rafmacalaba/datause-encoder-data huggingface dataset:
- 2 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

# **1 Introduction**

In 2007 the global urban population exceeded the global rural population, and it is projected that urban dwellers will double the rural dwellers by 2050 (United Nations, 2014). Latin America and the Caribbean (LAC) is the most urbanized region on the planet, with 80% of its population living in cities (UN-Habitat, 2012). This growth has not stopped, according to the World Development Indicators, LAC cities added 206 million people between 1960 and 1990, and 191 million people between 1990 and 2015 (The World Bank, 2017). This rapid level of urbanization imposes challenges for local authorities, as many cannot provide services and infrastructure for the new urban areas at the same speed as the urban growth. Urban form has an important influence on the emergence of agglomeration economies and congestion costs, and on cities’ economic productivity, as well as quality of life and environmental sustainability (Squires, 2002). The economies of agglomeration is strongly associated with intraurban connectivity levels, the level of compactness, and land use patterns (Ciccone and Hall, 1996; Cervero, 2001; Rosenthal and Strange, 2004). LAC cities are still spatially growing despite the deceleration in population growth (UN-Habitat, 2012).

However, there is a lack of empirical studies about the characteristics of LAC cities and their changes over time. The challenges of topography may have prevented dense development in some cases, as many Latin American cities are located in rugged topographies with natural barriers. Some of the urbanization in the region is in fact occurring over steep terrain, with slopes greater than 15%, and we verified the trend of urbanizing even steeper terrains in recent years. According to Gencer (2013), these levels of urban concentration of population increase the potential for disasters and most of the cities exposed to at least one natural hazard are located in Asia and Latin America. In some instances, the urbanization occurs at the cost of occupying adjacent protected areas. More than 2,400 square kilometers of protected areas have been lost to urban growth in the region from 1996 to 2010 according to the findings in this work. Rapid urbanization combined with limited infrastructure investments may have also led to urban forms that pose a barrier to the rise of agglomeration economies, limit firm interaction, and make it hard for workers to reach their jobs. A better understanding about the dynamics of urban form in the region can shed light on whether urban policy has a role to play in supporting city productivity, quality of life, and urban sustainability, while mitigating the increased vulnerability that usually comes with urban expansion.
_SchoolAttiat_ “ _α_ 0 _C_ 19 _Shockt_ ` _α_ 1 _C_ 19 _Shockt_ ˆ _Xia_ ` _γ_ 1 _CSat_ ` _γ_ 2 _HHsizeit_ ` _γ_ 3 _Ageit_ ` _ρi_ ` _εiat_ (2) where _α_ 1 estimates differential effects of the COVID-19’s lockdown measures for an individual with characteristics _Xia_ . _Xia_ can represent the gender of the schoolchild, the urban or rural sector of residence, whether the State of residence has been hit by a more severe lockdown (as defined in Section 2.1) during the first round of the pandemic between March and June, whether a schoolchild has benefited from distance learning during the lockdown period.

To identify the heterogeneous effects of the impact of COVID-19 by gender and zone, we estimate Equation 1 for all age groups (5-18, 5-11, and 12-18) through the following model where we interact the variable _C_ 19 _Shockt_ with gender and area of residence of respondents.

_SchoolAttiat_ “ _α_ 0 _C_ 19 _Shockt_ ` _α_ 1 _C_ 19 _Shockt_ ˆ _Girlsi_ ` _α_ 2 _C_ 19 _Shockt_ ˆ _Zonei_ Where _Girlsi_ measures the gender of the respondent and takes the value 1 if the respondent is a girl and 0 otherwise. The variable _Zonei_ which captures the geopolitical zone of residence of the individual is a dummy variable which takes the value 1 according to the zone that is highlighted in the model.<sup>21</sup> The other undefined variables of this Equation have the same meanings as those given in Equation 1.

The sum of the coefficients _α_ 0, _α_ 1, _α_ 2, and _α_ 3 capture the impact of COVID-19 on girls’ school attendance living in zone _i_ , while the sum of the coefficients _α_ 0 and _α_ 2 capture the impact of COVID-19 on boy school attendance in the same zone.

Data from suffer more than usual face-to-face of mea- phone surveys may surveys surement errors. Given that our school attendance variable pre- and post-COVID-19 is

> 21Nigeria is subdivided into 6 geopolitical zones, namely North-Central, North-East, North-West, South-East, South-South, and South-West.

