier (2007), which emphasize coastal access and the problems of being landlocked, respectively.

The remainder of the paper has the following structure. Section 2 provides a simple conceptual framework to facilitate interpretation. In section 3, I describe the lights and roads data and the methods used to integrate them. In section 4, I describe the econometric specification used, and in section 5, I report results. Section 6 concludes. An Appendix provides further details on the data and methods used.

# **2 Conceptual framework**

Economists often think about the role of intercity transport costs in city growth in the context of two-region New Economic Geography (NEG) models following Krugman (1991), or one of many variants,
from the Human Capital Index database, recently developed and then updated by the World Bank (2020); second, the rate of “learning poverty” defined as the share of children who are unable to read and understand a simple text by age 10 (World Bank, 2019a). For more details on these measures and our empirical specification, see Appendix 1. Figures 3 and 4 provide visual evidence of the association between teacher earnings differentials and student performance. Figure 3 shows the cross-country 19
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE underpricing of power is close to 1.6 percent of GDP, which is considerably higher than in comparator countries (figure 27). In the water sector, tariffs are $0.34 per cubic meter compared to the estimated cost recovery tariff of $1.07 per cubic meter. The financial burden associated with underpricing of water is 0.6 percent of GDP, which is lower than that for power in Mali but higher than in comparator countries.

**Figure 27. Underpricing of power and water in Mali is costly relative to comparator countries**

Financial burden of underpricing in 2007, as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>power water<br>Mali nonfragile low-income countries<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster 2009. Note: Estimations based on operational performance data up to 2007.

About 90 percent of people with electricity or piped water connections belong to the top income quintile; connections are virtually nonexistent for poorer households (figure 28). This highly inequitable distribution of connections virtually guarantees that any price subsidy to these services will be extremely regressive.

45
Using historical data on physician densities, GDP per capita, OOP spending per capita, and the size of the population aged 65 or over, we estimated the relationship between the economic and population indicators and physicians per 1,000 population using a generalized linear model (GLM). Missing data points for physicians per 1,000 between any two real data points were linearly interpolated. No adjustments were made where data was missing in projected physician densities.

For countries where historical data for total per capita health expenditures and OOP health spending was completely missing, mean yearly values for the specific World Bank region and income group combination to which the country belonged were substituted (see Annex C, below, _Projecting out‐of‐pocket expenditures_ ). No adjustments for missing data were made where data was not available for population or GDP per capita; this data tended to be missing for a handful of very small countries, where the projections would minimally affect overall shortage estimates. Thus, countries for which population or GDP per capita data were not available are excluded from the demand projections.

All variables were transformed into logs. To avoid endogeneity, GDP per capita, OOP spending per capita (OOPPC), and the size of the population aged 65 or over (Pop65) were all lagged up to five years to allow time for such factors to work through the economy and affect the labor market, as other authors have done in previous projection exercises (Getzen 1990; Scheffer et la. 2008). A stepwise approach was used to select the specific combination of year lags that maximized the predictive power of each variable. Lagged variables that achieved a minimum 1% level of significance after repeated iteration were kept within the model, resulting in the following optimal model: (Eq 1) ln( _physicians per 1000 populationit_ ) = β0 + β1*ln( _GDP per capitait‐1_ ) + β2*ln( _GDP per capitait‐4_ ) + β3*ln( _GDP per capitait‐5_ ) +β4*ln(
_ (2022) document similar levels of 65-75% over one year and 40-50% over three years in Australia. For those at the top 0.1%, where undistributed corporate profits matter much more in our settings, we measure much higher persistence levels – the probability of observing the same individuals at the top 0.1% over three years for example is 55-60% in Honduras vs. 30-40% in Norway and Australia.

# **7.3 Female participation at the top**

An additional dimension of income inequality we are able to observe using administrative tax records is the gender gap: what is the representation of women at the very top of the income distribution?

In Figure 9, we present the share of female taxpayers in the top quantiles of the distribution, pooled for the 2014-2019 period. We show the female share for two income definitions, excluding or including undistributed corporate profits. The overall

> 24While the previous exercises use a discrete measure of persistence at the top, asking if individuals remained at the top 1% or 0.1% earners over time, an alternatively measure is to ask how the rank of an individual in the entire distribution changes over time, given their ranking at some initial period – that is to compute the rank-rank correlation of individual income over time. We present that exercise in Figure A.3, where we show the median rank in 2014 and 2018 for each bin of income ranking in 2011<sup>25</sup> . What we document is a very strong, positive correlation between initial ranking and median ranking four and seven years after the initial measurement – individuals at any given position in the initial period are likely to stay in a similar position over the years.

