rafmacalaba/data-use-mentions-extended huggingface dataset:
sq km. Urban expansion rate is the percentage of newly urban areas in a given grid cell for a given SSP scenario (ranging from 0 to 1). Crop production for each grid cell is defined as the yield multiplied by harvested area in metric tons. We include seven food group: cereals, fruits, vegetables, oils, pulses, roots, and sugar. We exclude non-food groups from the database (Cotton, other fibre crops, arabica coffee, robusta coffee, cocoa, tea, tobacco, rest of crops). Here, the urbanization rate is defined as the percentage of urban population to total population, and ranges from the value of 0 to 1. The urban expansion projection data (Chen et al. 2020) are calculated using panel data regression based on historical population, urbanization rate, and gross domestic product (GDP) data.

Table 4 presents results for crop production for both 2040 (Panel A) and 2100 (Panel B). First, we find that across all SSP scenarios and in both 2040 and 2100, grids that are expected to have higher urban expansion rates are grids which have higher levels of crop production today. This indicates that in the future, cities are likely to grow into areas with more crop production, regardless of SSP scenario. Second, in both 2040 and 2100, the positive correlation between projected urban expansion and current crop production is highest under SSP3 and lowest under SSP5. Third, the positive correlation between projected urban expansion and current crop production is large in both 2040 and 2100, but the magnitude dampens over time. This means that the cumulative 22
choose the transportation mode that minimizes their transportation costs, and the probability to choose each employment subcenter depends on income net of commuting cost.

- iii. _Rents are adjusted_ , as changes in transportation costs modify the relative attractiveness of locations in the urban area. All else equal, locations that are furthest from jobs and where households largely commute by transportation modes subject to the tax become less attractive because they are tied with higher transportation costs. As a result, housing rents in these locations will decrease proportionally more than rents in more central or more job-dense locations.

- iv. _Households adjust their dwelling sizes_ in reaction to rents changes, if they live in the formal private sector (as we assume that dwellings have fixed size in informal housing, backyarding, and formal subsidized housing).

- v. _Households’ locations change_ , i.e. they move within the urban area to maximize their utility.

# 4. Data, calibration, and validation

## 4.1. Data and calibration

We use the same data as Pfeiffer et al. (2019). The spatial distribution of the population is taken from National Censuses for the years 2001 and 2011. Incomes are also from the 2011 Census, and reclassified in four groups. For transportation, we use the transport model used by the City of Cape Town to retrieve transportation times between pairs of transport zones for each transport mode and jobs’ locations. We also use aggregated statistics on modal shares and residence-workplace distances in Cape Town derived from Cape Town’s 2013 Transport Survey. We use housing prices from the housing sales registry of the City of Cape Town. Concerning land use, areas of subsidized housing are identified from the cadastre of the City of Cape Town. The area available for backyard housing is estimated as the yard size of these units. Informal settlements areas are taken from the 2011 census. Finally, amenities are taken from various sources (see Pfeiffer et al., 2019). For the estimation of the model’s parameters, we
by a major mobile phone operator in Afghanistan and processed by Blumenstock et al. (2018). For this paper, the data were made available at the district level, on a monthly basis, for the period from April 2013 to April 2016. Formal firms with active cell phone traffic are measured per 1,000 inhabitants.

_Illicit economic activity_ The land surface devoted to opium poppy cultivation is used to estimate Illicit economic activity . It is recognized that poppy yields per hectare may vary across different parts of the country and over time. YY<sup>OO</sup> Yields could also decline as the cultivated surface increases, and more marginal land is used. However, the relevant margin in this case is not between cultivated and uncultivated land, but between land devoted to poppies and land devoted to wheat, with both being similar in terms of their agricultural potential (Keohler and Zuercher 2007). Given the absence of detailed information on poppy yields across 12
# **Table A2: Robustness**

