rafmacalaba/data-use-mentions-extended huggingface dataset:
A short time series finds a slow transition to digital occupations in Southeast Asia, with only Vietnam showing a trend toward greater digitalization. When expanding the sample to annual labor force surveys in Malaysia (2011-17), Thailand (2014-20), and Vietnam (2014-15, 2017-18), we find that any changes in the occupational structure of labor markets in Malaysia and Thailand did not affect the distribution of the workforce across digital skills levels (Figure 10). In contrast, the data from Vietnam finds a decline in very low digital occupations (from 70 percent to 64 percent over the five-year period) and a 3-percentage point increase in low- and medium digital occupations. The micro-data explain these changes, where the share of workers in occupations related to subsistence agriculture — very low digital occupations — declined significantly over the period with slow increases in craft occupations (assemblers, construction, machine operators) and services (shop salespeople) which are classified as low or medium digital occupations.

33
surveys from the World Bank Global Labor Database (GLD) and the World Bank i2d2 database.<sup>6</sup>

– For our quantitative exercise, we use a core sample of six large economies India, – Indonesia, Mexico, Brazil, Canada, and the United States which additionally offer high-quality micro data on hours worked and compensation, enabling us to calculate workers’ hourly wages. In addition, these countries provide data for more than four

> 6A description of the database can be found at https://github.com/worldbank/gld.

7
The third column of Table 4 extends our main analysis by controlling for the effect of fear in citizens’ perception of COVID-19 on economic outcomes. The coefficient on the fear variable is negative and significant, indicating that a higher perception of fear among the population negatively affects economic activity. A 1 percent increase in the “fear” of the pandemic (search frequencies) reduces the electricity consumption by about 0.02 percent. Compared to the baseline specification shown in column (1), the introduction of the fear variable reduces slightly the magnitude of the wait interaction effects without affecting their significance. All other coefficients are quantitatively similar to the coefficients in the baseline specification. Similar results are observed in regressions when we used the terms “infection” and “COVID.” One of the problems with using Google trends data is the issues of attribution (e.g., Mavragani and Ochoa 2019). What if the searches of the term “death” are not connected with perceptions but rather reflect a time trend when people use Google more frequently for all searchers? To rule-out such possibility, we use searches for “neutral” terms “piano” and “milk.” We believe these terms not to be correlated with perceptions of fear. Our falsification test confirms that these terms have no significant impact on the consumption of electricity.

# **6. Assessing the role of press coverage**

The results presented in the last specification of Table 4 show that individual perceptions of the pandemic may have a role in explaining how economic activity varies along the reopening process. Fetzer et al. (2020) show that economic anxiety driven by the COVID-19 pandemic can be detected in the increase in the volume of searches for “recession” and “stock market crash” as soon as the first cases of the disease were detected in each location. This points to the importance of diffusion of information as a factor driving individual behaviors. In fact, individual attitudes and perceptions have also been shown to depend on media coverage, for
. Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br>US cents per KwH<br>Mali Niger Congo Benin Cape Verde Cameroon Rwanda Burkina Faso Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia DRC Zambia South Africa<br><!-- End of picture text --> Also Senegal has systematically increased its participation in regional initiatives. The country is active in the Senegal River Basin Organization,<sup>8</sup> and it is also interconnected to the West African Power Pool (WAPP). Its increased participation in the regional electricity market is an important step toward addressing the lack of sufficient electricity supply to the economy, which may be helped by importing hydroelectricity, mainly from Guinée through the OMVG<sup>9</sup> , and also gas-fueled electricity from Côte d’Ivoire.

> 8 Organisation pour la Mise en valeur du Fleuve Sénégal (OMVS).

> 9 Gambia River Basin Development Organisation.