18
**<br>**violence**<br>•<br>Raise awareness of the links<br>between violence and health<br>among service providers,<br>managers, and public health<br>policy makers.<br>•<br>Improve the quality of care for<br>survivors of violence, including<br>identification, treatment,<br>documentation, information,<br>referrals and follow-up.<br>•<br>Increase coordination with other<br>sectors that provide services or<br>workonviolence prevention.|•<br>Policies, procedures and protocols to<br>improve the health care response<br>•<br>Sensitization and training of health<br>professionals<br>•<br>Routine screening and referral systems<br>•<br>Development of information systems<br>such as epidemiological surveillance and<br>morbidity statistics on violence<br>•<br>Specialized survivor services<br>(counseling, support groups)<br>•<br>Improved coordination and referrals to<br>NGOs and other sectors<br>•<br>Curricular changes in training of nurses<br>and medical personnel|
|**Community**<br>**Mobilization**|**To increase community**<br>**mobilization to address GBV as a**<br>**public health problem**<br>•<br>Strengthen community support<br>for survivor services<br>•<br>Strengthen coalitions and<br>networks<br>•<br>Improve attitudes, norms,<br>practices and resources at the<br>communitylevel|•<br>Coalitions for public health research and<br>advocacy<br>•<br>Community level prevention and<br>mobilization initiatives<br>•<br
5 If the mean welfare ratio is equal to one, it indicates that on average households have consumption at the level of the poverty line. Growth is then measured by changes in the welfare ratios over time. Finally, Gini indices are also computed using normalized consumption levels, such that: G = 2 cov (Ci /Z, Fi )/W (3) where Fi is the normalized rank (taking a value between zero and one) of household i in the distribution of consumption, and the covariance is computed using the household weights. Note that at the area level, the Gini index computed with the lower poverty line (GL) is equal to the Gini with the upper poverty line (GU) since using one or the other regional poverty line just scales up all consumption measures without affecting inequality. However, at the national, urban, and rural levels, the Gini indices do depend on the poverty lines used since the poverty lines and the consumption distributions are not equal between areas.

# _1I.3 Results_

Table I gives poverty measures at the national, urban, and rural levels (measures for the fourteen geographical areas are given in appendix). We find decreasing poverty in the early 1980's, increasing poverty in the late 1980's and early 1990's, and again decreasing poverty thereafter (the results are similar with both sets of poverty lines). Broadly speaking, these results are consistent with previous research<sup>5</sup> .

Table 2 provides welfare ratios and Gini indices of inequality. The trends for welfare ratios are similar to those observed for poverty, which is not surprising since welfare ratios represent mean levels of consumption. Nationally, the welfare ratios increased from 1983-84 to 1985-96, then decreased from According to Rahman and Haque (1988), poverty decreased from the mid 1970's to the mid 1980's. For the later period, estimates based on group data for the Household Expenditure Surveys published by the Bangladesh Bureau of Statistics indicate
should be about half the number of proxy reports for households in the proxy assignment. In total, by design, there are more self‐reports than proxy reports. Because the survey experiment highly emphasized the importance of avoiding proxies when not assigned to this treatment, the project was successful at completing self‐reports when assigned. In about five percent of cases, the team was unable to interview a person selected for self‐report. While there were small deviations from the original design during the implementation, the overall realised design remained very close to the planned design, as shown in Table A.2. in annex. The results presented in this paper are unchanged if we exclude the observations where we had to deviate slightly from the planned design, or when we restrict ourselves to spouse instead of random proxies to reflect the more common approach in household surveys. Panel B of Table 2 reports balance tests at the individual level, and shows that allocation across treatments is generally well balanced. There appear to be some imbalances across individuals related to age, 15
- 11 -

# _Table 4 Regression Results for Transition Rate, 1992_

# Dependent Variable = Transition rate, 1992

||_Primary_|_Junior Secondary_|
|---|---|---|
|_Right hand_<br>_side variables_|_to_<br>_Secondary_|_to_<br>_Senior Secondary_|
|constant|-86.4*|-0.17|
||(-9.23)|(-0.77)|
|log Income per capita|12.91*<br>(2.02)|-0.571*<br>(-2.49)|
|log Expenditure per pupil|10.33*<br>(3.05)|-0.077<br>(-0.56)|
|log Agricultural income|1.69<br>(0.95)|-0.106<br>(-1.74)|
|Deviation of Grade 6 Achievement Score|0.112*|-NA-|
|From National Mean|(2.55)||
|N|29|29|
|Adj. R|0.655|0.305|

# Note: The T-statistics are in parenthesis.

(*) Significant at 5 % level.