## **Dependent Variable: Completion Binary [yes=1] in Columns 1 and 3 through 9; Completion Index in Column 2 Standard Errors: Clustered by Organization OLS Estimates**

||**(1) Baseline**|**(2) Average**<br>**Completion**<br>**Rate**|**(3) Excl. Org.**<br>**With Most**<br>**Projects**|**(4) Excl. Five**<br>**Orgs. With**<br>**Smallest No. of**<br>**Projects**|**(5) Excl. Orgs. Below**<br>**5% or Above 95%**<br>**of CS-Autonomy**<br>**Scale**|**(6) Excl. Orgs.**<br>**Below 5%**<br>**or Above 95% of**<br>**CS-Inc./Monit.**<br>**Scale**|**(7) Defining**<br>**Management**<br>**Scores Using**<br>**Orgs with**<br>**Project Data**|**(8) Fractional**<br>**regression**|**(9) Alternative**<br>**Fixed Effects**|
|---|---|---|---|---|---|---|---|---|---|
|**CS-Autonomy**|0.28***|0.
<!-- Start of picture text --> a. Projected crop losses by 2040 b. Projected crop losses by 2100<br>}<br>/<br>ecce € . Se|<br>S3 fh Nf<br>a a Ls<br>= “= vegetables ¢ ? — = A / “= vegetables<br>S 2 re! ~ mot é, ° ——~ f f ~ rots<br>6 . 3 = yy<br>& <a) Vv ~ cereals z A ie =<br>a . | ~ cits & i. J ~ of<br>c ° 4 : i co 7 .<br>1<br>5 = : —— ~ pulses ; 1 . ‘' ° ~ pulses<br>r) 0<br>SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 S$SP2 SSP3 SSP4 SSP5<br><!-- End of picture text -->
and presents the main results. Section 4 concludes.

# **2. The data**

This section describes the sources of the dependent variables, and the sources and construction of independent variables.

The main dependent variable used in previous studies is the poverty headcount ratio at $1.90 a day (at 2011 purchasing power parity) as a percent of the population. As an alternative variable, the poverty headcount ratio at $3.10 a day is sometimes used too. We use both indicators, the latter for robustness purposes. The Democratic Republic of Congo is the country with largest share of population living below $1.90 a day: 85.56%, followed by Madagascar and Burundi with 77.88% and 77.65%, respectively. Looking at the alternative definition of the poverty headcount ratio of $3.10 a day, these percentages increase significantly. The Democratic Republic of Congo has nearly its entire population (94.52%) living on less than $3.10 a day, Burundi — 92.17% and Madagascar — 91.08%. The correlation between the two variables is high, at 93.73%.

2
studies of intangible investment in developed countries, some of which go back to the 1970s. Future intangible asset studies on developing countries should expect a similar disadvantage in historical data availability.

Finally, while most existing studies of intangibles present only national aggregates, we present intangible investment estimates disaggregated to the industry level. This is a simple corollary of the CHS approach, since they take firm level spending as their starting point. For those intangibles for which we use firm level surveys, this is easily done by aggregating the firm level data to the industry level; the surveys are designed to be representative at the industry level. In cases where we use aggregate national accounts data, we estimate the amounts purchased by different industries by using the national accounts input 11
**Figure 5:** Self-Reported yield overestimation of crop-cut yield over quintiles of crop-cut yields and key inputs. _Notes:_ The y-axis represents the amount by which SR yields overestimate CC yields ( _Y_ = _SR − CC_ ) in kg/ha. The x-axis shows the quintile of the crop-cut yields or of the input per ha. We are restricted to the plots with crop-cut

**Crop−cut Yields**

<!-- Start of picture text --> GPS Measured Area<br><!-- End of picture text --> <!-- Start of picture text --> 1 2 3 4 5<br>200<br>0<br>−400<br>−800<br><!-- End of picture text --> <!-- Start of picture text --> 1 2 3 4 5<br>600<br>200<br>0<br>−400<br><!-- End of picture text -->