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The WEAI survey collects time use data for both primary and secondary (simultaneous) activities across the following 18 types of activities:<sup>6</sup>

- Personal activities: sleeping and resting, eating and drinking, personal care, school (including homework)

- Market work: work as employed, own business work, farming/livestock/fishing

- Unpaid (household) work: shopping/getting service, weaving/sewing/textile care, cooking, domestic work (including fetching wood and water), care for children/adults/elderly, traveling/commuting

- Leisure: watching TV/listening to radio/reading, exercising, social activities and hobbies, religious activities

Originally designed to be used as a paper survey, the WEAI time diary follows a format adapted from the Lesotho Household Budget Survey. In this format, rows representing each type of activity and columns representing the previous 24 hours divided into 15-minute intervals combine to form an 18 × 96 grid (see Figure 2).<sup>7</sup> Activities are recorded by drawing horizontal lines across the grid. In this way, the format itself acts to promote the accurate recording of data. By simply checking that a continuous (though, of course, staggered) line extends across the grid, enumerators are able to verify that all of the previous 24 hours have been counted and that no additional time has been recorded in the module. Indeed, errors of this sort are rare in data collected using the WEAI time diary; though, of course, without a proper comparison group we cannot say for certain whether this is truly the case. Moreover, the possibility exists that the grid format may be too complicated for some enumerators to handle, as reported by some implementers of the WEAI. As such, extra training may be required to prepare enumerators for the WEAI time diary.

More recently, the WEAI time diary has been adapted for use with computer-assisted personal interviewing (CAPI) software on a tablet or smartphone, and the grid format discarded in favor of a set of nested, “drop-down” menus that allow for greater specificity of listed activities, while also providing built-in checks for data consistency. The module is currently being piloted in several developing countries.

Survey-based time diaries are, at present, the most common method for collecting time use data in developing countries, having been implemented in at least 57 such countries around the world (Fisher 2015). The method is widely perceived to be both cost-effective to implement (albeit more costly than stylized questions) and to provide accurate estimates of how people spend their time. Hence, many researchers consider the survey-based time diary to be the “gold standard” for collecting time use data in developing countries.

> 6 An “Other (specify)” option is also included in the module to capture any activity that does not fall into one of the listed categories.

> 7 See Lawson (2007) for details on the Lesotho Household Budget Survey time use module.

6
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**Table 4.13: Decomposition of Poverty Incidence by State, 1985-93 (Poverty Line=N395.41)**

|||1985/86|||1992/93||
|---|---|---|---|---|---|---|
|**STATE**|**P0**|**P1**|**P2**|**Po**|**PI**|P2|
|Lagos (Urban)|0.6|0.3|0.3|0.0|0.0|0.0|
|FCT|0.0|0.0|0.0|1.4|1.9|2.2|
|Anambra|4.5|2.9|2.6|3.1|1.1|0.9|
|Bauchi|5.8|8.9|9.7|6.9|9.5|9.4|
|Bendel|4.1|3.7|3.5|2.0|0.7|0.7|
|Benue|4.4|4.9|5.1|4.0|4.1|4.7|
|Bomo|5.0|5.3|5.6|6.1|6.7|6.8|
|Cross River|6.0|5.2|4.7|4.4|4.1|4.3|
|Gongola|5.1|6.1|6.3|3.6|2.7|2.5|
|Imo|4.8|3.0|2.7|2.8|1.1|1.1|
|Kaduna|9.0|10.1|10.0|5.6|3.2|3.0|
|Kano|11.3|11.5|10.9|15.3|18.1|16.4|
|Kwara|3.0|2.4|2.1|3.0|2.4|2.5|
|Lagos (Rural)|_1.5_|1.3|1.2|4.6|3.9|3.6|
|Niger|2.8|4.9|6.4|3.3|3.9|4.2|
|Ogun|2.9|2.6|2.3|1.9|1.1|0.9|
|Ondo|3.9|4.3|4.6|3.7|2.5|2.5|
|Oyo|6.6|2.9|1.9|5.8|3.0|2.8|
|Plateau|3.9|4.1|4.3|6.1|6.8|7.1|
|Rivers|2.5|1.5|1.1|3.2|3.0|2.9|
|Sokoto|12.5|14.1|14.7|13.1|19.8|21.3|
|**All Nigeria**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|