**(b)** External **Efficiency.** Ascertaining a closer match between the education system's future output and the demand for educated labor requires rate of return analysis. Such analysis also assesses whether educational investment had any impact on household welfare, and where should resources be allocated in the sector so as to make it more externally efficient? A summary of economic rates of return to three levels of education, based on "elaborate" or "complete" method with the data from 1993 Labor Survey by the Ministry of Labor, is presented in Table 5. Both the private and social returns to each level of education were high in
only 3.6 percent, because the US$2 per day poverty-income elasticity is significantly lower than the US$1 per day poverty-income elasticity.

These poverty impacts from full merchandise trade reform are significantly lower than past estimates. For example in the 2002 GEP, the number of poor people living on a US$1 per day was estimated to fall by 110 million, and the number living on US$2 per day to fall by 320 million (World Bank 2002). The new numbers reflect three changes in increasing significance. The first is the change in the baseline poverty forecast. The baseline US$1 per day poverty forecast is currently 622 million, compared to 734 million in the 2004 GEP (World Bank 2004). Using the 2004 GEP forecast would raise those lifted out of poverty to 38 million and 80 million, respectively, for US$1 per day and US$2 per day poverty lines from the current estimate of 32 million and 66 million. The second change is the new estimate of the change in the food wage. Both the unweighted and population-weighted estimates of the food wage impacts from global reform are reduced by 50 percent compared to the previous results.<sup>29</sup> This is largely due to the role of the new tariff database (particularly preferences and the policy reforms that are now subsumed in the baseline). If the previous estimates of the change in the food wage are used, the reductions in poverty would rise to 63 million and 190 million, respectively. The third change reflects the use of region- and indicator-specific income poverty elasticities.<sup>30</sup> Using the old

> 29. Earlier studies with the Linkage model found an estimated impact of roughly an 8 percent increase in the food wage (averaged over all developing countries). The new impact is roughly a 4 percent increase. The largest changes occur in China and the Middle East and North Africa, the regions with the most significant revision in tariffs. For
# **Figure 1: Total Number of Climate and Environmental related Natural Disasters Over Time**

This figure shows the total number of disaster episodes around the world. For a disaggregated presentation across disaster groups, see Appendix, Figure 7 <!-- Start of picture text --> iv) a call for international assistance<br>iii) an official declaration of a state of emergency<br>400 ii) 100 or more people affected<br>i) 10 or more people reported killed<br>300<br>200<br>100<br>0<br>1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> countries covered, and hence decided against it. We focus on the instance of disaster instead of the precise damage figures for two reasons: 1) EM-DAT damage figures are missing for most developing countries and hence using damage figures will introduce a rich-country bias3 hard to correct for, in addition to significantly trimming the sample, and 2) anecdotal evidence and on-the-ground experiences tend to suggest that these damage figures are not very precise and accurate.

This section focuses on key stylized facts related to climate and environmental related natural disasters. We consider eight types of natural disasters related to climate change or environmental risks: wild fire, volcanic activity, earthquake, storm, landslide, drought, flood and extreme temperature. While a specific type of event can occur many times in a country each year, an episode is defined if the event occurs at least once in a country in a year. Therefore, there are typically more occurrences than episodes. The remainder of this paper focuses on the impacts of episodes.

Climate and environmental related natural disasters have increased in frequency between 1960 and 2020. Figure 1 presents the number of disaster episodes categorized by severity during the last 60 years. Between the periods 1960-79 and 2000-2020, the number of disaster episodes has almost tripled. Disaster episodes associated with an official declaration of a state
identical or nearly identical to the English word; and for 12 stimulus items, the words were suitable for piloting, but the pictures were inappropriate or unfamiliar. Eight plates had words that were more likely to be known in English and were not translated. Luo was the most difficult language to work with, as it had the most limitations in translating the stimulus words, so we created this list first. To increase our capacity to select the best words possible, we (PK, OO) engaged a focus group of 6 Luo-speaking mothers and teachers to review 32 words for which we either had no suitable translation, or that were candidate items not part of the original BPVS III.

We piloted 58 items with about 30 Luo-speaking children 2-6 years of age, and examined pass rates for each word by age group (younger or older than five years). Words were then grouped by estimated difficulty level (hard, average or easy), and we sought to have roughly the same number of items at each of the three difficulty levels. As we lacked a sufficient number of Luo words across the three categories, we created some new stimulus words based on results from the focus groups (lantana, bull, roar), providing our own plates of pictures for each. The final Luo test includes 27 items, with approximately 9 words in each difficulty level. Of these 27 items, three were new words and pictures, three involved translations including two words (no one-word translation was known), one used a distractor picture to replace the original stimulus picture (“boulder” replaced “mountain”), one replaced a plate with more familiar looking pictures, and one slightly changed the stimulus word (from “applauding” to “clapping,” as our translators were aware of no distinct word for “applauding.”).