**Quantity of Seeds per Ha**

**Days of Household Labor per Ha**

<!-- Start of picture text --> 1 2 3 4 5<br>600<br>200<br>0<br>−400<br><!-- End of picture text --> <!-- Start of picture text --> 1 2 3 4 5<br>600<br>200<br>0<br>−400<br><!-- End of picture text --> 30
I regress a given characteristic of the state to which a student was posted on available individual variables from administrative data at the time of posting (age, gender, marital status, whether the student graduated on time, and whether the student transferred to another department). In addition, during the survey, I collect additional variables on student characteristics that may affect their willingness to serve in different parts of the country and their ability to potentially influence their postings: religion (students may be more willing to serve in regions with the same religion), states of the country in which they lived before NYSC (people who have previously lived outside their ethnic groups may be willing to do so again), type of secondary school attended (private schools are typically more expensive compared to state or Federal government schools and may indicate socioeconomic status; also, Federal government schools are usually more diverse as they attract students from different states of the country and so present an avenue for prior interethnic exposure), and whether either parent has tertiary education (an indicator of socioeconomic influence and status). The regressions follow the same empirical specification as the results and include fixed effects for the course studied as well as the state of origin as these variables are used in assigning students.

Table 1 provides the results of the randomization balance checks for being posted to a non-Yoruba state (the primary independent variable used in this paper) as well as a continuous measure (share of Yoruba in state of posting). It uses participant baseline characteristics from both administrative and survey data for the main survey sample used in subsequent analyses (those that did not report influencing their postings). A joint F-test fails to reject the null hypothesis that student characteristics do not predict being posted to a non-Yoruba region with a p-value of 0.787. Appendix Table A2 reports the p-values of the joint F-test for the same analysis conducted using other state characteristics (distance, Muslim majority, rural and poverty level). Columns 1 and 2 cover the main analysis sample, 16
- Nagin, D. S. and G. Pogarsky (2004). Time and punishment: Delayed consequences and criminal behavior. _Journal of Quantitative Criminology 20_ (4), 295–317.

- Nelson, C. A., C. H. Zeanah, N. A. Fox, P. J. Marshall, A. T. Smyke, and D. Guthrie (2007). Cognitive recovery in socially deprived young children: The Bucharest Early Intervention Project. _Science 318_ (5858), 1937–1940.

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- Ponniah, K. and S. D. Hollon (2008). Empirically supported psychological interventions for social phobia in adults: a qualitative review of randomized controlled trials. _Psychological medicine 38_ (1), 3–14.

- Raine, A., K. A. Dodge, R. Loeber, L. Gatzke-Kopp, D. Lynam, C. Reynolds, M. Stouthamer-Loeber, and J. Liu (2006). The Reactive-Proactive Aggression Questionnaire: Differential Correlates of Reactive and Proactive Aggression in Adolescent Boys. _Aggressive Behavior 32_ , 159–171.

- Republic of Liberia (2012). Liberia: Poverty Reduction Strategy. Technical report, Government of Liberia, Monrovia.

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# **II. Literature Review and Empirical Strategy**

# **_An earnings model with an expanded measure of human capital_**

The basic Mincer (1965) model is in which the earnings ( _yi_ ) of individual _i_ are a function of that person’s human capital ( _Hi_ ) and a stochastic term ( _εi_ ) that represents idiosyncratic earnings differences, presumed to be orthogonal to _Hi_ . Due to the lack of direct measures of human capital, _H_ has been typically measured by years of schooling attained. This human capital earnings function has been the basis of a large empirical literature, including on developing countries.<sup>8</sup> We expand this basic earnings function to include other measures of human capital—in particular, cognitive skills and noncognitive skills—besides years of schooling and experience.<sup>9</sup> Glewwe (1996) and Hanushek et al. (2015), among others, find that including cognitive skills as a measure of human capital lowers the return to schooling estimated using just the basic earnings function.<sup>10</sup> A growing literature shows statistically significant earnings returns to noncognitive skills.<sup>11</sup>

> 8 A summary of the estimates for developing countries is provided by Psacharopoulos (1984) and Psacharopoulos and Patrinos (2004).

> 9 Even when measures of skills are included, we expect schooling years to affect earnings because of measurement error (that is, our skills measures are not likely to be a comprehensive set) and because years of schooling itself may be valued in the labor market where it is perceived to signal useful information about the individual (Spence, 1973; Belman and Heywood, 1991; Riley, 2001).