27
|NO|NO|YES|
|**(d)** **Impact of imports on employment**|(1)|(2)|(3)|(4)|(5)|(6)|
|log (Imports per worker), change|0.130|0.0816|0.0818|1.296***|-0.144|-0.145|
||(0.0866)|(0.0794)|(0.0794)|(0.378)|(0.244)|(0.243)|
|Observations|1,980|1,980|1,980|1,980|1,980|1,980|
|Demographic and employment controls (year 2000)|NO|YES|YES|NO|YES|YES|
|Median income control(year 2000)|NO|NO|YES|NO|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p < .01 ** p < .05 * p < .1. The dependent variable is the change from 2000 to 2015 in the labor force participation rate and employment rate. The explanatory variable is the change in the log of exports-toand imports-to-workers workers from 2004 to 2014. Demographic and employment controls include the proportion of rural population, the proportion of population aged 15 and more who are literate, total population, and the share of employment in the primary, secondary and tertiary sectors of economic activity (all variables in the year 2000).

# 3.2 Exports, imports and the labor income channel

Table 2 shows the impacts of exports and imports per worker on labor incomes. The weak impacts of exports on the employment rate in Table 1 may be due to the fact that higher exports per worker led to a net inflow of migrants, thereby increasing both the numerator and the denominator of the employment rate (
number of variables including personal, household and location characteristics as control variables and the log of disposable income per capita as the explanatory variable.<sup>22</sup> Results are shown in table 4.6. We can see that income is very significant and a positive variable explaining stayers in the wage category versus those that moved from wage to non-wage labor (col 1). This would suggest that staying in wage labor 'pays' better. In fact, we calculated the premium earned by moving from wage to non-wage employment and found a value of (-) 2.2%. In other words, those who moved have - on average - a disposable income 2.2% lower than those who stayed in wage labor.

Vice-versa, income is not a significant variable that distinguishes stayers in the non wage category from those who moved from the non wage category to the wage category (col. 2). In this case, the premium paid for moving into the wage category is (+)1.9% suggesting that moving into the wage category from the non wage category pays off. We can also note that all other variables which we used as control variables are in fact all significant and could potentially be explanatory variables themselves. Premiums for moving and not moving are small and the decision to move may be more related to non-income factors. However, the conclusion we have to draw from the probit regressions is that disposable income is not what is attracting workers into individual farming.

In sum, the flow from wage (employees) to non wage (self-employed) workers has increased during the 1990-1996 deep recession, continued during the 1998-1999 moderate recession and was still visible during the 2000-2002 growth period.<sup>23</sup> Our micro data for Moldova have fully supported in this respect the macro data we showed for the CIS-7 and provided by CIS-Stat. From the probit regressions on stayers and movers we concluded that disposable income is not what is attracting workers into individual farming
.443|
|Index of marketing practices|0.378|0.211|0.367|0.383|0.036|||||
|Index of accounting practices|0.271|0.235|0.273|0.270|0.975|||||
|Index of planning practices|0.170|0.262|0.164|0.173|0.143|||||
|Food sector|0.320|0.466|0.344|0.309|0.037|||||
|Beauty sector|0.104|0.306|0.099|0.106|0.605|||||
|Handicrafts sector|0.101|0.302|0.095|0.104|0.433|||||
|Service sector|0.302|0.459|0.294|0.306|0.453|||0.298|0.458|
|Essential business|0.189|0.391|0.200|0.184|0.284|||||

**Notes:** Baseline characteristics of firms involved in the program shown in first five columns. Characteristics of a representative sample of Mexican female entrepreneurs shown in columns 6 and 7 are from the 2023 ENOE (National Survey of Occupation and Employment). Columns 8 and 9 show characteristics of CREA’s in-person training clients taken from a 2014 survey. Not all characteristics are available in these other surveys. P-value in Column (5) correspond to the effect of treatment on the baseline covariate, controlling for strata fixed effects.
_</sup> in the total imports of _g_<sup>_′_</sup> by the destination country ( _MSg_<sup>_China′_</sup> _,_ 0 ) and summed over all HS-6 products _g_<sup>_′_</sup> within an HS-4 product _g_ to arrive at a measure of Chinese competition index at the Indonesian firm _i_ and HS-4 product _g_ level. We follow Mattoo et al. (2017) in constructing this index at the HS-4 level using dis-aggregated product data at HS-6 to reflect the fact that too narrowly defined products, such as HS8, may not accurately reflect the effects of Chinese competition while too broadly defined categories such as HS-2 may confound these effects.