Source: NCS, 1985/86 and 1992/93 Tables 4.14 and 4.15 shows the evolution of extreme poverty by state. Ten out of the 19 state structure show increases in extreme poverty. We find from the table that the following states have shown an increase in headcount of extreme poverty. Anambra (0.6 percentage points), Cross Rivers (6.7 percentage points), Plateau (4.0 percentage points), Sokoto (9.8 percentage points), Kaduna (7.7 percentage points), Kano (3.9 percentage points), Kwara (4.4 percentage points), Ogun (0.7 percentage points) and Rivers (12.6 percentage points). Also, eleven states have registered an increase in the depth of extreme poverty as follows: Anambra (0.2 percentage points), Benue (3.5 percentage points), Bauchi (0.9 percentage points), Cross River (4.1 percentage points), Kano (2.6 percentage points), Kwara (4 percentage points), Ondo (0.3 percentage points), Oyo (2.5 percentage points), Plateau (3.5 percentage points), and Rivers (4.6 percentage points).
technological change can be safely assumed to reinforce each other, in developing countries both factors probably operate in opposite directions and empirical evidence is crucial to determine which tends to prevail. This paper provides new evidence on that question, by estimating the net effect of the use of imported inputs, exports and FDI on the relative demand for skilled workers, using recently collected manufacturing plant-level data from Brazil and China, two developing countries which have experienced signi fi cant increases in their degree of international integration.

Previous empirical work on the U.S. and other OECD countries has generally found skilledbiased technological change to be more important than trade as the main factor underlying the aforementioned labor demand shifts. Thus, only a small share of the observed changes in the demand for skilled labor can be attributed to a reallocation of production towards skill-intensive — sectors as the trade hypothesis would predict. Rather, it appears that most of the shift away from unskilled labor has taken place within narrowly de fi ned industries, a result that is consistent with the hypothesis of skill-biased technical change. Moreover, studies using industry-level and plant-level data have found a direct link between the relative demand for skilled workers and investments in the adoption of computers or in research and development (R&D).<sup>3</sup> However, these studies have been less successful in uncovering the hypothesized effects of exports, imports and foreign investment fl ows on the demand for skilled labor.<sup>4</sup> The evidence for developing countries is more scarce. In Mexico, the relative wages of skilled workers increased dramatically after the country liberalized its trade regime and relaxed restrictions on FDI in the early 1980s. Feenstra and Hanson (1997) show that the states and

> 3 See Berman et al. (1994), Doms et al. (1997), Dunne et al. (1997), Autor et al. (1998), Machin and Van Reenen (1998), and Haskel and Heden (1999).

> 4 For U.S. industries, some con fl icting evidence is obtained. Slaughter (2000) and Bloningen and Slaughter (2001) fi nd no important effects of, respectively, outward and inward FDI on the demand for skilled labor. Feenstra and Hanson (1996a) encounter a signi fi cant link between outsourcing (encompassing imports and contract work overseas) and the demand for skilled labor but Autor et al. (1998) obtain non-signi fi cant effects of imports and outsourcing and positive effects of exports on skill upgrading.

2
**Figure 9 Factors affecting the competitiveness of SMRs**

<!-- Start of picture text --> (1) Economy of Scale- Assumes single unit and same<br>LR design concept (large plant directly scaled down)<br>Multiple 1<br>2 (2) Multiple Units – Cost savings for multiple<br>Unit<br>units at same site<br>(3) Learning – Cost reductions for site &<br>Learning<br>3 Curve program learning for additional units in series<br>(4) Construction Schedule – Reduced<br>Construct IDC from shorter construction time<br>4<br>Schedule (5) Unit Timing – Gradual<br>capacity additions to fit demand<br>Unit<br>5 (6) Plant Design – Cost<br>Timing reductions resulting<br>from design concept<br>Plant characteristics<br>6<br>Design<br>Present Value<br>Capital Cost<br>“SMR Concept”<br>0 300 600 900 1,200 1,500<br>Plant Capacity (Mwe)<br>Economy of Scale<br>Cost per Kwe<br><!-- End of picture text --> _Source_ : Mycoff et al (2010).

To estimate the impact of economies of scale, Mycoff et al assume a scaling factor n=0.6 and that the two plants are comparable in design and characteristics—i.e. that the single large reactor is scaled down in its entirety to 1⁄4 of its size. According to the standard scaling function, the hypothetical overnight cost (per unit of installed capacity) of the SMR-based power station will be 74 percent higher compared to a single large-scale reactor. Based on various studies in the literature, the authors posit that the combined impact of multiple units and learning effects is a 22 percent reduction in specific capital costs for the SMR-based station. To quantify the impact of construction schedule, the authors assume that the construction times of the large reactor and the SMR units are five and three years respectively. The shorter construction duration results in a 5 percent savings for the SMRs. Temporal flexibility (four sequentially deployed SMRs with the first going into operation at the same time as the large reactor and the rest every 9 months thereafter) and design simplification lead to 5 and 15 percent reductions in specific capital costs respectively for the SMRs. When all these factors are combined, the SMRbased station suffers a specific capital cost disadvantage of only 4 percent as compared to the single large reactor of the same capacity.