> 10 These two studies illustrate the types of data sources used for this growing literature. Glewwe (1996) uses a household survey in Ghana which administered tests to household members of abstract reasoning (Raven's Coloured Progressive Matrices), mathematics, and
that each country’s distribution is lognormal; that GDP per capita gives the correct mean income and that its under- or overestimation compared to household surveys is constant across poor and rich alike) and it is quasi impossible to tell the impact which each of the assumptions separately has on the results. Further, since even the minimal data requirements (national Ginis) are not satisfied annually, authors are led to make additional assumptions (for example, that national inequality does not change or changes in an assumed fashion), so that at the end

> 9 Available at <u>http://www.wider.unu.edu/wiid/wiid.htm</u> and http://econ.worldbank.org.

6
drinking water and roads in West Bengal and drinking water in Rajasthan (women collect drinking water in both places, and are employed building roads in West Bengal). They invest relatively less in public goods that are linked to men's concerns: education in West Bengal and roads in Rajasthan. In a follow-up study, Beaman et al. (2006) analyze how these different policies affect children. They provide evidence from two Indian states that children in villages headed by female leaders have higher immunization rates and that girls experience an improvement in school attendance.

# **2.6  Direct Evidence on Preference Differences**

One potential explanation for the observed differences in spending patterns is that women and men have different preferences. Croson and Gneezy (2009) provide an overview of the experimental evidence on gender differences in preferences. They conclude that there is robust evidence that women are more risk averse than men. They also document gender differences in other-regarding preferences such as altruism, inequality aversion, and reciprocity. However, women are not generally more other-regarding than men. It is not clear how these preference differences affect parents' attitudes towards children.

Bauer and Chylitová (2009) specifically examine how gender differences in preferences depend on the presence of children. They measure the discount rate and risk aversion using data from lab experiments in rural India. They find evidence of gender differences in the discount rate, but not in attitudes toward risk. Interestingly, childless men and women turn out to have the same discount rates, but women with children under 18 are more patient than men with children. The more children the women have, the more patient they are relative to men with the same number of children.

A different kind of evidence on gender differences in preferences is provided by Woolley (2004). The paper surveys a small sample of couples with children in Ottawa, Canada, to study gender differences in spending. People were asked in interviews how they spend their child benefits and what they would do with unexpected additional income. The
of individuals is 2,870,315. In the case of North Macedonia, the total number of households covered was 567,753 and the number of individuals was 2,076,247. In both cases we nowcast incomes to what would be observed in 2019 levels using official GDP per capita growth and passthrough rates for each country.<sup>14</sup> For Moldova we use the 2019 Household Budget Survey (HBS), which is a nationally representative household survey conducted annually that captures information on income, expenses, and consumption in addition to demographic and social indicators. The 2019 HBS covered 4,408 households containing 11,355 individuals.

In the simulations we use an expenditure-based poverty threshold of $5.5 per day; and the same threshold is used to calculate the poverty gap squared, our main welfare indicator. The estimated poverty rates for each country before the shocks are 32%, 12% and 17% for Albania, Moldova, and North Macedonia, respectively. The poverty gap squared indexes are 6.7, 1.3 and 3.5, while the Gini indexes are 0.37, 0.32 and 0.33.

# Methodology

To determine the poverty and distributional impacts of alternative responses to shocks, we use survey-based microsimulations (Bourguignon and Spadaro 2006). We consider three types of shocks and assess the impacts of the different responses described in Table 2 for each type of shock. As a baseline shock we use the first wave of the Covid-19 Pandemic shock as simulated by World Bank (2020d, 2020b, 2020a), where individuals were exposed to the shock to varying degrees depending on the industry where they work and the type of workers they are. We then simulate two additional shocks of the same magnitude (i.e., with the same aggregate labor income losses), but with different distributional impacts. The second shock consists of a proportional loss of income that is equal for all workers who report labor income. This is an
first examine how well the text analysis works against this gold standard for U.S. occupations. We do this using the narrow dictionary, which only contains strictly green terms. Second, as an alternative exercise, we test the reliability of the identification strategy by applying the same dictionary to a skill database and observing how the GTI correlates with green skills intensity.