We repeated a similar process for creating tests in Swahili (32 items) and English (34 items), each with roughly one-third of items in the three difficulty categories. The Swahili
# Figure 9.  Inter-industry wage inequality, average level of protection and unionism

- (a) Inter-industry wage inequality and  collective bargaining

<!-- Start of picture text --> 0 20 40 60 80 100<br>workers covered by collective bargaining agreements<br>Galbraith: Theil of inter-ind. wages Fitted values<br>(c ) Collective bargaining and<br>average tariff rate<br>0 20 40 60 80 100<br>workers covered by co l ective bargaining agreements<br>unweighted average tariff rate from WB sources Fitted values<br>.15<br>.1<br>.05<br>Galbraith: Theil of inter-ind. wages/Fitted values0<br>40<br>30<br>20<br>10<br>0<br>unweighted average tariff rate from WB sources/Fitted values<br><!-- End of picture text -->

- (b)  Average tariff rate and inter-industry wage inequality

<!-- Start of picture text --> 0 20 40 60 80 100<br>unweighted average tariff rate from WB sources<br>Galbraith: Theil of inter-ind. wages Fitted values<br>.2<br>.15<br>.1<br>.05<br>G alb ra ith : T he il o f inte r-ind . w ag es /F itte d v alu e s0<br><!-- End of picture text --> (d) Inter-industry wage inequality (controlled for collective bargaining) and average tariff rate <!-- Start of picture text --> 0 10 20 30 40<br>unweighted average tariff rate from WB sources<br>resid Fitted values<br>.1<br>.05<br>resid/Fitted values0<br>-.05<br><!-- End of picture text --> 45
human capital characteristics) relate to work by Borjas (2000), Duleep and Regets (1997) and Hu (2000).

# **_3.1. EMPIRICAL FRAMEWORK_**

The estimation employs OLS with country fixed effects to explain immigrants’ performance - proxied by occupational placement (OP) - as of Census year _t_ for migrant _i_ who came to the US in decade _d_ . Decades are attributed to cohorts in the sense used by Borjas (1985), where the implicit assumption is that migrants arriving within a decade are homogeneous with respect to ability _._ The estimations rely on data from the 5% PUMS samples of the US Censuses 1980, 1990 and 2000<sup>8</sup> and focus on employed male immigrants, not living in “group quarters”,<sup>9</sup> not enrolled in school as of 1980, 1990 or 2000, and whose reported years of immigration lie within the 19701989 interval. The sample in Census 1980 is random, each observation representing 20 individuals in the US population, while Censuses 1990 and 2000 are weighted so that each observation represents a different number of individuals in the US.

Three different metrics are used as the dependent variable, denoted as OP (Occupational Placement). The first indicates the prestige level and the other two quantify the educational content of each occupational category. The prestige index and ne educational content variables are available from Ruggles et al. (2009) whereas the third variable is based on our own calculations. The first metric, labeled as the Prestige Index, does not vary by census year, and measures the prestige associated with an occupation. The second, labeled Education Index 1, indicates the educational content of each occupation and is based on the share of individuals with undergraduate and graduate degrees who declare that as their occupation. Both the Prestige Index and Education Index take values from 1 to 100. The last variable, labeled Education Index 2, also indicates the educational content of an occupation, and
# 2.2 Limitations to the Methodology to Generate the List of Green Jobs

There are some shortcomings to this method. First, Granata and Posadas (2024) observe that the GTI may underestimate the level of greenness of an occupation as the ISCO-88 does not present a comprehensive list of tasks for every job in an occupational category, but rather a description of what the occupation involves for the majority of jobs. Second, emerging occupations, some of which are associated to the greening of the economy, may be under the residual occupation code “Not elsewhere classified” and may not be captured since the task description of these residual occupations tend to be quite general (Granata and Posadas 2024). Third, the ISCO descriptions of occupations are static and therefore we might fail to capture new and emerging green tasks overtime. Lastly, it is possible that the text analysis methodology underestimates the share of green tasks in managerial occupations because administrative tasks such as policy makers who form clean energy and environmental policies are not emphasized.<sup>21</sup>

# 2.3 Analytical Methodology

We use standard econometric techniques to understand the factors associated with being in a green job, being in a greener job, and earnings in green jobs.

# _2.3.1_ Factors associated with being in a green job

To understand the factors related to being in a green job, we estimate a logit model of the probability of being in a green job as specified in equation 2. 0 + α + η + δ + θ + ε � (2) ii ii pp ll tt ss ii Y is a binary dependent variable equal to one if the respondent is in a green job (GTI >0) and Pr( YY= 1 ∣XX) = FF�ββ + ββ XX zero otherwise. We use the strictly green job definition for the first round of estimates and the broadly green job definition for a second round of estimates. F is the logit function link while the individual characteristics (age, race