Intuitively, according to this measure, if the HS four-digit product, let’s say TVs, consists of only two items, color TVs and non-color TVs, the measure is simply the share of Chinese imports in Japan’s imports of each type of TV, weighted by the importance of each type of TV in firm _i_ ’s TV exports to Japan. If the firm faces no competition from 14
<mark>Montreal: Institute for the Study of International Development, McGill University (GrOW Research Working Paper Series# 5) (2017).</mark>

- <mark>Contreras, Dante, and Paulina Sepúlveda. "Effect of Lengthening the School Day on Mother's Labor Supply."</mark> _<mark>World Bank Economic Review</mark>_ <mark>31, no. 3 (2017): 747-766.</mark>

- <mark>Du, Fenglian, and Xiao-yuan Dong. "Women’s employment and child care choices in urban China during the economic transition."</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>62, no. 1 (2013): 131-155.</mark>

- <mark>Duflo, Esther. "Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment."</mark> _<mark>American Economic Review</mark>_ <mark>91, no. 4 (2001): 795-813.</mark>

- <mark>Felfe, Christina, Michael Lechner, and Petra Thiemann. "After-school care and parents' labor supply."</mark> _<mark>Labour Economics</mark>_ <mark>42 (2016): 64-75.</mark>

- <mark>Fitzpatrick, Maria Donovan. "Preschoolers enrolled and mothers at work? The effects of universal prekindergarten."</mark> _<mark>Journal of Labor Economics</mark>_ <mark>28, no. 1 (2010): 51-85.</mark>

- <mark>Frankenberg, Elizabeth, and Duncan Thomas. "The Indonesia Family Life Survey (IFLS): Study design and results from waves 1 and 2." (2000).</mark>

- <mark>Frankenberg, Elizabeth,
using and stretching short and long run targets, tracking outcomes, and taking appropriate action, and (iii) performance incentives: rewarding high-performing employees, and retraining or moving underperformers and careful hiring. To collect information on these practices, two alternative strategies could be deployed: (i) Open Ended questions (e.g. world management survey, WMS)<sup>11</sup> and (ii) Closed Ended questions (e.g. Management and Organizational Practices Surveys, MOPS).<sup>12</sup> **Table 2** summarizes the comparison of the two approaches on a number of dimensions.

> 11 Designing these surveys take some expertise in terms of selecting questions and response grids and hence intense training and monitoring of the interview team. The full questionnaire is available on <u>www.worldmanagementsurvey.com.</u> 12 The full questionnaire is available on http://bhs.econ.census.gov/bhs/mops/form.html
m*100 m, but to avoid excessive compute times, we aggregate the data into 250 m*250 m grid cells. This aggregation also ensures compatibility with other spatial layers that we use to classify cells as potentially habitable. As with other “top down” gridded population data sets, the constrained WorldPop algorithm derives a grid by taking population data from national censuses for larger sub-national administrative units and distributing this across the cells that fall within each unit. In the case of constrained WorldPop, population is spread unevenly across cells within a given admin unit based on weights derived from a Machine Learning (ML) model (Stevens et al. 2015), only allocating population to cells that are built-up. In turn, the classification of cells as builtup is based on the extremely detailed map of all building footprints in SSA that was derived under the Gates Foundation sponsored Digitize Africa project.<sup>18</sup> The quality and resolution of this building footprint data is comparable to, if not better than, the building footprint data that de Bellefon et al. (2021) use for France in their original application of the dartboard algorithm.

The spatial data used to define a country’s potentially habitable areas come from two sources. That used to identify water bodies and deserts come from the European Space Agency’s GlobCover product (Bontemps et al. 2013).<sup>19</sup> Meanwhile, the data used to measure elevation and slope come from SRTM30, which is a near-global digital elevation model (DEM) comprising a combination of data from the Space Shuttle Radar Topography Mission flown in February 2000 and the U.S. Geological Survey's GTOPO30 data set.<sup>20</sup>