# Reliability test #1: The proposed text analysis methodology applied to O*NET task taxonomy versus O*NET GEP

To carry out this test, we apply our dictionary and text analysis techniques to the O*NET task statements database (version 23.3, which uses O*NET SOC 2010 and contains green occupations and task statements). In total there are 19,636 unique task statements for 974 occupations. Our green narrow dictionary successfully captures 77 percent of O*NET GEP green tasks. At the same time, our green dictionary identifies 293 green tasks that the O*NET GEP missed. The differences may be due to the GEP focus being circumscribed to certain sectors and qualitative work. Table 3.4 shows the detailed comparison of the results of applying both methodologies. A thorough analysis of the task statements confirms the validity of our methodology. Tasks that are green to O*NET but are not green according to the text analysis are: _’coordinate with other marketing team members and workers such as graphic artists to develop and implement marketing programs’_ , or _’prepare, maintain, or revise quality assurance documentation or procedures_ ,’ or to ‘ _monitor the flow of energy in response to changes in consumer demand_ ‘, or ‘ _fill out defective equipment reports_ .‘ Our dictionary also detects 300 green tasks within occupations that are non-green to O*NET. Examples are the tasks _’determine or recommend radioactive decontamination procedures, according to the size and nature of equipment and the degree of contamination_ ,’ or _’conduct audits at hazardous waste sites or industrial sites or participate in hazardous waste site investigations_ .’
# _Distribution of Nitrogen Use Efficiency (NUE)_

Generating the NUE at the most disaggregated level possible is of particular interest for the analysis of spatial fertilizer profitability in this study. The “plot” multilevel model allows for the estimation of NUE at the plot-level. Moreover, as table 3 indicates, the estimated mean NUE of the “plot” multilevel model is very similar in magnitude to that of the mean plot-level FE model, which is our most consistent estimate. Using the “plot” multilevel model, the NUE is estimated to range from 2.82kg to 25.98kg with a mean and standard deviation of 11.82kg and 2.42 respectively (figure 1). NUE ranges between 10kg and 15kg for majority of the plots (75%); between 5kg and 10kg for 16% of the plots; between 15kg and 20kg for about 8% of the plots; and at least 20kg for only about 1% of the plots.

Overall, the average NUE estimated in this study is quite low, and lower than those calculated by the MPTF task force using data from master farmers in the late 1990s in Benson (1999) and used by the official FISP evaluation (Dorward et al. 2008), and other recent studies by Arndt et. Al (2015) and Harou et al. (2017). However, these estimates are consistent with other studies using recent data on smallholders in Malawi. NUE estimates in these studies range from 7.1 kg to 11.0 kg by Snapp et al (2013), between 6.6kg and 11.5kg by Ricker-Gilbert and Jayne (2011, 2012); and 11.3kg by Holden and Lunduka (2011). The low NUE in Malawi is likely to be one of the main reasons why the use of commercial fertilizer for crop production is very low in the country. The fertilizer use literature suggests that the low NUE observed in this and other studies in Malawi is not an isolated case in SSA. The NUE
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 15

# **Table 5.** Control variables (vector **X** ).

|**Dimension **|**Variable**|**Description **|**Datasource for calculation **|
|---|---|---|---|
|I. Size|pop_2010|Sum of population count within the urban extent|Urban extents 2010 and population count at pixel<br>level from GHS for 2015 (GHS_POP_<br>GPW42015_GLOBE_R2015A_54009_250_v1_0)|
||density|pop_2010/ areac_ue2010_km2|Urban extents 2010 and population count at pixel<br>level from GHS for 2015 (GHS_POP_<br>GPW42015_GLOBE_R2015A_54009_250_v1_0)<br>and Urban extents 2010|
|II. Location|pop_radio300km|Inhabitants in other FUAs within a 300 km radius of a<br>city/1,000,000|Urban extents 2010 and population count at pixel<br>level from GHS for 2015 (GHS_POP_<br>GPW42015_GLOBE_R2015A_54009_250_v1_0)|
|III. Natural Amenities|coast_2010|Dummy for location at the coast|MODIS Water Mask (Carroll et al., 2009) and<br>urban extents from deblurred and corrected DMSP-<br>OLS NTL RC 2010 data.|
|VI. Urban Amenities|port|Dummy for port|Lloyd’s List (http://directories.lloydslist.com)|