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In the South South region—that is, in the Niger Delta area—several militant groups, targeting primarily the oil industry, have caused disruptions to the oil-led economy. The conflict has long historical roots; some form of violent conflict has been ongoing since independence. Most recently, the conflict is related to demands for a more equitable redistribution of oil resources as well as concerns related to environmental degradation. In 2009, amnesty was declared to militants (Nwankpa 2014; Ajodo-Adebanjoko 2017). However, new militant groups have emerged since, and fatalities have increased during the last few years (figure B.1).

In the Niger Delta region, 22 percent of households in our data reported at least one conflict event between 2010 and 2017, a similar figure to the conflict intensity in the North Central region. Bandits and criminals were the most common type of perpetrator (42 percent of cases).

# 3.   Data

## 3.1.   Data sources

We combined the GHS panel data with data from a telephone survey on household victimization that were conducted with a subset of the GHS panel households during 2017. The GHS is an LSMS data set that has been collected in three waves between 2010 and 2016. The waves are 2010–11, 2012–13, and 2015–16, and they include two visits each: a postplanting visit during the autumn months and a postharvest visit during the spring.

A separate telephone-based conflict survey was administered to 717 of the GHS panel households selected from the most recent visit (wave 3, visit 2).<sup>7</sup> The purpose of the survey was to understand the extent to which households experienced conflict: whether they had become victims of violence or property-related crime or if they had experienced other events related to conflict and criminal activity since 2010. These events are thus based on participants’ recall of the period between January 2010 and May 2017. Participants were asked to recall events that occurred each year during this span. The survey covered the three most conflict-affected geographical zones within the 16 states of Nigeria.<sup>8</sup> Households from local government areas (LGAs) that had high conflict exposure were oversampled based on

> 7 The GHS waves include between 4,500 and 5,000 households. During the last visit—the second visit of the third round—the sample size was 4,579. 8 The telephone-based survey was conducted between March and May 2017. See appendix B for details on how the geopolitical zones were selected based on conflict exposure.

8
ness on returns to experience is likely to be overstated.<sup>13</sup> Controlling for country ...xed e¤ects in the last column of Table 3 allows for cross-country variation in the intercept and leads to an estimated return to home country experience of 1.9 percent evaluated at 5 years of labor market experience and the mean of log Open. When controlling for country ...xed e¤ects, the coe¢ cient on the interaction term between experience and openness remains signi...cant, but drops from 0.006 to 0.002. This implies that raising Open from 0.2 to 0.3 leads to an increase in the annual return to experience by 1 percentage point.

As pointed out above empirical evidence suggests that not only the intercept of the Mincerian earnings equation, but also the returns to individual characteristics, such as education or post-migration experience, are likely to vary across source countries of US immigrants. This issue can be addressed by estimating the e¤ect of openness on returns to experience by means of the two-step method.

# 5 Openness and Returns to Experience

The ...rst step of the two-step method consists in estimating the Mincerian earnings equation for each country separately, yielding estimates of the country-speci...c returns to home country experience.<sup>14</sup> Di¤erences in returns to home country experience across countries of origin of US immigrants are substantial. Evaluated at 5 (10) years of labor market experience statistically signi...cant returns range from 7.8 (6.6) percent for immigrants proceeding from Norway, Finland and Japan to 1.4 (1) percent for Philippines, Mexicans and Guatemalans (see Table 4). The relatively low average return to home country experience of US immigrants identi...ed in previous

> 13 Immigrants from more open economies may not only have higher returns to experience, but also higher entry wages. As pointed out above, Borjas (1998) ...nds no signi...cant e¤ect of openness on the entry wage level of US immigrants as soon as he controls for country ...xed e¤ects and/or initial educational attainment. However, he ...nds a positive and signi...cant e¤ect of GDP per capita on log entry wages. Given that GDP per capita and openness are positively correlated, trade is likely to a¤ect log entry wages as long as GDP per capita is not controlled for.

> 14 Returns to experience are only estimated for countries with at least 50 US immigrants that satisfy the election criteria, using data from the 1980 and 1990 census jointly. An alternative strategy would have been to estimate country-speci...c returns to experience per census year. However, variation in returns to experience across census years is not signi...cant, inducing me to stack the 1980 and 1990 census. This increases the number of observations used for estimating the country-speci...c returns to experience and therefore the precision of the estimates.

10
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