# **3. Descriptive Analysis of Overall Urbanization Patterns**

# **3.1.** **_Aggregate differences across countries_**

Table 1 presents summary statistics that describe key urbanization and other characteristics across SSA countries, while table 2 identifies the
With regard to MDG 1 (poverty), the model generates aggregate urban and rural poverty rates. As a first step, in the poverty module, Foster-Green-Thorbecke (FGT) poverty indicators are computed for the RHs (for each individually or for aggregations of RHs) on the basis of their percapita consumption, an initial poverty rate, and an initial Gini coefficient.<sup>21</sup> Drawing on these poverty indicators and the migration of parts of the population of some or all of the RHs that is imposed to generate urban and rural population shares that match the aggregate urbanization function (as discussed above), the final poverty indicators are computed for urban and rural population aggregates.<sup>22</sup>

> 21 In this application, for the purpose of poverty calculations, the rural low- and high-income RHs are aggregated into a raw rural household while, similarly, the urban low- and high-income RHs are aggregated into a raw urban household. Raw rural and urban poverty rates are computed on the basis of the simulated per-capita consumption for each aggregated group, its base-year poverty rate, and its Gini coefficient.

> 22 In order to verify that the computational procedure is without error, post-calculation checks verify that the poverty rate in each year is the same for national aggregations of the raw and final disaggregated poverty rates.

27
We derive two main testable predictions, respectively related to non-tariff measures and to tariffs. Specifically, we show that NTMs applied in a non-discriminatory fashion – that is, in compliance with the WTO’s “national treatment” clause, whereby imported and domestically-produced products must be treated alike – raise market power and PTM for incumbents if they induce the exit of smaller firms, e.g. through higher fixed costs.<sup>4</sup> Import tariffs, on the other hand, decrease the sales of incumbents but also induce the exit of small firms, leading to an ambiguous impact on market power. However, given their discriminatory nature, tariffs are more likely to displace foreign firms in favor of domestic ones, implying that the intensive margin effect dominates and in net tariffs reduce the market shares of exporters and thus their incentive to engage in PTM.<sup>5</sup> We then test these predictions using a large multi-country firm-level data set obtained from customs administrations in twelve developing countries, ranging from low income to middle income. We combine the firm-level data with destination-product specific data on bilateral applied tariffs as well as non-tariff measures. The latter cover a wide range of measures ranging from sanitary and phytosanitary measures to technical barriers to trade. Pooling together firm-level data from several developing countries allows us to provide evidence on the extent of exchange rate pass-through for countries and regions that have received little attention so far. It also allows us to explore more systematically the impact of trade policy on competition and market power using a sample of countries facing different trade policy arrangements, in particular preferential tariffs. Importantly, the high dimensionality of the data enables us to account for unobserved heterogeneity and for a large set of confounding influences at the level of both the firm and the market using a powerful array of fixed effects. The fact that our unit of observation is the firm rather than the product or the country also implies that our estimates are unlikely to be driven by large firms, which is important given the
Unwilling or unable to cheat? evidence from a tax audit experiment in denmark. _Econometrica 79_ (3), 651–692.

- Lee, K., E. Miguel, and C. Wolfram (2020). Experimental evidence on the economics of rural electrification. _Journal of Political Economy 128_ (4), 1523–1565.

- Liemberger, R. and A. Wyatt (2019). Quantifying the global non-revenue water problem. _Water Supply 19_ (3), 831–837.

- McRae, S. (2015). Infrastructure quality and the subsidy trap. _American Economic Review 105_ (1), 35–66.

24
# **4.2.2 Policy Experiments**

Our next task is running five policy experiments. The first two consider an acceleration of public capital formation. IBGE data imply that gross electricity infrastructure formation has decreased in Brazil from a 3.28 percent average share of GDP in the 1980s to an average of 0.9 percent for the period 1990 to 2007. As a consequence, as Calderón and Servén (2010) and Frischtak (2013) among others argue, Brazil’s infrastructure investment has fallen in the past few decades below the levels shown by other Latin American and emerging nations such as Chile, China, and India. We now ask our model, what would have been the impact on the different sectors and the economy as a whole if Brazil had maintained the public-capital formation levels of the 1980s during the 1990s and 2000s? This would have implied an average investment in electricity infrastructure of 3.0 percent for the period 1970-2007.

We conduct two different experiments to achieve a stock of _Gt_ similar to the one that the increase in the ratio of _Igt_ to GDP from 1.81 to 3.0 percent would deliver. In the first experiment (the “ _λt_ up” case), the rise comes as the result of stronger incentives to invest in grid capacity induced by an increase in the political ideology parameter _λt_ . Put differently, the policy maker increases the public capital formation perceived as valuable by her supporting group. For this, starting from 1990, we increase each period the value of _λt_ so that _Gt_ rises at a constant rate that implies the desired average ratio _Igt/GDP_ of 0.03 for the whole period 1970-2006. All other parameters take on their benchmark numbers.

The results are in Figure 6 and the fourth column of Table 7. The Figure shows values of the different variables from 1970 to 2006. However, the quantities reported in the Table – this time refer to the differences for the interval 1990-2006 this will be the case in all the policy experiments
- 7 -

# **III. DATA**

Our empirical data are collected from over 150 rural road contracts in 19 districts of Nepal where the World Bank has been assisting the Rural Access Improvement and Decentralization Project (RAIDP).<sup>2</sup> They are located mostly in the Tarai area (Figure 2). In each district, on average 8 road contracts were reviewed―half from World Bank-financed projects and half from government-owned projects. In Nepal the rural road projects are basically implemented at the local level. With the assistance of the Department of Local Infrastructure and Agricultural Roads (DOLIDAR), the District Development Committees (DDCs) are designing, procuring and managing rural road civil works and services.

Nepal is one of the least developed countries. According to the 2011 census, about 26.6 million people live in the country. About 34 percent still live on less than $1.25 a day at 2008 international prices. Gross domestic product (GDP) per capita was only $430 in 2009. The country has some 17,000 km of road network, which is among the lowest road densities in the world. The vast majority of rural residents have to spend more than 30 minutes to access paved roads. About one-third do not have any paved road within more than 3 hours in Nepal.

The sample contracts are relatively small. In Nepal the rural road standards are not particularly high. The average unit cost of rural road upgrading works is about NRs1.6 million or $23,000 per km.<sup>3</sup> The “size of contracts” also seems fairly small at 7km on average but ranges from less than 1 km to more than 30 km (Figure 3). Depending on size, the administrative efficiency of procurers or DDCs contracting out works may differ. In some cases, it takes more than 6 months (Figure 4). The contract performance also varies across contracts. Most of the contracts did not incur any
**Table 3. Poverty rates by geographic area, headship, and displacement**

|||Poverty rate<br>Non-||||Observati<br>Non-|ons||
|---|---|---|---|---|---|---|---|---|
||Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|
|All|77|77.3|66.5|70|2,220|2,427|21,610|26,257|
|_Geographic_<br>_area_|||||||||
|Urban||62.7|64.1|64||1,562|17,469|19,031|
|Rural||83.5|72.8|76.3||865|4,141|5,006|
|_Headship_|||||||||
|Female|70.8|70|64.8|66.7|1,254|1,009|10,969|13,232|
|_de jure_|68.8|64.4|67.7|67.5|383|292|3,124|3,799|
|_de facto_|70.1|72.2|63.2|65.7|856|690|7,627|9,173|
|Male|84.7|81.1|68|72.9|966|1,418|10,641|13,025|

_Source:_ Own elaboration based on Somali HFS 2017-18 _. Note_ : IDP stands for internally displaced persons.
<!-- Start of picture text --> Figure 6. Hours worked per week by gender, contractual and actual<br>(a) usual hours of work (b) actual hours of work<br>0 20 40 60 80 0 20 40 60 80 100<br>Usual hours of work per week in primary job (Q84ESAS) Actual hours of work last week in primary job (Q85ESAS)<br>Usual hours of work (women) Usual hours of work (men) Actual hours of work (women) Actual hours of work (men)<br>.1 .3<br>.08<br>.2<br>.06<br>Kernel density.04 Kernel density.1<br>.02<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan.<br><!-- End of picture text -->

**Figure 7. Usual weekly hours of work by age**

<!-- Start of picture text --> 45<br>19<br>40<br>14<br>35<br>30 9<br>25<br>4<br>20<br>‐1<br>15<br>10 ‐6<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age<br>Hours worked by women Hours worked by men Gender gap<br>Hours worked per week<br>Gender gap in weekly hours<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 36